1 OpenQuake-engine Manual 3.15.0#

Authors
Marco Pagani\(^1\), Vitor Silva\(^1\), Anirudh Rao\(^1\), Michele Simionato\(^1\), Kendra Johnson\(^1\)
Authors on previous versions
Helen Crowley\(^2\), Damiano Monelli\(^3\), Graeme Weatherill\(^4\), Robin Gee\(^5\)

\(^1\) GEM Foundation, via Ferrata 1, 27100 Pavia, Italy

\(^2\) EUCENTRE, via Ferrata 1, 27100 Pavia, Italy

\(^3\) RenRe, Beethovenstrasse 33, CH-8002 Zürich, Switzerland

\(^4\) GFZ, Helmholtzstraße 6/7, 14473 Potsdam, Germany

\(^5\) PartnerRe, Hardstrasse 301, CH-8002 Zürich, Switzerland

Email address for all current authors:
\(<\)firstname.lastname\(>\)@globalquakemodel.org
Citation
Please cite this document as: GEM (2022). The OpenQuake-engine User Manual. Global Earthquake Model (GEM) OpenQuake Manual for Engine version 3.15.0. doi: 10.13117/GEM.OPENQUAKE.MAN.ENGINE. 3.15.0
Disclaimer
The OpenQuake-engine User Manual is distributed in the hope that it will be useful, but without any warranty: without even the implied warranty of merchantability or fitness for a particular purpose. While every precaution has been taken in the preparation of this document, in no event shall the authors of the Manual and the GEM Foundation be liable to any party for direct, indirect, special, incidental, or consequential damages, including lost profits, arising out of the use of information contained in this document or from the use of programs and source code that may accompany it, even if the authors and GEM Foundation have been advised of the possibility of such damage. The Manual provided hereunder is on as “as is” basis, and the authors and GEM Foundation have no obligations to provide maintenance, support, updates, enhancements, or modifications.
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© 2013–2022 GEM Foundation
April 2022

Contents

2 Introduction#

The goal of this manual is to provide a comprehensive and transparent description of the features of the OpenQuake Engine. This manual is designed to be readable by someone with basic understanding of Probabilistic Seismic Hazard and Risk Analysis, but no previous knowledge of the OpenQuake engine is assumed.

The OpenQuake engine is an effort promoted and actively developed by the GEM Foundation, a public-private partnership initiated by the Global Science Forum of the Organisation for Economic Co-operation and Development (OECD) 1.

The OpenQuake engine is the result of an effort carried out jointly by the Information Technology and Scientific teams working at the GEM Foundation Secretariat. It is freely distributed under an Affero GPL license (http://www.gnu.org/licenses/agpl-3.0.html).

2.1 OpenQuake-engine Background#

2.1.1 Overview#

OpenQuake-engine is the seismic hazard and risk calculation software developed by the GEM Foundation. By following current standards in software developments like test-driven development and continuous integration, the OpenQuake engine aims at becoming an open, and community-driven tool for seismic hazard and risk analysis.

The source code of the OpenQuake engine is available on a public web-based repository at the following address: http://github.com/gem/oq-engine.

The OpenQuake engine is available for the Linux, macOS, and Windows platforms. It can be installed in several different ways. The following page provides a handy guide for users to choose the most appropriate installation method depending on their intended use cases:

https://github.com/gem/oq-engine/blob/master/doc/installing/README.md.

This user manual is for the command line interface for the OpenQuake engine.

2.1.2 Supplementary resources#

Guidance instructions for using the OpenQuake engine WebUI are available at https://github.com/gem/oq-engine/blob/master/doc/running/server.md.

A user manual for the QGIS plugin for the OpenQuake engine is available at https://docs.openquake.org/oq-irmt-qgis/latest/. In particular, instructions for using the plugin as an interface for running OpenQuake engine calculations are listed in Chapter 14, and methods for using the plugin for visualization of hazard and risk outputs are listed in Chapter 15.

A manual intended for more advanced users of the OpenQuake engine is available at https://docs.openquake.org/oq-engine/advanced/OpenQuakeforAdvancedUsers.pdf.

Interested users are also encouraged to peruse the OpenQuake Hazard Science and OpenQuake Risk Science books, which provide explanations of the scientific methodologies adopted in the implementation of the earthquake hazard and risk libraries of the OpenQuake engine.

Subscribe to the OpenQuake users mailing list to keep abreast of the latest announcements from the OpenQuake development team, to ask and answer questions related to the OpenQuake engine and participate in the ongoing discussions: https://groups.google.com/g/openquake-users

2.1.3 Launching a calculation#

An OpenQuake engine analysis is launched from the command line of a terminal.

A schematic list of the options that can be used for the execution of the OpenQuake engine can be obtained with the following command:

user@ubuntu:~$ oq engine --help

The result is the following:

usage: oq engine [-h] [--log-file LOG_FILE] [--no-distribute] [-y]
                 [-c CONFIG_FILE] [--make-html-report YYYY-MM-DD|today] [-u]
                 [-d] [-w] [--run JOB_INI [JOB_INI ...]]
                 [--list-hazard-calculations] [--list-risk-calculations]
                 [--delete-calculation CALCULATION_ID]
                 [--delete-uncompleted-calculations]
                 [--hazard-calculation-id HAZARD_CALCULATION_ID]
                 [--list-outputs CALCULATION_ID] [--show-log CALCULATION_ID]
                 [--export-output OUTPUT_ID TARGET_DIR]
                 [--export-outputs CALCULATION_ID TARGET_DIR] [-e]
                 [-l {debug, info, warn, error, critical}] [-r]
                 [--param PARAM]

Run a calculation using the traditional command line API

optional arguments:
  -h, --help            show this help message and exit
  --log-file LOG_FILE, -L LOG_FILE
                        Location where to store log messages; if not
                        specified, log messages will be printed to the console
                        (to stderr)
  --no-distribute, --nd
                        Disable calculation task distribution and run the
                        computation in a single process. This is intended for
                        use in debugging and profiling.
  -y, --yes             Automatically answer "yes" when asked to confirm an
                        action
  -c CONFIG_FILE, --config-file CONFIG_FILE
                        Custom openquake.cfg file, to override default
                        configurations
  --make-html-report YYYY-MM-DD|today, --r YYYY-MM-DD|today
                        Build an HTML report of the computation at the given
                        date
  -u, --upgrade-db      Upgrade the openquake database
  -d, --db-version      Show the current version of the openquake database
  -w, --what-if-I-upgrade
                        Show what will happen to the openquake database if you
                        upgrade
  --run JOB_INI [JOB_INI ...]
                        Run a job with the specified config file
  --list-hazard-calculations, --lhc
                        List hazard calculation information
  --list-risk-calculations, --lrc
                        List risk calculation information
  --delete-calculation CALCULATION_ID, --dc CALCULATION_ID
                        Delete a calculation and all associated outputs
  --delete-uncompleted-calculations, --duc
                        Delete all the uncompleted calculations
  --hazard-calculation-id HAZARD_CALCULATION_ID, --hc HAZARD_CALCULATION_ID
                        Use the given job as input for the next job
  --list-outputs CALCULATION_ID, --lo CALCULATION_ID
                        List outputs for the specified calculation
  --show-log CALCULATION_ID, --sl CALCULATION_ID
                        Show the log of the specified calculation
  --export-output OUTPUT_ID TARGET_DIR, --eo OUTPUT_ID TARGET_DIR
                        Export the desired output to the specified directory
  --export-outputs CALCULATION_ID TARGET_DIR, --eos CALCULATION_ID TARGET_DIR
                        Export all of the calculation outputs to the specified
                        directory
  -e, --exports         Comma-separated string specifing the export formats,
                        in order of priority
  -l, --log-level {debug, info, warn, error, critical}
                        Defaults to "info"
  -r, --reuse-input    Read the sources|exposures from the cache (if any)
  --param PARAM, -p PARAM
                        Override parameters specified with the syntax
                        NAME1=VALUE1,NAME2=VALUE2,...

3 Hazard#

3.1 Introduction to the Hazard Module#

The hazard component of the OpenQuake engine builds on top of the OpenQuake hazard library (oq-hazardlib), a Python-based library containing tools for PSHA calculations.

The web repository of this library is available at the following address:

In this section we briefly illustrate the main properties of the hazard component of the OpenQuake engine. In particular, we will describe the main typologies of sources supported and the main calculation workflows available.

3.1.1 Source typologies#

An OpenQuake engine seismic source input model contains a list of sources belonging to a finite set of possible typologies. Each source type is defined by a set of parameters - called source data - which are used to specify the source geometry and the properties of seismicity occurrence.

Currently the OpenQuake engine supports the following source types:

  • Sources for modelling distributed seismicity:

    • Point Source - The elemental source type used to model distributed seismicity. Grid and area sources (described below) are different containers of point sources.

    • Area Source - So far, the most frequently adopted source type in national and regional PSHA models.

    • Grid Source - A replacement for area sources admitting spatially variable seismicity occurrence properties.

  • Fault sources with floating ruptures:

    • Simple Fault Source - The simplest fault model in the OpenQuake engine. This source is habitually used to describe shallow seismogenic faults.

    • Complex Fault Source - Often used to model subduction interface sources with a complex geometry.

  • Fault sources with ruptures always covering the entire fault surface:

    • Characteristic Fault Source - A typology of source where ruptures always fill the entire fault surface.

    • Non-Parametric Source - A typology of source representing a collection of ruptures, each with their associated probabilities of 0, 1, 2 … occurrences in the investigation time

  • Sources for representing individual earthquake ruptures

    • Planar fault rupture - an individual fault rupture represented as a single rectangular plane

    • Multi-planar fault rupture - an individual rupture represented as a collection of rectangular planes

    • Simple fault rupture - an individual fault rupture represented as a simple fault surface

    • Complex fault rupture - an individual fault rupture represented as a complex fault surface

The OpenQuake engine contains some basic assumptions for the definition of these source typologies:

  • In the case of area and fault sources, the seismicity is homogeneously distributed over the source;

  • Seismicity temporal occurrence follows a Poissonian model.

The above sets of sources may be referred to as “parametric” sources, that is to say that the generation of the Earthquake Rupture Forecast is done by the OpenQuake engine based on the parameters of the sources set by the user. In some cases, particularly if the user wishes for the temporal occurrence model to be non-Poissonian (such as the lognormal or Brownian Passage Time models) a different type of behaviour is needed. For this OpenQuake-engine supports a Non-Parametric Source in which the Earthquake Rupture Forecast is provided explicitly by the user as a set of ruptures and their corresponding probabilities of occurrence.

3.1.1.1 Source typologies for modelling distributed seismicity#

3.1.1.1.1 Point sources#
Single rupture

Fig. 1 Single rupture#

The point source is the elemental source type adopted in the OpenQuake-engine for modelling distributed seismicity. The OpenQuake engine always performs calculations considering finite ruptures, even in the case of point sources.

These are the basic assumptions used to generate ruptures with point sources:

  • Ruptures have a rectangular shape

  • Rupture hypocenter is located in the middle of the rupture

  • Ruptures are limited at the top and at the bottom by two planes parallel to the sea level and placed at two characteristic depths named upper and lower seismogenic depths, respectively (see Fig. 1)

3.1.1.1.1.1 Source data#

For the definition of a point source the following parameters are required (Fig. 1 shows some of the parameters described below, together with an example of the surface of a generated rupture):

  • The coordinates of the point (i.e. longitude and latitude) [decimal degrees]

  • The upper and lower seismogenic depths [km]

  • One Magnitude-Frequency Distribution

  • One magnitude-scaling relationship

  • The rupture aspect ratio

  • A distribution of nodal planes i.e. one (or several) instances of the following set of parameters:

    • strike [degrees]

    • dip [degrees]

    • rake [degrees]

  • A magnitude independent depth distribution of hypocenters [km].

Fig. 2 shows ruptures generated by a point source for a range of magnitudes. Each rupture is centered on the single hypocentral position admitted by this point source. Ruptures are created by conserving the area computed using the specified magnitude-area scaling relatioship and the corresponding value of magnitude.

Point source with multiple ruptures. Note the change in the aspect ratio once the rupture width fills the entire seismogenic layer.

Fig. 2 Point source with multiple ruptures. Note the change in the aspect ratio once the rupture width fills the entire seismogenic layer.#

Below we provide the excerpt of an .xml file used to describe the properties of a point source. Note that in this example, ruptures occur on two possible nodal planes and two hypocentral depths. Fig. 3 shows the ruptures generated by the point source.

 1<pointSource id="1" name="point" tectonicRegion="Stable Continental Crust">
 2     <pointGeometry>
 3         <gml:Point>
 4             <gml:pos>-122.0 38.0</gml:pos>
 5         </gml:Point>
 6         <upperSeismoDepth>0.0</upperSeismoDepth>
 7         <lowerSeismoDepth>10.0</lowerSeismoDepth>
 8    </pointGeometry>
 9    <magScaleRel>WC1994</magScaleRel>
10    <ruptAspectRatio>0.5</ruptAspectRatio>
11    <truncGutenbergRichterMFD aValue="-3.5" bValue="1.0" minMag="5.0"
12                                maxMag="6.5" />
13    <nodalPlaneDist>
14         <nodalPlane probability="0.3" strike="0.0" dip="90.0" rake="0.0" />
15         <nodalPlane probability="0.7" strike="90.0" dip="45.0" rake="90.0" />
16    </nodalPlaneDist>
17    <hypoDepthDist>
18        <hypoDepth probability="0.5" depth="4.0" />
19        <hypoDepth probability="0.5" depth="8.0" />
20    </hypoDepthDist>
21</pointSource>
Ruptures produced by the source created using the information in the example .xml file described on page .

Fig. 3 Ruptures produced by the source created using the information in the example .xml file described on page .#

3.1.1.1.2 Grid sources#

A Grid Source is simply a collection of point sources distributed over a regular grid (usually equally spaced in longitude and latitude). In Probabilistic Seismic Hazard Analysis a grid source can be considered a model alternative to area sources, since they both model distributed seismicity. Grid sources are generally used to reproduce more faithfully the spatial pattern of seismicity depicted by the earthquakes occurred in the past; in some models (e.g. Petersen et al. (2008)) only events of low and intermediate magnitudes are considered. They are frequently, though not always, computed using seismicity smoothing algorithms (Frankel 1995; Woo 1996, amongst many others).

The use of smoothing algorithms to produce grid sources brings some advantages compared to area sources, since (1) it removes most of the unavoidable degree of subjectivity due to the definition of the geometries of the area sources and (2) it produces a spatial pattern of seismicity that is usually closer to what observed in the reality. Nevertheless, in many cases smoothing algorithms require an a-priori definition of some setup parameters that expose the calculation to a certain degree of partiality.

Grid sources are modeled in OpenQuake engine simply as a set of point sources; in other words, a grid source is just a long list of point sources specified as described in the previous section.

3.1.1.1.3 Area sources#

Area sources are usually adopted to describe the seismicity occurring over wide areas where the identification and characterization - i.e. the unambiguous definition of position, geometry and seismicity occurrence parameters - of single fault structures is difficult.

From a computation standpoint, area sources are comparable to grid sources since they are both represented in the engine by a list of point sources.

The OpenQuake engine using the source data parameters (see below) creates an equally spaced in distance grid of point sources where each point has the same seismicity occurrence properties (i.e. rate of events generated).

Below we provide a brief description of the parameters necessary to completely describe an area source.

3.1.1.1.3.1 Source data#
  • A polygon defining the external border of the area (i.e. a list of Longitude-Latitude [degrees] tuples) The current version of the OQ-engine doesn’t support the definition of internal borders.

  • The upper and lower seismogenic depths [km]

  • One Magnitude-Frequency Distribution

  • One Magnitude-Scaling Relationship

  • The rupture aspect ratio

  • A distribution of nodal planes i.e. one (or several) instances of the following set of parameters

    • strike [degrees]

    • dip [degrees]

    • rake [degrees]

  • A magnitude independent depth distribution of hypocenters [km].

Below we provide the excerpt of an .xml file used to describe the properties of an area source. The ruptures generated by the area source described in the example are controlled by two nodal planes and have hypocenters at localized at two distinct depths.

 1<areaSource id="1" name="Quito" tectonicRegion="Active Shallow Crust">
 2    <areaGeometry>
 3      <gml:Polygon>
 4        <gml:exterior>
 5          <gml:LinearRing>
 6            <gml:posList>
 7              -122.5 37.5
 8              -121.5 37.5
 9              -121.5 38.5
10              -122.5 38.5
11            </gml:posList>
12          </gml:LinearRing>
13        </gml:exterior>
14      </gml:Polygon>
15      <upperSeismoDepth>0.0</upperSeismoDepth>
16      <lowerSeismoDepth>10.0</lowerSeismoDepth>
17    </areaGeometry>
18    <magScaleRel>PeerMSR</magScaleRel>
19    <ruptAspectRatio>1.5</ruptAspectRatio>
20    <incrementalMFD minMag="6.55" binWidth="0.1">
21        <occurRates>0.0010614989 8.8291627E-4 7.3437777E-4 6.108288E-4
22                    5.080653E-4</occurRates>
23    </incrementalMFD>
24    <nodalPlaneDist>
25        <nodalPlane probability="0.3" strike="0.0" dip="90.0" rake="0.0"/>
26        <nodalPlane probability="0.7" strike="90.0" dip="45.0" rake="90.0"/>
27    </nodalPlaneDist>
28    <hypoDepthDist>
29        <hypoDepth probability="0.5" depth="4.0" />
30        <hypoDepth probability="0.5" depth="8.0" />
31    </hypoDepthDist>
32</areaSource>

3.1.1.2 Fault sources with floating ruptures#

Fault sources in the OpenQuake engine are classified according to the method adopted to distribute ruptures over the fault surface. Two options are currently supported:

  • With the first option, ruptures with a surface lower than the whole fault surface are floated so as to cover as much as possible homogeneously the fault surface. This model is compatible with all the supported magnitude-frequency distributions.

  • With the second option, ruptures always fill the entire fault surface. This model is compatible with magnitude-frequency distributions similar to a characteristic model (à la (Schwartz and Coppersmith 1984)).

In this subsection we discuss the different fault source types that support floating ruptures. In the next subsection we will illustrate the fault typology available to model a characteristic rupturing behaviour.

3.1.1.2.1 Simple Faults#

Simple Faults are the most common source type used to model shallow faults; the “simple” adjective relates to the geometry description of the source which is obtained by projecting the fault trace (i.e. a polyline) along a characteristic dip direction.

The parameters used to create an instance of this source type are described in the following paragraph.

3.1.1.2.1.1 Source data#
  • A horizontal Fault Trace (usually a polyline). It is a list of longitude-latitude tuples [degrees].

  • A Frequency-Magnitude Distribution

  • A Magnitude-Scaling Relationship

  • A representative value of the dip angle (specified following the Aki-Richards convention; see Aki and Richards (2002)) [degrees]

  • Rake angle (specified following the Aki-Richards convention; see Aki and Richards (2002)) [degrees]

  • Upper and lower depth values limiting the seismogenic interval [km]

For near-fault probabilistic seismic hazard analysis, two additional parameters are needed for characterising seismic sources:

  • A hypocentre list. It is a list of the possible hypocentral positions, and the corresponding weights, e.g., alongStrike=”0.25” downDip=”0.25” weight=”0.25”. Each hypocentral position is defined in relative terms using as a reference the upper left corner of the rupture and by specifying the fraction of rupture length and rupture width.

  • A slip list. It is a list of the possible rupture slip directions [degrees], and their corresponding weights. The angle describing each slip direction is measured counterclockwise using the fault strike direction as reference.

In near-fault PSHA calculations, the hypocentre list and the slip list are mandatory. The weights in each list must always sum to one. The available GMPE which currently supports the near-fault directivity PSHA calculation in OQ- engine is the ChiouYoungs2014NearFaultEffect GMPE developed by Brian S.-J. Chiou and Youngs (2014) (associated with an Active Shallow Crust tectonic region type).

We provide two examples of simple fault source files. The first is an excerpt of an xml file used to describe the properties of a simple fault source and the second example shows the excerpt of an xml file used to describe the properties of a simple fault source that can be used to perform a PSHA calculation taking into account directivity effects.

 1<simpleFaultSource id="1" name="Mount Diablo Thrust"
 2        tectonicRegion="Active Shallow Crust">
 3      <simpleFaultGeometry>
 4          <gml:LineString>
 5              <gml:posList>
 6                  -121.82290 37.73010
 7                  -122.03880 37.87710
 8              </gml:posList>
 9          </gml:LineString>
10          <dip>45.0</dip>
11          <upperSeismoDepth>10.0</upperSeismoDepth>
12          <lowerSeismoDepth>20.0</lowerSeismoDepth>
13      </simpleFaultGeometry>
14      <magScaleRel>WC1994</magScaleRel>
15      <ruptAspectRatio>1.5</ruptAspectRatio>
16      <incrementalMFD minMag="5.0" binWidth="0.1">
17          <occurRates>0.0010614989 8.8291627E-4 7.3437777E-4 6.108288E-4
18                      5.080653E-4</occurRates>
19      </incrementalMFD>
20      <rake>30.0</rake>
21      <hypoList>
22          <hypo alongStrike="0.25" downDip="0.25" weight="0.25"/>
23          <hypo alongStrike="0.25" downDip="0.75" weight="0.25"/>
24          <hypo alongStrike="0.75" downDip="0.25" weight="0.25"/>
25          <hypo alongStrike="0.75" downDip="0.75" weight="0.25"/>
26      </hypoList>
27      <slipList>
28          <slip weight="0.333">0.0</slip>
29          <slip weight="0.333">45.0</slip>
30          <slip weight="0.334">90.0</slip>
31      </slipList>
32</simpleFaultSource>
 1<simpleFaultSource id="1" name="Mount Diablo Thrust"
 2        tectonicRegion="Active Shallow Crust">
 3     <simpleFaultGeometry>
 4         <gml:LineString>
 5             <gml:posList>
 6                 -121.82290 37.73010
 7                 -122.03880 37.87710
 8             </gml:posList>
 9         </gml:LineString>
10         <dip>45.0</dip>
11         <upperSeismoDepth>10.0</upperSeismoDepth>
12         <lowerSeismoDepth>20.0</lowerSeismoDepth>
13     </simpleFaultGeometry>
14     <magScaleRel>WC1994</magScaleRel>
15     <ruptAspectRatio>1.5</ruptAspectRatio>
16     <incrementalMFD minMag="5.0" binWidth="0.1">
17         <occurRates>0.0010614989 8.8291627E-4 7.3437777E-4 6.108288E-4
18                     5.080653E-4</occurRates>
19     </incrementalMFD>
20     <rake>30.0</rake>
21     <hypoList>
22         <hypo alongStrike="0.25" downDip="0.25" weight="0.25"/>
23         <hypo alongStrike="0.25" downDip="0.75" weight="0.25"/>
24         <hypo alongStrike="0.75" downDip="0.25" weight="0.25"/>
25         <hypo alongStrike="0.75" downDip="0.75" weight="0.25"/>
26     </hypoList>
27     <slipList>
28         <slip weight="0.333">0.0</slip>
29         <slip weight="0.333">45.0</slip>
30         <slip weight="0.334">90.0</slip>
31     </slipList>
32</simpleFaultSource>
3.1.1.2.2 Complex Faults#

A complex fault differs from simple fault just by the way the geometry of the fault surface is defined and the fault surface is later created. The input parameters used to describe complex faults are, for the most part, the same used to describe the simple fault typology.

In the case of complex faults, the dip angle is not requested while the fault trace is substituted by two fault edges limiting the top and bottom of the fault surface. Additional curves lying over the fault surface can be specified to complement and refine the description of the fault surface geometry. Unlike the simple fault these edges are not required to be horizontal and may vary in elevation, i.e. the upper edge may represent the intersection between the exposed fault trace and the topographic surface, where positive values indicate below sea level, and negative values indicate above sea level.

Usually, we use complex faults to model intraplate megathrust faults such as the big subduction structures active in the Pacific (Sumatra, South America, Japan) but this source typology can be used also to create - for example - listric fault sources with a realistic geometry.

 1<complexFaultSource id="1" name="Cascadia Megathrust"
 2                    tectonicRegion="Subduction Interface">
 3    <complexFaultGeometry>
 4        <faultTopEdge>
 5            <gml:LineString>
 6                <gml:posList>
 7                    -124.704  40.363  0.5493260E+01
 8                    -124.977  41.214  0.4988560E+01
 9                    -125.140  42.096  0.4897340E+01
10                </gml:posList>
11            </gml:LineString>
12        </faultTopEdge>
13        <intermediateEdge>
14            <gml:LineString>
15                <gml:posList>
16                    -124.704  40.363  0.5593260E+01
17                    -124.977  41.214  0.5088560E+01
18                    -125.140  42.096  0.4997340E+01
19                </gml:posList>
20            </gml:LineString>
21        </intermediateEdge>
22        <intermediateEdge>
23            <gml:LineString>
24                <gml:posList>
25                    -124.704  40.363  0.5693260E+01
26                    -124.977  41.214  0.5188560E+01
27                    -125.140  42.096  0.5097340E+01
28                </gml:posList>
29            </gml:LineString>
30        </intermediateEdge>
31        <faultBottomEdge>
32            <gml:LineString>
33                <gml:posList>
34                    -123.829  40.347  0.2038490E+02
35                    -124.137  41.218  0.1741390E+02
36                    -124.252  42.115  0.1752740E+02
37                </gml:posList>
38            </gml:LineString>
39        </faultBottomEdge>
40    </complexFaultGeometry>
41    <magScaleRel>WC1994</magScaleRel>
42    <ruptAspectRatio>1.5</ruptAspectRatio>
43    <truncGutenbergRichterMFD aValue="-3.5" bValue="1.0" minMag="5.0"
44                              maxMag="6.5" />
45    <rake>30.0</rake>
46</complexFaultSource>

As with the previous examples, the red text highlights the parameters used to specify the source geometry, the parameters in green describe the rupture mechanism, the text in blue describes the magnitude-frequency distribution and the gray text describes the rupture properties.

3.1.1.3 Fault sources without floating ruptures#

3.1.1.3.1 Characteristic faults#

The characteristic fault source is a particular typology of fault created with the assumption that its ruptures will always cover the entire fault surface. As such, no floating is necessary on the surface. The characteristic fault may still take as input a magnitude frequency distribution. In this case, the fault surface can be represented either as a Simple Fault Source surface or as a Complex Fault Source surface or as a combination of rectangular ruptures as represented in Fig. 4. Mutiple surfaces containing mixed geometry types are also supported.

Geometry of a multi-segmented characteristic fault composed of four rectangular ruptures as modelled in OpenQuake.

Fig. 4 Geometry of a multi-segmented characteristic fault composed of four rectangular ruptures as modelled in OpenQuake.#

3.1.1.3.1.1 Source data#
  • The characteristic rupture surface is defined through one of the following options:

    • A list of rectangular ruptures (“planar surfaces”)

    • A Simple Fault Source geometry

    • A Complex Fault Source geometry

  • A Frequency-Magnitude Distribution.

  • Rake angle (specified following the Aki-Richards convention; see Aki and Richards (2002)).

  • Upper and lower depth values limiting the seismogenic interval.

A comprehensive example enumerating the possible rupture surface configurations is shown below.

 1<characteristicFaultSource id="5" name="characteristic source, simple fault"
 2                           tectonicRegion="Volcanic">
 3    <truncGutenbergRichterMFD aValue="-3.5" bValue="1.0"
 4                              minMag="5.0" maxMag="6.5" />
 5    <rake>30.0</rake>
 6    <surface>
 7        <!-- Characteristic Fault with a simple fault surface -->
 8        <simpleFaultGeometry>
 9            <gml:LineString>
10                <gml:posList>
11                    -121.82290 37.73010
12                    -122.03880 37.87710
13                </gml:posList>
14            </gml:LineString>
15            <dip>45.0</dip>
16            <upperSeismoDepth>10.0</upperSeismoDepth>
17            <lowerSeismoDepth>20.0</lowerSeismoDepth>
18        </simpleFaultGeometry>
19    </surface>
20</characteristicFaultSource>
 1<characteristicFaultSource id="6" name="characteristic source, complex fault"
 2                           tectonicRegion="Volcanic">
 3    <incrementalMFD minMag="5.0" binWidth="0.1">
 4        <occurRates>0.0010614989 8.8291627E-4 7.3437777E-4</occurRates>
 5    </incrementalMFD>
 6    <rake>60.0</rake>
 7    <surface>
 8        <!-- Characteristic source with a complex fault surface -->
 9        <complexFaultGeometry>
10            <faultTopEdge>
11                <gml:LineString>
12                    <gml:posList>
13                       -124.704  40.363  0.5493260E+01
14                       -124.977  41.214  0.4988560E+01
15                       -125.140  42.096  0.4897340E+01
16                    </gml:posList>
17                </gml:LineString>
18            </faultTopEdge>
19            <faultBottomEdge>
20                <gml:LineString>
21                    <gml:posList>
22                        -123.829  40.347  0.2038490E+02
23                        -124.137  41.218  0.1741390E+02
24                        -124.252  42.115  0.1752740E+02
25                    </gml:posList>
26                </gml:LineString>
27            </faultBottomEdge>
28        </complexFaultGeometry>
29    </surface>
30</characteristicFaultSource>
 1<characteristicFaultSource id="7" name="characteristic source, multi surface"
 2                           tectonicRegion="Volcanic">
 3    <truncGutenbergRichterMFD aValue="-3.6" bValue="1.0"
 4                              minMag="5.2" maxMag="6.4" />
 5    <rake>90.0</rake>
 6    <surface>
 7        <!-- Characteristic source with a collection of planar surfaces -->
 8        <planarSurface>
 9            <topLeft lon="-1.0" lat="1.0" depth="21.0" />
10            <topRight lon="1.0" lat="1.0" depth="21.0" />
11            <bottomLeft lon="-1.0" lat="-1.0" depth="59.0" />
12            <bottomRight lon="1.0" lat="-1.0" depth="59.0" />
13        </planarSurface>
14        <planarSurface strike="20.0" dip="45.0">
15             <topLeft lon="1.0" lat="1.0" depth="20.0" />
16             <topRight lon="3.0" lat="1.0" depth="20.0" />
17             <bottomLeft lon="1.0" lat="-1.0" depth="80.0" />
18             <bottomRight lon="3.0" lat="-1.0" depth="80.0" />
19         </planarSurface>
20    </surface>
21</characteristicFaultSource>

3.1.1.4 Non-Parametric Sources#

3.1.1.4.1 Non-Parametric Fault#

The non-parametric fault typology requires that the user indicates the rupture properties (rupture surface, magnitude, rake and hypocentre) and the corresponding probabilities of the rupture. The probabilities are given as a list of floating point values that correspond to the probabilities of \(0, 1, 2, \ldots ... N\) occurrences of the rupture within the specified investigation time. Note that there is not, at present, any internal check to ensure that the investigation time to which the probabilities refer corresponds to that specified in the configuration file. As the surface of the rupture is set explicitly, no rupture floating occurs, and, as in the case of the characteristic fault source, the rupture surface can be defined as either a single planar rupture, a list of planar ruptures, a Simple Fault Source geometry, a Complex Fault Source geometry, or a combination of different geometries.

