# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# LICENSE
#
# Copyright (c) 2010-2017, GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli.
#
# The Hazard Modeller's Toolkit is free software: you can redistribute
# it and/or modify it under the terms of the GNU Affero General Public
# License as published by the Free Software Foundation, either version
# 3 of the License, or (at your option) any later version.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>
#
# DISCLAIMER
#
# The software Hazard Modeller's Toolkit (openquake.hmtk) provided herein
# is released as a prototype implementation on behalf of
# scientists and engineers working within the GEM Foundation (Global
# Earthquake Model).
#
# It is distributed for the purpose of open collaboration and in the
# hope that it will be useful to the scientific, engineering, disaster
# risk and software design communities.
#
# The software is NOT distributed as part of GEM's OpenQuake suite
# (https://www.globalquakemodel.org/tools-products) and must be considered as a
# separate entity. The software provided herein is designed and implemented
# by scientific staff. It is not developed to the design standards, nor
# subject to same level of critical review by professional software
# developers, as GEM's OpenQuake software suite.
#
# Feedback and contribution to the software is welcome, and can be
# directed to the hazard scientific staff of the GEM Model Facility
# (hazard@globalquakemodel.org).
#
# The Hazard Modeller's Toolkit (openquake.hmtk) is therefore distributed WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details.
#
# The GEM Foundation, and the authors of the software, assume no
# liability for use of the software.
'''
Module: openquake.hmtk.faults.fault_model implements the set of classes to allow for a
calculation of the magnitude frequency distribution from the geological
slip rate
'''
import numpy as np
from math import fabs
from openquake.hazardlib.scalerel import get_available_scalerel
from openquake.hazardlib.mfd.evenly_discretized import EvenlyDiscretizedMFD
from openquake.hmtk.models import IncrementalMFD
from openquake.hmtk.faults.fault_geometries import (SimpleFaultGeometry,
ComplexFaultGeometry)
from openquake.hmtk.sources.simple_fault_source import mtkSimpleFaultSource
from openquake.hmtk.sources.complex_fault_source import mtkComplexFaultSource
from openquake.hmtk.faults import mfd
MFD_MAP = mfd.get_available_mfds()
SCALE_REL_MAP = get_available_scalerel()
DEFAULT_MSR_SIGMA = [(0., 1.0)]
def _update_slip_rates_with_aseismic(slip_rate, aseismic):
'''
For all the slip rates in the slip rate tuple, multiply by the aseismic
coefficient
:param list slip_rate:
List of tuples (Slip Value, Weight) defining the slip distribution
:param float aseismic:
Fractional proportion of slip release aseismically
:returns:
slip - List of tuples (Slip Value, Weight) for adjusted slip rates
'''
return [(slip_val * (1.0 - aseismic), weight)
for slip_val, weight in slip_rate]
[docs]class RecurrenceBranch(object):
'''
:class:`openquake.hmtk.faults.fault_model.RecurrenceBranch` is an object
to store a set of parameters for recurrence calculations and the
corresponding total weight
:param str branch_id:
Unique branch id
:param float area:
Fault area (km ^ 2)
:param float slip:
Fault slip rate (mm / yr)
:param msr:
Magnitude scaling relation as instance of
:class: openquake.hazardlib.scale_rel.base.BaseASR
:param float rake:
Rake of fault (degrees)
:param float shear_modulus:
Shear modulus of fault (GPa)
:param float disp_length_ratio:
Displacement to length ratio of the fault
:param float weight:
Weight of recurrence model branch
:param recurrence:
Magnitude frquency distribution as instance of
:class: openquake.hmtk.models.IncrementalMFD
:param float max_mag:
Maximum magnitude from the magnitude frequency distribution
:param numpy.ndarray magnitudes:
Magnitudes of MFD
'''
def __init__(self, area, slip, msr, rake, shear_modulus,
disp_length_ratio=None, msr_sigma=0., weight=1.0):
self.branch_id = None
self.area = area
self.slip = slip
self.msr = msr
self.msr_sigma = msr_sigma
self.rake = rake
self.shear_modulus = shear_modulus
self.disp_length_ratio = disp_length_ratio
self.weight = weight
self.recurrence = None
self.max_mag = None
self.magnitudes = None
[docs] def update_weight(self, new_weight):
'''
Updates the weight by multiplying by the new weight
:param float new_weight:
Weight to be multiplied by existing weight
'''
self.weight = self.weight * new_weight
[docs] def get_recurrence(self, config):
'''
Calculates the recurrence model for the given settings as
an instance of the openquake.hmtk.models.IncrementalMFD
:param dict config:
Configuration settings of the magnitude frequency distribution.
