# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2018 GEM Foundation
#
# OpenQuake 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.
#
# OpenQuake 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. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from openquake.hazardlib.gsim.base import CoeffsTable
from openquake.hazardlib.gsim.abrahamson_2015 import (
AbrahamsonEtAl2015SInter, AbrahamsonEtAl2015SInterLow,
AbrahamsonEtAl2015SInterHigh, AbrahamsonEtAl2015SSlab,
AbrahamsonEtAl2015SSlabLow, AbrahamsonEtAl2015SSlabHigh)
# Total epistemic uncertainty factors from Abrahamson et al. (2018)
BCHYDRO_SIGMA_MU = CoeffsTable(sa_damping=5, table="""
imt SIGMA_MU_SINTER SIGMA_MU_SSLAB
pga 0.3 0.50
0.010 0.3 0.50
0.020 0.3 0.50
0.030 0.3 0.50
0.050 0.3 0.50
0.075 0.3 0.50
0.100 0.3 0.50
0.150 0.3 0.50
0.200 0.3 0.50
0.250 0.3 0.46
0.300 0.3 0.42
0.400 0.3 0.38
0.500 0.3 0.34
0.600 0.3 0.30
0.750 0.3 0.30
1.000 0.3 0.30
1.500 0.3 0.30
2.000 0.3 0.30
2.500 0.3 0.30
3.000 0.3 0.30
4.000 0.3 0.30
5.000 0.3 0.30
6.000 0.3 0.30
7.500 0.3 0.30
10.00 0.3 0.30
""")
[docs]def get_stress_factor(imt, slab=False):
"""
Returns the stress adjustment factor for the BC Hydro GMPE according to
Abrahamson et al. (2018)
"""
if slab:
sigma_mu = BCHYDRO_SIGMA_MU[imt]["SIGMA_MU_SSLAB"]
else:
sigma_mu = BCHYDRO_SIGMA_MU[imt]["SIGMA_MU_SINTER"]
return sigma_mu / 1.65
[docs]class BCHydroSERASInter(AbrahamsonEtAl2015SInter):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events with
theta6 calibrated to Mediterranean data.
Introduces two configurable parameters:
:param float theta6_adjustment:
The amount to increase or decrease the theta6 - should be +0.0015 (for
slower attenuation) and -0.0015 (for faster attenuation)
:param float sigma_mu_epsilon:
The number of standard deviations above or below the mean to apply the
statistical uncertainty sigma_mu term.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=False)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\
((self.theta6_adj + C['theta6']) * dists.rrup)
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00721467 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00719296 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00712619 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00701600 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00687258 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00671633 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00657867 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00648602 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00643709 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00639138 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00629147 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00609857 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00581454 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00548905 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00520499 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00505022 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00507967 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00529221 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00564790 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00607621 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00647922 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00676355 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00686566 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")
[docs]class BCHydroSERASInterLow(AbrahamsonEtAl2015SInterLow):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the low magnitude
scaling branch.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=False)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\
((self.theta6_adj + C['theta6']) * dists.rrup)
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00721467 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00719296 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00712619 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00701600 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00687258 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00671633 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00657867 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00648602 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00643709 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00639138 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00629147 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00609857 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00581454 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00548905 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00520499 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00505022 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00507967 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00529221 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00564790 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00607621 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00647922 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00676355 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00686566 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")
[docs]class BCHydroSERASInterHigh(AbrahamsonEtAl2015SInterHigh):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the high
magnitude scaling branch.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=False)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\
((self.theta6_adj + C['theta6']) * dists.rrup)
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00721467 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00719296 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00712619 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00701600 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00687258 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00671633 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00657867 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00648602 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00643709 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00639138 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00629147 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00609857 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00581454 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00548905 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00520499 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00505022 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00507967 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00529221 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00564790 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00607621 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00647922 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00676355 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00686566 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")
[docs]class BCHydroSERASSlab(AbrahamsonEtAl2015SSlab):
"""
SERA Adjustment of the BC Hydro GMPE for subduction in-slab events with
theta6 calibrated to Mediterranean data.
Introduces two configurable parameters:
a6_adjustment - the amount to increase or decrease the theta6 (should be
+0.0015 (for slower attenuation) and -0.0015 (for faster attenuation)
sigma_mu_epsilon - number of standard deviations above or below the mean
to apply the statistical uncertainty sigma_mu term.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=True)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00278801 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00275821 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00268517 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00261360 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00259240 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00264688 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00277703 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00296427 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00318216 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00340820 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00363798 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00388267 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00415403 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00445479 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00478084 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00513159 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00550694 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00590809 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00634283 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00680074 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00722208 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00752097 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00762908 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")
[docs]class BCHydroSERASSlabLow(AbrahamsonEtAl2015SSlabLow):
"""
SERA Adjustment of the BC Hydro GMPE for subduction in-slab events
with theta6 calibrated to Mediterranean data, for the low magnitude
scaling branch.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=True)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00278801 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00275821 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00268517 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00261360 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00259240 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00264688 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00277703 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00296427 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00318216 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00340820 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00363798 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00388267 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00415403 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00445479 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00478084 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00513159 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00550694 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00590809 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00634283 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00680074 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00722208 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00752097 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00762908 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")
[docs]class BCHydroSERASSlabHigh(AbrahamsonEtAl2015SSlabHigh):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the high magnitude
scaling branch.