Comprehensive examples enumerating the possible configurations are shown below:

 1<nonParametricSeismicSource id="1" name="A Non Parametric Planar Source"
 2                            tectonicRegion="Some TRT">
 3    <singlePlaneRupture probs_occur="0.544 0.456">
 4        <magnitude>8.3</magnitude>
 5        <rake>90.0</rake>
 6        <hypocenter depth="26.101" lat="40.726" lon="143.0"/>
 7        <planarSurface>
 8            <topLeft depth="9.0" lat="41.6" lon="143.1"/>
 9            <topRight depth="9.0" lat="40.2" lon="143.91"/>
10            <bottomLeft depth="43.202" lat="41.252" lon="142.07"/>
11            <bottomRight depth="43.202" lat="39.852" lon="142.91"/>
12        </planarSurface>
13    </singlePlaneRupture>
14    <multiPlanesRupture probs_occur="0.9244 0.0756">
15        <magnitude>6.9</magnitude>
16        <rake>0.0</rake>
17        <hypocenter depth="7.1423" lat="35.296" lon="139.31"/>
18        <planarSurface>
19            <topLeft depth="2.0" lat="35.363" lon="139.16"/>
20            <topRight depth="2.0" lat="35.394" lon="138.99"/>
21            <bottomLeft depth="14.728" lat="35.475" lon="139.19"/>
22            <bottomRight depth="14.728" lat="35.505" lon="139.02"/>
23        </planarSurface>
24        <planarSurface>
25            <topLeft depth="2.0" lat="35.169" lon="139.34"/>
26            <topRight depth="2.0" lat="35.358" lon="139.17"/>
27            <bottomLeft depth="12.285" lat="35.234" lon="139.45"/>
28            <bottomRight depth="12.285" lat="35.423" lon="139.28"/>
29        </planarSurface>
30    </multiPlanesRupture>
31</nonParametricSeismicSource>
 1<nonParametricSeismicSource id="2" name="A Non Parametric (Simple) Source"
 2                            tectonicRegion="Some TRT">
 3    <simpleFaultRupture probs_occur="0.157 0.843">
 4        <magnitude>7.8</magnitude>
 5        <rake>90.0</rake>
 6        <hypocenter depth="22.341" lat="43.624" lon="147.94"/>
 7        <simpleFaultGeometry>
 8            <gml:LineString>
 9                <gml:posList>
10                    147.96 43.202
11                    148.38 43.438
12                    148.51 43.507
13                    148.68 43.603
14                    148.76 43.640
15                </gml:posList>
16            </gml:LineString>
17            <dip>30.0</dip>
18            <upperSeismoDepth>14.5</upperSeismoDepth>
19            <lowerSeismoDepth>35.5</lowerSeismoDepth>
20        </simpleFaultGeometry>
21    </simpleFaultRupture>
22</nonParametricSeismicSource>
 1<nonParametricSeismicSource id="3" name="A Non Parametric (Complex) Source"
 2                            tectonicRegion="Some TRT">
 3    <complexFaultRupture probs_occur="0.157 0.843">
 4        <magnitude>7.8</magnitude>
 5        <rake>90.0</rake>
 6        <hypocenter depth="22.341" lat="43.624" lon="147.94"/>
 7        <complexFaultGeometry>
 8            <faultTopEdge>
 9                <gml:LineString>
10                    <gml:posList>
11                        148.76 43.64 5.0
12                        148.68 43.603 5.0
13                        148.51 43.507 5.0
14                        148.38 43.438 5.0
15                        147.96 43.202 5.0
16                    </gml:posList>
17                </gml:LineString>
18            </faultTopEdge>
19            <faultBottomEdge>
20               <gml:LineString>
21                    <gml:posList>
22                        147.92 44.002 35.5
23                        147.81 43.946 35.5
24                        147.71 43.897 35.5
25                        147.5 43.803 35.5
26                        147.36 43.727 35.5
27                    </gml:posList>
28                </gml:LineString>
29            </faultBottomEdge>
30        </complexFaultGeometry>
31    </complexFaultRupture>
32</nonParametricSeismicSource>

3.1.2 Magnitude-frequency distributions#

The magnitude-frequency distributions currently supported by the OpenQuake engine are the following:

A discrete incremental magnitude-frequency distribution

It is the simplest distribution supported. It is defined by the minimum value of magnitude (representing the mid point of the first bin) and the bin width. The distribution itself is simply a sequence of floats describing the annual number of events for different bins. The maximum magnitude admitted by this magnitude-frequency distribution is just the sum of the minimum magnitude and the product of the bin width by the number annual rate values. Below we provide an example of the xml that should be incorporated in a seismic source description in order to define this Magnitude-Frequency Distribution.

1<incrementalMFD minMag="5.05" binWidth="0.1">
2    <occurRates>0.15 0.08 0.05 0.03 0.015</occurRates>
3</incrementalMFD>

The magnitude-frequency distribution obtained with the above parameters is represented in Fig. 5.

Example of an incremental magnitude-frequency distribution.

Fig. 5 Example of an incremental magnitude-frequency distribution.#

A double truncated Gutenberg-Richter distribution

This distribution is described by means of a minimum minMag and maximum magnitude maxMag and by the \(a\) and \(b\) values of the Gutenberg-Richter relationship.

The syntax of the xml used to describe this magnitude-frequency distribution is rather compact as demonstrated in the following example:

1<truncGutenbergRichterMFD aValue="5.0" bValue="1.0" minMag="5.0"
2                          maxMag="6.0"/>

Fig. 6 shows the magnitude-frequency distribution obtained using the parameters of the considered example.

Example of a double truncated Gutenberg-Richter magnitude-frequency distribution.

Fig. 6 Example of a double truncated Gutenberg-Richter magnitude-frequency distribution.#

Hybrid Characteristic earthquake model (à la (Youngs and Coppersmith 1985))

The hybrid characteristic earthquake model, presented by (Youngs and Coppersmith 1985), distributes seismic moment proportionally between a characteristic model (for larger magnitudes) and an exponential model. The rate of events is dependent on the magnitude of the characteristic earthquake, the b-value and the total moment rate of the system (Fig. 7). However, the total moment rate may be defined in one of two ways. If the total moment rate of the source is known, as may be the case for a fault surface with known area and slip rate, then the distribution can be defined from the total moment rate (in N-m) of the source directly. Alternatively, the distribution can be defined from the rate of earthquakes in the characteristic bin, which may be preferable if the distribution is determined from observed seismicity behaviour. The option to define the distribution according to the total moment rate is input as:

1<YoungsCoppersmithMFD minmag="5.0" bValue="1.0" binWidth="0.1"
2                      characteristicMag="7.0" totalMomentRate="1.05E19"/>

whereas the option to define the distribution from the rate of the characteristic events is given as:

1<YoungsCoppersmithMFD minmag="5.0" bValue="1.0" binWidth="0.1"
2                      characteristicMag="7.0" characteristicRate="0.005"/>

Note that in this distribution the width of the magnitude bin must be defined explicitly in the model.

(Youngs and Coppersmith 1985) magnitude-frequency distribution.

Fig. 7 (Youngs and Coppersmith 1985) magnitude-frequency distribution.#

“Arbitrary” Magnitude Frequency Distribution

The arbitrary magnitude frequency distribution is another non-parametric form of MFD, in which the rates are defined explicitly. Here, the magnitude frequency distribution is defined by a list of magnitudes and their corresponding rates of occurrence. There is no bin-width as the rates correspond exactly to the specific magnitude. Unlike the evenly discretised MFD, there is no requirement that the magnitudes be equally spaced. This distribution (illustrated in Fig. 8) can be input as:

1<arbitraryMFD>
2    <occurRates>0.12 0.036 0.067 0.2</occurRates>
3    <magnitudes>8.1 8.47 8.68 9.02</magnitude>
4</arbitraryMFD>
“Arbitrary” magnitude-frequency distribution.

Fig. 8 “Arbitrary” magnitude-frequency distribution.#

3.1.3 Magnitude-scaling relationships#

We provide below a list of the magnitude-area scaling relationships implemented in the OpenQuake hazard library (oq-hazardlib):

3.1.3.1 Relationships for shallow earthquakes in active tectonic regions#

  • (Wells and Coppersmith 1994) - One of the most well known magnitude scaling relationships, based on a global database of historical earthquake ruptures. The implemented relationship is the one linking magnitude to rupture area, and is called with the keyword WC1994

3.1.3.2 Magnitude-scaling relationships for subduction earthquakes#

  • (Strasser, Arango, and Bommer 2010) - Defines several magnitude scaling relationships for interface and in-slab earthquakes. Only the magnitude to rupture-area scaling relationships are implemented here, and are called with the keywords StrasserInterface and StrasserIntraslab respectively.

  • (Thingbaijam, Mai, and Goda 2017) - Define magnitude scaling relationships for interface. Only the magnitude to rupture-area scaling relationships are implemented here, and are called with the keywords ThingbaijamInterface.

3.1.3.3 Magnitude-scaling relationships stable continental regions#

  • (EPRI 2011) - Defines a single magnitude to rupture-area scaling relationship for use in the central and eastern United States: \(Area = 10.0^{M_W - 4.336}\). It is called with the keyword CEUS2011

3.1.3.4 Miscellaneous Magnitude-Scaling Relationships#

  • PeerMSR defines a simple magnitude scaling relation used as part of the Pacific Earthquake Engineering Research Center verification of probabilistic seismic hazard analysis programs: \(Area = 10.0 ^{M_W - 4.0}\).

  • PointMSR approximates a ‘point’ source by returning an infinitesimally small area for all magnitudes. Should only be used for distributed seismicity sources and not for fault sources.

3.1.4 Calculation workflows#

The hazard component of the OpenQuake engine can compute seismic hazard using various approaches. Three types of analysis are currently supported:

  • Classical Probabilistic Seismic Hazard Analysis (PSHA), allowing calculation of hazard curves and hazard maps following the classical integration procedure ((Cornell 1968), McGuire (1976)) as formulated by (Field, Jordan, and Cornell 2003).

  • Event-Based Probabilistic Seismic Hazard Analysis, allowing calculation of ground-motion fields from stochastic event sets. Traditional results - such as hazard curves - can be obtained by post- processing the set of computed ground-motion fields.

  • Scenario Based Seismic Hazard Analysis, allowing the calculation of ground motion fields from a single earthquake rupture scenario taking into account ground-motion aleatory variability.

Each workflow has a modular structure, so that intermediate results can be exported and analyzed. Each calculator can be extended independently of the others so that additional calculation options and methodologies can be easily introduced, without affecting the overall calculation workflow.

3.1.4.1 Classical Probabilistic Seismic Hazard#

Analysis Input data for the classical Probabilistic Seismic Hazard Analysis consist of a PSHA input model provided together with calculation settings.

The main calculators used to perform this analysis are the following:

  1. Logic Tree Processor

    The Logic Tree Processor (LTP) takes as an input the Probabilistic Seismic Hazard Analysis Input Model and creates a Seismic Source Model. The LTP uses the information in the Initial Seismic Source Models and the Seismic Source Logic Tree to create a Seismic Source Input Model (i.e. a model describing geometry and activity rates of each source without any epistemic uncertainty).

    Following a procedure similar to the one just described the Logic Tree Processor creates a Ground Motion model (i.e. a data structure that associates to each tectonic region considered in the calculation a Ground Motion Prediction Equation).

  2. Earthquake Rupture Forecast Calculator

    The produced Seismic Source Input Model becomes an input information for the Earthquake Rupture Forecast (ERF) calculator which creates a list earthquake ruptures admitted by the source model, each one characterized by a probability of occurrence over a specified time span.

  3. Classical PSHA Calculator

    The classical PSHA calculator uses the ERF and the Ground Motion model to compute hazard curves on each site specified in the calculation settings.

3.1.4.2 Event-Based Probabilistic Seismic Hazard#

Analysis Input data for the Event-Based PSHA - as in the case of the Classical Probabilistic Seismic Hazard Analysis calculator - consists of a PSHA Input Model and a set of calculation settings.

The main calculators used to perform this analysis are:

  1. Logic Tree Processor

    The Logic Tree Processor works in the same way described in the description of the Classical Probabilistic Seismic Hazard Analysis workflow (see Section  3.2.4.1   Classical PSHA).

  2. Earthquake Rupture Forecast Calculator

    The Earthquake Rupture Forecast Calculator was already introduced in the description of the PSHA workflow (see Section  3.2.4.1   Classical PSHA).

  3. Stochastic Event Set Calculator

    The Stochastic Event Set Calculator generates a collection of stochastic event sets by sampling the ruptures contained in the ERF according to their probability of occurrence.

    A Stochastic Event Set (SES) thus represents a potential realisation of the seismicity (i.e. a list of ruptures) produced by the set of seismic sources considered in the analysis over the time span fixed for the calculation of hazard.

  4. Ground Motion Field Calculator

    The Ground Motion Field Calculator computes for each event contained in a Stochastic Event Set a realization of the geographic distribution of the shaking by taking into account the aleatory uncertainties in the ground- motion model. Eventually, the Ground Motion Field calculator can consider the spatial correlation of the ground-motion during the generation of the Ground Motion Field.

  5. Event-based PSHA Calculator

    The event-based PSHA calculator takes a (large) set of ground-motion fields representative of the possible shaking scenarios that the investigated area can experience over a (long) time span and for each site computes the corresponding hazard curve.

    This procedure is computationally intensive and is not recommended for investigating the hazard over large areas.

3.1.4.3 Scenario based Seismic Hazard Analysis#

In case of Scenario Based Seismic Hazard Analysis, the input data consist of a single earthquake rupture model and one or more ground-motion models. Using the Ground Motion Field Calculator, multiple realizations of ground shaking can be computed, each realization sampling the aleatory uncertainties in the ground-motion model. The main calculator used to perform this analysis is the Ground Motion Field Calculator, which was already introduced during the description of the event based PSHA workflow (see Section  3.2.4.3   Event based PSHA).

As the scenario calculator does not need to determine the probability of occurrence of the specific rupture, but only sufficient information to parameterise the location (as a three-dimensional surface), the magnitude and the style-of-faulting of the rupture, a more simplified NRML structure is sufficient compared to the source model structures described previously in 3.1.1   Source typologies. A rupture model XML can be defined in the following formats:

  1. Simple Fault Rupture - in which the geometry is defined by the trace of the fault rupture, the dip and the upper and lower seismogenic depths. An example is shown in the listing below:

     1      <?xml version='1.0' encoding='utf-8'?>
     2      <nrml xmlns:gml="http://www.opengis.net/gml"
     3            xmlns="http://openquake.org/xmlns/nrml/0.5">
     4
     5          <simpleFaultRupture>
     6            <magnitude>6.7</magnitude>
     7            <rake>180.0</rake>
     8            <hypocenter lon="-122.02750" lat="37.61744" depth="6.7"/>
     9            <simpleFaultGeometry>
    10              <gml:LineString>
    11                <gml:posList>
    12                  -121.80236 37.39713
    13                  -121.91453 37.48312
    14                  -122.00413 37.59493
    15                  -122.05088 37.63995
    16                  -122.09226 37.68095
    17                  -122.17796 37.78233
    18                </gml:posList>
    19              </gml:LineString>
    20              <dip>76.0</dip>
    21              <upperSeismoDepth>0.0</upperSeismoDepth>
    22              <lowerSeismoDepth>13.4</lowerSeismoDepth>
    23            </simpleFaultGeometry>
    24          </simpleFaultRupture>
    25
    26      </nrml>
    
  2. Planar & Multi-Planar Rupture - in which the geometry is defined as a collection of one or more rectangular planes, each defined by four corners. An example of a multi-planar rupture is shown below in the listing below:

     1<?xml version='1.0' encoding='utf-8'?>
     2<nrml xmlns:gml="http://www.opengis.net/gml"
     3      xmlns="http://openquake.org/xmlns/nrml/0.5">
     4
     5    <multiPlanesRupture>
     6        <magnitude>8.0</magnitude>
     7        <rake>90.0</rake>
     8        <hypocenter lat="-1.4" lon="1.1" depth="10.0"/>
     9            <planarSurface strike="90.0" dip="45.0">
    10                <topLeft lon="-0.8" lat="-2.3" depth="0.0" />
    11                <topRight lon="-0.4" lat="-2.3" depth="0.0" />
    12                <bottomLeft lon="-0.8" lat="-2.3890" depth="10.0" />
    13                <bottomRight lon="-0.4" lat="-2.3890" depth="10.0" />
    14            </planarSurface>
    15            <planarSurface strike="30.94744" dip="30.0">
    16                <topLeft lon="-0.42" lat="-2.3" depth="0.0" />
    17                <topRight lon="-0.29967" lat="-2.09945" depth="0.0" />
    18                <bottomLeft lon="-0.28629" lat="-2.38009" depth="10.0" />
    19                <bottomRight lon="-0.16598" lat="-2.17955" depth="10.0" />
    20            </planarSurface>
    21    </multiPlanesRupture>
    22
    23</nrml>
    
  3. Complex Fault Rupture - in which the geometry is defined by the upper, lower and (if applicable) intermediate edges of the fault rupture. An example of a complex fault rupture is shown below in the listing below:

     1<?xml version='1.0' encoding='utf-8'?>
     2<nrml xmlns:gml="http://www.opengis.net/gml"
     3      xmlns="http://openquake.org/xmlns/nrml/0.5">
     4
     5    <complexFaultRupture>
     6        <magnitude>8.0</magnitude>
     7        <rake>90.0</rake>
     8        <hypocenter lat="-1.4" lon="1.1" depth="10.0"/>
     9        <complexFaultGeometry>
    10            <faultTopEdge>
    11                <gml:LineString>
    12                    <gml:posList>
    13                        0.6 -1.5 2.0
    14                        1.0 -1.3 5.0
    15                        1.5 -1.0 8.0
    16                    </gml:posList>
    17                </gml:LineString>
    18            </faultTopEdge>
    19            <intermediateEdge>
    20                <gml:LineString>
    21                    <gml:posList>
    22                        0.65 -1.55 4.0
    23                        1.1  -1.4  10.0
    24                        1.5  -1.2  20.0
    25                    </gml:posList>
    26                </gml:LineString>
    27            </intermediateEdge>
    28            <faultBottomEdge>
    29                <gml:LineString>
    30                    <gml:posList>
    31                        0.65 -1.7 8.0
    32                        1.1  -1.6 15.0
    33                        1.5  -1.7 35.0
    34                    </gml:posList>
    35                </gml:LineString>
    36            </faultBottomEdge>
    37        </complexFaultGeometry>
    38    </complexFaultRupture>
    39
    40</nrml>
    

3.2 Using the Hazard Module#

This Chapter summarises the structure of the information necessary to define a PSHA input model to be used with the OpenQuake engine.

Input data for probabilistic based seismic hazard analysis (Classical, Event based, Disaggregation, and Uniform Hazard Spectra) are organised into:

  • A general configuration file.

  • A file describing the Seismic Source System, that is the set of initial source models and associated epistemic uncertainties needed to model the seismic activity in the region of interest.

  • A file describing the Ground Motion System, that is the set of ground motion prediction equations, per tectonic region type, needed to model the ground motion shaking in the region of interest.

Fig. 9 summarises the structure of a PSHA input model for the OpenQuake engine and the relationships between the different files.

PSHA Input Model structure

Fig. 9 PSHA Input Model structure#

3.2.1 Defining Logic Trees#

The main components of a logic tree structure in the OpenQuake engine are the following:

Branch

: the simplest component of a logic tree structure. A Branch represent a possible interpretation of a value assignment for a specific type of uncertainty. It is fully described by the tuple (parameter or model, weight).

Branching set

: it is a key component in the logic tree structure used by the OpenQuake engine. It groups a set of branches i.e. alternative interpretations of a parameter or a model. Each branching set is defined by:

This set of uncertainties can be applied to the whole initial seismic source input model or just to a subset of seismic sources. The sum of the weights/probabilities assigned to the set of branches always correspond to one.

Below we provide a simple schema illustrating the skeleton of xml file containing the desciption of a logic tree:

1    <logicTreeBranchSet branchSetID=ID
2                        uncertaintyType=TYPE>
3        <logicTreeBranch>
4            <uncertaintyModel>VALUE</uncertaintyModel>
5            <uncertaintyWeight>WEIGHT</uncertaintyWeight>
6        </logicTreeBranch>
7    </logicTreeBranchSet>

As it appears from this example, the structure of a logic tree is a set of nested elements.

A schematic representation of the elemental components of a logic tree structure is provided in Fig. 10. A Branch set identifies a collection of branches (i.e. individual branches) whose weights sum to 1.

Generic Logic Tree structure as described in terms of *Branch* sets, and individual branches.

Fig. 10 Generic Logic Tree structure as described in terms of Branch sets, and individual branches.#

3.2.1.1 Logic trees as described in the nrml schema#

In the NRML schema, a logic tree structure is defined through the logicTree element:

1<logicTree logicTreeID="ID">
2...
3</logicTree>

A logicTree contains as a sequence of logicTreeBranchSet elements.

There are no restrictions on the number of Branch set that can be defined.

Each logicTreeBranchSet has two required attributes: branchSetID and uncertaintyType. The latter defines the type of epistemic uncertainty this Branch set is describing.

 1<logicTree logicTreeID="ID">
 2             <logicTreeBranchSet branchSetID="ID_1"
 3                     uncertaintyType="UNCERTAINTY_TYPE">
 4                     ...
 5             </logicTreeBranchSet>
 6             <logicTreeBranchSet branchSetID="ID_2"
 7                     uncertaintyType="UNCERTAINTY_TYPE">
 8                     ...
 9             </logicTreeBranchSet>
10             ...
11             <logicTreeBranchSet branchSetID="ID_N"
12                     uncertaintyType="UNCERTAINTY_TYPE">
13                     ...
14             </logicTreeBranchSet>
15...
16</logicTree>

Possible values for the uncertaintyType attribute are:

  • gmpeModel: indicates epistemic uncertainties on ground motion prediction equations

  • sourceModel: indicates epistemic uncertainties on source models

  • maxMagGRRelative: indicates relative (i.e. increments) epistemic uncertainties to be added (or subtracted, depending on the sign of the increment) to the Gutenberg-Richter maximum magnitude value.

  • bGRRelative: indicates relative epistemic uncertainties to be applied to the Gutenberg-Richter b value.

  • abGRAbsolute: indicates absolute (i.e. values used to replace original values) epistemic uncertainties on the Gutenberg-Richter a and b values.

  • maxMagGRAbsolute: indicates (absolute) epistemic uncertainties on the Gutenberg-Richter maximum magnitude.

  • incrementalMFDAbsolute: indicates (absolute) epistemic uncertainties on the incremental magnitude frequency distribution (i.e. alternative rates and/or minimum magnitude) of a specific source (can only be applied to individual sources)

  • simpleFaultGeometryAbsolute: indicates alternative representations of the simple fault geometry for an individual simple fault source

  • simpleFaultDipRelative: indicates a relative increase or decrease in fault dip for one or more simple fault sources

  • simpleFaultDipAbsolute: indicates alternative values of fault dip for one or more simple fault sources

  • complexFaultGeometryAbsolute: indicates alternative representations of complex fault geometry for an individual complex fault source

  • characteristicFaultGeometryAbsolute: indicates alternative representations of the characteristic fault geometry for an individual characteristic fault source

A branchSet is defined as a sequence of logicTreeBranch elements, each specified by an uncertaintyModel element (a string identifying an uncertainty model; the content of the string varies with the uncertaintyType attribute value of the branchSet element) and the uncertaintyWeight element (specifying the probability/weight associated to the uncertaintyModel):

 1< logicTree  logicTreeID="ID">
 2...
 3
 4             < logicTreeBranchSet  branchSetID="ID_#"
 5                             uncertaintyType="UNCERTAINTY_TYPE">
 6                     < logicTreeBranch  branchID="ID_1">
 7                             <uncertaintyModel>
 8                                 UNCERTAINTY_MODEL
 9                             </uncertaintyModel>
10                             <uncertaintyWeight>
11                                 UNCERTAINTY_WEIGHT
12                             </uncertaintyWeight>
13                     </ logicTreeBranch >
14                     ...
15                     < logicTreeBranch  branchID="ID_N">
16                             <uncertaintyModel>
17                                 UNCERTAINTY_MODEL
18                             </uncertaintyModel>
19                             <uncertaintyWeight>
20                                 UNCERTAINTY_WEIGHT
21                             </uncertaintyWeight>
22                     </logicTreeBranch>
23             </logicTreeBranchSet>
24...
25</logicTree >

Depending on the uncertaintyType the content of the <uncertaintyModel> element changes:

  • if uncertaintyType="gmpeModel", the uncertainty model contains the name of a ground motion prediction equation (a list of available GMPEs can be obtained using oq info gsims and these are also documented at: http://docs.openquake.org/oq-engine/stable/openquake.hazardlib.gsim.html):

    <uncertaintyModel>GMPE_NAME</uncertaintyModel>
    
  • if uncertaintyType="sourceModel", the uncertainty model contains the paths to a source model file, e.g.:

    1<uncertaintyModel>SOURCE_MODEL_FILE_PATH</uncertaintyModel>
    
  • if uncertaintyType="maxMagGRRelative", the uncertainty model contains the increment to be added (or subtracted, depending on the sign) to the Gutenberg-Richter maximum magnitude:

    <uncertaintyModel>MAX_MAGNITUDE_INCREMENT</uncertaintyModel>
    
  • if uncertaintyType="bGRRelative", the uncertainty model contains the increment to be added (or subtracted, depending on the sign) to the Gutenberg-Richter b value:

    <uncertaintyModel>B_VALUE_INCREMENT</uncertaintyModel>
    
  • if uncertaintyType="abGRAbsolute", the uncertainty model must contain one a and b pair:

    <uncertaintyModel>A_VALUE B_VALUE</uncertaintyModel>
    
  • if uncertaintyType="maxMagGRAbsolute", the uncertainty model must contain one Gutenberg-Richter maximum magnitude value:

    <uncertaintyModel>MAX_MAGNITUDE</uncertaintyModel>
    
  • if uncertaintyType="incrementalMFDAbsolute", the uncertainty model must contain an instance of the incremental MFD node:

    1<uncertaintyModel>
    2    <incrementalMFD
    3        minMag="MIN MAGNITUDE"
    4        binWidth="BIN WIDTH">
    5        <occurRates>RATE_1 RATE_2 ... RATE_N</occurRates>
    6    </incrementalMFD>
    7</uncertaintyModel>
    
  • if uncertaintyType="simpleFaultGeometryAbsolute" then the uncertainty model must contain a valid instance of the simpleFaultGeometry node as described in section 3.1.1.2.1   Simple Faults

  • if uncertaintyType="simpleFaultDipRelative" then the uncertainty model must specify the number of degrees to increase (positive) or decrease (negative) the fault dip. Note that if this increase results in an adjusted fault dip greater than \(90^{\circ}\) or less than \(0^{\circ}\) an error will occur.

    <uncertaintyModel>DIP_INCREMENT</uncertaintyModel>
    
  • if uncertaintyType="simpleFaultDipAbsolute" then the uncertainty model must specify the dip angle (in degrees)

    <uncertaintyModel>DIP</uncertaintyModel>
    
  • if uncertaintyType="complexFaultGeometryAbsolute" then the uncertainty model must contain a valid instance of the complexFaultGeometry source node as described in section 3.1.1.2.2   Complex Faults

  • if uncertaintyType="characteristicFaultGeometryAbsolute" then the uncertainty model must contain a valid instance of the characteristicFaultGeometry source node, as described in section 3.1.1.3.1   Characteristic faults

There are no restrictions on the number of logicTreeBranch elements that can be defined in a logicTreeBranchSet, as long as the uncertainty weights sum to 1.0.

The logicTreeBranchSet element offers also a number of optional attributes allowing for complex tree definitions:

  • applyToBranches: specifies to which logicTreeBranch elements (one or more), in the previous Branch sets, the Branch set is linked to. The linking is established by defining the IDs of the branches to link to:

    applyToBranches="branchID1 branchID2 .... branchIDN"
    

    The default is the keyword ALL, which means that a Branch set is by default linked to all branches in the previous Branch set. By specifying one or more branches to which the Branch set links to, non- symmetric logic trees can be defined.

  • applyToSources: specifies to which source in a source model the uncertainty applies to. Sources are specified in terms of their IDs:

    applyToSources="srcID1 srcID2 .... srcIDN"
    
  • applyToTectonicRegionType: specifies to which tectonic region type the uncertainty applies to. Only one tectonic region type can be defined (Active Shallow Crust, Stable Shallow Crust, Subduction Interface, Subduction IntraSlab, Volcanic), e.g.:

    applyToTectonicRegionType="Active Shallow Crust"
    

3.2.2 The Seismic Source System#

The Seismic Source System contains the model (or the models) describing position, geometry and activity of seismic sources of engineering importance for a set of sites as well as the possible epistemic uncertainties to be incorporated into the calculation of seismic hazard.

3.2.2.1 The Seismic Source Logic Tree#

The structure of the Seismic Source Logic Tree consists of at least one Branch Set. The example provided below shows the simplest Seismic Source Logic Tree structure that can be defined in a Psha Input Model for OpenQuake engine. It’s a logic tree with just onebranchset with one Branch used to define the initial seismic source model (its weight will be equal to one).

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5            <logicTreeBranchSet uncertaintyType="sourceModel"
 6                                branchSetID="bs1">
 7                <logicTreeBranch branchID="b1">
 8                    <uncertaintyModel>seismic_source_model.xml
 9                    </uncertaintyModel>
10                    <uncertaintyWeight>1.0</uncertaintyWeight>
11                </logicTreeBranch>
12            </logicTreeBranchSet>
13    </logicTree>
14</nrml>

The optional branching levels will contain rules that modify parameters of the sources in the initial seismic source model.

For example, if the epistemic uncertainties to be considered are source geometry and maximum magnitude, the modeller can create a logic tree structure with three initial seismic source models (each one exploring a different definition of the geometry of sources) and one branching level accounting for the epistemic uncertainty on the maximum magnitude.

Below we provide an example of such logic tree structure. Note that the uncertainty on the maximum magnitude is specified in terms of relative increments with respect to the initial maximum magnitude defined for each source in the initial seismic source models.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5
 6            <logicTreeBranchSet uncertaintyType="sourceModel"
 7                                branchSetID="bs1">
 8                <logicTreeBranch branchID="b1">
 9                    <uncertaintyModel>seismic_source_model_A.xml
10                    </uncertaintyModel>
11                    <uncertaintyWeight>0.2</uncertaintyWeight>
12                </logicTreeBranch>
13                <logicTreeBranch branchID="b2">
14                    <uncertaintyModel>seismic_source_model_B.xml
15                    </uncertaintyModel>
16                    <uncertaintyWeight>0.3</uncertaintyWeight>
17                </logicTreeBranch>
18                <logicTreeBranch branchID="b3">
19                    <uncertaintyModel>seismic_source_model_C.xml
20                    </uncertaintyModel>
21                    <uncertaintyWeight>0.5</uncertaintyWeight>
22                </logicTreeBranch>
23            </logicTreeBranchSet>
24
25            <logicTreeBranchSet branchSetID="bs21"
26                    uncertaintyType="maxMagGRRelative">
27                <logicTreeBranch branchID="b211">
28                    <uncertaintyModel>+0.0</uncertaintyModel>
29                    <uncertaintyWeight>0.6</uncertaintyWeight>
30                </logicTreeBranch>
31                <logicTreeBranch branchID="b212">
32                    <uncertaintyModel>+0.5</uncertaintyModel>
33                    <uncertaintyWeight>0.4</uncertaintyWeight>
34                </logicTreeBranch>
35            </logicTreeBranchSet>
36
37    </logicTree>
38</nrml>

Starting from OpenQuake engine v2.4, it is also possible to split a source model into several files and read them as if they were a single file. The file names for the different files comprising a source model should be provided in the source model logic tree file. For instance, a source model could be split by tectonic region using the following syntax in the source model logic tree:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5            <logicTreeBranchSet uncertaintyType="sourceModel"
 6                                branchSetID="bs1">
 7                <logicTreeBranch branchID="b1">
 8                    <uncertaintyModel>
 9                     active_shallow_sources.xml
10                     stable_shallow_sources.xml
11                    </uncertaintyModel>
12                    <uncertaintyWeight>1.0</uncertaintyWeight>
13                </logicTreeBranch>
14            </logicTreeBranchSet>
15    </logicTree>
16</nrml>

3.2.2.2 The Seismic Source Model#

The structure of the xml file representing the seismic source model corresponds to a list of sources, each one modelled using one out of the five typologies currently supported. Below we provide a schematic example of a seismic source model:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5            <logicTreeBranchSet uncertaintyType="sourceModel"
 6                                branchSetID="bs1">
 7                <logicTreeBranch branchID="b1">
 8                    <uncertaintyModel>seismic_source_model.xml
 9                    </uncertaintyModel>
10                    <uncertaintyWeight>1.0</uncertaintyWeight>
11                </logicTreeBranch>
12            </logicTreeBranchSet>
13    </logicTree>
14</nrml>

3.2.3 The Ground Motion System The Ground Motion#

System defines the models and the possible epistemic uncertainties related to ground motion modelling to be incorporated into the calculation.