'''
model = MFD_MAP[config['Model_Name']]()
model.setUp(config)
model.get_mmax(config, self.msr, self.rake, self.area)
model.mmax = model.mmax + (self.msr_sigma * model.mmax_sigma)
# As the Anderson & Luco arbitrary model requires the input of the
# displacement to length ratio
if 'AndersonLucoAreaMmax' in config['Model_Name']:
if not self.disp_length_ratio:
# If not defined then default to 1.25E-5
self.disp_length_ratio = 1.25E-5
min_mag, bin_width, occur_rates = model.get_mfd(
self.slip,
self.area, self.shear_modulus, self.disp_length_ratio)
else:
min_mag, bin_width, occur_rates = model.get_mfd(self.slip,
self.area,
self.shear_modulus)
self.recurrence = IncrementalMFD(min_mag, bin_width, occur_rates)
self.magnitudes = min_mag + np.cumsum(
bin_width *
np.ones(len(occur_rates), dtype=float)) - bin_width
self.max_mag = np.max(self.magnitudes)
[docs]class mtkActiveFault(object):
'''
Main class to represent fault source
:param int identifier:
Identifier Code
:param str name:
Fault Name
:param geometry:
Instance of :class:`openquake.hmtk.faults.fault_model.SimpleFaultGeometry` or
:class:`openquake.hmtk.faults.fault_model.ComplexFaultGeometry`
:param list slip_rate:
Slip rate (mm/yr) as list of tuples [(Value, Weight)]
:param float aseismic:
Aseismic slip coefficient
:param float rake:
Rake of the fault slip (degrees)
:param neotectonic_fault:
Instance of :class:`openquake.hmtk.faults.faulted_earth.NeotectonicFault`
:param str trt:
Tectonic region type
:param scale_rel:
Scaling relation as list of tuples [(:class:
openquake.hazardlib.scalerel.base.BaseASR, Weight)]
:param float aspect_ratio:
Aspect ratio on fault
:param tuple mfd:
Tuple ([MFD], [Weight], [Scale_Rel]) defining the magnitude
frequency distribution
:param list shear_modulus:
Shear Modulus (GPa) as list of tuples [(Value, Weight)]
:param list disp_length_ratio:
Displacement to length ratio as list of tuples [(Value, Weight)]
:param list mfd_models:
Magnitude frequency distributions as list of instances of :class:
openquake.hmtk.faults.fault_model.RecurrenceBranch
:param list mfd_models:
Magnitude frequency distributions as list of instances of :class:
openquake.hmtk.models.IncrementalMFD
:param float area:
Area of fault (km ^ 2)
:param dict config:
Dictionary of configuration paramters for magnitude freuency
distribution calculation
'''
def __init__(self, identifier, name, geometry, slip_rate, rake, trt,
aseismic=0.0, msr_sigma=None,
neotectonic_fault=None, scale_rel=None, aspect_ratio=None,
shear_modulus=None, disp_length_ratio=None):
'''
'''
self.id = identifier
self.name = name
msr_sigma = msr_sigma or DEFAULT_MSR_SIGMA
cond = (not isinstance(geometry, SimpleFaultGeometry) and
not isinstance(geometry, ComplexFaultGeometry))
if cond:
raise IOError('Geometry must be instance of '
'openquake.hmtk.faults.fault_geometries.BaseFaultGeometry')
self.geometry = geometry
self.aseismic = aseismic
# Assert that the sum of the slip rates is 1.0
if fabs(np.sum([val[1] for val in slip_rate]) - 1.) > 1E-7:
raise ValueError('Slip rate weightings must sum to 1.0')
self.slip = _update_slip_rates_with_aseismic(slip_rate, self.aseismic)
self.rake = rake
self.neotectonic_fault = neotectonic_fault
self.trt = trt
self.rupt_aspect_ratio = aspect_ratio
self.mfd = ([], [], [])
self.shear_modulus = shear_modulus
self.disp_length_ratio = disp_length_ratio
self.mfd_models = []
self.msr = scale_rel
self.msr_sigma = msr_sigma
self.area = self.geometry.get_area()
self.config = None
self.regionalisation = None
self.catalogue = None
[docs] def get_tectonic_regionalisation(self, regionalisation, region_type=None):
'''
Defines the tectonic region and updates the shear modulus,
magnitude scaling relation and displacement to length ratio using
