Special features of the engine

There are a few less frequently used features of the engine that are not documented in the general user’s manual, since their usage is quite specific. They are documented here.

The custom_site_id

Since engine 3.13, it is possible to assign 6-character ASCII strings as unique identifiers for the sites. This can be convenient in various situations, especially when splitting a calculation in geographic regions. The way to enable it is to add a field called custom_site_id to the site model file, which must be unique for each site.

The hazard curve and ground motion field exporters have been modified to export the custom_site_id instead of the site_id (if present).

We used this feature to split the ESHM20 model in two parts (Northern Europe and Southern Europe). Then creating the full hazard map was as trivial as joining the generated CSV files. Without the custom_site_id the site IDs would overlap, thus making impossible to join the outputs.

A geohash string (see https://en.wikipedia.org/wiki/Geohash) makes a good custom_site_id since it can enable the unique identification of all potential sites across the globe.

The minimum_distance parameter

GMPEs often have a prescribed range of validity. In particular they may give unexpected results for points too close to ruptures. To avoid this problem the engine recognizes a minimum_distance parameter: if it is set, then for distances below the specified minimum distance, the GMPEs return the ground-motion value at the minimum distance. This avoids producing extremely large (and physically unrealistic) ground-motion values at small distances. The minimum distance is somewhat heuristic. It may be useful to experiment with different values of the minimum_distance, to see how the hazard and risk change.

GMPE logic trees with weighted IMTs

In order to support Canada’s 6th Generation seismic hazard model, the engine now has the ability to manage GMPE logic trees where the weight assigned to each GMPE may be different for each IMT. For instance you could have a particular GMPE applied to PGA with a certain weight, to SA(0.1) with a different weight, and to SA(1.0) with yet another weight. The user may want to assign a higher weight to the IMTs where the GMPE has a small uncertainty and a lower weight to the IMTs with a large uncertainty. Moreover a particular GMPE may not be applicable for some periods, and in that case the user can assign to a zero weight for those periods, in which case the engine will ignore it entirely for those IMTs. This is useful when you have a logic tree with multiple GMPEs per branchset, some of which are applicable for some IMTs and not for others. Here is an example:

<logicTreeBranchSet uncertaintyType="gmpeModel" branchSetID="bs1"
    <logicTreeBranch branchID="BooreEtAl1997GeometricMean">
        <uncertaintyWeight imt="PGA">0.25</uncertaintyWeight>
        <uncertaintyWeight imt="SA(0.5)">0.5</uncertaintyWeight>
        <uncertaintyWeight imt="SA(1.0)">0.5</uncertaintyWeight>
        <uncertaintyWeight imt="SA(2.0)">0.5</uncertaintyWeight>
    <logicTreeBranch branchID="SadighEtAl1997">
        <uncertaintyWeight imt="PGA">0.25</uncertaintyWeight>
        <uncertaintyWeight imt="SA(0.5)">0.5</uncertaintyWeight>
        <uncertaintyWeight imt="SA(1.0)">0.5</uncertaintyWeight>
        <uncertaintyWeight imt="SA(2.0)">0.5</uncertaintyWeight>
    <logicTreeBranch branchID="MunsonThurber1997Hawaii">
        <uncertaintyWeight imt="PGA">0.25</uncertaintyWeight>
        <uncertaintyWeight imt="SA(0.5)">0.0</uncertaintyWeight>
        <uncertaintyWeight imt="SA(1.0)">0.0</uncertaintyWeight>
        <uncertaintyWeight imt="SA(2.0)">0.0</uncertaintyWeight>
    <logicTreeBranch branchID="Campbell1997">
        <uncertaintyWeight imt="PGA">0.25</uncertaintyWeight>
        <uncertaintyWeight imt="SA(0.5)">0.0</uncertaintyWeight>
        <uncertaintyWeight imt="SA(1.0)">0.0</uncertaintyWeight>
        <uncertaintyWeight imt="SA(2.0)">0.0</uncertaintyWeight>

Clearly the weights for each IMT must sum up to 1, otherwise the engine will complain. Note that this feature only works for the classical and disaggregation calculators: in the event based case only the default uncertaintyWeight (i.e. the first in the list of weights, the one without imt attribute) would be taken for all IMTs.

Equivalent Epicenter Distance Approximation

The equivalent epicenter distance approximation (reqv for short) was introduced in engine 3.2 to enable the comparison of the OpenQuake engine with time-honored Fortran codes using the same approximation.

You can enable it in the engine by adding a [reqv] section to the job.ini, like in our example in openquake/qa_tests_data/classical/case_2/job.ini:

reqv_hdf5 = {'active shallow crust': 'lookup_asc.hdf5',
             'stable shallow crust': 'lookup_sta.hdf5'}

For each tectonic region type to which the approximation should be applied, the user must provide a lookup table in .hdf5 format containing arrays mags of shape M, repi of shape N and reqv of shape (M, N).

The examples in openquake/qa_tests_data/classical/case_2 will give you the exact format required. M is the number of magnitudes (in the examples there are 26 magnitudes ranging from 6.05 to 8.55) and N is the number of epicenter distances (in the examples ranging from 1 km to 1000 km).