3.2.3.1 The Ground Motion Logic Tree#

The structure of the Ground-Motion Logic Tree consists of a list of ground motion prediction equations for each tectonic region used to characterise the sources in the PSHA input model.

The example below in shows a simple Ground-Motion Logic Tree. This logic tree assumes that all the sources in the PSHA input model belong to “Active Shallow Crust” and uses for calculation the B. S.-J. Chiou and Youngs (2008) Ground Motion Prediction Equation.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5            <logicTreeBranchSet uncertaintyType="gmpeModel"
 6                    branchSetID="bs1"
 7                    applyToTectonicRegionType="Active Shallow Crust">
 8
 9                <logicTreeBranch branchID="b1">
10                    <uncertaintyModel>
11                    ChiouYoungs2008
12                    </uncertaintyModel>
13                    <uncertaintyWeight>1.0</uncertaintyWeight>
14                </logicTreeBranch>
15
16            </logicTreeBranchSet>
17    </logicTree>
18</nrml>

3.2.4 Configuration file#

The configuration file is the primary file controlling both the definition of the input model as well as parameters governing the calculation. We illustrate in the following different examples of the configuration file addressing different types of seismic hazard calculations.

3.2.4.1 Classical PSHA#

In the following we describe the overall structure and the most typical parameters of a configuration file to be used for the computation of a seismic hazard map using a classical PSHA methodology.

Calculation type and model info

1[general]
2description = A demo OpenQuake-engine .ini file for classical PSHA
3calculation_mode = classical
4random_seed = 1024

In this section the user specifies the following parameters:

  • description: a parameter that can be used to designate the model

  • calculation_mode: it is used to set the kind of calculation. In this case it corresponds to classical. Alternative options for the calculation_mode are described later in this manual.

  • random_seed: is used to control the random generator so that when Monte Carlo procedures are used calculations are replicable (if the same random_seed is used you get exactly the same results).

Geometry of the area (or the sites) where hazard is computed

This section is used to specify where the hazard will be computed. Two options are available:

The first option is to define a polygon (usually a rectangle) and a distance (in km) to be used to discretize the polygon area. The polygon is defined by a list of longitude-latitude tuples.

An example is provided below:

5[geometry]
6region = 10.0 43.0, 12.0 43.0, 12.0 46.0, 10.0 46.0
7region_grid_spacing = 10.0

The second option allows the definition of a number of sites where the hazard will be computed. Each site is specified in terms of a longitude, latitude tuple. Optionally, if the user wants to consider the elevation of the sites, a value of depth [km] can also be specified, where positive values indicate below sea level, and negative values indicate above sea level (i.e. the topographic surface). If no value of depth is given for a site, it is assumed to be zero. An example is provided below:

8[geometry]
9sites = 10.0 43.0, 12.0 43.0, 12.0 46.0, 10.0 46.0

If the list of sites is too long the user can specify the name of a csv file as shown below:

10[geometry]
11sites_csv = <name_of_the_csv_file>

The format of the csv file containing the list of sites is a sequence of points (one per row) specified in terms of the longitude, latitude tuple. Depth values are again optional. An example is provided below:

179.0,90.0
178.0,89.0
177.0,88.0

Logic tree sampling

The OpenQuake engine provides two options for processing the whole logic tree structure. The first option uses Montecarlo sampling; the user in this case specifies a number of realizations.

In the second option all the possible realizations are created. Below we provide an example for the latter option. In this case we set the number_of_logic_tree_samples to 0. OpenQuake engine will perform a complete enumeration of all the possible paths from the roots to the leaves of the logic tree structure.

12[logic_tree]
13number_of_logic_tree_samples = 0

If the seismic source logic tree and the ground motion logic tree do not contain epistemic uncertainties the engine will create a single PSHA input.

Generation of the earthquake rupture forecast

14[erf]
15rupture_mesh_spacing = 5
16width_of_mfd_bin = 0.1
17area_source_discretization = 10

This section of the configuration file is used to specify the level of discretization of the mesh representing faults, the grid used to delineate the area sources and, the magnitude-frequency distribution. Note that the smaller is the mesh spacing (or the bin width) the larger are (1) the precision in the calculation and (2) the computation demand.

In cases where the source model may contain a mixture of simple and complex ruptures it is possible to define a different rupture mesh spacing for complex faults only. This may be helpful in models that permit floating ruptures over large subduction sources, in which the nearest source to site distances may be larger than 20 - 30 km, and for which a small mesh spacing would produce a very large number of ruptures. The spacing for complex faults only can be configured by the line:

18complex_fault_mesh_spacing = 10

Parameters describing site conditions

18[site_params]
19reference_vs30_type = measured
20reference_vs30_value = 760.0
21reference_depth_to_2pt5km_per_sec = 5.0
22reference_depth_to_1pt0km_per_sec = 100.0

In this section the user specifies local soil conditions. The simplest solution is to define uniform site conditions (i.e. all the sites have the same characteristics).

Alternatively it is possible to define spatially variable soil properties in a separate file; the engine will then assign to each investigation location the values of the closest point used to specify site conditions.

23[site_params]
24site_model_file = site_model.csv

The file containing the site model has the following structure:

lon,lat,vs30,z1pt0,z2pt5,vs30measured,backarc
10.0,40.0,800.0,19.367196734,0.588625072259,0,0
10.1,40.0,800.0,19.367196734,0.588625072259,0,0
10.2,40.0,800.0,19.367196734,0.588625072259,0,0
10.3,40.0,800.0,19.367196734,0.588625072259,0,0
10.4,40.0,800.0,19.367196734,0.588625072259,0,0

Notice that the \(0\) for the field vs30measured means that the vs30 field is inferred, not measured. Most of the GMPEs are not sensitive to it, so you can usually skip it. For the backarc parameter \(0\) means false and this is the default, so you can skip such column. All columns that have defaults or are not needed by the GMPEs you are using can be skipped, while you will get an error if a relevant column is missing.

If the closest available site with soil conditions is at a distance greater than 5 km from the investigation location, a warning is generated.

Note: For backward-compatibility reasons, the site model file can also be given in XML format. That old format is deprecated but there are no plans to remove it any soon.

Calculation configuration

25[calculation]
26source_model_logic_tree_file = source_model_logic_tree.xml
27gsim_logic_tree_file = gmpe_logic_tree.xml
28investigation_time = 50.0
29intensity_measure_types_and_levels = {"PGA": [0.005, ..., 2.13]}
30truncation_level = 3
31maximum_distance = 200.0

This section of the OpenQuake engine configuration file specifies the parameters that are relevant for the calculation of hazard. These include the names of the two files containing the Seismic Source System and the Ground Motion System, the duration of the time window used to compute the hazard, the ground motion intensity measure types and levels for which the probability of exceedence will be computed, the level of truncation of the Gaussian distribution of the logarithm of ground motion used in the calculation of hazard and the maximum integration distance (i.e. the distance within which sources will contribute to the computation of the hazard).

The maximum distance refers to the largest distance between a rupture and the target calculation sites in order for the rupture to be considered in the PSHA calculation. This can be input directly in terms of kilometres (as above). There may be cases, however, in which it may be appropriate to have a different maximum source to site distance depending on the tectonic region type. This may be used, for example, to eliminate the impact of small, very far-field sources in regions of high attenuation (in which case maximum distance is reduced), or conversely it may be raised to allow certain source types to contribute to the hazard at greater distances (such as in the case when the region has lower attenuation). An example configuration for a maximum distance in Active Shallow Crust of 150 km, and in Stable Continental Crust of 200 km, is shown below:

31maximum_distance = {'Active Shallow Crust': 150.0,
32                    'Stable Continental Crust': 200.0}

Output

31[output]
32export_dir = outputs/
33# given the specified `intensity_measure_types_and_levels`
34mean = true
35quantiles = 0.1 0.5 0.9
36uniform_hazard_spectra = false
37poes = 0.1

The final section of the configuration file is the one that contains the parameters controlling the types of output to be produced. Providing an export directory will tell OpenQuake where to place the output files when the --exports flag is used when running the program. Setting mean to true will result in a specific output containing the mean curves of the logic tree, likewise quantiles will produce separate files containing the quantile hazard curves at the quantiles listed (0.1, 0.5 and 0.9 in the example above, leave blank or omit if no quantiles are required). Setting uniform_hazard_spectra to true will output the uniform hazard spectra at the same probabilities of exceedence (poes) as those specified by the later option poes. The probabilities specified here correspond to the set investigation time. Specifying poes will output hazard maps. For more information about the outputs of the calculation, see the section: “Description of hazard output” (page ).

3.2.4.2 Seismic hazard disaggregation#

In this section we describe the structure of the configuration file to be used to complete a seismic hazard disaggregation. Since only a few parts of the standard configuration file need to be changed we can use the description given in Section  3.2.4.1   Classical PSHA as a reference and we emphasize herein major differences.

1[general]
2description = A demo .ini file for PSHA disaggregation
3calculation_mode = disaggregation
4random_seed = 1024

The calculation mode parameter in this case is set as disaggregation.

Geometry of the area (or the sites) where hazard is computed

5[geometry]
6sites = 11.0 44.5

In the section it is necessary to specify the geographic coordinates of the site(s) where the disaggregation will be performed. The coordinates of multiple site should be separated with a comma.

Disaggregation parameters

The disaggregation parameters need to be added to the the standard configuration file. They are shown in the following example and a description of each parameter is provided below.

 7[disaggregation]
 8poes_disagg = 0.02, 0.1
 9mag_bin_width = 1.0
10distance_bin_width = 25.0
11coordinate_bin_width = 1.5
12num_epsilon_bins = 3
13disagg_outputs = Mag_Dist_Eps Mag_Lon_Lat
14num_rlzs_disagg = 3
  • poes_disagg: disaggregation is performed for the intensity measure levels corresponding to the probability of exceedance value(s) provided here. The computations use the investigation_time and the intensity_measure_types_and_levels defined in the “Calculation configuration” section (see page ). For the poes_disagg the intensity measure level(s) for the disaggregation are inferred by performing a classical calculation and by inverting the hazard curves.

  • iml_disagg: the intensity measure level(s) to be disaggregated can be directly defined by specifying iml_disagg. Note that a disaggregation computation requires either poes_disagg or iml_disagg to be defined, but both cannot be defined at the same time.

  • mag_bin_width: mandatory; specifies the width of every magnitude histogram bin of the disaggregation matrix computed

  • distance_bin_width: specifies the width of every distance histogram bin of the disaggregation matrix computed (km)

  • coordinate_bin_width: specifies the width of every longitude-latitude histogram bin of the disaggregation matrix computed (decimal degrees)

  • num_epsilon_bins: mandatory; specifies the number of Epsilon histogram bins of the disaggregation matrix. The width of the Epsilon bins depends on the truncation_level defined in the “Calculation configuration” section (page )

  • disagg_outputs: optional; specifies the type(s) of disaggregation to be computed. The options are: Mag, Dist, Lon_Lat, Lon_Lat_TRT, Mag_Dist, Mag_Dist_Eps, Mag_Lon_Lat, TRT. If none are specified, then all are computed. More details of the disaggregation output are given in the “Outputs from Hazard Disaggregation” section, see page )

  • disagg_by_src: optional; if specified and set to true, disaggregation by source is computed, if possible.

  • num_rlzs_disagg: optional; specifies the number of realizations to be used, selecting those that yield intensity measure levels closest to the mean.

Alternatively to num_rlzs_disagg, the user can specify the index or indices of the realizations to disaggregate as a list of comma-separated integers. For example:

7[disaggregation]
8rlz_index = 22,23

If num_rlzs_disagg is specified, the user cannot specify rlz_index, and vice versa. If num_rlzs_disagg or rlz_index are specified, the mean disaggregation is automatically computed from the selected realizations. If neither is specified, the realization that yields the intensity measure level closest to the mean level will be selected.

As mentioned above, the user also has the option to perform disaggregation by directly specifying the intensity measure level to be disaggregated, rather than specifying the probability of exceedance. An example is shown below:

7[disaggregation]
8iml_disagg = {'PGA': 0.1}

If iml_disagg is specified, the user should not include intensity_measure_types_and_levels in the “Calculation configuration” section (see page ) since it is explicitly given here.

3.2.4.3 Event based PSHA#

In the following we describe the sections of the configuration file that are required to complete event based PSHA calculations

Calculation type and model info

This part is almost identical to the corresponding one described in Section  3.2.4.1   Classical PSHA.

Note the setting of the calculation_mode parameter which now corresponds to event_based.

1[general]
2description = A demo OpenQuake-engine .ini file for event based PSHA
3calculation_mode = event_based
4random_seed = 1024

Event based parameters

This section is used to specify the number of stochastic event sets to be generated for each logic tree realisation (each stochastic event set represents a potential realisation of seismicity during the investigation_time specified in the calculation_configuration part). Additionally, in this section the user can specify the spatial correlation model to be used for the generation of ground motion fields.

ses_per_logic_tree_path = 5
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

The acceptable flags for the parameter vs30_clustering are False and True, with a capital F and T respectively. 0 and 1 are also acceptable flags.

Output

This part substitutes the Output part described in the configuration file example described in the Section 3.2.4.1   Classical PSHA.

[output]
export_dir = /tmp/xxx
ground_motion_fields = true
# post-process ground motion fields into hazard curves,
# given the specified `intensity_measure_types_and_levels`
hazard_curves_from_gmfs = true
mean = true
quantiles = 0.15, 0.50, 0.85
poes = 0.1, 0.2

Starting from OpenQuake engine22, it is now possible to export information about the ruptures directly in CSV format.

The option hazard_curves_from_gmfs instructs the user to use the event- based ground motion values to provide hazard curves indicating the probabilities of exceeding the intensity measure levels set previously in the intensity_measure_types_and_levels option.

3.2.4.4 Scenario hazard#

In order to run this calculator, the parameter calculation_mode needs to be set to scenario. The basic job configuration file required for running a scenario hazard calculation is shown in the listing below.

[general]
description = Scenario Hazard Config File
calculation_mode = scenario

[sites]
sites_csv = sites.csv

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[site_params]
site_model_file = site_model.xml

[correlation]
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

[hazard_calculation]
intensity_measure_types = PGA, SA(0.3), SA(1.0)
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim = BooreAtkinson2008
number_of_ground_motion_fields = 1000

Most of the job configuration parameters required for running a scenario hazard calculation seen in the example in the listing <lst:config_scenario_hazard> above are the same as those described in the previous sections for the classical PSHA calculator (Section  3.2.4.1   Classical PSHA) and the event-based PSHA calculator (Section  3.2.4.3   Event based PSHA). The set of sites at which the ground motion fields will be produced can be specifed by using either the sites or sites_csv parameters, or the region and region_grid_spacing parameters, similar to the classical PSHA and event-based PSHA calculators. The parameter unique to the scenario calculator is described below:

  • number_of_ground_motion_fields: this parameter is used to specify the number of Monte Carlo simulations of the ground motion values at the specified sites

  • gsim: this parameter indicates the name of a ground motion prediction equation (a list of available GMPEs can be obtained using oq info gsims and these are also documented at: http://docs.openquake.org/oq-engine/stable/openquake.hazardlib.gsim.html)

Multiple ground motion prediction equations can be used for a scenario hazard calculation by providing a GMPE logic tree file (described previously in Section 3.2.3.1   The Ground Motion Logic Tree) using the parameter gsim_logic_tree_file. In this case, the OpenQuake engine generates ground motion fields for all GMPEs specified in the logic tree file. The Branch weights in the logic tree file are ignored in a scenario analysis and only the individual Branch results are computed. Mean or quantile ground motion fields will not be generated.

The ground motion fields will be computed at each of the sites and for each of the intensity measure types specified in the job configuration file. The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 260 completed in 3 seconds. Results:
  id | name
 569 | Ground Motion Fields

3.3 Hazard Calculations and Results#

In this Chapter we provide a desciption of the main commands available for running hazard with the OpenQuake engine and the file formats used to represent the results of the analyses.

A general introduction on the use of the OpenQuake engine is provided in 2.1   OpenQuake-engine Background. The reader is invited to consult this part before diving into the following sections.

3.3.1 Running OpenQuake-engine for hazard calculations#

The execution of a hazard analysis using the OpenQuake-engine is straightforward. Below we provide an example of the simplest command that can be used to launch a hazard calculation. It consists in the invocation of oq engine together with the --run option, and the name of a configuration file (in the example below it corresponds to job.ini):

user@ubuntu:$ oq engine --run job.ini

The amount of information prompted during the execution of the analysis can be controlled through the --log-level flag as shown in the example below:

user@ubuntu:$ oq engine --run job.ini --log-level debug

In this example we ask the engine to provide an extensive amount of information (usually not justified for a standard analysis). Alternative options are: debug, info, warn, error, critical.

3.3.2 Exporting results from a hazard calculation#

There are two alternative ways to get results from the OpenQuake-engine: directly through the calculation or by exporting them from the internal OpenQuake engine database once a calculation is completed.

The first option is defined at the OpenQuake-engine invocation through the flag –exports xml, as shown in the example below:

user@ubuntu:~$ oq engine --run job.ini --exports xml

This will export the results to the results directory specified in the job.ini file.

The second option allows the user to export the computed results or just a subset of them whenever they want. In order to obtain the list of results of the hazard calculations stored in the OpenQuake engine database the user can utilize the --lhc command (‘list hazard calculations’) to list the hazard calculations:

user@ubuntu:~$ oq engine --lhc

The execution of this command will produce a list similar to the one provided below (the numbers in red are the calculations IDs):

user@ubuntu:~$ oq engine --lhc
job_id | status | start_time | description
1 | failed | 2013-03-01 09:49:34 | Classical PSHA
2 | successful | 2013-03-01 09:49:56 | Classical PSHA
3 | failed | 2013-03-01 10:24:04 | Classical PSHA
4 | failed | 2013-03-01 10:28:16 | Classical PSHA
5 | failed | 2013-03-01 10:30:04 | Classical PSHA
6 | successful | 2013-03-01 10:31:53 | Classical PSHA
7 | failed | 2013-03-09 08:15:14 | Classical PSHA
8 | successful | 2013-03-09 08:18:04 | Classical PSHA

Subsequently the user can get the list of result stored for a specific hazard analysis by using the --list-outputs, or --lo, command, as in the example below (note that the number in blue emphasizes the result ID):

user@ubuntu:~$ oq engine --lo <calc_id>
id | name
3 | hcurves

and finally extract an xml file for a specific hazard result:

user@ubuntu:~$ oq engine --export-outputs <result_id> <output_folder>

3.3.3 Description of hazard outputs#

The results generated by the OpenQuake-engine are fundamentally of two distinct typologies differentiated by the presence (or absence) of epistemic uncertainty in the PSHA input model.

When epistemic uncertainty is incorporated into the calculation, the OpenQuake-engine calculators (e.g. Classical PSHA, Event Based PSHA, Disaggregation, UHS) produce a set of results (i.e. hazard curves, ground motion fields, disaggregation outputs, UHS, for each logic-tree realisation) which reflects epistemic uncertainties introduced in the PSHA input model. For each logic tree sample, results are computed and stored. Calculation of results statistics (mean, standard deviation, quantiles) are supported by all the calculators.

By default, OpenQuake will export only the statistical results, i.e. mean curves and quantiles. If the user requires the complete results for all realizations, there is a flag to specify, please see the FAQ https://github.com/gem/oq-engine/blob/master/doc/faq-hazard.md. Beware that if the logic tree contains a large number of end branches the process of exporting the results from each end Branch can add a significant amount of time - possibly longer than the computation time - and result in a large volume of disk spaced being used. In this case it is best to postprocess the data programmatically. Please contact us and we will be happy to give directions on how to do that in Python.

NB: in the literature there are different algorithms for the computation of the quantiles. The OpenQuake engine uses an algorithm based on interpolation which is implemented here:

https://github.com/gem/oq-engine/tree/master/openquake/hazardlib/stats.py

In particular, the median is computed as the q=0.5 quantile.

3.3.3.1 Outputs from Classical PSHA#

By default, the classical PSHA calculator computes and stores hazard curves for each logic tree sample considered.

When the PSHA input model doesn’t contain epistemic uncertainties the results is a set of hazard curves (one for each investigated site). The command below illustrates how is possible to retrieve the group of hazard curves obtained for a calculation with a given identifier <calc_id> (see Section 3.3.2   Exporting results from a hazard calculation for an explanation about how to obtain the list of calculations performed with their corresponding ID):

user@ubuntu:~$ oq engine --lo <calc_id>
id | name
*3 | Hazard Curves
4 | Realizations

To export from the database the outputs (in this case hazard curves) contained in one of the output identifies, one can do so with the following command:

user@ubuntu:~$ oq engine --export-output <output_id> <output_directory>

Alternatively, if the user wishes to export all of the outputs associated with a particular calculation then they can use the --export-outputs with the corresponding calculation key:

user@ubuntu:~$ oq engine --export-outputs <calc_id> <output_directory>

The exports will produce one or more nrml files containing the seismic hazard curves, as represented below in the listing <lst:output_hazard_curves_xml> below.

 1<?xml version="1.0" encoding="utf-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4  <hazardCurves sourceModelTreePath="b1|b212"
 5                gsimTreePath="b2" IMT="PGA"
 6                investigationTime="50.0">
 7    <IMLs>0.005 0.007 0.0098 ... 1.09 1.52 2.13</IMLs>
 8    <hazardCurve>
 9      <gml:Point>
10        <gml:pos>10.0 45.0</gml:pos>
11      </gml:Point>
12      <poEs>1.0 1.0 1.0 ... 0.000688359310522 0.0 0.0</poEs>
13    </hazardCurve>
14    ...
15    <hazardCurve>
16      <gml:Point>
17        <gml:pos>lon lat</gml:pos>
18      </gml:Point>
19      <poEs>poe1 poe2 ... poeN</poEs>
20    </hazardCurve>
21  </hazardCurves>
22</nrml>

Notwithstanding the intuitiveness of this file, let’s have a brief overview of the information included. The overall content of this file is a list of hazard curves, one for each investigated site, computed using a PSHA input model representing one possible realisation obtained using the complete logic tree structure.

The attributes of the hazardCurves element (see text in red) specify the path of the logic tree used to create the seismic source model (sourceModelTreePath) and the ground motion model (gsimTreePath) plus the intensity measure type and the investigation time used to compute the probability of exceedance.

The IMLs element (in green in the example) contains the values of shaking used by the engine to compute the probability of exceedance in the investigation time. For each site this file contains a hazardCurve element which has the coordinates (longitude and latitude in decimal degrees) of the site and the values of the probability of exceedance for all the intensity measure levels specified in the IMLs element.

If the hazard calculation is configured to produce results including seismic hazard maps and uniform hazard spectra, then the list of outputs would display the following:

user@ubuntu:~$ oq engine --lo <calc_id>
id | name
2 | Full Report
3 | Hazard Curves
4 | Hazard Maps
5 | Realizations
6 | Uniform Hazard Spectra

The first listing below shows a sample of the nrml file used to describe a hazard map, and and the second listing below shows a sample of the nrml used to describe a uniform hazard spectrum.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4  <hazardMap sourceModelTreePath="b1" gsimTreePath="b1"
 5             IMT="PGA" investigationTime="50.0" poE="0.1">
 6    <node lon="119.596690957" lat="21.5497682591" iml="0.204569990197"/>
 7    <node lon="119.596751048" lat="21.6397004197" iml="0.212391638188"/>
 8    <node lon="119.596811453" lat="21.7296325803" iml="0.221407505615"/>
 9    ...
10  </hazardMap>
11</nrml>
 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <uniformHazardSpectra sourceModelTreePath="b1_b2_b4"
 5                        gsimTreePath="b1_b2"
 6                        investigationTime="50.0" poE="0.1">
 7        <periods>0.0 0.025 0.1 0.2</periods>
 8        <uhs>
 9            <gml:Point>
10                <gml:pos>0.0 0.0</gml:pos>
11            </gml:Point>
12            <IMLs>0.3 0.5 0.2 0.1</IMLs>
13        </uhs>
14        <uhs>
15            <gml:Point>
16                <gml:pos>0.0 1.0</gml:pos>
17            </gml:Point>
18            <IMLs>0.3 0.5 0.2 0.1</IMLs>
19        </uhs>
20    </uniformHazardSpectra>
21</nrml>

3.3.3.2 Outputs from Hazard Disaggregation#

The OpenQuake engine output of a disaggregation analysis corresponds to the combination of a hazard curve and a multidimensional matrix containing the results of the disaggregation. For a typical disaggregation calculation the list of outputs are the following:

user@ubuntu:~$ oq engine --lo <calc_id>
id | name
**3** | Disaggregation Outputs
5 | Full Report
6 | Realizations

Running --export-output to export the disaggregation results will produce individual files for each site. In presence of a nontrivial logic tree the user can specify the realization on which to perform the disaggregation by setting the rlz_index parameter in the job.ini file. If not specified, for each site the engine will determine the realization closest to the mean hazard curve and will use that realization to perform the disaggregation.

3.3.3.3 Outputs from Event Based PSHA#

The Event Based PSHA calculator computes and stores stochastic event sets and the corresponding ground motion fields. This calculator can also produce hazard curves and hazard maps, similar to the Classical PSHA calculator. The inset below shows an example of the list of results provided by the OpenQuake engine at the end of an event-based PSHA calculation:

user@ubuntu:~$ oq engine --lo <calc_id>
id | name
10 | Ground Motion Fields
11 | Hazard Curves
12 | Hazard Maps
13 | Realizations
14 | Earthquake Ruptures
15 | Events
16 | Uniform Hazard Spectra

Exporting the outputs from the ruptures will produce a CSV file with the following colums:

  1. rup_id: incremental number identifying the rupture

  2. multiplicity: how many times the rupture occurs in the effective investigation time

  3. mag: float specifying the magnitude of the rupture

  4. centroid_lon: longitude of the centroid of the rupture

  5. centroid_lat: latitude of the centroid of the rupture

  6. centroid_depth: depth (in km) of the centroid of the rupture

  7. trt: string specifying the tectonic region type

  8. strike: strike angle of the rupture surface

  9. dip: dip angle of the rupture surface

  10. rake: rake angle of the rupture surface

The outputs from the Ground Motion Fields can be exported in the csv format. Exporting the outputs from the Ground Motion Fields in the csv format results in two csv files illustrated in the example files in Table 1 and Table 4. The sites csv file provides the association between the site ids in the Ground Motion Fields csv file with their latitude and longitude coordinates.

Table 1 Example of a ground motion fields csv output file for an event based hazard calculation#

rlz_id

site_id

event_id

gmv_PGA

gmv_SA(0.3)

gmv_SA(1.0)

0

0

48

0.0089

0.0686

0.0065

0

0

54

0.0219

0.0325

0.0164

0

6

75

0.0246

0.0244

0.0036

1

0

76

0.0189

0.0327

0.0094

1

0

77

0.0286

0.0683

0.0471

7

6

1754

0.3182

1.2973

0.6127

7

6

1755

0.2219

0.6200

0.5069

The Events output produces a csv file with fields event_id, rup_id and rlz_id. The event_id is a 32 bit integer that identifies uniquely the event; the rup_id is a 32 bit integer that identifies uniquely the rupture; the rlz_id is a 16 bit integer that identifies uniquely the realization. The association between the event_id and the rup_id is stored inside the Events output.

The Realizations output produces a csv file listing the source model and the combination of ground shaking intensity models for each path sampled from the logic tree. An example of such a file is shown below in Table 2.

Table 2 Example of a realizations file#

ordinal

branch_path

weight

0

b1\(\sim\)b11_b21

0.1125

1

b1\(\sim\)b11_b22

0.075

2

b1\(\sim\)b12_b21

0.0375

3

b1\(\sim\)b12_b22

0.025

4

b2\(\sim\)b11_b21

0.3375

5

b2\(\sim\)b11_b22

0.225

6

b2\(\sim\)b12_b21

0.1125

7

b2\(\sim\)b12_b22

0.075

3.3.3.4 Outputs from Scenario Hazard Analysis#

By default, the scenario hazard calculator computes and stores Ground Motion Fields for each GMPE specified in the job configuration file. The Ground Motion Fields will be computed at each of the sites and for each of the intensity measure types specified in the job configuration file.

Exporting the outputs from the Ground Motion Fields in the csv format results in two csv files illustrated in the example files in Table 3 and Table 4. The sites csv file provides the association between the site ids in the Ground Motion Fields csv file with their latitude and longitude coordinates.

Table 3 Example of a ground motion fields csv output file for a scenario (Download example)#

rlzi

sid

eid

gmv_PGA

gmv_SA(0.3)

gmv_SA(1.0)

0

0

0

0.062

0.119

0.157

0

1

0

0.086

1.533

0.260

0

2

0

0.223

1.647

0.232

1

4

99

2.467

0.750

1.918

1

5

99

0.601

0.828

2.272

1

6

99

0.514

0.340

1.202

In this example, the gmfs have been computed using two different GMPEs, so the realization indices (’rlzi’) in the first column of the example gmfs file are either 0 or 1. The gmfs file lists the ground motion values for 100 simulations of the scenario, so the event indices (’eid’) in the third column go from 0–99. There are seven sites with indices 0–6 (’sid’) which are repeated in the second column for each of the 100 simulations of the event and for each of the two GMPEs. Finally, the subsequent columns list the ground motion values for each of the intensity measure types specified in the job configuration file.

Table 4 Example of a sites csv output file for a scenario (Download example)#

site_id

lon

lat

0

-122.57000

38.11300

1

-122.11400

38.11300

2

-122.00000

37.91000

3

-122.00000

38.00000

4

-122.00000

38.11300

5

-122.00000

38.22500

6

-121.88600

38.11300

3.4 Demonstrative Examples#

A number of hazard calculation demos are provided with the OpenQuake engine installation,showing different examples of input and configuration files, for different use cases.

This is the list of demos which illustrate how to use the OpenQuake engine for various seismic hazard analysis:

  • AreaSourceClassicalPSHA

  • CharacteristicFaultSourceCase1ClassicalPSHA

  • CharacteristicFaultSourceCase2ClassicalPSHA

  • CharacteristicFaultSourceCase3ClassicalPSHA

  • ComplexFaultSourceClassicalPSHA

  • Disaggregation

  • EventBasedPSHA

  • LogicTreeCase1ClassicalPSHA

  • LogicTreeCase2ClassicalPSHA

  • LogicTreeCase3ClassicalPSHA

  • PointSourceClassicalPSHA

  • SimpleFaultSourceClassicalPSHA

3.4.1 Classical PSHA Demos#

A number of demos have been designed to show how to perform a classical PSHA calculation using the different available source typologies and how to define non-trivial logic trees. It should be noted that the input files that will be illustrated are valid not only for a classical PSHA calculation but also for event based and disaggregation analysis.

All the classical PSHA demos illustrating the different source typologies (all demos but the ones about Logic Tree definition) share the same GSIM logic tree file, which for clarity is provided in the listing below.