the regional values, if not previously defined for the fault
:param regionalistion:
Instance of the :class:
openquake.hmtk.faults.tectonic_regionalisaion.TectonicRegionalisation
:param str region_type:
Name of the region type - if not in regionalisation an error will
be raised
'''
if region_type:
self.trt = region_type
if not self.trt in regionalisation.key_list:
raise ValueError('Tectonic region classification missing or '
'not defined in regionalisation')
for iloc, key_val in enumerate(regionalisation.key_list):
if self.trt in key_val:
self.regionalisation = regionalisation.regionalisation[iloc]
# Update undefined shear modulus from tectonic regionalisation
if not self.shear_modulus:
self.shear_modulus = self.regionalisation.shear_modulus
# Update undefined scaling relation from tectonic
# regionalisation
if not self.msr:
self.msr = self.regionalisation.scaling_rel
# Update undefined displacement to length ratio from tectonic
# regionalisation
if not self.disp_length_ratio:
self.disp_length_ratio = \
self.regionalisation.disp_length_ratio
break
return
[docs] def select_catalogue(self, selector, distance, distance_metric="rupture",
upper_eq_depth=None, lower_eq_depth=None):
"""
Select earthquakes within a specied distance of the fault
"""
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
# rupture metric is selected
if ('rupture' in distance_metric):
# Use rupture distance
self.catalogue = selector.within_rupture_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth)
else:
# Use Joyner-Boore distance
self.catalogue = selector.within_joyner_boore_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth)
def _generate_branching_index(self):
'''
Generates a branching index (i.e. a list indicating the number of
branches in each branching level. Current branching levels are:
1) Slip
2) MSR
3) Shear Modulus
4) DLR
5) MSR_Sigma
6) Config
:returns:
* branch_index - A 2-D numpy.ndarray where each row is a pointer
to a particular combination of values
* number_branches - Total number of branches (int)
'''
branch_count = np.array([len(self.slip),
len(self.msr),
len(self.shear_modulus),
len(self.disp_length_ratio),
len(self.msr_sigma),
len(self.config)])
n_levels = len(branch_count)
number_branches = np.prod(branch_count)
branch_index = np.zeros([number_branches, n_levels], dtype=int)
cumval = 1
dstep = 1E-9
for iloc in range(0, n_levels):
idx = np.linspace(0.,
float(branch_count[iloc]) - dstep,
number_branches / cumval)
branch_index[:, iloc] = np.reshape(np.tile(idx, [cumval, 1]),
number_branches)
cumval *= branch_count[iloc]
return branch_index.tolist(), number_branches
[docs] def generate_config_set(self, config):
'''
Generates a list of magnitude frequency distributions and renders as
a tuple
:param dict/list config:
Configuration paramters of magnitude frequency distribution
'''
if isinstance(config, dict):
# Configuration list contains only one element
self.config = [(config, 1.0)]
elif isinstance(config, list):
# Multiple configurations with correscponding weights
total_weight = 0.
self.config = []
for params in config:
weight = params['Model_Weight']
total_weight += params['Model_Weight']
self.config.append((params, weight))
if fabs(total_weight - 1.0) > 1E-7:
raise ValueError('MFD config weights do not sum to 1.0 for '
'fault %s' % self.id)
else:
raise ValueError('MFD config must be input as dictionary or list!')
[docs] def generate_recurrence_models(
self, collapse=False, bin_width=0.1,
config=None, rendered_msr=None):
'''
Iterates over the lists of values defining epistemic uncertainty
in the parameters and calculates the corresponding recurrence model
At present epistemic uncertainty is supported for: 1) slip rate,
2) magnitude scaling relation, 3) shear modulus, 4) displacement
to length ratio) and 5) recurrence model.