Depending on the tectonic region type and rupture magnitude, the engine converts the epicentral distance repi` into an equivalent distance by looking at the lookup table and use it to determine the ``rjb and rrup distances, instead of the regular routines. This means that within this approximation ruptures are treated as pointwise and not rectangular as the engine usually does.

Notice that the equivalent epicenter distance approximation only applies to ruptures coming from PointSources/AreaSources/MultiPointSources, fault sources are untouched.

Ruptures in CSV format

Since engine v3.10 there is a way to serialize ruptures in CSV format. The command to give is:

$ oq extract "ruptures?min_mag=<mag>" <calc_id>`

For instance, assuming there is an event based calculation with ID 42, we can extract the ruptures in the datastore with magnitude larger than 6 with oq extract "ruptures?min_mag=6" 42: this will generate a CSV file. Then it is possible to run scenario calculations starting from that rupture by simply setting

rupture_model_file = ruptures-min_mag=6_42.csv

in the job.ini file. The format is provisional and may change in the future, but it will stay a CSV with JSON fields. Here is an example for a planar rupture, i.e. a rupture generated by a point source:

#,,,,,,,,,,"trts=['Active Shallow Crust']"
24,5.050000E+00,0.000000E+00,0.08456,0.15503,5.000000E+00,1,Active Shallow Crust,ParametricProbabilisticRupture PlanarSurface,"[[[[0.08456, 0.08456, 0.08456, 0.08456]], [[0.13861, 0.17145, 0.13861, 0.17145]], [[3.17413, 3.17413, 6.82587, 6.82587]]]]","{""occurrence_rate"": 4e-05}"

The format is meant to support all kind of ruptures, including ruptures generated by simple and complex fault sources, characteristic sources, nonparametric sources and new kind of sources that could be introduced in the engine in the future. The header will be the same for all kind of ruptures that will be stored in the same CSV. Here is description of the fields as they are named now (engine 3.11):

the random seed used to compute the GMFs generated by the rupture
the magnitude of the rupture
the rake angle of the rupture surface in degrees
the longitude of the hypocenter in degrees
the latitude of the hypocenter in degrees
the depth of the hypocenter in km
the number of occurrences of the rupture (i.e. number of events)
the tectonic region type of the rupture; must be consistent with the trts listed in the pre-header of the file
a space-separated string listing the rupture class and the surface class used in the engine
3 times nested list with lon, lat, dep of the points of the discretized rupture geometry for each underlying surface
extra parameters of the rupture as a JSON dictionary, for instance the rupture occurrence rate

Notice that using a CSV file generated with an old version of the engine is inherently risky: for instance if we changed the ParametricProbabilisticRupture class or the PlanarSurface classes in an incompatible way with the past, then a scenario calculation starting with the CSV would give different results in the new version of the engine. We never changed the rupture classes or the surface classes, but we changed the seed algorithm often, and that too would cause different numbers to be generated (hopefully, statistically consistent). A bug fix or change of logic in the calculator can also change the numbers across engine versions.


There is a parameter in the job.ini called max_sites_disagg, with a default value of 10. This parameter controls the maximum number of sites on which it is possible to run a disaggregation. If you need to run a disaggregation on a large number of sites you will have to increase that parameter. Notice that there are technical limits: trying to disaggregate 100 sites will likely succeed, trying to disaggregate 100,000 sites will most likely cause your system to go out of memory or out of disk space, and the calculation will be terribly slow. If you have a really large number of sites to disaggregate, you will have to split the calculation and it will be challenging to complete all the subcalculations.

The parameter max_sites_disagg is extremely important not only for disaggregation, but also for classical calculations. Depending on its value and then number of sites (N) your calculation can be in the few sites regime or the many sites regime.

In the few sites regime (N <= max_sites_disagg) the engine stores information for each rupture in the model (in particular the distances for each site) and therefore uses more disk space. The problem is mitigated since the engine uses a relatively aggressive strategy to collapse ruptures, but that requires more RAM available.

In the many sites regime (N > max_sites_disagg) the engine does not store rupture information (otherwise it would immediately run out of disk space, since typical hazard models have tens of millions of ruptures) and uses a much less aggressive strategy to collapse ruptures, which has the advantage of requiring less RAM.


Starting from engine 3.9 there is a new feature in the source model logic tree: the ability to define new branches by adding sources to a base model. An example will explain it all:

<?xml version="1.0" encoding="UTF-8"?>
<nrml xmlns:gml="http://www.opengis.net/gml"
  <logicTree logicTreeID="lt1">
    <logicTreeBranchSet uncertaintyType="sourceModel"
      <logicTreeBranch branchID="b01">
      <logicTreeBranch branchID="b02">
    <logicTreeBranchSet uncertaintyType="extendModel" applyToBranches="b01"
      <logicTreeBranch branchID="b11">
      <logicTreeBranch branchID="b12">

In this example there are two base source models, named commom1.xml and common2.xml; the branchset with uncertaintyType = "extendModel" is telling the engine to generate two effective source models by extending common1.xml first with extra1.xml and then with extra2.xml. If we removed the constraint applyToBranches="b01" then two additional effective source models would be generated by applying extra1.xml and extra2.xml to common2.xml.