Since this logic tree consideres only one tectonic region (i.e. Active Shallow Crust) all the seismic sources will belong be considered active shallow crust sources.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5            <logicTreeBranchSet uncertaintyType="gmpeModel"
 6                    branchSetID="bs1"
 7                    applyToTectonicRegionType="Active Shallow Crust">
 8
 9                <logicTreeBranch branchID="b1">
10                    <uncertaintyModel>
11                    ChiouYoungs2008
12                    </uncertaintyModel>
13                    <uncertaintyWeight>1.0</uncertaintyWeight>
14                </logicTreeBranch>
15
16            </logicTreeBranchSet>
17    </logicTree>
18</nrml>

3.4.1.1 Classical PSHA with different source typologies#

This section discusses the following examples:

  • AreaSourceClassicalPSHA

  • CharacteristicFaultSourceCase1ClassicalPSHA

  • CharacteristicFaultSourceCase2ClassicalPSHA

  • CharacteristicFaultSourceCase3ClassicalPSHA

  • ComplexFaultSourceClassicalPSHA

  • PointSourceClassicalPSHA

  • SimpleFaultSourceClassicalPSHA

The configuration file in the listing is defined to compute hazard curves for several intensity measure types (PGV, PGA and Spectral acceleration at different periods), hazard maps and uniform hazard spectra for different probabilities of exceedance:

[general]
description = ...
calculation_mode = classical
random_seed = 23

[geometry]
region = ...
region_grid_spacing = 5.0

[logic_tree]
number_of_logic_tree_samples = 0

[erf]
rupture_mesh_spacing = 2
width_of_mfd_bin = 0.1
area_source_discretization = 5.0

[site_params]
reference_vs30_type = measured
reference_vs30_value = 600.0
reference_depth_to_2pt5km_per_sec = 5.0
reference_depth_to_1pt0km_per_sec = 100.0

[calculation]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gmpe_logic_tree.xml
investigation_time = 50.0
intensity_measure_types_and_levels = {"PGV": [2, 4, 6 ,8, 10, ...],
                                      "PGA": [0.005, 0.007, ...],
                                      "SA(0.025)": [...],
                                      "SA(0.05)": [...],
                                      "SA(0.1)": [...],
                                      "SA(0.2)": [...],
                                      "SA(0.5)": [...],
                                      "SA(1.0)": [...],
                                      "SA(2.0)": [...]}
truncation_level = 3
maximum_distance = 200.0

[output]
mean = false
quantiles = 0.15, 0.50, 0.85
hazard_maps = true
uniform_hazard_spectra = true
poes = 0.10, 0.02

Hazard maps (for PGA, 10% in 50 years) for the different demos using various oq-engine source typologies are shown in the figures below:

Point Source

Fig. 11 Point Source (a).#

Area Source

Fig. 12 Area source (b). The solid black line represents the area boundary.#

Simple Fault Source

Fig. 13 Simple Fault Source (c). The dashed line represents the fault trace, while the solid line the fault surface projection.#

Complex Fault Source

Fig. 14 (d) Complex Fault Source. The solid line represent the fault surface projection#

Hazard maps (for PGA, 10% in 50 years) as obtained from characteristic fault sources with simple fault geometry (a), complex fault geometry (b), and collection of planar surfaces (c)

characteristic fault source with simple fault geometry

Fig. 15 characteristic fault source with simple fault geometry#

characteristic fault source with complex fault geometry

Fig. 16 characteristic fault source with complex fault geometry#

characteristic fault source with collection of planar surfaces

Fig. 17 characteristic fault source with collection of planar surfaces#

3.4.1.2 Classical PSHA with non trivial logic trees#

Three demos are provided to illustrate how the logic tree formalism can be used to express epistemic uncertainties in seismic hazard analysis.

LogicTreeCase1ClassicalPSHA shows an example of logic tree defining two alternative source models, with sources belonging to two different tectonic region types, and with two alternative GMPEs for each tectonic region type. The source model logic tree is therefore defined as shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5
 6            <logicTreeBranchSet uncertaintyType="sourceModel"
 7                                branchSetID="bs1">
 8                <logicTreeBranch branchID="b1">
 9                    <uncertaintyModel>
10                      source_model_1.xml
11                    </uncertaintyModel>
12                    <uncertaintyWeight>0.5</uncertaintyWeight>
13                </logicTreeBranch>
14                <logicTreeBranch branchID="b2">
15                    <uncertaintyModel>
16                       source_model_2.xml
17                    </uncertaintyModel>
18                    <uncertaintyWeight>0.5</uncertaintyWeight>
19                </logicTreeBranch>
20            </logicTreeBranchSet>
21
22    </logicTree>
23</nrml>

The two source models are defined in two separate files: source_model_1.xml and source_model_2.xml each one associated to a corresponding weight (0.5 for both).

The GSIM logic tree file contains the structure as shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2
 3<nrml xmlns:gml="http://www.opengis.net/gml"
 4      xmlns="http://openquake.org/xmlns/nrml/0.5">
 5    <logicTree logicTreeID="lt1">
 6
 7            <logicTreeBranchSet uncertaintyType="gmpeModel"
 8               applyToTectonicRegionType="Active Shallow Crust"
 9               branchSetID="bs1">
10                <logicTreeBranch branchID="b11">
11                   <uncertaintyModel>
12                      BooreAtkinson2008
13                   </uncertaintyModel>
14                   <uncertaintyWeight>0.5</uncertaintyWeight>
15                </logicTreeBranch>
16                <logicTreeBranch branchID="b12">
17                   <uncertaintyModel>
18                      ChiouYoungs2008
19                   </uncertaintyModel>
20                   <uncertaintyWeight>0.5</uncertaintyWeight>
21                </logicTreeBranch>
22            </logicTreeBranchSet>
23
24            <logicTreeBranchSet uncertaintyType="gmpeModel"
25              applyToTectonicRegionType="Stable Continental Crust"
26              branchSetID="bs2">
27              <logicTreeBranch branchID="b21">
28                <uncertaintyModel>
29                   ToroEtAl2002</uncertaintyModel>
30                <uncertaintyWeight>0.5</uncertaintyWeight>
31                </logicTreeBranch>
32                <logicTreeBranch branchID="b22">
33                  <uncertaintyModel>
34                     Campbell2003</uncertaintyModel>
35                  <uncertaintyWeight>0.5</uncertaintyWeight>
36                </logicTreeBranch>
37            </logicTreeBranchSet>
38
39    </logicTree>
40</nrml>

The source model contains sources belonging to Active Shallow Crust and Stable Continental Crust, therefore the GSIM logic tree defines two branching levels, one for each considered tectonic region type. Moreover for each tectonic region a Branch set with two GMPEs is defined: Boore and Atkinson 2008 and Chiou and Youngs 2008 for Active Shallow Crust and Toro et al. 2003 and Campbell 2003 for Stable Continental Crust. By processing the above logic tree files using the logic tree path enumeration mode (enabled by setting in the configuration file number_of_logic_tree_samples = 0) hazard results are computed for 8 logic tree paths (2 source models x 2 GMPEs for Active x 2 GMPEs for Stable).

LogicTreeCase2ClassicalPSHA defines a single source model consisting of only two sources (area and simple fault) belonging to different tectonic region types (Active Shallow Crust and Stable Continental Region) and both characterized by a truncated Gutenberg-Richter distribution. The logic tree defines uncertainties for G-R a and b values (three possible pairs for each source), maximum magnitude (three values for each source) and uncertainties on the GMPEs for each tectonic region type (two GMPE per region type).

To accommodate such a structure the GSIM logic tree is defined as shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5
 6            <logicTreeBranchSet uncertaintyType="sourceModel"
 7                                branchSetID="bs1">
 8                <logicTreeBranch branchID="b11">
 9                    <uncertaintyModel>
10                     source_model.xml
11                    </uncertaintyModel>
12                    <uncertaintyWeight>1.0</uncertaintyWeight>
13                </logicTreeBranch>
14            </logicTreeBranchSet>
15
16            <logicTreeBranchSet uncertaintyType="abGRAbsolute"
17                                applyToSources="1"
18                                branchSetID="bs21">
19                <logicTreeBranch branchID="b21">
20                    <uncertaintyModel>4.6 1.1</uncertaintyModel>
21                    <uncertaintyWeight>0.333</uncertaintyWeight>
22                </logicTreeBranch>
23                <logicTreeBranch branchID="b22">
24                    <uncertaintyModel>4.5 1.0</uncertaintyModel>
25                    <uncertaintyWeight>0.333</uncertaintyWeight>
26                </logicTreeBranch>
27                <logicTreeBranch branchID="b23">
28                    <uncertaintyModel>4.4 0.9</uncertaintyModel>
29                    <uncertaintyWeight>0.334</uncertaintyWeight>
30                </logicTreeBranch>
31            </logicTreeBranchSet>
32
33            <logicTreeBranchSet uncertaintyType="abGRAbsolute"
34                                applyToSources="2"
35                                branchSetID="bs31">
36                <logicTreeBranch branchID="b31">
37                    <uncertaintyModel>3.3 1.0</uncertaintyModel>
38                    <uncertaintyWeight>0.333</uncertaintyWeight>
39                </logicTreeBranch>
40                <logicTreeBranch branchID="b32">
41                    <uncertaintyModel>3.2 0.9</uncertaintyModel>
42                    <uncertaintyWeight>0.333</uncertaintyWeight>
43                </logicTreeBranch>
44                <logicTreeBranch branchID="b33">
45                    <uncertaintyModel>3.1 0.8</uncertaintyModel>
46                    <uncertaintyWeight>0.334</uncertaintyWeight>
47                </logicTreeBranch>
48            </logicTreeBranchSet>
49
50            <logicTreeBranchSet uncertaintyType="maxMagGRAbsolute"
51                                applyToSources="1"
52                                branchSetID="bs41">
53                <logicTreeBranch branchID="b41">
54                    <uncertaintyModel>7.0</uncertaintyModel>
55                    <uncertaintyWeight>0.333</uncertaintyWeight>
56                </logicTreeBranch>
57                <logicTreeBranch branchID="b42">
58                    <uncertaintyModel>7.3</uncertaintyModel>
59                    <uncertaintyWeight>0.333</uncertaintyWeight>
60                </logicTreeBranch>
61                <logicTreeBranch branchID="b43">
62                    <uncertaintyModel>7.6</uncertaintyModel>
63                    <uncertaintyWeight>0.334</uncertaintyWeight>
64                </logicTreeBranch>
65            </logicTreeBranchSet>
66
67            <logicTreeBranchSet uncertaintyType="maxMagGRAbsolute"
68                                applyToSources="2"
69                                branchSetID="bs51">
70                <logicTreeBranch branchID="b51">
71                    <uncertaintyModel>7.5</uncertaintyModel>
72                    <uncertaintyWeight>0.333</uncertaintyWeight>
73                </logicTreeBranch>
74                <logicTreeBranch branchID="b52">
75                    <uncertaintyModel>7.8</uncertaintyModel>
76                    <uncertaintyWeight>0.333</uncertaintyWeight>
77                </logicTreeBranch>
78                <logicTreeBranch branchID="b53">
79                    <uncertaintyModel>8.0</uncertaintyModel>
80                    <uncertaintyWeight>0.334</uncertaintyWeight>
81                </logicTreeBranch>
82            </logicTreeBranchSet>
83
84    </logicTree>
85</nrml>

The first branching level defines the source model. For each source, two branching levels are created, one defining uncertainties on G-R a and b values (defined by setting uncertaintyType="abGRAbsolute") and G-R maximum magnitude (uncertaintyType="maxMagGRAbsolute").

It is important to notice that each Branch set is applied to a specific source by defining the attribute applyToSources, followed by the source ID. The GSIM logic tree file is the same as used for LogicTreeCase1ClassicalPSHA. By setting in the configuration file number_of_logic_tree_samples = 0, hazard results are obtained for 324 paths (1 source model x 3 (a, b) pairs for source 1 x 3 (a, b) pairs for source 2 x 3 max magnitude values for source 1 x 3 max magnitude values for source 2 x 2 GMPEs for Active Shallow Crust X 2 GMPEs for Stable Continental Crust), see Fig. 18.

_images/hazard-curves-ltcase2.png

Fig. 18 Hazard curves as obtained from the LogicTreeCase2 demo. Solid gray lines represent individual hazard curves from the different logic tree path (a total of 324 curves). The red dashed line represents the mean hazard curve, while the red dotted lines depict the quantile levels (0.15, 0.5, 0.95).#

LogicTreeCase3ClassicalPSHA illustrates an example of logic tree defining relative uncertainties on G-R maximum magnitude and b value. A single source model is considered containing two sources belonging to different tectonic region types and both characterized by a G-R magnitude frequency distribution. The source model logic tree for this demo is as shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4    <logicTree logicTreeID="lt1">
 5
 6            <logicTreeBranchSet uncertaintyType="sourceModel"
 7                                branchSetID="bs1">
 8                <logicTreeBranch branchID="b11">
 9                    <uncertaintyModel>
10                     source_model.xml
11                    </uncertaintyModel>
12                    <uncertaintyWeight>1.0</uncertaintyWeight>
13                </logicTreeBranch>
14            </logicTreeBranchSet>
15
16            <logicTreeBranchSet uncertaintyType="bGRRelative"
17                                branchSetID="bs21">
18                <logicTreeBranch branchID="b21">
19                    <uncertaintyModel>+0.1</uncertaintyModel>
20                    <uncertaintyWeight>0.333</uncertaintyWeight>
21                </logicTreeBranch>
22                <logicTreeBranch branchID="b22">
23                    <uncertaintyModel>0.0</uncertaintyModel>
24                    <uncertaintyWeight>0.333</uncertaintyWeight>
25                </logicTreeBranch>
26                <logicTreeBranch branchID="b23">
27                    <uncertaintyModel>-0.1</uncertaintyModel>
28                    <uncertaintyWeight>0.334</uncertaintyWeight>
29                </logicTreeBranch>
30            </logicTreeBranchSet>
31
32            <logicTreeBranchSet uncertaintyType="maxMagGRRelative"
33                                branchSetID="bs31">
34                <logicTreeBranch branchID="b31">
35                    <uncertaintyModel>0.0</uncertaintyModel>
36                    <uncertaintyWeight>0.333</uncertaintyWeight>
37                </logicTreeBranch>
38                <logicTreeBranch branchID="b32">
39                    <uncertaintyModel>+0.5</uncertaintyModel>
40                    <uncertaintyWeight>0.333</uncertaintyWeight>
41                </logicTreeBranch>
42                <logicTreeBranch branchID="b33">
43                    <uncertaintyModel>+1.0</uncertaintyModel>
44                    <uncertaintyWeight>0.334</uncertaintyWeight>
45                </logicTreeBranch>
46            </logicTreeBranchSet>
47
48    </logicTree>
49</nrml>

After the first branching level defining the source model, two additional branching levels are defined, one defining relative uncertainties on b value (bGRRelative applied consistently to all sources in the source model) and the second uncertainties on maximum magnitude (maxMagGRRelative). Similar to the other cases, two GMPEs are considered for each tectonic region type and therefore the total number of logic tree path is 36 (1 source model x 3 b value increments x 3 maximum magnitude increments x 2 GMPE for Active x 2 GMPEs for Stable).

3.4.2 Hazard Disaggregation Demos#

An example of disaggregation calculation is given considering a source model consisting of two sources (area and simple fault) belonging to two different tectonic region types.

The calculation is defined with the following configuration file:

[general]
description = ...
calculation_mode = disaggregation
random_seed = 23

[geometry]
sites = 0.5 -0.5

[logic_tree]
number_of_logic_tree_samples = 0

[erf]
rupture_mesh_spacing = 2
width_of_mfd_bin = 0.1
area_source_discretization = 5.0

[site_params]
reference_vs30_type = measured
reference_vs30_value = 600.0
reference_depth_to_2pt5km_per_sec = 5.0
reference_depth_to_1pt0km_per_sec = 100.0

[calculation]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gmpe_logic_tree.xml
investigation_time = 50.0
intensity_measure_types_and_levels = {"PGA": [...]}
truncation_level = 3
maximum_distance = 200.0

[disaggregation]
poes_disagg = 0.1
mag_bin_width = 1.0
distance_bin_width = 10.0
coordinate_bin_width = 0.2
num_epsilon_bins = 3

[output]
export_dir = ...

Disaggregation matrices are computed for a single site (located between the two sources) for a ground motion value corresponding to a probability value equal to 0.1 (poes_disagg = 0.1). Magnitude values are classified in one magnitude unit bins (mag_bin_width = 1.0), distances in bins of 10 km (distance_bin_width = 10.0), coordinates in bins of 0.2 degrees (coordinate_bin_width = 0.2). 3 epsilons bins are considered (num_epsilon_bins = 3).

3.4.3 Event Based PSHA Demos#

A demo showing an example of Event Based PSHA calculation is provided with the following configuration file:

[general]
description = Event Based PSHA using Area Source
calculation_mode = event_based
random_seed = 23

[geometry]
sites = 0.5 -0.5

[logic_tree]
number_of_logic_tree_samples = 0

[erf]
rupture_mesh_spacing = 2
width_of_mfd_bin = 0.1
area_source_discretization = 5.0

[site_params]
reference_vs30_type = measured
reference_vs30_value = 600.0
reference_depth_to_2pt5km_per_sec = 5.0
reference_depth_to_1pt0km_per_sec = 100.0

[calculation]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gmpe_logic_tree.xml
investigation_time = 50.0
intensity_measure_types_and_levels = {"PGA": [...]}
truncation_level = 3
maximum_distance = 200.0

[event_based_params]
ses_per_logic_tree_path = 100
ground_motion_correlation_model =
ground_motion_correlation_params =

[output]
export_dir = ...
ground_motion_fields = true
hazard_curves_from_gmfs = true
mean = false
quantiles =
hazard_maps = true
poes = 0.1

The source model consist of one source (area). 100 stochastic event sets are generated (ses_per_logic_tree_path = 100) (an example can be seen in Fig. 19). Ground motion fields are computed (ground_motion_fields = true, Fig. 20 and Fig. 21) and also hazard curves from ground motion fields are extracted (hazard_curves_from_gmfs = true). The corresponding hazard maps for 0.1 probability are also calculated (hazard_maps = true)

_images/ses.png

Fig. 19 A stochastic event set generated with the event based PSHA demo. The area source defines a nodal plane distribution which distributes events among vertical and dipping (50 degrees) faults with equal weights. Vertical ruptures are then distributed equally in the range 0-180 degrees while the dipping ones in the range 0-360, both with a step of 45 degrees.#

_images/gmf-no-corr.png

Fig. 20 Ground motion fields (PGA) with no spatial correlation#

_images/gmf-corr.png

Fig. 21 Ground motion fields (PGA) with spatial correlation#

4 Risk#

4.1 Introduction to the Risk Module#

The seismic risk results are calculated using the OpenQuake risk library, an open-source suite of tools for seismic risk assessment and loss estimation. This library is written in the Python programming language and available in the form of a “developers” release at the following location: https://github.com/gem/oq-engine/tree/master/openquake/risklib.

The risk component of the OpenQuake engine can compute both scenario-based and probabilistic seismic damage and risk using various approaches. The following types of analysis are currently supported:

  • Scenario Damage Assessment, for the calculation of damage distribution statistics for a portfolio of buildings from a single earthquake rupture scenario taking into account aleatory and epistemic ground-motion variability.

  • Scenario Risk Assessment, for the calculation of individual asset and portfolio loss statistics due to a single earthquake rupture scenario taking into account aleatory and epistemic ground-motion variability. Correlation in the vulnerability of different assets of the same typology can also be taken into consideration.

  • Classical Probabilistic Seismic Damage Analysis, for the calculation of damage state probabilities over a specified time period, and probabilistic collapse maps, starting from the hazard curves computed following the classical integration procedure ((Cornell 1968), McGuire (1976)) as formulated by (Field, Jordan, and Cornell 2003).

  • Classical Probabilistic Seismic Risk Analysis, for the calculation of loss curves and loss maps, starting from the hazard curves computed following the classical integration procedure ((Cornell 1968), McGuire (1976)) as formulated by (Field, Jordan, and Cornell 2003).

  • Stochastic Event Based Probabilistic Seismic Damage Analysis, for the calculation of event damage tables starting from stochastic event sets. Other results such as damage-state-exceedance curves, probabilistic damage maps, and average annual damages or collapses can be obtained by post-processing the event damage tables.

  • Stochastic Event Based Probabilistic Seismic Risk Analysis, for the calculation of event loss tables starting from stochastic event sets. Other results such as loss-exceedance curves, probabilistic loss maps, and average annual losses can be obtained by post-processing the event loss tables.

  • Retrofit Benefit-Cost Ratio Analysis, which is useful in estimating the net-present value of the potential benefits of performing retrofitting for a portfolio of assets (in terms of decreased losses in seismic events), measured relative to the upfront cost of retrofitting.

Each calculation workflow has a modular structure, so that intermediate results can be saved and analyzed. Moreover, each calculator can be extended independently of the others so that additional calculation options and methodologies can be easily introduced, without affecting the overall calculation workflow. Each workflow is described in more detail in the following sections.

4.1.1 Scenario Damage Assessment#

The scenario damage calculator computes damage distribution statistics for all assets in a given Exposure Model for a single specified rupture. Damage distribution statistics include the mean and standard deviation of damage fractions for different damage states. This calculator requires the definition of a finite Rupture Model, an Exposure Model and a Fragility Model; the main results are the damage distribution statistics per asset, aggregated damage distribution statistics per taxonomy, aggregated damage distribution statistics for the region, and collapse maps, which contain the spatial distribution of the number or area of collapsed buildings throughout the region of interest.

The rupture characteristics—i.e. the magnitude, hypocenter and fault geometry—are modelled as deterministic in the scenario calculators. Multiple simulations of different possible Ground Motion Fields due to the single rupture are generated, taking into consideration both the inter-event variability of ground motions, and the intra-event residuals obtained from a spatial correlation model for ground motion residuals. The use of logic trees allows for the consideration of uncertainty in the choice of a ground motion model for the given tectonic region.

As an alternative to computing the Ground Motion Fields with OpenQuake engine, users can also provide their own sets of Ground Motion Fields as input to the scenario damage calculator.

Note: The damage simulation algorithm for the scenario damage calculator has changed starting from OpenQuake engine39 to use a full Monte Carlo simulation of damage states.

For each Ground Motion Field, a damage state is simulated for each building for every asset in the Exposure Model using the provided Fragility Model, and finally the mean damage distribution across all realizations is calculated. The calculator also provides aggregated damage distribution statistics for the portfolio, such as mean damage fractions for each taxonomy in the Exposure Model, and the mean damage for the entire region of study.

The required input files required for running a scenario damage calculation and the resulting output files are depicted in Fig. 22.

Scenario Damage Calculator input/output structure.

Fig. 22 Scenario Damage Calculator input/output structure.#

Consequence Model files can also be provided as inputs for a scenario damage calculation in addition to fragilitymodels files, in order to estimate consequences based on the calculated damage distribution. The user may provide one Consequence Model file corresponding to each loss type (amongst structural, nonstructural, contents, and business interruption) for which a Fragility Model file is provided. Whereas providing a Fragility Model file for at least one loss type is mandatory for running a Scenario Damage calculation, providing corresponding Consequence Model files is optional.

4.1.2 Scenario Risk Assessment#

The scenario risk calculator computes loss statistics for all assets in a given Exposure Model for a single specified rupture. Loss statistics include the mean and standard deviation of ground-up losses for each loss type considered in the analysis. Loss statistics can currently be computed for five different loss types using this calculator: structural losses, nonstructural losses, contents losses, downtime losses, and occupant fatalities. This calculator requires the definition of a finite Rupture Model, an Exposure Model and a Vulnerability Model for each loss type considered; the main results are the loss statistics per asset and mean loss maps.

The rupture characteristics—i.e. the magnitude, hypocenter and fault geometry—are modelled as deterministic in the scenario calculators. Multiple simulations of different possible Ground Motion Fields due to the single rupture are generated, taking into consideration both the inter-event variability of ground motions, and the intra-event residuals obtained from a spatial correlation model for ground motion residuals. The use of logic trees allows for the consideration of uncertainty in the choice of a ground motion model for the given tectonic region.

As an alternative to computing the Ground Motion Fields with OpenQuake, users can also provide their own sets of Ground Motion Fields as input to the scenario risk calculator.

For each Ground Motion Field simulation, a loss ratio is sampled for every asset in the Exposure Model using the provided probabilistic Vulnerability Model taking into consideration the correlation model for vulnerability of different assets of a given taxonomy. Finally loss statistics, i.e., the mean loss and standard deviation of loss for ground-up losses across all simulations, are calculated for each asset. Mean loss maps are also generated by this calculator, describing the mean ground-up losses caused by the scenario event for the different assets in the Exposure Model.

The required input files required for running a scenario risk calculation and the resulting output files are depicted in Fig. 23.

Scenario Risk Calculator input/output structure.

Fig. 23 Scenario Risk Calculator input/output structure.#

4.1.3 Classical Probabilistic Seismic Damage Analysis#

The classical PSHA-based damage calculator integrates the fragility functions for an asset with the seismic hazard curve at the location of the asset, to give the expected damage distribution for the asset within a specified time period. The calculator requires the definition of an Exposure Model, a Fragility Model with fragilityfunctions for each taxonomy represented in the Exposure Model, and hazard curves calculated in the region of interest. The main results of this calculator are the expected damage distribution for each asset, which describe the probability of the asset being in different damage states, and collapse maps for the region, which describe the probability of collapse for different assets in the portfolio over the specified time period. Damage distribution aggregated by taxonomy or of the total portfolio (considering all assets in the Exposure Model) can not be extracted using this calculator, as the spatial correlation of the ground motion residuals is not taken into consideration.

The hazard curves required for this calculator can be calculated by the OpenQuake engine for all asset locations in the Exposure Model using the classical PSHA approach (Cornell 1968; McGuire 1976).

The required input files required for running a classical probabilistic damage calculation and the resulting output files are depicted in Fig. 24.

Classical PSHA-based Damage Calculator input/output structure.

Fig. 24 Classical PSHA-based Damage Calculator input/output structure.#

4.1.4 Classical Probabilistic Seismic Risk Analysis#

The classical PSHA-based risk calculator convolves through numerical integration, the probabilistic vulnerability functions for an asset with the seismic hazard curve at the location of the asset, to give the loss distribution for the asset within a specified time period. The calculator requires the definition of an Exposure Model, a Vulnerability Model for each loss type of interest with vulnerabilityfunctions for each taxonomy represented in the Exposure Model, and hazard curves calculated in the region of interest. Loss curves and loss maps can currently be calculated for five different loss types using this calculator: structural losses, nonstructural losses, contents losses, downtime losses, and occupant fatalities. The main results of this calculator are loss exceedance curves for each asset, which describe the probability of exceedance of different loss levels over the specified time period, and loss maps for the region, which describe the loss values that have a given probability of exceedance over the specified time

Unlike the probabilistic event-based risk calculator, an aggregate loss curve (considering all assets in the Exposure Model) can not be extracted using this calculator, as the correlation of the ground motion residuals and vulnerability uncertainty is not taken into consideration in this calculator.

The hazard curves required for this calculator can be calculated by the OpenQuake engine for all asset locations in the Exposure Model using the classical PSHA approach (Cornell 1968; McGuire 1976). The use of logic- trees allows for the consideration of model uncertainty in the choice of a ground motion prediction equation for the different tectonic region types in the region. Unlike what was described in the previous calculator, a total loss curve (considering all assets in the Exposure Model) can not be extracted using this calculator, as the correlation of the ground motion residuals and vulnerability uncertainty is not taken into consideration.

The required input files required for running a classical probabilistic risk calculation and the resulting output files are depicted in Fig. 25.

Classical PSHA-based Risk Calculator input/output structure.

Fig. 25 Classical PSHA-based Risk Calculator input/output structure.#

4.1.5 Stochastic Event Based Probabilistic Seismic#

Damage Analysis This calculator employs an event-based Monte Carlo simulation approach to probabilistic damage assessment in order to estimate the damage distribution for individual assets and aggregated damage distribution for a spatially distributed portfolio of assets within a specified time period. The calculator requires the definition of an Exposure Model, a Fragility Model for each loss type of interest with fragilityfunctions for each damage state for every typology represented in the Exposure Model, and a Stochastic Event Set representative of the seismicity of the region over the specified time period. Damage state curves and damage maps corresponding to specified return periods can also be obtained using this calculator.

As an alternative to computing the Ground Motion Fields with OpenQuake engine, users can also provide their own sets of Ground Motion Fields as input to the event-based damage calculator.

The main results of this calculator are the event damage tables; these tables describe the total number of buildings in each damage state for the portfolio of assets for each seismic event in the Stochastic Event Set.

Asset-level event damage tables are generated by the calculator, but are not exportable in csv format due to the large file sizes that may be involved. Interested users can access the asset-level event damage tables within the datastore for the completed calculation.

This calculator relies on the probabilistic event-based hazard calculator, which simulates the seismicity of the chosen time period \(T\) by producing a Stochastic Event Set. For each rupture generated by a Seismic Source, the number of occurrences in the given time span \(T\) is simulated by sampling the corresponding probability distribution as given by \(P_{rup}(k | T)\). A Stochastic Event Set is therefore a sample of the full population of ruptures as defined by a Seismic Source Model. Each rupture is present zero, one or more times, depending on its probability. Symbolically, we can define a Stochastic Event Set as:

\[\begin{aligned} SES(T) = \left\{k \times rup,\;k\sim P_{rup}(k | T)\;\;\forall\;rup\;in\;Src\;\forall\;Src\;in\;SSM\right\}\end{aligned}\]

where \(k\), the number of occurrences, is a random sample of \(P_{rup}(k | T)\), and \(k \times rup\) means that rupture \(rup\) is repeated \(k\) times in the Stochastic Event Set.

For each rupture or event in the Stochastic Event Sets, a spatially correlated Ground Motion Field realisation is generated, taking into consideration both the inter-event variability of ground motions, and the intra-event residuals obtained from a spatial correlation model for ground motion residuals (if one is specified in the job file). The use of logic trees allows for the consideration of uncertainty in the choice of a Seismic Source Model, and in the choice of groundmotionmodels for the different tectonic regions.

For each Ground Motion Field realization, a damage state is siumulated for each building of every asset in the Exposure Model using the provided Fragility Model. The asset-level event damage table is saved to the datastore. Time-averaged damage distributions at the asset-level can be obtained from the event damage table. Finally damage state exceedance curves can be computed.

The required input files required for running a probabilistic stochastic event-based damage calculation and the resulting output files are depicted in Fig. 26.

Probabilistic Event-based Damage Calculator input/output structure.

Fig. 26 Probabilistic Event-based Damage Calculator input/output structure.#

Similar to the scenario damage calculator, Consequence Model files can also be provided as inputs for an event-based damage calculation in addition to fragilitymodels files, in order to estimate consequences based on the calculated damage distribution. The user may provide one Consequence Model file corresponding to each loss type (amongst structural, nonstructural, contents, and business interruption) for which a Fragility Model file is provided. Whereas providing a Fragility Model file for at least one loss type is mandatory for running an Event-Based Damage calculation, providing corresponding Consequence Model files is optional.

4.1.6 Stochastic Event Based Probabilistic Seismic#

Risk Analysis This calculator employs an event-based Monte Carlo simulation approach to probabilistic risk assessment in order to estimate the loss distribution for individual assets and aggregated loss distribution for a spatially distributed portfolio of assets within a specified time period. The calculator requires the definition of an Exposure Model, a Vulnerability Model for each loss type of interest with vulnerabilityfunctions for each taxonomy represented in the Exposure Model, and a Stochastic Event Set (also known as a synthetic catalog) representative of the seismicity of the region over the specified time period. Loss curves and loss maps can currently be calculated for five different loss types using this calculator: structural losses, nonstructural losses, contents losses, downtime losses, and occupant fatalities.

As an alternative to computing the Ground Motion Fields with OpenQuake engine, users can also provide their own sets of Ground Motion Fields as input to the event-based risk calculator, starting from OpenQuake engine28.

The main results of this calculator are loss exceedance curves for each asset, which describe the probability of exceedance of different loss levels over the specified time period, and loss maps for the region, which describe the loss values that have a given probability of exceedance over the specified time period. Aggregate loss exceedance curves can also be produced using this calculator; these describe the probability of exceedance of different loss levels for all assets in the portfolio. Finally, event loss tables can be produced using this calculator; these tables describe the total loss across the portfolio for each seismic event in the Stochastic Event Set.

This calculator relies on the probabilistic event-based hazard calculator, which simulates the seismicity of the chosen time period \(T\) by producing a Stochastic Event Set. For each rupture generated by a Seismic Source, the number of occurrences in the given time span \(T\) is simulated by sampling the corresponding probability distribution as given by \(P_{rup}(k | T)\). A Stochastic Event Set is therefore a sample of the full population of ruptures as defined by a Seismic Source Model. Each rupture is present zero, one or more times, depending on its probability. Symbolically, we can define a Stochastic Event Set as:

\[\begin{aligned} SES(T) = \left\{k \times rup,\;k\sim P_{rup}(k | T)\;\;\forall\;rup\;in\;Src\;\forall\;Src\;in\;SSM\right\}\end{aligned}\]

where \(k\), the number of occurrences, is a random sample of \(P_{rup}(k | T)\), and \(k \times rup\) means that rupture \(rup\) is repeated \(k\) times in the Stochastic Event Set.