:param list config:
List of MFD model configurations
:param bool collapse:
Boolean flag indicating whether to collapse the logic tree branches
:param float bin_width:
If collapsing the logic tree branches the reference mfd must be
defined. The minimum and maximum magnitudes are updated from the
model, but the bin width must be specified here
:param list/dict config:
Configuration (or sets of configurations) of the recurrence
calculations
:param rendered_msr:
If collapsing the logic tree branches a resulting magnitude
scaling relation must be defined as instance of
:class: openquake.hazardlib.scalerel.base.BaseASR
'''
if collapse and not rendered_msr:
raise ValueError('Collapsing logic tree branches requires input '
'of a single msr for rendering sources')
# Generate a set of tuples with corresponding weights
if config is not None:
self.generate_config_set(config)
if not isinstance(self.config, list):
raise ValueError('MFD configuration missing or incorrectly '
'formatted')
# Generate the branching index
branch_index, _number_branches = self._generate_branching_index()
mmin = np.inf
mmax = -np.inf
for idx in branch_index:
tuple_list = []
# Get slip
tuple_list.append(self.slip[idx[0]])
# Get msr
tuple_list.append(self.msr[idx[1]])
# Get shear modulus
tuple_list.append(self.shear_modulus[idx[2]])
# Get displacement length ratio
tuple_list.append(self.disp_length_ratio[idx[3]])
# Get msr sigma
tuple_list.append(self.msr_sigma[idx[4]])
# Get config
tuple_list.append(self.config[idx[5]])
# Calculate branch weight as product of tuple weights
branch_weight = np.prod(np.array([val[1] for val in tuple_list]))
# Instantiate recurrence model
model = RecurrenceBranch(self.area,
tuple_list[0][0],
tuple_list[1][0],
self.rake,
tuple_list[2][0],
tuple_list[3][0],
tuple_list[4][0],
weight=branch_weight)
model.get_recurrence(tuple_list[5][0])
self.mfd_models.append(model)
# Update the total minimum and maximum magnitudes for the fault
if model.recurrence.min_mag < mmin:
mmin = model.recurrence.min_mag
if np.max(model.magnitudes) > mmax:
mmax = np.max(model.magnitudes)
if collapse:
self.mfd = ([self.collapse_branches(mmin, bin_width, mmax)],
[1.0],
[rendered_msr])
else:
mfd_mods = []
mfd_wgts = []
mfd_msr = []
for model in self.mfd_models:
mfd_mods.append(IncrementalMFD(model.recurrence.min_mag,
model.recurrence.bin_width,
model.recurrence.occur_rates))
mfd_wgts.append(model.weight)
mfd_msr.append(model.msr)
self.mfd = (mfd_mods, mfd_wgts, mfd_msr)
[docs] def collapse_branches(self, mmin, bin_width, mmax):
'''
Collapse the logic tree branches into a single IncrementalMFD
:param float mmin:
Minimum magnitude of reference mfd
:param float bin_width:
Bin width of reference mfd
:param float mmax:
Maximum magnitude of reference mfd
:returns:
:class: openquake.hmtk.models.IncrementalMFD
'''
master_mags = np.arange(mmin, mmax + (bin_width / 2.), bin_width)
master_rates = np.zeros(len(master_mags), dtype=float)
for model in self.mfd_models:
id0 = np.logical_and(
master_mags >= np.min(model.magnitudes) - 1E-9,
master_mags <= np.max(model.magnitudes) + 1E-9)
# Use interpolation in log10-y values
yvals = np.log10(model.recurrence.occur_rates)
interp_y = np.interp(master_mags[id0],
model.magnitudes,
yvals)
master_rates[id0] = master_rates[id0] + (model.weight *
10. ** interp_y)
return IncrementalMFD(mmin, bin_width, master_rates)
[docs] def generate_fault_source_model(self):
'''
Creates a resulting `openquake.hmtk` fault source set.
:returns:
source_model - list of instances of either the :class:
`openquake.hmtk.sources.simple_fault_source.mtkSimpleFaultSource`
or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model
'''
source_model = []
model_weight = []
for iloc in range(0, self.get_number_mfd_models()):
model_mfd = EvenlyDiscretizedMFD(
self.mfd[0][iloc].min_mag,
self.mfd[0][iloc].bin_width,
self.mfd[0][iloc].occur_rates.tolist())
if isinstance(self.geometry, ComplexFaultGeometry):
# Complex fault class
source = mtkComplexFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_edges = self.geometry.trace
else:
# Simple Fault source
source = mtkSimpleFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.geometry.dip,
self.geometry.upper_depth,
self.geometry.lower_depth,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_trace = self.geometry.trace
source_model.append(source)
model_weight.append(self.mfd[1][iloc])
return source_model, model_weight
[docs] def get_number_mfd_models(self):
'''
Returns the number of mfd models for a given fault model
'''
return len(self.mfd[0])