"""
experimental = True
def __init__(self, theta6_adjustment=0.0, sigma_mu_epsilon=0.0):
super().__init__(theta6_adjustment=theta6_adjustment,
sigma_mu_epsilon=sigma_mu_epsilon)
self.theta6_adj = theta6_adjustment
self.sigma_mu_epsilon = sigma_mu_epsilon
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_stress_factor(imt, slab=True)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt vlin b theta1 theta2 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma sigma_ss
pga 865.1000 -1.1860 4.2203 -1.3500 -0.00278801 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0200 865.1000 -1.1860 4.2203 -1.3500 -0.00275821 1.0988 -1.4200 3.1200 0.0130 0.9800 -0.0135 -0.4000 0.9969 -1.0000 0.6000 0.4300 0.7400 0.6000
0.0500 1053.5000 -1.3460 4.5371 -1.4000 -0.00268517 1.2536 -1.6500 3.3700 0.0130 1.2880 -0.0138 -0.4000 1.1030 -1.1800 0.6000 0.4300 0.7400 0.6000
0.0750 1085.7000 -1.4710 5.0733 -1.4500 -0.00261360 1.4175 -1.8000 3.3700 0.0130 1.4830 -0.0142 -0.4000 1.2732 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1000 1032.5000 -1.6240 5.2892 -1.4500 -0.00259240 1.3997 -1.8000 3.3300 0.0130 1.6130 -0.0145 -0.4000 1.3042 -1.3600 0.6000 0.4300 0.7400 0.6000
0.1500 877.6000 -1.9310 5.4563 -1.4500 -0.00264688 1.3582 -1.6900 3.2500 0.0130 1.8820 -0.0153 -0.4000 1.2600 -1.3000 0.6000 0.4300 0.7400 0.6000
0.2000 748.2000 -2.1880 5.2684 -1.4000 -0.00277703 1.1648 -1.4900 3.0300 0.0129 2.0760 -0.0162 -0.3500 1.2230 -1.2500 0.6000 0.4300 0.7400 0.6000
0.2500 654.3000 -2.3810 5.0594 -1.3500 -0.00296427 0.9940 -1.3000 2.8000 0.0129 2.2480 -0.0172 -0.3100 1.1600 -1.1700 0.6000 0.4300 0.7400 0.6000
0.3000 587.1000 -2.5180 4.7945 -1.2800 -0.00318216 0.8821 -1.1800 2.5900 0.0128 2.3480 -0.0183 -0.2800 1.0500 -1.0600 0.6000 0.4300 0.7400 0.6000
0.4000 503.0000 -2.6570 4.4644 -1.1800 -0.00340820 0.7046 -0.9800 2.2000 0.0127 2.4270 -0.0206 -0.2300 0.8000 -0.7800 0.6000 0.4300 0.7400 0.6000
0.5000 456.6000 -2.6690 4.0181 -1.0800 -0.00363798 0.5799 -0.8200 1.9200 0.0125 2.3990 -0.0231 -0.1900 0.6620 -0.6200 0.6000 0.4300 0.7400 0.6000
0.6000 430.3000 -2.5990 3.6055 -0.9900 -0.00388267 0.5021 -0.7000 1.7000 0.0124 2.2730 -0.0256 -0.1600 0.5800 -0.5000 0.6000 0.4300 0.7400 0.6000
0.7500 410.5000 -2.4010 3.2174 -0.9100 -0.00415403 0.3687 -0.5400 1.4200 0.0120 1.9930 -0.0296 -0.1200 0.4800 -0.3400 0.6000 0.4300 0.7400 0.6000
1.0000 400.0000 -1.9550 2.7981 -0.8500 -0.00445479 0.1746 -0.3400 1.1000 0.0114 1.4700 -0.0363 -0.0700 0.3300 -0.1400 0.6000 0.4300 0.7400 0.6000
1.5000 400.0000 -1.0250 2.0123 -0.7700 -0.00478084 -0.0820 -0.0500 0.7000 0.0100 0.4080 -0.0493 0.0000 0.3100 0.0000 0.6000 0.4300 0.7400 0.6000
2.0000 400.0000 -0.2990 1.4128 -0.7100 -0.00513159 -0.2821 0.1200 0.7000 0.0085 -0.4010 -0.0610 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
2.5000 400.0000 0.0000 0.9976 -0.6700 -0.00550694 -0.4108 0.2500 0.7000 0.0069 -0.7230 -0.0711 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
3.0000 400.0000 0.0000 0.6443 -0.6400 -0.00590809 -0.4466 0.3000 0.7000 0.0054 -0.6730 -0.0798 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
4.0000 400.0000 0.0000 0.0657 -0.5800 -0.00634283 -0.4344 0.3000 0.7000 0.0027 -0.6270 -0.0935 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
5.0000 400.0000 0.0000 -0.4624 -0.5400 -0.00680074 -0.4368 0.3000 0.7000 0.0005 -0.5960 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
6.0000 400.0000 0.0000 -0.9809 -0.5000 -0.00722208 -0.4586 0.3000 0.7000 -0.0013 -0.5660 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
7.5000 400.0000 0.0000 -1.6017 -0.4600 -0.00752097 -0.4433 0.3000 0.7000 -0.0033 -0.5280 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
10.0000 400.0000 0.0000 -2.2937 -0.4000 -0.00762908 -0.4828 0.3000 0.7000 -0.0060 -0.5040 -0.0980 0.0000 0.3000 0.0000 0.6000 0.4300 0.7400 0.6000
""")