For each rupture or event in the Stochastic Event Sets, a spatially correlated Ground Motion Field realisation is generated, taking into consideration both the inter-event variability of ground motions, and the intra-event residuals obtained from a spatial correlation model for ground motion residuals (if one is specified in the job file). The use of logic trees allows for the consideration of uncertainty in the choice of a Seismic Source Model, and in the choice of groundmotionmodels for the different tectonic regions.

For each Ground Motion Field realization, a loss ratio is sampled for every asset in the Exposure Model using the provided probabilistic Vulnerability Model, taking into consideration the correlation model for vulnerability of different assets of a given taxonomy. Finally loss exceedance curves are computed for ground-up losses.

The required input files required for running a probabilistic stochastic event-based risk calculation and the resulting output files are depicted in Fig. 27.

Probabilistic Event-based Risk Calculator input/output structure.

Fig. 27 Probabilistic Event-based Risk Calculator input/output structure.#

4.1.7 Retrofit Benefit-Cost Ratio Analysis#

This calculator represents a decision-support tool for deciding whether the employment of retrofitting measures to a collection of existing buildings is advantageous from an economical point of view. For this assessment, the expected losses considering the original and retrofitted configuration of the buildings are estimated, and the economic benefit due to the better seismic design is divided by the retrofitting cost, leading to the benefit/cost ratio. These loss curves are computed using the previously described Classical PSHA- based Risk calculator. The output of this calculator is a benefit/cost ratio for each asset, in which a ratio above one indicates that employing a retrofitting intervention is economically viable.

In Fig. 28, the input/output structure for this calculator is depicted.

Retrofitting Benefit/Cost Ratio Calculator input/output structure.

Fig. 28 Retrofitting Benefit/Cost Ratio Calculator input/output structure.#

For further information regarding the theoretical background of the methodologies used for each calculator, users are referred to the OpenQuake- engine Book (Risk).

4.2 Risk Input Models#

The following sections describe the basic inputs required for a risk calculation, including exposuremodels, fragilitymodels, consequencemodels, and vulnerabilitymodels. In addition, each risk calculator also requires the appropriate hazard inputs computed in the region of interest. Hazard inputs include hazard curves for the classical probabilistic damage and risk calculators, Ground Motion Field for the scenario damage and risk calculators, or Stochastic Event Sets for the probabilistic event based calculators.

4.2.1 Exposure Models#

All risk calculators in the OpenQuake engine require an Exposure Model that needs to be provided in the Natural hazards’ Risk Markup Language schema, the use of which is illustrated through several examples in this section. The information included in an Exposure Model comprises a metadata section listing general information about the exposure, followed by a cost conversions section that describes how the different areas, costs, and occupancies for the assets will be specified, followed by data regarding each individual asset in the portfolio.

Note: Starting from OpenQuake engine30, the Exposure Model may be provided using csv files listing the asset information, along with an xml file conatining the metadata section for the exposure model that has been described in the examples above. See Example 8 below for an illustration of an exposure model using csv files.

A simple Exposure Model comprising a single asset is shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <assets>
18    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
19      <location lon="-122.000" lat="38.113" />
20      <costs>
21        <cost type="structural" value="10000" />
22      </costs>
23      <occupancies>
24        <occupancy occupants="20" period="day" />
25      </occupancies>
26    </asset>
27  </assets>
28
29</exposureModel>
30
31</nrml>

Let us take a look at each of the sections in the above example file in turn. The first part of the file contains the metadata section:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <assets>
18    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
19      <location lon="-122.000" lat="38.113" />
20      <costs>
21        <cost type="structural" value="10000" />
22      </costs>
23      <occupancies>
24        <occupancy occupants="20" period="day" />
25      </occupancies>
26    </asset>
27  </assets>
28
29</exposureModel>
30
31</nrml>

The information in the metadata section is common to all of the assets in the portfolio and needs to be incorporated at the beginning of every Exposure Model file. There are a number of parameters that compose the metadata section, which is intended to provide general information regarding the assets within the Exposure Model. These parameters are described below:

  • id: mandatory; a unique string used to identify the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (–), and underscores (_), with a maximum of 100 characters.

  • category: an optional string used to define the type of assets being stored (e.g: buildings, lifelines).

  • taxonomySource: an optional attribute used to define the taxonomy being used to classify the assets.

  • description: mandatory; a brief string (ASCII) with further information about the Exposure Model.

Next, let us look at the part of the file describing the area and cost conversions:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <assets>
18    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
19      <location lon="-122.000" lat="38.113" />
20      <costs>
21        <cost type="structural" value="10000" />
22      </costs>
23      <occupancies>
24        <occupancy occupants="20" period="day" />
25      </occupancies>
26    </asset>
27  </assets>
28
29</exposureModel>
30
31</nrml>

Notice that the costType element defines a name, a type, and a unit attribute.

The Natural hazards’ Risk Markup Language schema for the Exposure Model allows the definition of a structural cost, a nonstructural components cost, a contents cost, and a business interruption or downtime cost for each asset in the portfolio. Thus, the valid values for the name attribute of the costType element are the following:

  • structural: used to specify the structural replacement cost of assets

  • nonstructural: used to specify the replacement cost for the nonstructural components of assets

  • contents: used to specify the contents replacement cost

  • business_interruption: used to specify the cost that will be incurred per unit time that a damaged asset remains closed following an earthquake

The Exposure Model shown in the example above defines only the structural values for the assets. However, multiple cost types can be defined for each asset in the same Exposure Model.

The unit attribute of the costType element is used for specifying the currency unit for the corresponding cost type. Note that the OpenQuake engine itself is agnostic to the currency units; the unit is thus a descriptive attribute which is used by the OpenQuake engine to annotate the results of a risk assessment. This attribute can be set to any valid Unicode string.

The type attribute of the costType element specifies whether the costs will be provided as an aggregated value for an asset, or per building or unit comprising an asset, or per unit area of an asset. The valid values for the type attribute of the costType element are the following:

  • aggregated: indicates that the replacement costs will be provided as an aggregated value for each asset

  • per_asset: indicates that the replacement costs will be provided per structural unit comprising each asset

  • per_area: indicates that the replacement costs will be provided per unit area for each asset

If the costs are to be specified per_area for any of the costTypes, the area element will also need to be defined in the conversions section. The area element defines a type, and a unit attribute.

The unit attribute of the area element is used for specifying the units for the area of an asset. The OpenQuake engine itself is agnostic to the area units; the unit is thus a descriptive attribute which is used by the OpenQuake engine to annotate the results of a risk assessment. This attribute can be set to any valid ASCII string.

The type attribute of the area element specifies whether the area will be provided as an aggregated value for an asset, or per building or unit comprising an asset. The valid values for the type attribute of the area element are the following:

  • aggregated: indicates that the area will be provided as an aggregated value for each asset

  • per_asset: indicates that the area will be provided per building or unit comprising each asset

The way the information about the characteristics of the assets in an Exposure Model are stored can vary strongly depending on how and why the data was compiled. As an example, if national census information is used to estimated the distribution of assets in a given region, it is likely that the number of buildings within a given geographical area will be used to define the dataset, and will be used for estimating the number of collapsed buildings for a scenario earthquake. On the other hand, if simplified methodologies based on proxy data such as population distribution are used to develop the Exposure Model, then it is likely that the built up area or economic cost of each building typology will be directly derived, and will be used for the estimation of economic losses.

Finally, let us look at the part of the file describing the set of assets in the portfolio to be used in seismic damage or risk calculations:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <assets>
18    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
19      <location lon="-122.000" lat="38.113" />
20      <costs>
21        <cost type="structural" value="10000" />
22      </costs>
23      <occupancies>
24        <occupancy occupants="20" period="day" />
25      </occupancies>
26    </asset>
27  </assets>
28
29</exposureModel>
30
31</nrml>

Each asset definition involves specifiying a set of mandatory and optional attributes concerning the asset. The following set of attributes can be assigned to each asset based on the current schema for the Exposure Model:

  • id: mandatory; a unique string used to identify the given asset, which is used by the OpenQuake engine to relate each asset with its associated results. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • taxonomy: mandatory; this string specifies the building typology of the given asset. The taxonomy strings can be user-defined, or based on an existing classification scheme such as the GEM Taxonomy, PAGER, or EMS-98.

  • number: the number of individual structural units comprising a given asset. This attribute is mandatory for damage calculations. For risk calculations, this attribute must be defined if either the area or any of the costs are provided per structural unit comprising each asset.

  • area: area of the asset, at a given location. As mentioned earlier, the area is a mandatory attribute only if any one of the costs for the asset is specified per unit area.

  • location: mandatory; specifies the longitude (between -180\(^{\circ}\) to 180\(^{\circ}\)) and latitude (between -90\(^{\circ}\) to 90 \(^{\circ}\)) of the given asset, both specified in decimal degrees 2.

  • costs: specifies a set of costs for the given asset. The replacement value for different cost types must be provided on separate lines within the costs element. As shown in the example above, each cost entry must define the type and the value. Currently supported valid options for the cost type are: structural, nonstructural, contents, and business_interruption.

  • occupancies: mandatory only for probabilistic or scenario risk calculations that specify an occupants_vulnerability_file. Each entry within this element specifies the number of occupants for the asset for a particular period of the day. As shown in the example above, each occupancy entry must define the period and the occupants. Currently supported valid options for the period are: day, transit, and night. Currently, the number of occupants for an asset can only be provided as an aggregated value for the asset.

For the purposes of performing a retrofitting benefit/cost analysis, it is also necessary to define the retrofitting cost (retrofitted). The combination between the possible options in which these three attributes can be defined leads to four ways of storing the information about the assets. For each of these cases a brief explanation and example is provided in this section.

Example 1

This example illustrates an Exposure Model in which the aggregated cost (structural, nonstructural, contents and business interruption) of the assets of each taxonomy for a set of locations is directly provided. Thus, in order to indicate how the various costs will be defined, the following information needs to be stored in the Exposure Model file, as shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with aggregated replacement costs for each asset
10  </description>
11  <conversions>
12    <costTypes>
13      <costType name="structural" type="aggregated" unit="USD" />
14      <costType name="nonstructural" type="aggregated" unit="USD" />
15      <costType name="contents" type="aggregated" unit="USD" />
16      <costType name="business_interruption" type="aggregated" unit="USD/month"/>
17    </costTypes>
18  </conversions>
19  <assets>
20    <asset id="a1" taxonomy="Adobe" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="20000" />
24        <cost type="nonstructural" value="30000" />
25        <cost type="contents" value="10000" />
26        <cost type="business_interruption" value="4000" />
27      </costs>
28    </asset>
29  </assets>
30</exposureModel>
31
32</nrml>

In this case, the cost type of each component as been defined as aggregated. Once the way in which each cost is going to be defined has been established, the values for each asset can be stored according to the format shown in the listing.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with aggregated replacement costs for each asset
10  </description>
11  <conversions>
12    <costTypes>
13      <costType name="structural" type="aggregated" unit="USD" />
14      <costType name="nonstructural" type="aggregated" unit="USD" />
15      <costType name="contents" type="aggregated" unit="USD" />
16      <costType name="business_interruption" type="aggregated" unit="USD/month"/>
17    </costTypes>
18  </conversions>
19  <assets>
20    <asset id="a1" taxonomy="Adobe" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="20000" />
24        <cost type="nonstructural" value="30000" />
25        <cost type="contents" value="10000" />
26        <cost type="business_interruption" value="4000" />
27      </costs>
28    </asset>
29  </assets>
30</exposureModel>
31
32</nrml>

Each asset is uniquely identified by its id. Then, a pair of coordinates (latitude and longitude) for a location where the asset is assumed to exist is defined. Each asset must be classified according to a taxonomy, so that the OpenQuake engine is capable of employing the appropriate Vulnerability Function or Fragility Function in the risk calculations. Finally, the cost values of each type are stored within the costs attribute. In this example, the aggregated value for all structural units (within a given asset) at each location is provided directly, so there is no need to define other attributes such as number or area. This mode of representing an Exposure Model is probably the simplest one.

Example 2

In the snippet shown in the listing below, an Exposure Model containing the number of structural units and the associated costs per unit of each asset is presented.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per building for each asset
10  </description>
11  <conversions>
12    <costTypes>
13      <costType name="structural" type="per_asset" unit="USD" />
14      <costType name="nonstructural" type="per_asset" unit="USD" />
15      <costType name="contents" type="per_asset" unit="USD" />
16      <costType name="business_interruption" type="per_asset" unit="USD/month"/>
17    </costTypes>
18  </conversions>
19  <assets>
20    <asset id="a1" number="2" taxonomy="Adobe" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="7500" />
24        <cost type="nonstructural" value="11250" />
25        <cost type="contents" value="3750" />
26        <cost type="business_interruption" value="1500" />
27      </costs>
28    </asset>
29  </assets>
30</exposureModel>
31
32</nrml>

For this case, the cost type has been set to per_asset. Then, the information from each asset can be stored following the format shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per building for each asset
10  </description>
11  <conversions>
12    <costTypes>
13      <costType name="structural" type="per_asset" unit="USD" />
14      <costType name="nonstructural" type="per_asset" unit="USD" />
15      <costType name="contents" type="per_asset" unit="USD" />
16      <costType name="business_interruption" type="per_asset" unit="USD/month"/>
17    </costTypes>
18  </conversions>
19  <assets>
20    <asset id="a1" number="2" taxonomy="Adobe" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="7500" />
24        <cost type="nonstructural" value="11250" />
25        <cost type="contents" value="3750" />
26        <cost type="business_interruption" value="1500" />
27      </costs>
28    </asset>
29  </assets>
30</exposureModel>
31
32</nrml>

In this example, the various costs for each asset is not provided directly, as in the previous example. In order to carry out the risk calculations in which the economic cost of each asset is provided, the OpenQuake engine multiplies, for each asset, the number of units (buildings) by the “per asset” replacement cost. Note that in this case, there is no need to specify the attribute area.

Example 3

The example shown in the listing below comprises an Exposure Model containing the built up area of each asset, and the associated costs are provided per unit area.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per unit area;
10    and areas provided as aggregated values for each asset
11  </description>
12  <conversions>
13    <area type="aggregated" unit="SQM" />
14    <costTypes>
15      <costType name="structural" type="per_area" unit="USD" />
16      <costType name="nonstructural" type="per_area" unit="USD" />
17      <costType name="contents" type="per_area" unit="USD" />
18      <costType name="business_interruption" type="per_area" unit="USD/month"/>
19    </costTypes>
20  </conversions>
21  <assets>
22    <asset id="a1" area="1000" taxonomy="Adobe" >
23      <location lon="-122.000" lat="38.113" />
24      <costs>
25        <cost type="structural" value="5" />
26        <cost type="nonstructural" value="7.5" />
27        <cost type="contents" value="2.5" />
28        <cost type="business_interruption" value="1" />
29      </costs>
30    </asset>
31  </assets>
32</exposureModel>
33
34</nrml>

In order to compile an Exposure Model with this structure, the cost type should be set to per_area. In addition, it is also necessary to specify if the area that is being store represents the aggregated area of number of units within an asset, or the average area of a single unit. In this particular case, the area that is being stored is the aggregated built up area per asset, and thus this attribute was set to aggregated. The listing below illustrates the definition of the assets for this example.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per unit area;
10    and areas provided as aggregated values for each asset
11  </description>
12  <conversions>
13    <area type="aggregated" unit="SQM" />
14    <costTypes>
15      <costType name="structural" type="per_area" unit="USD" />
16      <costType name="nonstructural" type="per_area" unit="USD" />
17      <costType name="contents" type="per_area" unit="USD" />
18      <costType name="business_interruption" type="per_area" unit="USD/month"/>
19    </costTypes>
20  </conversions>
21  <assets>
22    <asset id="a1" area="1000" taxonomy="Adobe" >
23      <location lon="-122.000" lat="38.113" />
24      <costs>
25        <cost type="structural" value="5" />
26        <cost type="nonstructural" value="7.5" />
27        <cost type="contents" value="2.5" />
28        <cost type="business_interruption" value="1" />
29      </costs>
30    </asset>
31  </assets>
32</exposureModel>
33
34</nrml>

Once again, the OpenQuake engine needs to carry out some calculations in order to compute the different costs per asset. In this case, this value is computed by multiplying the aggregated built up area of each asset by the associated cost per unit area. Notice that in this case, there is no need to specify the attribute number.

Example 4

This example demonstrates an Exposure Model that defines the number of structural units for each asset, the average built up area per structural unit and the associated costs per unit area. The listing below shows the metadata definition for an Exposure Model built in this manner.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per unit area;
10    and areas provided per building for each asset
11  </description>
12  <conversions>
13    <area type="per_asset" unit="SQM" />
14    <costTypes>
15      <costType name="structural" type="per_area" unit="USD" />
16      <costType name="nonstructural" type="per_area" unit="USD" />
17      <costType name="contents" type="per_area" unit="USD" />
18      <costType name="business_interruption" type="per_area" unit="USD/month"/>
19    </costTypes>
20  </conversions>
21  <assets>
22    <asset id="a1" number="3" area="400" taxonomy="Adobe" >
23      <location lon="-122.000" lat="38.113" />
24      <costs>
25        <cost type="structural" value="10" />
26        <cost type="nonstructural" value="15" />
27        <cost type="contents" value="5" />
28        <cost type="business_interruption" value="2" />
29      </costs>
30    </asset>
31  </assets>
32</exposureModel>
33
34</nrml>

Similarly to what was described in the previous example, the various costs type also need to be established as per_area, but the type of area is now defined as per_asset. The listing below illustrates the definition of the assets for this example.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>
 9    Exposure model with replacement costs per unit area;
10    and areas provided per building for each asset
11  </description>
12  <conversions>
13    <area type="per_asset" unit="SQM" />
14    <costTypes>
15      <costType name="structural" type="per_area" unit="USD" />
16      <costType name="nonstructural" type="per_area" unit="USD" />
17      <costType name="contents" type="per_area" unit="USD" />
18      <costType name="business_interruption" type="per_area" unit="USD/month"/>
19    </costTypes>
20  </conversions>
21  <assets>
22    <asset id="a1" number="3" area="400" taxonomy="Adobe" >
23      <location lon="-122.000" lat="38.113" />
24      <costs>
25        <cost type="structural" value="10" />
26        <cost type="nonstructural" value="15" />
27        <cost type="contents" value="5" />
28        <cost type="business_interruption" value="2" />
29      </costs>
30    </asset>
31  </assets>
32</exposureModel>
33
34</nrml>

In this example, the OpenQuake engine will make use of all the parameters to estimate the various costs of each asset, by multiplying the number of structural units by its average built up area, and then by the respective cost per unit area.

Example 5

In this example, additional information will be included, which is required for other risk analysis besides loss estimation, such as the benefit/cost analysis.

In order to perform a benefit/cost assessment, it is necessary to indicate the retrofitting cost. This parameter is handled in the same manner as the structural cost, and it should be stored according to the format shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure model illustrating retrofit costs</description>
 9  <conversions>
10    <costTypes>
11      <costType name="structural" type="aggregated" unit="USD"
12                retrofittedType="per_asset" retrofittedUnit="USD" />
13    </costTypes>
14  </conversions>
15  <assets>
16    <asset id="a1" taxonomy="Adobe" number="1" >
17      <location lon="-122.000" lat="38.113" />
18      <costs>
19        <cost type="structural" value="10000" retrofitted="2000" />
20      </costs>
21    </asset>
22  </assets>
23</exposureModel>
24
25</nrml>

Despite the fact that for the demonstration of how the retrofitting cost can be stored the per building type of cost structure described in Example 1 was used, it is important to mention that any of the other cost storing approaches can also be employed (Examples 2–4).

Example 6

The OpenQuake engine is also capable of estimating human losses, based on the number of occupants in an asset, at a certain time of the day. The example Exposure Model shown in the listing below illustrates how this parameter is defined for each asset. In addition, this example also serves the purpose of presenting an Exposure Model in which three cost types have been defined using three different options.

As previously mentioned, in this example only three costs are being stored, and each one follows a different approach. The structural cost is being defined as the aggregate replacement cost for all of the buildings comprising the asset (Example 1), the nonstructural value is defined as the replacement cost per unit area where the area is defined per building comprising the asset (Example 4), and the contents and business_interruption values are provided per building comprising the asset (Example 2). The number of occupants at different times of the day are also provided as aggregated values for all of the buildings comprising the asset.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure model example with occupants</description>
 9  <conversions>
10    <costTypes>
11      <costType name="structural" type="aggregated" unit="USD" />
12      <costType name="nonstructural" type="per_area" unit="USD" />
13      <costType name="contents" type="per_asset" unit="USD" />
14      <costType name="business_interruption" type="per_asset" unit="USD/month" />
15    </costTypes>
16    <area type="per_asset" unit="SQM" />
17  </conversions>
18  <assets>
19    <asset id="a1" taxonomy="Adobe" number="5" area="200" >
20      <location lon="-122.000" lat="38.113" />
21      <costs>
22        <cost type="structural" value="20000" />
23        <cost type="nonstructural" value="15" />
24        <cost type="contents" value="2400" />
25        <cost type="business_interruption" value="1500" />
26      </costs>
27      <occupancies>
28        <occupancy occupants="6" period="day" />
29        <occupancy occupants="10" period="transit" />
30        <occupancy occupants="20" period="night" />
31      </occupancies>
32    </asset>
33  </assets>
34</exposureModel>
35
36</nrml>

Example 7

Starting from OpenQuake engine27, the user may also provide a set of tags for each asset in the Exposure Model. The primary intended use case for the tags is to enable aggregation or accumulation of risk results (casualties / damages / losses) for each tag. The tags could be used to specify location attributes, occupancy types, or insurance policy codes for the different assets in the Exposure Model.

The example Exposure Model shown in the listing below illustrates how one or more tags can be defined for each asset.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example_with_tags"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example with Tags</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <tagNames>state county tract city zip cresta</tagNames>
18
19  <assets>
20    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="10000" />
24      </costs>
25      <occupancies>
26        <occupancy occupants="20" period="day" />
27      </occupancies>
28      <tags state="California" county="Solano" tract="252702"
29            city="Suisun" zip="94585" cresta="A.11"/>
30    </asset>
31  </assets>
32
33</exposureModel>
34
35</nrml>

The list of tag names that will be used in the Exposure Model must be provided in the metadata section of the exposure file, as shown in the following snippet from the full file:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example_with_tags"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example with Tags</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <tagNames>state county tract city zip cresta</tagNames>
18
19  <assets>
20    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="10000" />
24      </costs>
25      <occupancies>
26        <occupancy occupants="20" period="day" />
27      </occupancies>
28      <tags state="California" county="Solano" tract="252702"
29            city="Suisun" zip="94585" cresta="A.11"/>
30    </asset>
31  </assets>
32
33</exposureModel>
34
35</nrml>

The tag values for the different tags can then be specified for each asset as shown in the following snippet from the same file:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example_with_tags"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_2.0">
 8  <description>Exposure Model Example with Tags</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="per_area" unit="USD" />
13    </costTypes>
14    <area type="per_asset" unit="SQM" />
15  </conversions>
16
17  <tagNames>state county tract city zip cresta</tagNames>
18
19  <assets>
20    <asset id="a1" taxonomy="Adobe" number="5" area="100" >
21      <location lon="-122.000" lat="38.113" />
22      <costs>
23        <cost type="structural" value="10000" />
24      </costs>
25      <occupancies>
26        <occupancy occupants="20" period="day" />
27      </occupancies>
28      <tags state="California" county="Solano" tract="252702"
29            city="Suisun" zip="94585" cresta="A.11"/>
30    </asset>
31  </assets>
32
33</exposureModel>
34
35</nrml>

Note that it is not mandatory that every tag name specified in the metadata section must be provided with a tag value for each asset.

Example 8

This example illustrates the use of multiple csv files containing the assets information, in conjunction with the metadata section in the usual xml format.

Let us take a look at the metadata section of the Exposure Model, which is listed as usual in an xml file:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<exposureModel id="exposure_example_with_csv_files"
 6               category="buildings"
 7               taxonomySource="GEM_Building_Taxonomy_3.0">
 8  <description>Exposure Model Example with CSV Files</description>
 9
10  <conversions>
11    <costTypes>
12      <costType name="structural" type="aggregated" unit="USD" />
13      <costType name="nonstructural" type="aggregated" unit="USD" />
14      <costType name="contents" type="aggregated" unit="USD" />
15    </costTypes>
16    <area type="per_asset" unit="SQFT" />
17  </conversions>
18
19  <occupancyPeriods>night</occupancyPeriods>
20
21  <tagNames>occupancy state_id state county_id county tract</tagNames>
22
23  <assets>
24    Washington.csv
25    Oregon.csv
26    California.csv
27  </assets>
28
29</exposureModel>
30
31</nrml>

As in all previous examples, the information in the metadata section is common to all of the assets in the portfolio.

The asset data can be provided in one or more csv files. The path to each of the csv files containing the asset data must be listed between the <assets> and </assets> xml tags.

In the example shown above, the exposure information is provided in three csv files, Washington.csv, Oregon.csv, and California.csv. To illustrate the format of the csv files, we have shown below the header and first few lines of the file Washington.csv in Table 5.

Table 5 Example exposure csv file#

id

lon

lat

taxonomy

number

structural

area

occupancy

state

county

A1

-122.7

46.5

AGR1-W1-LC

7.6

898000

18

Agr

Washington

Lewis County

A2

-122.7

46.5

AGR1-PC1-LC

0.6

67000

1

Agr

Washington

Lewis County

A3

-122.7

46.5

AGR1-C2L-PC

0.6

67000

1

Agr

Washington

Lewis County

A4

-122.7

46.5

AGR1-PC1-PC

1.5

179000

4

Agr

Washington

Lewis County

A5

-122.7

46.5

AGR1-S2L-LC

0.6

67000

1

Agr

Washington

Lewis County

A6

-122.7

46.5

AGR1-S1L-PC

1.1

133000

3

Agr

Washington

Lewis County

A7

-122.7

46.5

AGR1-S2L-PC

1.5

182000

4

Agr

Washington

Lewis County

A8

-122.7

46.5

AGR1-S3-PC

1.1

133000

3

Agr

Washington

Lewis County

A9

-122.7

46.5

AGR1-RM1L-LC

0.6

68000

1

Agr

Washington

Lewis County

Note that the xml metadata section for exposure models provided using csv files must include the xml tag <occupancyPeriods> listing the periods of day for which the number of occupants in each asset will be listed in the csv files. In case the number of occupants are not listed in the csv files, a self-closing tag <occupancyPeriods /> should be included in the xml metadata section.

A web-based tool to build an Exposure Model in the Natural hazards’ Risk Markup Language schema starting from a csv file or a spreadsheet can be found at the OpenQuake platform at the following address: https://platform.openquake.org/ipt/.

4.2.2 Fragility Models#

This section describes the schema currently used to store fragilitymodels, which are required for the Scenario Damage Calculator and the Classical Probabilistic Seismic Damage Calculator. In order to perform probabilistic or scenario damage calculations, it is necessary to define a Fragility Function for each building typology present in the Exposure Model. A Fragility Model defines a set of fragilityfunctions, describing the probability of exceeding a set of limit, or damage, states. The fragilityfunctions can be defined using either a discrete or a continuous format, and the Fragility Model file can include a mix of both types of fragilityfunctions.

For discrete fragilityfunctions, sets of probabilities of exceedance (one set per limit state) are defined for a list of intensity measure levels, as illustrated in Fig. 29.

Graphical representation of a discrete fragility model

Fig. 29 Graphical representation of a discrete fragility model#

The fragilityfunctions can also be defined as continuous functions, through the use of cumulative lognormal distribution functions. In Fig. 30, a continuous Fragility Model is presented.

Graphical representation of a continuous fragility model

Fig. 30 Graphical representation of a continuous fragility model#

An example Fragility Model comprising one discrete Fragility Function and one continuous Fragility Function is shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns="http://openquake.org/xmlns/nrml/0.5">
 3
 4<fragilityModel id="fragility_example"
 5                assetCategory="buildings"
 6                lossCategory="structural">
 7
 8  <description>Fragility Model Example</description>
 9  <limitStates>slight moderate extensive complete</limitStates>
10
11  <fragilityFunction id="Woodframe_TwoStorey" format="discrete">
12    <imls imt="PGA" noDamageLimit="0.05">0.005 0.2 0.4 0.6 0.8 1.0 1.2</imls>
13    <poes ls="slight">0.00 0.01 0.15 0.84 0.99 1.00 1.00</poes>
14    <poes ls="moderate">0.00 0.00 0.01 0.12 0.35 0.57 0.74</poes>
15    <poes ls="extensive">0.00 0.00 0.00 0.08 0.19 0.32 0.45</poes>
16    <poes ls="complete">0.00 0.00 0.00 0.06 0.17 0.26 0.35</poes>
17  </fragilityFunction>
18
19  <fragilityFunction id="RC_LowRise" format="continuous" shape="logncdf">
20    <imls imt="SA(0.3)" noDamageLimit="0.05" minIML="0.0" maxIML="5.0"/>
21    <params ls="slight" mean="0.50" stddev="0.10"/>
22    <params ls="moderate" mean="1.00" stddev="0.40"/>
23    <params ls="extensive" mean="1.50" stddev="0.90"/>
24    <params ls="complete" mean="2.00" stddev="1.60"/>
25  </fragilityFunction>
26
27</fragilityModel>
28
29</nrml>

The initial portion of the schema contains general information that describes some general aspects of the Fragility Model. The information in this metadata section is common to all of the functions in the Fragility Model and needs to be included at the beginning of every Fragility Model file. The parameters of the metadata section are shown in the snippet below and described after the snippet:

4<fragilityModel id="fragility_example"
5                assetCategory="buildings"
6                lossCategory="structural">
7
8  <description>Fragility Model Example</description>
9  <limitStates>slight moderate extensive complete</limitStates>
  • id: mandatory; a unique string used to identify the Fragility Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • assetCategory: an optional string used to specify the type of assets for which fragilityfunctions will be defined in this file (e.g: buildings, lifelines).

  • lossCategory: mandatory; valid strings for this attribute are “structural”, “nonstructural”, “contents”, and “business_interruption”.

  • description: mandatory; a brief string (ASCII) with further relevant information about the Fragility Model, for example, which building typologies are covered or the source of the functions in the Fragility Model.

  • limitStates: mandatory; this field is used to define the number and nomenclature of each limit state. Four limit states are employed in the example above, but it is possible to use any number of discrete states, as long as a fragility curve is always defined for each limit state. The limit states must be provided as a set of strings separated by whitespaces between each limit state. Each limit state string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_). Please ensure that there is no whitespace within the name of any individual limit state.

The following snippet from the above Fragility Model example file defines a discrete Fragility Function:

19  <fragilityFunction id="Woodframe_TwoStorey" format="discrete">
20    <imls imt="PGA" noDamageLimit="0.05">0.005 0.2 0.4 0.6 0.8 1.0 1.2</imls>
21    <poes ls="slight">0.00 0.01 0.15 0.84 0.99 1.00 1.00</poes>
22    <poes ls="moderate">0.00 0.00 0.01 0.12 0.35 0.57 0.74</poes>
23    <poes ls="extensive">0.00 0.00 0.00 0.08 0.19 0.32 0.45</poes>
24    <poes ls="complete">0.00 0.00 0.00 0.06 0.17 0.26 0.35</poes>
25  </fragilityFunction>

The following attributes are needed to define a discrete Fragility Function:

  • id: mandatory; a unique string used to identify the taxonomy for which the function is being defined. This string is used to relate the Fragility Function with the relevant asset in the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • format: mandatory; for discrete fragilityfunctions, this attribute should be set to “discrete”.

  • imls: mandatory; this attribute specifies the list of intensity levels for which the limit state probabilities of exceedance will be defined. In addition, it is also necessary to define the intensity measure type (imt). Optionally, a noDamageLimit can be specified, which defines the intensity level below which the probability of exceedance for all limit states is taken to be zero.

  • poes: mandatory; this field is used to define the probabilities of exceedance (poes) for each limit state for each discrete Fragility Function. It is also necessary to specify which limit state the exceedance probabilities are being defined for using the attribute ls. The probabilities of exceedance for each limit state must be provided on a separate line; and the number of exceedance probabilities for each limit state defined by the poes attribute must be equal to the number of intensity levels defined by the attribute imls. Finally, the number and names of the limit states in each fragility function must be equal to the number of limit states defined earlier in the metadata section of the Fragility Model using the attribute limitStates.

The following snippet from the above Fragility Model example file defines a continuous Fragility Function:

11  <fragilityFunction id="RC_LowRise" format="continuous" shape="logncdf">
12    <imls imt="SA(0.3)" noDamageLimit="0.05" minIML="0.0" maxIML="5.0"/>
13    <params ls="slight" mean="0.50" stddev="0.10"/>
14    <params ls="moderate" mean="1.00" stddev="0.40"/>
15    <params ls="extensive" mean="1.50" stddev="0.90"/>
16    <params ls="complete" mean="2.00" stddev="1.60"/>
17  </fragilityFunction>

The following attributes are needed to define a continuous Fragility Function:

  • id: mandatory; a unique string used to identify the taxonomy for which the function is being defined. This string is used to relate the Fragility Function with the relevant asset in the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • format: mandatory; for continuous fragilityfunctions, this attribute should be set to “continuous”.

  • shape: mandatory; for continuous fragilityfunctions using the lognormal cumulative distrution, this attribute should be set to “logncdf”. At present, only the lognormal cumulative distribution function can be used for representing continuous fragilityfunctions.

  • imls: mandatory; this element specifies aspects related to the intensity measure used by the the Fragility Function. The range of intensity levels for which the continuous fragilityfunctions are valid is specified using the attributes minIML and maxIML. In addition, it is also necessary to define the intensity measure type imt. Optionally, a noDamageLimit can be specified, which defines the intensity level below which the probability of exceedance for all limit states is taken to be zero.

  • params: mandatory; this field is used to define the parameters of the continuous curve for each limit state for this Fragility Function. For a lognormal cumulative distrbution function, the two parameters required to specify the function are the mean and standard deviation of the intensity level. These parameters are defined for each limit state using the attributes mean and stddev respectively. The attribute ls specifies the limit state for which the parameters are being defined. The parameters for each limit state must be provided on a separate line. The number and names of the limit states in each Fragility Function must be equal to the number of limit states defined earlier in the metadata section of the Fragility Model using the attribute limitStates. A point worth clarifying is that the parameters to be defined in the fragility input file are the mean and standard deviation of the intensity measure level (IML) for each damage state, and not the mean and standard deviation of log(IML). Thus, if the intensity measure is PGA or SA for instance, the units for the input parameters will be ’g’.

Note that the schema for representing fragilitymodels has changed between Natural hazards’ Risk Markup Language v0.4 (used prior to OpenQuake engine17) and Natural hazards’ Risk Markup Language v0.5 (introduced in OpenQuake engine17).

A deprecation warning is printed every time you attempt to use a Fragility Model in the old Natural hazards’ Risk Markup Language v0.4 format in an OpenQuake engine17 (or later) risk calculation. To get rid of the warning you must upgrade the old fragilitymodels files to Natural hazards’ Risk Markup Language v0.5. You can use the command upgrade_nrml with oq to do this as follows:

user@ubuntu:~$ oq upgrade_nrml <directory-name>

The above command will upgrade all of your old Fragility Model files to Natural hazards’ Risk Markup Language v0.5. The original files will be kept, but with a .bak extension appended. Notice that you will need to set the lossCategory attribute to its correct value manually. This is easy to do, since if you try to run a computation you will get a clear error message telling the expected value for the lossCategory for each file.

Several methodologies to derive fragilityfunctions are currently being evaluated by GEM Foundation and have been included as part of the Risk Modeller’s Toolkit, the code for which can be found on a public repository at GitHub at the following address: http://github.com/gemsciencetools/rmtk.

A web-based tool to build a Fragility Model in the Natural hazards’ Risk Markup Language schema are also under development, and can be found at the OpenQuake platform at the following address: https://platform.openquake.org/ipt/.

4.2.3 Consequence Models#

Starting from OpenQuake engine17, the Scenario Damage calculator also accepts consequence models in addition to fragility models, in order to estimate consequences based on the calculated damage distribution. The user may provide one Consequence Model file corresponding to each loss type (amongst structural, nonstructural, contents, and business interruption) for which a Fragility Model file is provided. Whereas providing a Fragility Model file for at least one loss type is mandatory for running a Scenario Damage calculation, providing corresponding Consequence Model files is optional.

This section describes the schema currently used to store consequencemodels, which are optional inputs for the Scenario Damage Calculator. A Consequence Model defines a set of consequencefunctions, describing the distribution of the loss (or consequence) ratio conditional on a set of discrete limit (or damage) states. These Consequence Function can be currently defined in OpenQuake engine by specifying the parameters of the continuous distribution of the loss ratio for each limit state specified in the fragility model for the corresponding loss type, for each taxonomy defined in the exposure model.

An example Consequence Model is shown in the listing below.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns="http://openquake.org/xmlns/nrml/0.5">
 3
 4<consequenceModel id="consequence_example"
 5                  assetCategory="buildings"
 6                  lossCategory="structural">
 7
 8  <description>Consequence Model Example</description>
 9  <limitStates>slight moderate extensive complete</limitStates>
10
11  <consequenceFunction id="RC_LowRise" dist="LN">
12    <params ls="slight" mean="0.04" stddev="0.00"/>
13    <params ls="moderate" mean="0.16" stddev="0.00"/>
14    <params ls="extensive" mean="0.32" stddev="0.00"/>
15    <params ls="complete" mean="0.64" stddev="0.00"/>
16  </consequenceFunction>
17
18</consequenceModel>
19
20</nrml>

The initial portion of the schema contains general information that describes some general aspects of the Consequence Model. The information in this metadata section is common to all of the functions in the Consequence Model and needs to be included at the beginning of every Consequence Model file. The parameters are described below:

  • id: a unique string used to identify the Consequence Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • assetCategory: an optional string used to specify the type of assets for which consequencefunctions will be defined in this file (e.g: buildings, lifelines).

  • lossCategory: mandatory; valid strings for this attribute are “structural”, “nonstructural”, “contents”, and “business_interruption”.

  • description: mandatory; a brief string (ASCII) with further information about the Consequence Model, for example, which building typologies are covered or the source of the functions in the Consequence Model.

  • limitStates: mandatory; this field is used to define the number and nomenclature of each limit state. Four limit states are employed in the example above, but it is possible to use any number of discrete states. The limit states must be provided as a set of strings separated by whitespaces between each limit state. Each limit state string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_). Please ensure that there is no whitespace within the name of any individual limit state. The number and nomenclature of the limit states used in the Consequence Model should match those used in the corresponding Fragility Model.

4<consequenceModel id="consequence_example"
5                  assetCategory="buildings"
6                  lossCategory="structural">
7
8  <description>Consequence Model Example</description>
9  <limitStates>slight moderate extensive complete</limitStates>

The following snippet from the above Consequence Model example file defines a Consequence Function using a lognormal distribution to model the uncertainty in the consequence ratio for each limit state:

11  <consequenceFunction id="RC_LowRise" dist="LN">
12    <params ls="slight" mean="0.04" stddev="0.00"/>
13    <params ls="moderate" mean="0.16" stddev="0.00"/>
14    <params ls="extensive" mean="0.32" stddev="0.00"/>
15    <params ls="complete" mean="0.64" stddev="0.00"/>
16  </consequenceFunction>

The following attributes are needed to define a Consequence Function:

  • id: mandatory; a unique string used to identify the taxonomy for which the function is being defined. This string is used to relate the Consequence Function with the relevant asset in the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • dist: mandatory; for vulnerability function which use a continuous distribution to model the uncertainty in the conditional loss ratios, this attribute should be set to either “LN” if using the lognormal distribution, or to “BT” if using the Beta distribution 3.

  • params: mandatory; this field is used to define the parameters of the continuous distribution used for modelling the uncertainty in the loss ratios for each limit state for this Consequence Function. For a lognormal distrbution, the two parameters required to specify the function are the mean and standard deviation of the consequence ratio. These parameters are defined for each limit state using the attributes mean and stddev respectively. The attribute ls specifies the limit state for which the parameters are being defined. The parameters for each limit state must be provided on a separate line. The number and names of the limit states in each Consequence Function must be equal to the number of limit states defined in the corresponding Fragility Model using the attribute limitStates.

4.2.4 Vulnerability Models#

In order to perform probabilistic or scenario risk calculations, it is necessary to define a Vulnerability Function for each building typology present in the Exposure Model. In this section, the schema for the Vulnerability Model is described in detail. A graphical representation of a Vulnerability Model (mean loss ratio for a set of intensity measure levels) is illustrated in Fig. 31.

Graphical representation of a vulnerability model

Fig. 31 Graphical representation of a vulnerability model#

Note that although the uncertainty for each loss ratio is not represented in Fig. 31, it can be considered in the input file, by means of a coefficient of variation per loss ratio and a probabilistic distribution, which can currently be set to lognormal (LN), Beta (BT); or by specifying a discrete probability mass (PM) 4 distribution of the loss ratio at a set of intensity levels. An example of a Vulnerability Function that models the uncertainty in the loss ratio at different intensity levels using a lognormal distribution is illustrated in Fig. 32.

Graphical representation of a vulnerability function that models the uncertainty in the loss ratio using a lognormal distribution. The mean loss ratios and coefficients of variation are illustrated for a set of intensity levels.

Fig. 32 Graphical representation of a vulnerability function that models the uncertainty in the loss ratio using a lognormal distribution. The mean loss ratios and coefficients of variation are illustrated for a set of intensity levels.#

In general, defining vulnerabilityfunctions requires the user to specify the distribution of the loss ratio for a set of intensity levels. The loss ratio distributions can be defined using either a discrete or a continuous format, and the Vulnerability Model file can include a mix of both types of vulnerabilityfunctions. It is also possible to define a Vulnerability Function using a set of deterministic loss ratios corresponding to a set of intensity levels (i.e., ignoring the uncertainty in the conditional loss ratios).

An example Vulnerability Model comprising three vulnerability functions is shown in the listing below. This Vulnerability Model contains one function that uses the lognormal distribution to represent the uncertainty in the loss ratio at different intensity levels, one function that uses the Beta distribution, and one function that is defined using a discrete probability mass distribution.

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns="http://openquake.org/xmlns/nrml/0.5">
 3
 4<vulnerabilityModel id="vulnerability_example"
 5                    assetCategory="buildings"
 6                    lossCategory="structural">
 7
 8  <description>vulnerability model</description>
 9
10  <vulnerabilityFunction id="W1_Res_LowCode" dist="LN">
11   <imls imt="PGA">0.005 0.15 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0</imls>
12   <meanLRs>0.01 0.04 0.10 0.20 0.33 0.50 0.67 0.80 0.90 0.96 0.99</meanLRs>
13   <covLRs>0.03 0.12 0.24 0.32 0.38 0.40 0.38 0.32 0.24 0.12 0.03</covLRs>
14  </vulnerabilityFunction>
15
16
17  <vulnerabilityFunction id="S1_Res_HighCode" dist="BT">
18   <imls imt="SA(0.3)">0.05 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0</imls>
19   <meanLRs>0.01 0.03 0.07 0.15 0.24 0.37 0.50 0.60 0.67 0.72 0.75</meanLRs>
20   <covLRs>0.03 0.12 0.24 0.32 0.38 0.40 0.38 0.32 0.24 0.12 0.03</covLRs>
21  </vulnerabilityFunction>
22
23
24  <vulnerabilityFunction id="ATC13_URM_Res" dist="PM">
25   <imls imt="MMI">6 7 8 9 10 11 12</imls>
26   <probabilities lr="0.000">0.95 0.49 0.30 0.14 0.03 0.01 0.00</probabilities>
27   <probabilities lr="0.005">0.03 0.38 0.40 0.30 0.10 0.03 0.01</probabilities>
28   <probabilities lr="0.050">0.02 0.08 0.16 0.24 0.30 0.10 0.01</probabilities>
29   <probabilities lr="0.200">0.00 0.02 0.08 0.16 0.26 0.30 0.03</probabilities>
30   <probabilities lr="0.450">0.00 0.02 0.03 0.10 0.18 0.30 0.18</probabilities>
31   <probabilities lr="0.800">0.00 0.01 0.02 0.04 0.10 0.18 0.39</probabilities>
32   <probabilities lr="1.000">0.00 0.01 0.01 0.02 0.03 0.08 0.38</probabilities>
33  </vulnerabilityFunction>
34
35</vulnerabilityModel>
36
37</nrml>

The initial portion of the schema contains general information that describes some general aspects of the Vulnerability Model. The information in this metadata section is common to all of the functions in the Vulnerability Model and needs to be included at the beginning of every Vulnerability Model file. The parameters are illustrated in the snippet shown and described below:

4<vulnerabilityModel id="vulnerability_example"
5                    assetCategory="buildings"
6                    lossCategory="structural">
7
8  <description>vulnerability model</description>
  • id: a unique string (ASCII) used to identify the Vulnerability Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • assetCategory: an optional string (ASCII) used to specify the type of assets for which vulnerabilityfunctions will be defined in this file (e.g: buildings, lifelines).

  • lossCategory: mandatory; valid strings for this attribute are “structural”, “nonstructural”, “contents”, “business_interruption”, and “occupants”.

  • description: mandatory; a brief string with further information about the Vulnerability Model, for example, which building typologies are covered or the source of the functions in the Vulnerability Model.

The following snippet from the above Vulnerability Model example file defines a Vulnerability Function modelling the uncertainty in the conditional loss ratios using a (continuous) lognormal distribution:

10  <vulnerabilityFunction id="W1_Res_LowCode" dist="LN">
11   <imls imt="PGA">0.005 0.15 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0</imls>
12   <meanLRs>0.01 0.04 0.10 0.20 0.33 0.50 0.67 0.80 0.90 0.96 0.99</meanLRs>
13   <covLRs>0.03 0.12 0.24 0.32 0.38 0.40 0.38 0.32 0.24 0.12 0.03</covLRs>
14  </vulnerabilityFunction>

The following attributes are needed to define a Vulnerability Function which uses a continuous distribution to model the uncertainty in the conditional loss ratios:

  • id: a unique string (ASCII) used to identify the taxonomy for which the function is being defined. This string is used to relate the Vulnerability Function with the relevant asset in the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • dist: mandatory; for vulnerabilityfunctions which use a continuous distribution to model the uncertainty in the conditional loss ratios, this attribute should be set to either “LN” if using the lognormal distribution, or to “BT” if using the Beta distribution.

  • imls: mandatory; this attribute specifies the list of intensity levels for which the parameters of the conditional loss ratio distributions will be defined. In addition, it is also necessary to define the intensity measure type (imt).

  • meanLRs: mandatory; this field is used to define the mean loss ratios for this Vulnerability Function for each of the intensity levels defined by the attribute imls. The number of mean loss ratios defined by the meanLRs attribute must be equal to the number of intensity levels defined by the attribute imls.

  • covLRs: mandatory; this field is used to define the coefficient of variation for the conditional distribution of the loss ratios for this Vulnerability Function for each of the intensity levels defined by the attribute imls. The number of coefficients of variation of loss ratios defined by the covLRs attribute must be equal to the number of intensity levels defined by the attribute imls. The uncertainty in the conditional loss ratios can be ignored by setting all of the covLRs for a given Vulnerability Function to zero.

The next snippet from the Vulnerability Model example file of the Vulnerability Function listing above defines a Vulnerability Function which models the uncertainty in the conditional loss ratios using a (discrete) probability mass distribution:

24  <vulnerabilityFunction id="ATC13_URM_Res" dist="PM">
25   <imls imt="MMI">6 7 8 9 10 11 12</imls>
26   <probabilities lr="0.000">0.95 0.49 0.30 0.14 0.03 0.01 0.00</probabilities>
27   <probabilities lr="0.005">0.03 0.38 0.40 0.30 0.10 0.03 0.01</probabilities>
28   <probabilities lr="0.050">0.02 0.08 0.16 0.24 0.30 0.10 0.01</probabilities>
29   <probabilities lr="0.200">0.00 0.02 0.08 0.16 0.26 0.30 0.03</probabilities>
30   <probabilities lr="0.450">0.00 0.02 0.03 0.10 0.18 0.30 0.18</probabilities>
31   <probabilities lr="0.800">0.00 0.01 0.02 0.04 0.10 0.18 0.39</probabilities>
32   <probabilities lr="1.000">0.00 0.01 0.01 0.02 0.03 0.08 0.38</probabilities>
33  </vulnerabilityFunction>

The following attributes are needed to define a Vulnerability Function which uses a discrete probability mass distribution to model the uncertainty in the conditional loss ratios:

  • id: a unique string (ASCII) used to identify the taxonomy for which the function is being defined. This string is used to relate the Vulnerability Function with the relevant asset in the Exposure Model. This string can contain letters (a–z; A–Z), numbers (0–9), dashes (-), and underscores (_), with a maximum of 100 characters.

  • dist: mandatory; for vulnerabilityfunctions which use a discrete probability mass distribution to model the uncertainty in the conditional loss ratios, this attribute should be set to “PM”.

  • imls: mandatory; this attribute specifies the list of intensity levels for which the parameters of the conditional loss ratio distributions will be defined. In addition, it is also necessary to define the intensity measure type (imt).

  • probabilities: mandatory; this field is used to define the probability of observing a particular loss ratio (specified for each row of probabilities using the attribute lr), conditional on the set of intensity levels specified using the attribute imls. for this Vulnerability Function. Thus, the number of probabilities defined by each probabilities attribute must be equal to the number of intensity levels defined by the attribute imls. On the other hand, there is no limit to the number of loss ratios for which probabilities can be defined. In the example shown here, notice that the set of probabilities conditional on any particular intensity level, say, \(MMI = 8\), sum up to one.

Note that the schema for representing vulnerabilitymodels has changed between Natural hazards’ Risk Markup Language v0.4 (used prior to OpenQuake engine17) and Natural hazards’ Risk Markup Language v0.5 (introduced in OpenQuake engine17).

A deprecation warning is printed every time you attempt to use a Vulnerability Model in the old Natural hazards’ Risk Markup Language v0.4 format in an OpenQuake engine17 (or later) risk calculation. To get rid of the warning you must upgrade the old vulnerabilitymodels files to Natural hazards’ Risk Markup Language v0.5. You can use the command upgrade_nrml with oq to do this as follows:

user@ubuntu:~$ oq upgrade_nrml <directory-name>

The above command will upgrade all of your old Vulnerability Model files to Natural hazards’ Risk Markup Language v0.5. The original files will be kept, but with a .bak extension appended. Notice that you will need to set the lossCategory attribute to its correct value manually. This is easy to do, since if you try to run a computation you will get a clear error message telling the expected value for the lossCategory for each file.

Several methodologies to derive vulnerabilityfunctions are currently being evaluated by GEM Foundation and have been included as part of the Risk Modeller’s Toolkit, the code for which can be found on a public repository at GitHub at: http://github.com/gemsciencetools/rmtk.

A web-based tool to build an Vulnerability Model in the Natural hazards’ Risk Markup Language schema are also under development, and can be found at the OpenQuake platform at the following address: https://platform.openquake.org/ipt/.

4.3 Using the Risk Module#

This Chapter summarises the structure of the information necessary to define the different input data to be used with the OpenQuake engine risk calculators. Input data for scenario-based and probabilistic seismic damage and risk analysis using the OpenQuake engine are organised into:

  • An exposure model file in the NRML format, as described in Section 4.2.1   Exposure Models.

  • A file describing the Vulnerability Model (Section 4.2.4   Vulnerability Models) for loss calculations, or a file describing the Fragility Model (Section 4.2.2   Fragility Models) for damage calculations. Optionally, a file describing the Consequence Model (Section 4.2.3   Consequence Models) can also be provided in order to calculate losses from the estimated damage distributions.

  • A general calculation configuration file.

  • Hazard inputs. These include hazard curves for the classical probabilistic damage and risk calculators, ground motion fields for the scenario damage and risk calculators, or stochastic event sets for the probabilistic event based calculators. As of OpenQuake engine21, in general, there are five different ways in which hazard calculation parameters or results can be provided to the OpenQuake engine in order to run the subsequent risk calculations:

    • Use a single configuration file for running the hazard and risk calculations sequentially (preferred)

    • Use separate configuration files for running the hazard and risk calculations sequentially (legacy)

    • Use a configuration file for the risk calculation along with all hazard outputs from a previously completed, compatible OpenQuake engine hazard calculation

    • Use a configuration file for the risk calculation along with hazard input files in the OpenQuake NRML format

The file formats for Exposure models, Fragility Models, Consequence Models, and Vulnerability models have been described earlier in Chapter 4.2   Risk Input Models. The configuration file is the primary file that provides the OpenQuake engine information regarding both the definition of the input models (e.g. exposure, site parameters, fragility, consequence, or vulnerability models) as well as the parameters governing the risk calculation.

Information regarding the configuration file for running hazard calculations using the OpenQuake engine can be found in Section 3.2.4   Configuration file. Some initial mandatory parameters of the configuration file common to all of the risk calculators are presented in the listing. The remaining parameters that are specific to each risk calculator are discussed in subsequent sections.

[general]
description = Example risk calculation
calculation_mode = scenario_risk

[exposure]
exposure_file = exposure_model.xml

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
  • description: a parameter that can be used to include some information about the type of calculations that are going to be performed.

  • calculation_mode: this parameter specifies the type of calculation to be run. Valid options for the calculation_mode for the risk calculators are: scenario_damage, scenario_risk, classical_damage, classical_risk, event_based_risk, and classical_bcr.

  • exposure_file: this parameter is used to specify the path to the Exposure Model file. Typically this is the path to the xml file containing the exposure, or the xml file containing the metadata sections for the case where the assets are listed in one or more csv files. For particularly large exposure models, it may be more convenient to provide the path to a single compressed zip file that contains the exposure xml file and the exposure csv files (if any).

Depending on the type of risk calculation, other parameters besides the aforementioned ones may need to be provided. We illustrate in the following sections different examples of the configuration file for the different risk calculators.

4.3.1 Scenario Damage Calculator#

For this calculator, the parameter calculation_mode should be set to scenario_damage.

Example 1

This example illustrates a scenario damage calculation which uses a single configuration file to first compute the ground motion fields for the given rupture model and then calculate damage distribution statistics based on the ground motion fields. A minimal job configuration file required for running a scenario damage calculation is shown in the listing below.

[general]
description = Scenario damage using a single config file
calculation_mode = scenario_damage

[exposure]
exposure_file = exposure_model.xml

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[site_params]
site_model_file = site_model.xml

[hazard_calculation]
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim = BooreAtkinson2008
number_of_ground_motion_fields = 1000

[fragility]
structural_fragility_file = structural_fragility_model.xml

The general parameters description and calculation_mode, and exposure_file have already been described earlier. The other parameters seen in the above example configuration file are described below:

  • rupture_model_file: a parameter used to define the path to the earthquake Rupture Model file describing the scenario event.

  • rupture_mesh_spacing: a parameter used to specify the mesh size (in km) used by the OpenQuake engine to discretize the rupture. Note that the smaller the mesh spacing, the greater will be (1) the precision in the calculation and (2) the computational demand.

  • structural_fragility_file: a parameter used to define the path to the structural Fragility Model file.

In this case, the ground motion fields will be computed at each of the locations of the assets in the exposure model. Ground motion fields will be generated for each of the intensity measure types found in the provided set of fragility models. The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 2680 completed in 13 seconds. Results:
  id | name
5069 | Average Asset Damages

Note that one or more of the following parameters can be used in the same job configuration file to provide the corresponding fragility model files:

  • structural_fragility_file: a parameter used to define the path to a structural Fragility Model file

  • nonstructural_fragility_file: a parameter used to define the path to a nonstructural Fragility Model file

  • contents_fragility_file: a parameter used to define the path to a contents Fragility Model file

  • business_interruption_fragility_file: a parameter used to define the path to a business interruption Fragility Model file

It is important that the lossCategory parameter in the metadata section for each provided fragility model file (“structural”, “nonstructural”, “contents”, or “business_interruption”) should match the loss type defined in the configuration file by the relevant keyword above.

Example 2

This example illustrates a scenario damage calculation which uses separate configuration files for the hazard and risk parts of a scenario damage assessment. The first configuration file shown in the first listing below contains input models and parameters required for the computation of the ground motion fields due to a given rupture. The second configuration file shown in the second listing contains input models and parameters required for the calculation of the damage distribution for a portfolio of assets due to the ground motion fields.

Scenario hazard example

[general]
description = Scenario hazard example
calculation_mode = scenario

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[sites]
sites_csv = sites.csv

[site_params]
site_model_file = site_model.xml

[hazard_calculation]
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim = BooreAtkinson2008
intensity_measure_types = PGA, SA(0.3)
number_of_ground_motion_fields = 1000
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

Scenario damage example

[general]
description = Scenario damage example
calculation_mode = scenario_damage

[exposure]
exposure_file = exposure_model.xml

[boundaries]
region = -123.0 38.3, -121.0 38.3, -121.0 36.5, -123.0 36.5

[hazard]
asset_hazard_distance = 20

[fragility]
structural_fragility_file = structural_fragility_model.xml

[risk_calculation]
time_event = night

In this example, the set of intensity measure types for which the ground motion fields should be generated is specified explicitly in the configuration file using the parameter intensity_measure_types. If the hazard calculation outputs are intended to be used as inputs for a subsequent scenario damage or risk calculation, the set of intensity measure types specified here must include all intensity measure types that are used in the fragility or vulnerability models for the subsequent damage or risk calculation.

In the hazard configuration file illustrated above, the list of sites at which the ground motion values will be computed is provided in a CSV file, specified using the sites_csv parameter. The sites used for the hazard calculation need not be the same as the locations of the assets in the exposure model used for the following risk calculation. In such cases, it is recommended to set a reasonable search radius (in km) using the asset_hazard_distance parameter for the OpenQuake engine to look for available hazard values, as shown in the job_damage.ini example file above.

The only new parameters introduced in the risk configuration file for this example are the region, asset_hazard_distance, and time_event parameters, which are described below; all other parameters have already been described in earlier examples.

  • region: this is an optional parameter which defines the polygon that will be used for filtering the assets from the exposure model. Assets outside of this region will not be considered in the risk calculations. This region is defined using pairs of coordinates that indicate the vertices of the polygon, which should be listed in the Well-known text (WKT) format:

    region = lon_1 lat_1, lon_2 lat_2, …, lon_n lat_n

    For each point, the longitude is listed first, followed by the latitude, both in decimal degrees. The list of points defining the polygon can be provided either in a clockwise or counter-clockwise direction.

    If the region is not provided, all assets in the exposure model are considered for the risk calculation.

    This parameter is useful in cases where the exposure model covers a region larger than the one that is of interest in the current calculation.

  • asset_hazard_distance: this parameter indicates the maximum allowable distance between an asset and the closest hazard input. Hazard inputs can include hazard curves or ground motion intensity values. If no hazard input site is found within the radius defined by the asset_hazard_distance, the asset is skipped and a message is provided mentioning the id of the asset that is affected by this issue.

    If multiple hazard input sites are found within the radius defined by the this parameter, the hazard input site with the shortest distance from the asset location is associated with the asset. It is possible that the associated hazard input site might be located outside the polygon defined by the region.

  • time_event: this parameter indicates the time of day at which the event occurs. The values that this parameter can be set to are currently limited to one of the three strings: day, night, and transit. This parameter will be used to compute the number of fatalities based on the number of occupants present in the various assets at that time of day, as specified in the exposure model.

Now, the above calculations described by the two configuration files “job_hazard.ini” and “job_damage.ini” can be run separately. The calculation id for the hazard calculation should be provided to the OpenQuake engine while running the risk calculation using the option --hazard-calculation-id (or --hc). This is shown below:

user@ubuntu:~$ oq engine --run job_hazard.ini

After the hazard calculation is completed, a message similar to the one below will be displayed in the terminal:

Calculation 2681 completed in 4 seconds. Results:
  id | name
5072 | Ground Motion Fields

In the example above, the calculation id of the hazard calculation is 2681. There is only one output from this calculation, i.e., the Ground Motion Fields.

The risk calculation for computing the damage distribution statistics for the portfolio of assets can now be run using:

user@ubuntu:~$ oq engine --run job_damage.ini --hc 2681

After the calculation is completed, a message similar to the one listed above in Example 1 will be displayed.

In order to retrieve the calculation id of a previously run hazard calculation, the option --list-hazard-calculations (or --lhc) can be used to display a list of all previously run hazard calculations:

job_id |     status |         start_time |         description
  2609 | successful | 2015-12-01 14:14:14 | Mid Nepal earthquake
  ...
  2681 | successful | 2015-12-12 10:00:00 | Scenario hazard example

The option --list-outputs (or --lo) can be used to display a list of all outputs generated during a particular calculation. For instance,

user@ubuntu:~$ oq engine --lo 2681

will produce the following display:

  id | name
5072 | Ground Motion Fields

Example 3

The example shown in the listing below illustrates a scenario damage calculation which uses a file listing a precomputed set of Ground Motion Fields. These Ground Motion Fields can be computed using the OpenQuake engine or some other software. The Ground Motion Fields must be provided in either the Natural hazards’ Risk Markup Language schema or the csv format as presented in Section 3.3.3.4   Outputs from Scenario Hazard Analysis. The damage distribution is computed based on the provided Ground Motion Fields.

[general]
description = Scenario damage using user-defined ground motion fields (NRML)
calculation_mode = scenario_damage

[hazard]
gmfs_file = gmfs.xml

[exposure]
exposure_file = exposure_model.xml

[fragility]
structural_fragility_file = structural_fragility_model.xml
  • gmfs_file: a parameter used to define the path to the Ground Motion Fields file in the Natural hazards’ Risk Markup Language schema. This file must define Ground Motion Fields for all of the intensity measure types used in the Fragility Model.

The listing below shows an example of a Ground Motion Fields file in the Natural hazards’ Risk Markup Language schema and Table 3 shows an example of a Ground Motion Fields file in the csv format. If the Ground Motion Fields file is provided in the csv format, an additional csv file listing the site ids must be provided using the parameter sites_csv. See Table 4 for an example of the sites csv file, which provides the association between the site ids in the Ground Motion Fields csv file with their latitude and longitude coordinates.

[general]
description = Scenario damage using user-defined ground motion fields (CSV)
calculation_mode = scenario_damage

[hazard]
sites_csv = sites.csv
gmfs_csv = gmfs.csv

[exposure]
exposure_file = exposure_model.xml

[fragility]
structural_fragility_file = structural_fragility_model.xml
  • gmfs_csv: a parameter used to define the path to the Ground Motion Fields file in the csv format. This file must define Ground Motion Fields for all of the intensity measure types used in the Fragility Model. (Download an example file here).

  • sites_csv: a parameter used to define the path to the sites file in the csv format. This file must define site id, longitude, and latitude for all of the sites for the Ground Motion Fields file provided using the gmfs_csv parameter. (Download an example file here).

The above calculation(s) can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

Example 4

This example illustrates a the hazard job configuration file for a scenario damage calculation which uses two Ground Motion Prediction Equations instead of only one. Currently, the set of Ground Motion Prediction Equations to be used for a scenario calculation can be specified using a logic tree file, as demonstrated in 3.2.3.1   The Ground Motion Logic Tree. As of OpenQuake engine18, the weights in the logic tree are ignored, and a set of Ground Motion Fields will be generated for each Ground Motion Prediction Equation in the logic tree file. Correspondingly, damage distribution statistics will be generated for each set of Ground Motion Field.

The file shown in the listing below lists the two Ground Motion Prediction Equations to be used for the hazard calculation:

 1<?xml version="1.0" encoding="UTF-8"?>
 2<nrml xmlns:gml="http://www.opengis.net/gml"
 3      xmlns="http://openquake.org/xmlns/nrml/0.5">
 4
 5<logicTree logicTreeID="lt1">
 6    <logicTreeBranchSet uncertaintyType="gmpeModel"
 7                        branchSetID="bs1"
 8                        applyToTectonicRegionType="Active Shallow Crust">
 9
10      <logicTreeBranch branchID="b1">
11        <uncertaintyModel>BooreAtkinson2008</uncertaintyModel>
12        <uncertaintyWeight>0.75</uncertaintyWeight>
13      </logicTreeBranch>
14
15      <logicTreeBranch branchID="b2">
16        <uncertaintyModel>ChiouYoungs2008</uncertaintyModel>
17        <uncertaintyWeight>0.25</uncertaintyWeight>
18      </logicTreeBranch>
19
20    </logicTreeBranchSet>
21</logicTree>
22
23</nrml>

The only change that needs to be made in the hazard job configuration file is to replace the gsim parameter with gsim_logic_tree_file, as demonstrated in the listing below.

[general]
description = Scenario hazard example using multiple GMPEs
calculation_mode = scenario

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[sites]
sites_csv = sites.csv

[site_params]
site_model_file = site_model.xml

[hazard_calculation]
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim_logic_tree_file = gsim_logic_tree.xml
intensity_measure_types = PGA, SA(0.3)
number_of_ground_motion_fields = 1000
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

Example 5

This example illustrates a scenario damage calculation which specifies fragility models for calculating damage to structural and nonstructural components of structures, and also specifies Consequence Model files for calculation of the corresponding losses.

A minimal job configuration file required for running a scenario damage calculation followed by a consequences analysis is shown in the listing below.

[general]
description = Scenario damage and consequences
calculation_mode = scenario_damage

[exposure]
exposure_file = exposure_model.xml

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[site_params]
site_model_file = site_model.xml

[hazard_calculation]
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim = BooreAtkinson2008
number_of_ground_motion_fields = 1000
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

[fragility]
structural_fragility_file = structural_fragility_model.xml
nonstructural_fragility_file = nonstructural_fragility_model.xml

[consequence]
structural_consequence_file = structural_consequence_model.xml
nonstructural_consequence_file = nonstructural_consequence_model.xml

Note that one or more of the following parameters can be used in the same job configuration file to provide the corresponding Consequence Model files:

  • structural_consequence_file: a parameter used to define the path to a structural Consequence Model file

  • nonstructural_consequence_file: a parameter used to define the path to a nonstructural Consequence Model file

  • contents_consequence_file: a parameter used to define the path to a contents Consequence Model file

  • business_interruption_consequence_file: a parameter used to define the path to a business interruption Consequence Model file

It is important that the lossCategory parameter in the metadata section for each provided Consequence Model file (“structural”, “nonstructural”, “contents”, or “business_interruption”) should match the loss type defined in the configuration file by the relevant keyword above.

The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 1579 completed in 37 seconds. Results:
  id | name
8990 | Average Asset Losses
8993 | Average Asset Damages

4.3.2 Scenario Risk Calculator#

In order to run this calculator, the parameter calculation_mode needs to be set to scenario_risk.

Most of the job configuration parameters required for running a scenario risk calculation are the same as those described in the previous section for the scenario damage calculator. The remaining parameters specific to the scenario risk calculator are illustrated through the examples below.

Example 1

This example illustrates a scenario risk calculation which uses a single configuration file to first compute the ground motion fields for the given rupture model and then calculate loss statistics for structural losses and nonstructural losses, based on the ground motion fields. The job configuration file required for running this scenario risk calculation is shown in the listing below.

[general]
description = Scenario risk using a single config file
calculation_mode = scenario_risk

[exposure]
exposure_file = exposure_model.xml

[rupture]
rupture_model_file = rupture_model.xml
rupture_mesh_spacing = 2.0

[site_params]
site_model_file = site_model.xml

[hazard_calculation]
random_seed = 42
truncation_level = 3.0
maximum_distance = 200.0
gsim = BooreAtkinson2008
number_of_ground_motion_fields = 1000
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
nonstructural_vulnerability_file = nonstructural_vulnerability_model.xml

[risk_calculation]
master_seed = 24
asset_correlation = 1

Whereas a scenario damage calculation requires one or more fragility and/or consequence models, a scenario risk calculation requires the user to specify one or more vulnerability model files. Note that one or more of the following parameters can be used in the same job configuration file to provide the corresponding vulnerability model files:

  • structural_vulnerability_file: this parameter is used to specify the path to the structural Vulnerability Model file

  • nonstructural_vulnerability_file: this parameter is used to specify the path to the nonstructuralvulnerabilitymodel file

  • contents_vulnerability_file: this parameter is used to specify the path to the contents Vulnerability Model file

  • business_interruption_vulnerability_file: this parameter is used to specify the path to the business interruption Vulnerability Model file

  • occupants_vulnerability_file: this parameter is used to specify the path to the occupants Vulnerability Model file

It is important that the lossCategory parameter in the metadata section for each provided vulnerability model file (“structural”, “nonstructural”, “contents”, “business_interruption”, or “occupants”) should match the loss type defined in the configuration file by the relevant keyword above.

The remaining new parameters introduced in this example are the following:

  • master_seed: this parameter is used to control the random number generator in the loss ratio sampling process. If the same master_seed is defined at each calculation run, the same random loss ratios will be generated, thus allowing reproducibility of the results.

  • asset_correlation: if the uncertainty in the loss ratios has been defined within the Vulnerability Model, users can specify a coefficient of correlation that will be used in the Monte Carlo sampling process of the loss ratios, between the assets that share the same taxonomy. If the asset_correlation is set to one, the loss ratio residuals will be perfectly correlated. On the other hand, if this parameter is set to zero, the loss ratios will be sampled independently. If this parameter is not defined, the OpenQuake engine will assume zero correlation in the vulnerability. As of OpenQuake engine18, asset_correlation applies only to continuous vulnerabilityfunctions using the lognormal or Beta distribution; it does not apply to vulnerabilityfunctions defined using the PMF distribution. Although partial correlation was supported in previous versions of the engine, beginning from OpenQuake engine22, values between zero and one are no longer supported due to performance considerations. The only two values permitted are asset_correlation = 0 and asset_correlation = 1.

In this case, the ground motion fields will be computed at each of the locations of the assets in the exposure model and for each of the intensity measure types found in the provided set of vulnerability models. The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 2735 completed in 10 seconds. Results:
  id | name
5328 | Aggregate Asset Losses
5329 | Average Asset Losses
5330 | Aggregate Event Losses

All of the different ways of running a scenario damage calculation as illustrated through the examples of the previous section are also applicable to the scenario risk calculator, though the examples are not repeated here.

A few additional parameters related to the event based risk calculator that may be useful for controlling specific aspects of the calculation are listed below:

  • ignore_covs: this parameter controls the propagation of vulnerability uncertainty to losses. The vulnerability functions using continuous distributions (such as the lognormal distribution or beta distribution) to characterize the uncertainty in the loss ratio conditional on the shaking intensity level, specify the mean loss ratios and the corresponding coefficients of variation for a set of intensity levels. They are used to build the so called Epsilon matrix within the engine, which is how loss ratios are sampled from the distribution for each asset. There is clearly a performance penalty associated with the propagation of uncertainty in the vulnerability to losses. The Epsilon matrix has to be computed and stored, and then the worker processes have to read it, which involves large quantities of data transfer and memory usage. Setting ignore_covs = true in the job file will result in the engine using just the mean loss ratio conditioned on the shaking intensity and ignoring the uncertainty. This tradeoff of not propagating the vulnerabilty uncertainty to the loss estimates can lead to a significant boost in performance and tractability. The default value of ignore_covs is false.

4.3.3 Classical Probabilistic Seismic Damage Calculator#

In order to run this calculator, the parameter calculation_mode needs to be set to classical_damage.

Most of the job configuration parameters required for running a classical probabilistic damage calculation are the same as those described in the section for the scenario damage calculator. The remaining parameters specific to the classical probabilistic damage calculator are illustrated through the examples below.

Example 1

This example illustrates a classical probabilistic damage calculation which uses a single configuration file to first compute the hazard curves for the given source model and ground motion model and then calculate damage distribution statistics based on the hazard curves. A minimal job configuration file required for running a classical probabilistic damage calculation is shown in the listing below.

[general]
description = Classical probabilistic damage using a single config file
calculation_mode = classical_damage

[exposure]
exposure_file = exposure_model.xml

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2
area_source_discretization = 20

[site_params]
site_model_file = site_model.xml

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[hazard_calculation]
random_seed = 42
investigation_time = 1
truncation_level = 3.0
maximum_distance = 200.0

[fragility]
structural_fragility_file = structural_fragility_model.xml

The general parameters description and calculation_mode, and exposure_file have already been described earlier in Section 4.3.1   Scenario Damage Calculator. The parameters related to the hazard curves computation have been described earlier in Section  3.2.4.1   Classical PSHA.

In this case, the hazard curves will be computed at each of the locations of the assets in the exposure model, for each of the intensity measure types found in the provided set of fragilitymodels. The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 2741 completed in 12 seconds. Results:
  id | name
5359 | Asset Damage Distribution

Example 2

This example illustrates a classical probabilistic damage calculation which uses separate configuration files for the hazard and risk parts of a classical probabilistic damage assessment. The first configuration file shown in the listing below contains input models and parameters required for the computation of the hazard curves.

[general]
description = Classical probabilistic hazard
calculation_mode = classical

[sites]
region = -123.0 38.3, -121.0 38.3, -121.0 36.5, -123.0 36.5
region_grid_spacing = 0.5

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2
area_source_discretization = 20

[site_params]
site_model_file = site_model.xml

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[hazard_calculation]
random_seed = 42
investigation_time = 1
truncation_level = 3.0
maximum_distance = 200.0
intensity_measure_types_and_levels = {
 "PGA": logscale(0.05, 3.0, 30),
 "SA(1.0)": logscale(0.05, 3.0, 30)}

The second configuration file shown in the listing below contains input models and parameters required for the calculation of the probabilistic damage distribution for a portfolio of assets based on the hazard curves and fragility models.

[general]
description = Classical probabilistic damage example
calculation_mode = classical_damage

[exposure]
exposure_file = exposure_model.xml

[hazard]
asset_hazard_distance = 20

[fragility]
structural_fragility_file = structural_fragility_model.xml

[risk_calculation]
risk_investigation_time = 50
steps_per_interval = 4

Now, the above calculations described by the two configuration files “job_hazard.ini” and “job_damage.ini” can be run sequentially or separately, as illustrated in Example 2 in Section 4.3.1   Scenario Damage Calculator. The new parameters introduced in the above example configuration file are described below:

  • risk_investigation_time: an optional parameter that can be used in probabilistic damage or risk calculations where the period of interest for the risk calculation is different from the period of interest for the hazard calculation. If this parameter is not explicitly set, the OpenQuake engine will assume that the risk calculation is over the same time period as the preceding hazard calculation.

  • steps_per_interval: an optional parameter that can be used to specify whether discrete fragility functions in the fragility models should be discretized further, and if so, how many intermediate steps to use for the discretization. Setting

    steps_per_interval = n

    will result in the OpenQuake engine discretizing the discrete fragility models using (n - 1) linear interpolation steps between each pair of intensity level, poe points.

    The default value of this parameter is one, implying no interpolation.

4.3.4 Classical Probabilistic Seismic Risk Calculator#

In order to run this calculator, the parameter calculation_mode needs to be set to classical_risk.

Most of the job configuration parameters required for running a classical probabilistic risk calculation are the same as those described in the previous section for the classical probabilistic damage calculator. The remaining parameters specific to the classical probabilistic risk calculator are illustrated through the examples below.

Example 1

This example illustrates a classical probabilistic risk calculation which uses a single configuration file to first compute the hazard curves for the given source model and ground motion model and then calculate loss exceedance curves based on the hazard curves. An example job configuration file for running a classical probabilistic risk calculation is shown in the listing below.

[general]
description = Classical probabilistic risk using a single config file
calculation_mode = classical_risk

[exposure]
exposure_file = exposure_model.xml

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2
area_source_discretization = 20

[site_params]
site_model_file = site_model.xml

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[hazard_calculation]
random_seed = 42
investigation_time = 1
truncation_level = 3.0
maximum_distance = 200.0

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
nonstructural_vulnerability_file = nonstructural_vulnerability_model.xml

Apart from the calculation mode, the only difference with the example job configuration file shown in Example 1 of Section 4.3.3   Classical Probabilistic Seismic Damage Calculator is the use of a vulnerability model instead of a fragility model.

As with the Scenario Risk calculator, it is possible to specify one or more Vulnerability Model files in the same job configuration file, using the parameters:

  • structural_vulnerability_file,

  • nonstructural_vulnerability_file,

  • contents_vulnerability_file,

  • business_interruption_vulnerability_file, and/or

  • occupants_vulnerability_file

It is important that the lossCategory parameter in the metadata section for each provided vulnerability model file (“structural”, “nonstructural”, “contents”, “business_interruption”, or “occupants”) should match the loss type defined in the configuration file by the relevant keyword above.

In this case, the hazard curves will be computed at each of the locations of the assets in the Exposure Model, for each of the intensity measure types found in the provided set of vulnerabilitymodels. The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 2749 completed in 24 seconds. Results:
  id | name
3980 | Asset Loss Curves Statistics
3981 | Asset Loss Maps Statistics
3983 | Average Asset Loss Statistics

Example 2

This example illustrates a classical probabilistic risk calculation which uses separate configuration files for the hazard and risk parts of a classical probabilistic risk assessment. The first configuration file shown in the listing contains input models and parameters required for the computation of the hazard curves.

[general]
description = Classical probabilistic hazard
calculation_mode = classical

[sites]
region = -123.0 38.3, -121.0 38.3, -121.0 36.5, -123.0 36.5
region_grid_spacing = 0.5

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2
area_source_discretization = 20

[site_params]
site_model_file = site_model.xml

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[hazard_calculation]
random_seed = 42
investigation_time = 1
truncation_level = 3.0
maximum_distance = 200.0
intensity_measure_types_and_levels = {
 "PGA": logscale(0.05, 3.0, 30),
 "SA(1.0)": logscale(0.05, 3.0, 30)}

The second configuration file shown in the listing below contains input models and parameters required for the calculation of the loss exceedance curves and probabilistic loss maps for a portfolio of assets based on the hazard curves and vulnerabilitymodels.

[general]
description = Classical probabilistic risk
calculation_mode = classical_risk

[exposure]
exposure_file = exposure_model.xml

[hazard]
asset_hazard_distance = 20

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
nonstructural_vulnerability_file = nonstructural_vulnerability_model.xml

[risk_calculation]
risk_investigation_time = 50
lrem_steps_per_interval = 2

[risk_outputs]
quantiles = 0.15, 0.50, 0.85
conditional_loss_poes = 0.02, 0.10

Now, the above calculations described by the two configuration files “job_hazard.ini” and “job_risk.ini” can be run sequentially or separately, as illustrated in Example 2 in Section 4.3.1   Scenario Damage Calculator. The new parameters introduced in the above risk configuration file example are described below:

  • lrem_steps_per_interval: this parameter controls the number of intermediate values between consecutive loss ratios (as defined in the Vulnerability Model) that are considered in the risk calculations. A larger number of loss ratios than those defined in each Vulnerability Function should be considered, in order to better account for the uncertainty in the loss ratio distribution. If this parameter is not defined in the configuration file, the OpenQuake engine assumes the lrem_steps_per_interval to be equal to 5. More details are provided in the OpenQuake Book (Risk).

  • quantiles: this parameter can be used to request the computation of quantile loss curves for computations involving non-trivial logic trees. The quantiles for which the loss curves should be computed must be provided as a comma separated list. If this parameter is not included in the configuration file, quantile loss curves will not be computed.

  • conditional_loss_poes: this parameter can be used to request the computation of probabilistic loss maps, which give the loss levels exceeded at the specified probabilities of exceedance over the time period specified by risk_investigation_time. The probabilities of exceedance for which the loss maps should be computed must be provided as a comma separated list. If this parameter is not included in the configuration file, probabilistic loss maps will not be computed.

4.3.5 Stochastic Event Based Seismic Damage#

Calculator The parameter calculation_mode needs to be set to event_based_damage in order to use this calculator.

Most of the job configuration parameters required for running a stochastic event based damage calculation are the same as those described in the previous sections for the scenario damage calculator and the classical probabilistic damage calculator. The remaining parameters specific to the stochastic event based damage calculator are illustrated through the example below.

Example 1

This example illustrates a stochastic event based damage calculation which uses a single configuration file to first compute the Stochastic Event Sets and Ground Motion Fields for the given source model and ground motion model, and then calculate event loss tables, loss exceedance curves and probabilistic loss maps for structural losses, nonstructural losses and occupants, based on the Ground Motion Fields. The job configuration file required for running this stochastic event based damage calculation is shown in the listing below.

[general]
description = Stochastic event based damage using a single job file
calculation_mode = event_based_damage

[exposure]
exposure_file = exposure_model.xml

[site_params]
site_model_file = site_model.xml

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2.0
area_source_discretization = 10.0

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[correlation]
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

[hazard_calculation]
random_seed = 24
truncation_level = 3
maximum_distance = 200.0
investigation_time = 1
ses_per_logic_tree_path = 10000

[fragility]
structural_fragility_file = structural_fragility_model.xml

[consequence]
structural_consequence_file = structural_consequence_model.xml

[risk_calculation]
master_seed = 42
risk_investigation_time = 1
return_periods = 5, 10, 25, 50, 100, 250, 500, 1000

Similar to that the procedure described for the Scenario Damage calculator, a Monte Carlo sampling process is also employed in this calculator to take into account the uncertainty in the conditional loss ratio at a particular intensity level. Hence, the parameters asset_correlation and master_seed may be defined as previously described for the Scenario Damage calculator in Section 4.3.1   Scenario Damage Calculator. The parameter “risk_investigation_time” specifies the time period for which the average damage values will be calculated, similar to the Classical Probabilistic Damage calculator. If this parameter is not provided in the risk job configuration file, the time period used is the same as that specifed in the hazard calculation using the parameter “investigation_time”.

The new parameters introduced in this example are described below:

  • minimum_intensity: this optional parameter specifies the minimum intensity levels for each of the intensity measure types in the risk model. Ground motion fields where each ground motion value is less than the specified minimum threshold are discarded. This helps speed up calculations and reduce memory consumption by considering only those ground motion fields that are likely to contribute to losses. It is also possible to set the same threshold value for all intensity measure types by simply providing a single value to this parameter. For instance: “minimum_intensity = 0.05” would set the threshold to 0.05 g for all intensity measure types in the risk calculation. If this parameter is not set, the OpenQuake engine extracts the minimum thresholds for each intensity measure type from the vulnerability models provided, picking the lowest intensity value for which a mean loss ratio is provided.

  • return_periods: this parameter specifies the list of return periods (in years) for computing the asset / aggregate damage curves. If this parameter is not set, the OpenQuake engine uses a default set of return periods for computing the loss curves. The default return periods used are from the list: [5, 10, 25, 50, 100, 250, 500, 1000, …], with its upper bound limited by (ses_per_logic_tree_path × investigation_time)

    \[\begin{split}\begin{split} average\_damages & = sum(event\_damages) \\ & \div (hazard\_investigation\_time \times ses\_per\_logic\_tree\_path) \\ & \times risk\_investigation\_time \end{split}\end{split}\]

The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

Computation of the damage curves, and average damages for each individual asset in the Exposure Model can be resource intensive, and thus these outputs are not generated by default.

4.3.6 Stochastic Event Based Seismic Risk Calculator#

The parameter calculation_mode needs to be set to event_based_risk in order to use this calculator.

Most of the job configuration parameters required for running a stochastic event based risk calculation are the same as those described in the previous sections for the scenario risk calculator and the classical probabilistic risk calculator. The remaining parameters specific to the stochastic event based risk calculator are illustrated through the example below.

Example 1

This example illustrates a stochastic event based risk calculation which uses a single configuration file to first compute the Stochastic Event Sets and Ground Motion Fields for the given source model and ground motion model, and then calculate event loss tables, loss exceedance curves and probabilistic loss maps for structural losses, nonstructural losses and occupants, based on the Ground Motion Fields. The job configuration file required for running this stochastic event based risk calculation is shown in the listing below.

[general]
description = Stochastic event based risk using a single job file
calculation_mode = event_based_risk

[exposure]
exposure_file = exposure_model.xml

[site_params]
site_model_file = site_model.xml

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2.0
area_source_discretization = 10.0

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml

[correlation]
ground_motion_correlation_model = JB2009
ground_motion_correlation_params = {"vs30_clustering": True}

[hazard_calculation]
random_seed = 24
truncation_level = 3
maximum_distance = 200.0
investigation_time = 1
number_of_logic_tree_samples = 0
ses_per_logic_tree_path = 100000
minimum_intensity = {"PGA": 0.05, "SA(0.4)": 0.10, "SA(0.8)": 0.12}

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
nonstructural_vulnerability_file = nonstructural_vulnerability_model.xml

[risk_calculation]
master_seed = 42
risk_investigation_time = 1
asset_correlation = 0
return_periods = [5, 10, 25, 50, 100, 250, 500, 1000]

[risk_outputs]
avg_losses = true
quantiles = 0.15, 0.50, 0.85
conditional_loss_poes = 0.02, 0.10

Similar to that the procedure described for the Scenario Risk calculator, a Monte Carlo sampling process is also employed in this calculator to take into account the uncertainty in the conditional loss ratio at a particular intensity level. Hence, the parameters asset_correlation and master_seed may be defined as previously described for the Scenario Risk calculator in Section 4.1.2   Scenario Risk Assessment. The parameter “risk_investigation_time” specifies the time period for which the event loss tables and loss exceedance curves will be calculated, similar to the Classical Probabilistic Risk calculator. If this parameter is not provided in the risk job configuration file, the time period used is the same as that specifed in the hazard calculation using the parameter “investigation_time”.

The new parameters introduced in this example are described below:

  • minimum_intensity: this optional parameter specifies the minimum intensity levels for each of the intensity measure types in the risk model. Ground motion fields where each ground motion value is less than the specified minimum threshold are discarded. This helps speed up calculations and reduce memory consumption by considering only those ground motion fields that are likely to contribute to losses. It is also possible to set the same threshold value for all intensity measure types by simply providing a single value to this parameter. For instance: “minimum_intensity = 0.05” would set the threshold to 0.05 g for all intensity measure types in the risk calculation. If this parameter is not set, the OpenQuake engine extracts the minimum thresholds for each intensity measure type from the vulnerability models provided, picking the lowest intensity value for which a mean loss ratio is provided.

  • return_periods: this parameter specifies the list of return periods (in years) for computing the aggregate loss curve. If this parameter is not set, the OpenQuake engine uses a default set of return periods for computing the loss curves. The default return periods used are from the list: [5, 10, 25, 50, 100, 250, 500, 1000, …], with its upper bound limited by (ses_per_logic_tree_path × investigation_time)

  • avg_losses: this boolean parameter specifies whether the average asset losses over the time period “risk_investigation_time” should be computed. The default value of this parameter is true.

    \[\begin{split}\begin{split} average\_loss & = sum(event\_losses) \\ & \div (hazard\_investigation\_time \times ses\_per\_logic\_tree\_path) \\ & \times risk\_investigation\_time \end{split}\end{split}\]

The above calculation can be run using the command line:

user@ubuntu:$ oq engine --run job.ini

Computation of the loss tables, loss curves, and average losses for each individual asset in the Exposure Model can be resource intensive, and thus these outputs are not generated by default, unless instructed to by using the parameters described above.

Users may also begin an event based risk calculation by providing a precomputed set of Ground Motion Fields to the OpenQuake engine. The following example describes the procedure for this approach.

Example 2

This example illustrates a stochastic event based risk calculation which uses a file listing a precomputed set of Ground Motion Fields. These Ground Motion Fields can be computed using the OpenQuake engine or some other software. The Ground Motion Fields must be provided in the csv format as presented in Section  3.2.4.3   Event based PSHA. Table 1 shows an example of a Ground Motion Fields file in the csv format.

An additional csv file listing the site ids must also be provided using the parameter sites_csv. See Table 4 for an example of the sites csv file, which provides the association between the site ids in the Ground Motion Fields csv file with their latitude and longitude coordinates.

Starting from the input Ground Motion Fields, the OpenQuake engine can calculate event loss tables, loss exceedance curves and probabilistic loss maps for structural losses, nonstructural losses and occupants. The job configuration file required for running this stochastic event based risk calculation starting from a precomputed set of Ground Motion Fields is shown in the listing below.

[general]
description = Stochastic event based risk using precomputed gmfs
calculation_mode = event_based_risk

[hazard]
sites_csv = sites.csv
gmfs_csv = gmfs.csv
investigation_time = 50

[exposure]
exposure_file = exposure_model.xml

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml

[risk_calculation]
risk_investigation_time = 1
return_periods = [5, 10, 25, 50, 100, 250, 500, 1000]

[risk_outputs]
avg_losses = true
quantiles = 0.15, 0.50, 0.85
conditional_loss_poes = 0.02, 0.10

Additional parameters

A few additional parameters related to the event based risk calculator that may be useful for controlling specific aspects of the calculation are listed below:

  • individual_curves: this boolean parameter is used to specify if the asset loss curves for each Branch realization should be saved to the datastore. For the asset loss curves output, by default the engine only saves and exports statistical results, i.e. the mean and quantile asset loss curves. If you want the asset loss curves for each of the individual Branch realizations, you must set individual_curves=true in the job file. Please take care: if you have hundreds of realizations, the data transfer and disk space requirements will be orders of magnitude larger than just returning the mean and quantile asset loss curves, and the calculation might fail. The default value of individual_curves is false.

  • asset_correlation: if the uncertainty in the loss ratios has been defined within the Vulnerability Model, users can specify a coefficient of correlation that will be used in the Monte Carlo sampling process of the loss ratios, between the assets that share the same taxonomy. If the asset_correlation is set to one, the loss ratio residuals will be perfectly correlated. On the other hand, if this parameter is set to zero, the loss ratios will be sampled independently. If this parameter is not defined, the OpenQuake engine will assume zero correlation in the vulnerability. As of OpenQuake engine18, asset_correlation applies only to continuous vulnerabilityfunctions using the lognormal or Beta distribution; it does not apply to vulnerabilityfunctions defined using the PMF distribution. Although partial correlation was supported in previous versions of the engine, beginning from OpenQuake engine22, values between zero and one are no longer supported due to performance considerations. The only two values permitted are asset_correlation = 0 and asset_correlation = 1.

  • ignore_covs: this parameter controls the propagation of vulnerability uncertainty to losses. The vulnerability functions using continuous distributions (such as the lognormal distribution or beta distribution) to characterize the uncertainty in the loss ratio conditional on the shaking intensity level, specify the mean loss ratios and the corresponding coefficients of variation for a set of intensity levels. They are used to build the so called Epsilon matrix within the engine, which is how loss ratios are sampled from the distribution for each asset. There is clearly a performance penalty associated with the propagation of uncertainty in the vulnerability to losses. The Epsilon matrix has to be computed and stored, and then the worker processes have to read it, which involves large quantities of data transfer and memory usage. Setting ignore_covs = true in the job file will result in the engine using just the mean loss ratio conditioned on the shaking intensity and ignoring the uncertainty. This tradeoff of not propagating the vulnerabilty uncertainty to the loss estimates can lead to a significant boost in performance and tractability. The default value of ignore_covs is false.

4.3.7 Retrofit Benefit-Cost Ratio Calculator#

As previously explained, this calculator uses loss exceedance curves which are calculated using the Classical Probabilistic risk calculator. In order to run this calculator, the parameter calculation_mode needs to be set to classical_bcr.

Most of the job configuration parameters required for running a classical retrofit benefit-cost ratio calculation are the same as those described in the previous section for the classical probabilistic risk calculator. The remaining parameters specific to the classical retrofit benefit-cost ratio calculator are illustrated through the examples below.

Example 1

This example illustrates a classical probabilistic retrofit benefit-cost ratio calculation which uses a single configuration file to first compute the hazard curves for the given source model and ground motion model, then calculate loss exceedance curves based on the hazard curves using both the original vulnerability model and the vulnerability model for the retrofitted structures, then calculate the reduction in average annual losses due to the retrofits, and finally calculate the benefit-cost ratio for each asset. A minimal job configuration file required for running a classical probabilistic retrofit benefit-cost ratio calculation is shown in the listing below.

[general]
description = Classical cost-benefit analysis using a single config file
calculation_mode = classical_bcr

[exposure]
exposure_file = exposure_model.xml

[erf]
width_of_mfd_bin = 0.1
rupture_mesh_spacing = 2
area_source_discretization = 20

[site_params]
site_model_file = site_model.xml

[logic_trees]
source_model_logic_tree_file = source_model_logic_tree.xml
gsim_logic_tree_file = gsim_logic_tree.xml
number_of_logic_tree_samples = 0

[hazard_calculation]
random_seed = 42
investigation_time = 1
truncation_level = 3.0
maximum_distance = 200.0

[vulnerability]
structural_vulnerability_file = structural_vulnerability_model.xml
structural_vulnerability_retrofitted_file = retrofit_vulnerability_model.xml

[risk_calculation]
interest_rate = 0.05
asset_life_expectancy = 50
lrem_steps_per_interval = 1

The new parameters introduced in the above example configuration file are described below:

  • vulnerability_retrofitted_file: this parameter is used to specify the path to the Vulnerability Model file containing the vulnerabilityfunctions for the retrofitted asset

  • interest_rate: this parameter is used in the calculation of the present value of potential future benefits by discounting future cash flows

  • asset_life_expectancy: this variable defines the life expectancy or design life of the assets, and is used as the time-frame in which the costs and benefits of the retrofit will be compared

The above calculation can be run using the command line:

user@ubuntu:~$ oq engine --run job.ini

After the calculation is completed, a message similar to the following will be displayed:

Calculation 2776 completed in 25 seconds. Results:
  id | name
5422 | Benefit-cost ratio distribution | BCR Map. type=structural, hazard=5420

4.3.8 Exporting Risk Results#

To obtain a list of all risk calculations that have been previously run (successfully or unsuccessfully), or that are currently running, the following command can be employed:

user@ubuntu:~$ oq engine --list-risk-calculations

or simply:

user@ubuntu:~$ oq engine --lrc

Which will display a list of risk calculations as presented below.

job_id |     status |          start_time |     description
     1 |   complete | 2015-12-02 08:50:30 | Scenario damage example
     2 |     failed | 2015-12-03 09:56:17 | Scenario risk example
     3 |   complete | 2015-12-04 10:45:32 | Scenario risk example
     4 |   complete | 2015-12-04 10:48:33 | Classical risk example
     5 |   complete | 2020-07-09 13:47:45 | Event based risk aggregation example

Then, in order to display a list of the risk outputs from a given job which has completed successfully, the following command can be used:

user@ubuntu:~$ oq engine --list-outputs <risk_calculation_id>

or simply:

user@ubuntu:~$ oq engine --lo <risk_calculation_id>

which will display a list of outputs for the calculation requested, as presented below:

Calculation 5 results:
  id | name
  11 | Aggregate Event Losses
   1 | Aggregate Loss Curves
   2 | Aggregate Loss Curves Statistics
   3 | Aggregate Losses
   4 | Aggregate Losses Statistics
   5 | Average Asset Losses Statistics
  13 | Earthquake Ruptures
   6 | Events
   7 | Full Report
  10 | Input Files
  12 | Realizations
  14 | Source Loss Table
  15 | Total Loss Curves
  16 | Total Loss Curves Statistics
  17 | Total Losses
  18 | Total Losses Statistics

Then, in order to export all of the risk calculation outputs in the default file format (csv for most outputs), the following command can be used:

user@ubuntu:~$ oq engine --export-outputs <risk_calculation_id> <output_directory>

or simply:

user@ubuntu:~$ oq engine --eos <risk_calculation_id> <output_directory>

If, instead of exporting all of the outputs from a particular calculation, only particular output files need to be exported, this can be achieved by using the --export-output option and providing the id of the required output:

user@ubuntu:~$ oq engine --export-output <risk_output_id> <output_directory>

or simply:

user@ubuntu:~$ oq engine --eo <risk_output_id> <output_directory>

4.4 Risk Results#

This following sections describe the different output files produced by the risk calculators.

4.4.1 Scenario Damage Outputs#

The Scenario Damage Calculator produces the following output file for all loss types (amongst “structural”, “nonstructural”, “contents”, or “business_interruption”) for which a fragility model file was provided in the configuration file:

  1. Event Damages: this file contains the damage distribution statistics for each individual events generated by the scenario, for each event and for every GMPE specified in the job file. For each event, the total number of buildings in each damage state are listed in this file.

  2. Average Asset Damages: this file contains the damage distribution statistics for each of the individual assets defined in the Exposure Model that fall within the region and have a computed Ground Motion Field value available within the defined asset_hazard_distance. For each asset, the mean number of buildings (mean) in each damage state are listed in this file.

In addition, if the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Damage Calculation, the following additional outputs can be exported:

  1. dmg_by_tag: this file contains the aggregated damage distribution statistics for each of the tags defined in the Exposure Model. For each tag, the mean number of buildings (mean) in each damage state are listed in this file.

  2. dmg_total: this file contains the aggregated damage distribution statistics for the entire portfolio of assets defined in the Exposure Model. The mean (mean) and associated standard deviation (stddev) of the total number of buildings in each damage state are listed in this file.

In addition to the above asset-level damage output file which is produced for all Scenario Damage calculations, the following output file is also produced for all loss types (amongst “structural”, “nonstructural”, “contents”, or “business_interruption”) for which a Consequence Model file was also provided in the configuration file:

  1. Event Losses: this file contains the scenario consequence statistics for each of the individual events generated by the scenario for every GMPE specified in the job file. For each event, the total consequences considering the entire portfolio of assets are listed in this file.

  2. Average Asset Losses: this file contains the scenario consequence statistics for each of the individual assets defined in the Exposure Model that fall within the region and have a computed Ground Motion Field value available within the defined asset_hazard_distance. For each asset, the mean consequences (mean) and associated standard deviation (stddev) are listed in this file.

In addition, if the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Damage Calculation, the following additional outputs can be exported:

  1. losses_by_tag: this file contains the aggregated scenario consequence statistics for each of the tags defined in the Exposure Model. For each tag, the mean consequences (mean) and associated standard deviation (stddev) are listed in this file.

  2. losses_total: this file contains the aggregated scenario consequence statistics for the entire portfolio of assets defined in the Exposure Model. The mean consequences (mean) and associated standard deviation (stddev) are listed in this file.

If the calculation involves multiple Ground Motion Prediction Equations as described in Example 4 in Section 4.3.1   Scenario Damage Calculator, separate output files are generated for each of the above outputs, for each of the different Ground Motion Prediction Equations used in the calculation.

These different output files for Scenario Damage calculations are described in more detail in the following subsections.

4.4.1.1 Scenario damage statistics#

4.4.1.1.1 Event damage statistics#

This output contains the total damage distribution statistics for each of the individual events generated for the scenario. An example output file for structural damage is shown in the file snippet in Table 6.

Table 6 Example of a scenario event damage distribution output file#

event_id

rlz_id

ds0

ds1

ds2

ds3

ds4

0

0

5

1

0

0

1

1

0

4

1

1

1

0

2

0

6

0

0

0

1

100

1

5

0

1

0

1

101

1

4

1

1

1

0

102

1

5

1

0

0

1

The output file lists the total number of buildings in each damage state for each simulated event for the scenario, for each GMPE specified in the job file.

4.4.1.1.2 Asset damage statistics#

This output contains the damage distribution statistics for each of the individual assets defined in the Exposure Model that fall within the region and have a computed Ground Motion Field value available within the defined asset_hazard_distance. An example output file for structural damage is shown in the file snippet in Table 7.

Table 7 Example of a scenario asset damage distribution output file#

asset_ref

taxonomy

lon

lat

ds0_mean

ds1_mean

ds2_mean

ds3_mean

a1

tax1

-122.000

38.113

2.43E-01

6.60E-01

3.00E-02

0.00E+00

a2

tax2

-122.114

38.113

8.18E-01

1.00E-01

8.00E-02

2.50E-03

a3

tax1

-122.570

38.113

9.90E-01

1.00E-02

0.00E+00

0.00E+00

a4

tax3

-122.000

38.000

3.93E-01

2.35E-01

2.95E-01

7.25E-02

a5

tax1

-122.000

37.910

9.90E-01

0.00E+00

0.00E+00

0.00E+00

a6

tax2

-122.000

38.225

4.08E-01

2.35E-01

1.73E-01

1.28E-01

a7

tax1

-121.886

38.113

9.70E-01

2.00E-02

0.00E+00

0.00E+00

The output file lists the mean of the number of buildings in each damage state for each asset in the exposure model for all loss types (amongst ‘structural’’, “nonstructural”, “contents”, or “business_interruption”) for which a Consequence Model file was also provided in the configuration file in addition to the corresponding Fragility Model file.

4.4.1.1.3 Damage statistics by tag#

If the OpenQuake-QGIS plugin is used for visualizing or exporting the results, the Scenario Damage calculator can also estimate the expected total number of buildings of a certain combination of tags in each damage state and made available for export as a csv file. This distribution of damage per building tag is depicted in the example output file snippet in Table 8.

Table 8 Example of a scenario damage distribution per tag output file#

taxonomy

structural ds0_mean

structural ds1_mean

structural ds2_mean

structural ds3_mean

… …

taxo nomy=wood

3,272.48

592.55

479.19

422.34

taxonomy =concrete

1,241.94

389.94

272.69

91.63

taxon omy=steel

460.72

279.44

152.18

57.43

The output file lists the mean of the total number of buildings in each damage state for each tag found in the exposure model for all loss types (amongst “structural”, “nonstructural”, “contents”, or “business_interruption”).

4.4.1.1.4 Total damage statistics#

Finally, a total damage distribution output file can also be generated if the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Damage Calculation, which will contain the mean and standard deviation of the total number of buildings in each damage state, as illustrated in the example file in Table 9.

Table 9 Example of a scenario total damage distribution output file#

loss_type

damage_state

damage_value

structural

no_damage_mean

4,975.13

structural

ds1_mean

904.06

structural

ds2_mean

564.35

structural

ds3_mean

246.44

structural

ds4_mean

310.03

4.4.1.2 Scenario consequence statistics#

4.4.1.2.1 Asset consequence statistics#

This output contains the consequences statistics for each of the individual assets defined in the Exposure Model that fall within the region and have a computed Ground Motion Field value available within the defined asset_hazard_distance. An example output file for structural damage consequences is shown in Table 10.

Table 10 Example of a scenario asset consequences output file#

asset_ref

lon

lat

nonstructural-mean

nonstructural-stddev

a3

-122.57000

38.11300

428.29

281.49

a2

-122.11400

38.11300

1220.84

1111.4

a5

-122.00000

37.91000

1390.59

859.10

a4

-122.00000

38.00000

2889.04

1663.33

a1

-122.00000

38.11300

3191.30

1707.41

a6

-122.00000

38.22500

3310.62

2069.87

a7

-121.88600

38.11300

1415.19

845.83

The output file lists consequence statistics for all loss types (amongst “structural”, “nonstructural”, “contents”, or “business_interruption”) for which a Consequence Model file was also provided in the configuration file in addition to the corresponding Fragility Model file.

4.4.1.2.2 Total consequence statistics#

Finally, if the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Damage Calculation, a total consequences output file can also be generated, which will contain the mean and standard deviation of the total consequences for the selected scenario, as illustrated in the example shown in Table 11.

Table 11 Example of a scenario total consequences output file#

contents-mean

contents-stddev

structural-mean

structural-stddev

13845.87

6517.61

2270.29

2440.90

4.4.2 Scenario Risk Outputs#

The Scenario Risk Calculator produces the following set of output files:

  1. Aggregate Asset Losses: this file contains the aggregated scenario loss statistics for the entire portfolio of assets defined in the Exposure Model. The mean (mean) and standard deviation (stddev) of the total loss for the portfolio of assets are listed in this file.

  2. Average Asset Losses: this file contains mean (mean) and associated standard deviation (stddev) of the scenario loss for all assets at each of the unique locations in the Exposure Model.

  3. Aggregate Event Losses: this file contains the total loss for the portfolio of assets defined in the Exposure Model for each realization of the scenario generated in the Monte Carlo simulation process.

In addition, if the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Risk Calculation, the following additional outputs can be exported:

  1. losses_by_tag: this file contains the scenario loss statistics for each of the tags defined in the Exposure Model. For each tag, the mean (mean) and associated standard deviation (stddev) of the losses for each tag are listed in this file.

If the calculation involves multiple Ground Motion Prediction Equations, separate output files are generated for each of the above outputs, for each of the different Ground Motion Prediction Equations used in the calculation.

These different output files for Scenario Risk calculations are described in more detail in the following subsections.

4.4.2.1 Scenario loss statistics#

4.4.2.1.1 Asset loss statistics#

This output is always produced for a Scenario Risk calculation and comprises a mean total loss and associated standard deviation for each of the individual assets defined in the Exposure Model that fall within the region and have a computed Ground Motion Field value available within the defined asset_hazard_distance. These results are stored in a comma separate value (.csv) file as illustrated in the example shown in Table 12.

Table 12 Example of a scenario asset loss distribution output file#

structural

structural

asset_ref

taxonomy

lon

lat

mean

stddev

a3

wood

-122.57000

38.11300

686,626

1,070,680

a2

concrete

-122.11400

38.11300

1,496,360

2,121,790

a5

wood

-122.00000

37.91000

3,048,910

4,339,480

a4

steel

-122.00000

38.00000

9,867,070

15,969,600

a1

wood

-122.00000

38.11300

12,993,800

22,136,700

a6

concrete

-122.00000

38.22500

5,632,180

9,508,760

a7

wood

-121.88600

38.11300

2,966,190

5,270,480

4.4.2.1.2 Tag loss statistics#

If the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Risk Calculation, the total expected losses for assets of each tag will be computed and made available for export as a csv file. This distribution of losses per asset tag is depicted in the example output file snippet in Table 13.

Table 13 Example of a scenario loss distribution per tag output file#

tag

contents

nonstructural

structural

taxonomy=wood

526,754.0

759,653.0

393,912.0

taxonomy=concrete

587,773.0

1,074,620.0

142,571.0

taxonomy=steel

407,821.0

923,281.0

197,341.0

The output file lists the mean loss aggregated for each tags present in the exposure model and selected by the for all loss types (amongst “structural”, “nonstructural”, “contents”, or “business_interruption”) for which a Vulnerability Model file was provided in the configuration file.

4.4.2.1.3 Total loss statistics#

If the OpenQuake-QGIS plugin is used for visualizing or exporting the results from a Scenario Risk Calculation, the mean total loss and associated standard deviation for the selected earthquake rupture will be computed and made available for export as a csv file, as illustrated in the example shown in Table 14.

Table 14 Example of a scenario total loss output file#

LossType

Unit

Mean

Standard Deviation

structural

USD

8717775315.66

2047771108.36

4.4.2.2 Scenario losses by event#

The losses by event output lists the total losses for each realization of the scenario generated in the Monte Carlo simulation process for all loss types for which a Vulnerability Model file was provided in the configuration file. These results are exported in a comma separate value (.csv) file as illustrated in the example shown in Table 15.

Table 15 Example of a scenario losses by event output file#

event

structural

nonstructural

1

2,194.74

20,767.00

2

4,037.57

20,905.70

3

2,950.80

18,635.50

4

7,787.75

19,041.40

5

3,964.19

30,982.80

6

19,394.60

40,274.60

4.4.3 Probabilistic Damage Outputs#

4.4.3.1 Probabilistic damage distribution#

The Classical Probabilistic Damage calculator provides the expected damage distribution per asset as a csv file, an example of which is shown in Table 16.

Table 16 Example of a probabilistic damage distribution output file#

asset_ref

no_damage

slight

moderate

extreme

complete

a1

4.4360E-06

6.3482E-03

3.4851E-01

4.7628E-01

1.6884E-01

a2

1.0391E-05

9.1856E-03

3.7883E-01

4.6140E-01

1.5056E-01

a998

6.9569E-02

6.4106E+00

7.4108E+01

5.7563E+01

1.7848E+01

a999

1.2657E-01

8.1294E+00

7.6249E+01

5.4701E+01

1.6792E+01

This file lists the expected number of structural units in each damage state for each asset, for the time period specified by the parameter risk_investigation_time.

4.4.4 Probabilistic Risk Outputs#

Probabilistic risk calculations can be run using either the Classical Probabilistic Risk Calculator or the Stochastic Event-Based Probabilistic Risk Calculator. The following set of outputs is generated by both calculators:

  1. loss_curves: loss exceedance curves describe the probabilities of exceeding a set of loss ratios or loss values, within a given time span (or investigation interval).

  2. loss_maps: loss maps describe the loss (value) that is exceeded at the selected probability of exceedance (poE) within the specified time period for all assets at each of the unique locations in the Exposure Model.

  3. avg_losses: the average losses output describes the expected loss (value) within the time period specified by risk_investigation_time for all assets in the Exposure Model.

In addition, with the Stochastic Event-Based Probabilistic Risk Calculator, it is also possible to calculate the following types of outputs:

  1. agg_curves: aggregate loss curves describe the exceedance probabilities for a set of loss values for the entire portfolio of assets defined in the Exposure Model.

  2. losses_by_event: an event loss table contains the aggregate loss across all assets in the Exposure Model for each of the simulated ruptures in the Stochastic Event Set.

4.4.4.1 Loss exceedance curves#

Loss exceedance curves describe the probabilities of exceeding a set of loss ratios or loss values, within a given time span (or investigation interval). Depending upon the type of calculator used and the options defined before running a probabilistic risk calculation, one or more of the sets of loss exceedance curves described in the following subsections will be generated for all loss types (amongst “structural”, “nonstructural”, “contents”, “occupants”, or “business_interruption”) for which a vulnerability model file was provided in the configuration file.

4.4.4.1.1 Asset loss exceedance curves#

Individual asset loss exceedance curves for ground-up losses are always generated for the Classical Probabilistic Risk Calculator. On the other hand, individual asset loss exceedance curves are not generated for the Stochastic Event-Based Probabilistic Risk Calculator. These results are stored in a comma separate value (.csv) file as illustrated in the example shown in Table 17.

Table 17 Example of an asset loss curve output file#

asset

loss_type

loss

poe

asset

loss_type

loss

poe

a1

structural

0

1.00E+00

a1

structural

100

1.00E+00

a1

structural

400

8.43E-01

a1

structural

1000

4.70E-01

a1

structural

2000

1.78E-01

a1

structural

3300

7.31E-02

a1

structural

5000

3.30E-02

a1

structural

6700

1.68E-02

a1

structural

8000

1.01E-02

a1

structural

9000

6.62E-03

a1

structural

9600

4.95E-03

a1

structural

9900

4.12E-03

a1

structural

10000

3.86E-03

4.4.4.1.2 Mean loss exceedance curves#

For calculations involving multiple hazard branches, mean asset loss exceedance curves are also generated for both the Classical Probabilistic Risk Calculator and the Stochastic Event-Based Probabilistic Risk Calculator (if the parameter “loss_ratios” is defined in the configuration file). The structure of the file is identical to that of the individual asset loss exceedance curve output file.

4.4.4.1.3 Quantile loss exceedance curves#

For calculations involving multiple hazard branches, quantile asset loss exceedance curves can also be generated for both the Classical Probabilistic Risk Calculator and the Stochastic Event-Based Probabilistic Risk Calculator (if the parameter “loss_ratios” is defined in the configuration file). The quantiles for which loss curves will be calculated should have been defined in the job configuration file for the calculation using the parameter quantiles. The structure of the file is identical to that of the individual asset loss exceedance curve output file.

4.4.4.1.4 Aggregate loss exceedance curves#

Aggregate loss exceedance curves are generated only by the Stochastic Event- Based Probabilistic Risk Calculator and describe the probabilities of exceedance of the total loss across the entire portfolio for a set of loss values within a given time span (or investigation interval). These results are exported in a comma separate value (.csv) file as illustrated in the example shown in Table 18.

Table 18 Example of an aggregate loss curve#

annual _frequency_o of_exceedence

return _period

structural

nonstructural

1E+00

1

1,440.07

5E-01

2

246.95

2,122.25

2E-01

5

506.42

2,714.08

1E-01

10

740.06

3,226.47

5E-02

20

1,040.54

4,017.06

2E-02

50

1,779.61

6,610.49

1E-02

100

2,637.58

9,903.82

5E-03

200

3,742.73

14,367.00

2E-03

500

5,763.20

21,946.50

1E-03

1,000

7,426.77

25,161.00

5E-04

2,000

9,452.61

28,937.50

2E-04

5,000

12,021.00

35,762.20

1E-04

10,000

14,057.90

38,996.60

Same as described previously for individual assets, mean aggregate loss exceedance curves and quantile aggregate loss exceedance curves will also be generated when relevant.

4.4.4.2 Probabilistic loss maps#

A probabilistic loss map contains the losses that have a specified probability of exceedance within a given time span (or investigation interval) throughout the region of interest. This result can be generated using either the Stochastic Event-Based Probabilistic Risk Calculator or the Classical Probabilistic Risk Calculator.

The file snippet included in Table 19 shows an example probabilistic loss map output file.

Table 19 Example of a probabilistic loss map output file#

asset_ref

taxonomy

lon

lat

structural poe-0.02

structural poe-0.1

a1

wood

-122.000

38.113

6,686.10

3,241.80

a2

concrete

-122.114

38.113

597.59

328.07

a3

wood

-122.570

38.113

251.73

136.64

a4

steel

-122.000

38.000

3,196.66

1,610.98

a5

wood

-122.000

37.910

949.26

431.26

a6

concrete

-122.000

38.225

1,549.72

577.30

a7

wood

-121.886

38.113

1,213.54

677.16

4.4.4.3 Stochastic event loss tables#

The Stochastic Event-Based Probabilistic Risk Calculator will also produce an aggregate event loss table. Each row of this table contains the rupture id, and aggregated loss (sum of the losses from the collection of assets within the region of interest), for each event in the stochastic event sets. The rupture id listed in this table is linked with the rupture ids listed in the stochastic event sets files.

The file snippet included in Table 20 shows an example stochastic event loss table output file.

Table 20 Example aggregate event loss table#

event_id

rup_id

year

structural

0

486

8

1

486

8

2

486

8

375.12

3

486

8

177.71

4

486

12

173.75

5

486

12

200.51

6

486

12

267.50

7

486

12

196.72

8

785

12

4,720.67

9

785

12

1,002.59

10

785

12

6,693.98

11

785

12

1,135.40

12

483

13

111.85

Asset event loss tables provide calculated losses for each of the assets in the exposure model, for each event within the stochastic event sets. Considering that the amount of data usually contained in an asset event loss table is substantial, this table is not generated by default and even when it is generated it cannot be exported: it can only be accessed programmatically from the datastore. It is there for debugging purposes only.

4.4.5 Benefit-Cost Ratio Outputs#

4.4.5.1 Retrofitting benefit/cost ratio maps#

Ratio maps from the Retrofitting Benefit/Cost Ratio calculator require loss exceedance curves, which can be calculated using the Classical Probabilistic Risk calculator. For this reason, the parameters sourceModelTreePath and gsimTreePath are also included in this NRML schema, so the whole calculation process can be traced back. The results for each asset are stored as depicted in Table 21.

Table 21 Example benefit-cost ratio map output#

lon

lat

asset_ref

aal_original

aal_retrofitted

bcr

80.0888

28.8612

a1846

966,606

53,037

1.72

80.0888

28.8612

a4119

225,788

26,639

1.46

80.0888

28.8612

a6444

444,595

16,953

1.33

80.0888

28.8612

a8717

106,907

10,086

0.39

80.0888

28.9362

a1784

964,381

53,008

1.92

80.0888

28.9362

a4057

225,192

26,597

1.64

80.0888

28.9362

a6382

443,388

16,953

1.49

80.0888

28.9362

a8655

106,673

10,081

0.44

80.1292

29.0375

a2250

1,109,310

60,989

2.08

80.1292

29.0375

a4523

2,785,790

329,083

1.78

  • interestRate: this parameter represents the interest rate used in the time-value of money calculations

  • assetLifeExpectancy: this parameter specifies the life expectancy (or design life) of the assets considered for the calculations

  • node: this schema follows the same node structure already presented for the loss maps, however, instead of losses for each asset, the benefit/cost ratio (ratio), the average annual loss considering the original vulnerability (aalOrig) and the average annual loss for the retrofitted (aalRetr) configuration of the assets are provided.

4.5 Demonstrative Examples#

The following sections describe the set of demos that have been compiled to demonstrate some of the features and usage of the risk calculators of the OpenQuake engine. These demos can be found in a public repository on GitHub at the following link: https://github.com/gem/oq-engine/tree/master/demos/risk.

These examples are purely demonstrative and are not intended to represent accurately the seismicity, vulnerability or exposure characteristics of the region selected, but simply to provide example input files that can be used as a starting point for users planning to employ the OpenQuake engine in seismic risk and loss estimation studies.

It is also noted that in the demonstrative examples presented in this section, illustrations about the various messages from the engine displayed in the command line interface are presented. These messages often contain information about the calculation id and output id, which will certainly be different for each user.

Following is the list of demos which illustrate how to use the OpenQuake engine for various scenario-based and probabilistic seismic damage and risk analyses:

  • ClassicalBCR

  • ClassicalDamage

  • ClassicalRisk

  • EventBasedDamage

  • EventBasedRisk

  • ScenarioDamage

  • ScenarioRisk

These seven demos use Nepal as the region of interest. An example Exposure Model has been developed for this region, comprising 9,063 assets distributed amongst 2,221 locations (due to the existence of more than one asset at the same location). A map with the distribution of the number of buildings throughout Nepal is presented in Fig. 33.

Distribution of number of buildings in Nepal

Fig. 33 Distribution of number of buildings in Nepal#

The building portfolio was organised into four classes for the rural areas (adobe, dressed stone, unreinforced fired brick, wooden frames), and five classes for the urban areas (the aforementioned typologies, in addition to reinforced concrete buildings). For each one of these building typologies, vulnerabilityfunctions and fragilityfunctions were collected from the published literature available for the region. These input models are only for demonstrative purposes and for further information about the building characteristics of Nepal, users are advised to contact the National Society for Earthquake Technology of Nepal (NSET - http:www.nset.org.np/).

The following sections include instructions not only on how to run the risk calculations, but also on how to produce the necessary hazard inputs. Thus, each demo comprises the configuration file, Exposure Model and fragility or vulnerability models fundamental for the risk calculations. Each demo folder also a configuration file and the input models to produce the relevant hazard inputs.

4.5.1 Scenario Damage#

Demos A rupture of magnitude Mw 7 in the central part of Nepal is considered in this demo. The characteristics of this rupture (geometry, dip, rake, hypocentre, upper and lower seismogenic depth) are defined in the fault_rupture.xml file, and the hazard and risk calculation settings are specified in the job.ini file.

To run the Scenario Damage demo, users should navigate to the folder where the required files have been placed and employ following command:

user@ubuntu:~$ oq engine --run job_hazard.ini && oq engine --run job_risk.ini --hc=-1

The hazard calculation should produce the following outputs:

Calculation 8967 completed in 4 seconds. Results:
  id | name
9060 | Ground Motion Fields
9061 | Realizations

and the following outputs should be produced by the risk calculation:

Calculation 8968 completed in 16 seconds. Results:
  id | name
9062 | Average Asset Damages
9063 | Average Asset Losses

4.5.2 Scenario Risk Demos#

The same rupture described in the Scenario Damage demo is also used for this demo. In this case, a combined job file, job.ini, is used to specify the configuration parameters for the hazard and risk calculations.

To run the Scenario Risk demo, users should navigate to the folder where the required files have been placed and employ following command:

user@ubuntu:~$ oq engine --run job.ini

and the following outputs should be produced:

Calculation 8970 completed in 16 seconds. Results:
  id | name
9071 | Aggregate Asset Losses
9072 | Full Report
9073 | Ground Motion Fields
9074 | Average Asset Losses
9075 | Aggregate Event Losses
9076 | Realizations

4.5.3 Classical Probabilistic Seismic Damage Demos#

The seismic source model developed within the Global Seismic Hazard Assessment Program (GSHAP) is used with the (B. S.-J. Chiou and Youngs 2008) ground motion prediction equation to produce the hazard input for this demo. No uncertainties are considered in the seismic source model and since only one GMPE is being considered, there will be only one possible path in the logic tree. Therefore, only one set of seismic hazard curves will be produced. To run the hazard calculation, the following command needs to be employed:

user@ubuntu:~$ oq engine --run job_hazard.ini

which will produce the following sample hazard output:

Calculation 8971 completed in 34 seconds. Results:
  id | name
9074 | Hazard Curves
9075 | Realizations

The risk job calculates the probabilistic damage distribution for each asset in the Exposure Model starting from the above generated hazard curves. The following command launches the risk calculations:

user@ubuntu:~$ oq engine --run job_risk.ini --hc 8971

and the following sample outputs are obtained:

Calculation 8972 completed in 16 seconds. Results:
  id | name
9076 | Asset Damage Distribution
9077 | Asset Damage Statistics

4.5.4 Classical Probabilistic Seismic Risk Demos#

The same hazard input as described in the Classical Probabilistic Damage demo is used for this demo. Thus, the workflow to produce the set of hazard curves described in Section 4.5.3   Classical Probabilistic Seismic Damage Demos is also valid herein. Then, to run the Classical Probabilistic Risk demo, users should navigate to the folder containing the demo input models and configuration files and employ the following command:

user@ubuntu:~$ oq engine --run job_hazard.ini

which will produce the following hazard output:

Calculation 8971 completed in 34 seconds. Results:
  id | name
9074 | Hazard Curves
9075 | Realizations

In this demo, loss exceedance curves for each asset and two probabilistic loss maps (for probabilities of exceedance of 1% and 10%) are produced. The following command launches these risk calculations:

user@ubuntu:~$ oq engine --run job_risk.ini --hc 8971

and the following outputs are expected:

Calculation 8973 completed in 16 seconds. Results:
  id | name
9077 | Asset Loss Curves Statistics
9078 | Asset Loss Maps Statistics
9079 | Average Asset Loss Statistics

4.5.5 Event Based Probabilistic Seismic Damage Demos#

This demo uses the same probabilistic seismic hazard assessment (PSHA) model described in the previous examples in Section 4.5.3   Classical Probabilistic Seismic Damage Demos and Section 4.5.4   Classical Probabilistic Seismic Risk Demos. However, instead of hazard curves, sets of ground motion fields will be generated by the hazard calculation of this demo. Again, since there is only one Branch in the logic tree, only one set of ground motion fields will be used in the risk calculations. The hazard and risk jobs are defined in a single configuration file for this demo. To trigger the hazard and risk calculations the following command needs to be used:

user@ubuntu:~$ oq engine --run job.ini

and the following results are expected:

Calculation 2 completed in 29 seconds. Results:
  id | name
  24 | Aggregate Event Damages
  30 | Aggregate Event Losses
  20 | Average Asset Damages
  21 | Average Asset Damages Statistics
  22 | Average Asset Losses
  23 | Average Asset Losses Statistics
  32 | Earthquake Ruptures
  25 | Events
  26 | Full Report
  27 | Ground Motion Fields
  28 | Hazard Curves
  29 | Input Files
  31 | Realizations

4.5.6 Event Based Probabilistic Seismic Risk Demos#

This demo uses the same probabilistic seismic hazard assessment (PSHA) model described in the previous examples in Section 4.5.3   Classical Probabilistic Seismic Damage Demos and Section 4.5.4   Classical Probabilistic Seismic Risk Demos. However, instead of hazard curves, sets of ground motion fields will be generated by the hazard calculation of this demo. Again, since there is only one Branch in the logic tree, only one set of ground motion fields will be used in the risk calculations. The hazard and risk jobs are defined in a single configuration file for this demo. To trigger the hazard and risk calculations the following command needs to be used:

user@ubuntu:~$ oq engine --run job.ini

and the following results are expected:

Calculation 8974 completed in 229 seconds. Results:
  id | name
1820 | Total Loss Curves
1821 | Total Loss Curves Statistics
1822 | Aggregate Loss Table
1823 | Average Asset Losses
1824 | Average Asset Loss Statistics
1826 | Asset Loss Maps
1827 | Asset Loss Maps Statistics
1828 | Average Asset Losses
1829 | Average Asset Losses Statistics
1830 | Earthquake Ruptures
1831 | Events
1832 | Realizations

The number and the name of the outputs can change between different versions of the engine.

4.5.7 Retrofit Benefit-Cost Ratio Demos#

The loss exceedance curves used within this demo are produced using the Classical Probabilistic Risk calculator. Thus, the process to produce the seismic hazard curves described in Section 4.5.4   Classical Probabilistic Seismic Risk Demos can be employed here. Then, the risk calculations can be initiated using the following command:

user@ubuntu:~$ oq engine --run job_risk.ini --hc 8971

which should produce the following output:

Calculation 8976 completed in 14 seconds. Results:
  id | name
9087 | Benefit Cost Ratios

4.6 Bibliography#


Aki, K., and P. G. Richards. 2002. Quantitative Seismology. Sausalito, California: University Science Books.

Chiou, B. S.-J., and R. R. Youngs. 2008. “An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra.” Earthquake Spectra 24: 173–215.

Chiou, Brian S.-J., and Robert R. Youngs. 2014. “Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra.” Earthquake Spectra 30 (3): 1117–53.

Cornell, C. A. 1968. “Engineering Seismic Risk Analysis.” Bulletin of the Seismological Society of America 58: 1583–1606.

EPRI, Electric Power Research Institute. 2011. “Technical Report: Central and Eastern United States Seismic Source Characterisation for Nuclear Facilities.” Report. EPRI, Palo Alto, CA. U. S. DoE,; U. S. NERC.

Field, E. H., T. H. Jordan, and C. A. Cornell. 2003. “OpenSHA - a Developing Community-Modeling Environment for Seismic Hazard Analysis.” Seismological Research Letters 74: 406–19.

Frankel, A. 1995. “Mapping Seismic Hazard in the Central and Eastern United States.” Seismological Research Letters 66 (4): 8–21.

McGuire, K. K. 1976. “FORTRAN Computer Program for Seismic Risk Analysis.” Open-File report 76-67. United States Department of the Interior, Geological Survey.

Petersen, M. D., A. D. Frankel, S. C. Harmsen, C. S. Mueller, K. M. Haller, R. L. Wheeler, R. L. Wesson, et al. 2008. “Documentation for the 2008 Update of the United States National Seismic Hazard Maps.” Open File Report 2008-1128. U.S. Department of the Interior, U.S. Geological Survey.

Schwartz, D. P., and K. J. Coppersmith. 1984. “Fault Behaviour and Characteristic Earthquakes: Examples from the Wasatch and San Andreas Fault Zones.” Journal of Geophysical Research 89 (B7): 5681–98.

Strasser, F. O., M. C. Arango, and J. J. Bommer. 2010. “Scaling of the Source Dimensions of Interface and Intraslab Subduction-zone Earthquakes with Moment Magnitude.” Seismological Research Letters 81: 941–50.

Thingbaijam, K. K. S., P. M. Mai, and K. Goda. 2017. “New Empirical Earthquake Source-Scaling Laws.” Bulletin of the Seismological Society of America 107 (5): 2225–2946. https://doi.org/10.1785/0120170017.

Wells, D. L., and K. J. Coppersmith. 1994. “New Empirical Relationships Among Magnitude, Rupture Length, Rupture Width, Rupture Area, and Surface Displacement.” Bulletin of the Seismological Society of America 84 (4): 974–1002.

Woo, G. 1996. “Kernel Estimation Methods for Seismic Hazard Area Source Modeling.” Bulletin of the Seismological Society of America 86 (2): 353–62.

Youngs, R. R., and K. J. Coppersmith. 1985. “Implications of Fault Slip Rates and Earthquake Recurrence Models to Probabilistic Seismic Hazard Estimates.” Bulletin of the Seismological Society of America 75 (April): 939–64.

1
A short description of the process promoted by OECD is available here:
2

Within the OpenQuake engine, longitude and latitude coordinates are internally rounded to a precision of 5 digits after the decimal point.

3

Note that as of OpenQuake engine18, the uncertainty in the consequence ratios is ignored, and only the mean consequence ratios for the set of limit states is considered when computing the consequences from the damage distribution. Consideration of the uncertainty in the consequence ratios is planned for future releases of the OpenQuake engine.

4

As of OpenQuake engine18, the “PM” option for defining vulnerabilityfunctions is supported by the Scenario Risk and the Stochastic Event-Based Probabilistic Risk Calculators, but not by the Classical Probabilistic Risk Calculator.