# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2016 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/>.
"""
Module exports :class:`MontalvaEtAl2015SInter`
:class:`MontalvaEtAl2015SSlab`
"""
from __future__ import division
import numpy as np
from openquake.hazardlib.gsim.base import CoeffsTable
from openquake.hazardlib.imt import PGA
from openquake.hazardlib.gsim.abrahamson_2015 import (AbrahamsonEtAl2015SInter,
AbrahamsonEtAl2015SSlab)
[docs]class MontalvaEtAl2015SInter(AbrahamsonEtAl2015SInter):
"""
Adaptation of the Abrahamson et al. (2015) BC Hydro subduction interface
GMPE, calibrated to Chilean strong motion data
"""
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extract dictionaries of coefficients specific to required
# intensity measure type and for PGA
C = self.COEFFS[imt]
C_PGA = self.COEFFS[PGA()]
dc1_pga = C_PGA["DC1"]
# compute median pga on rock (vs30=1000), needed for site response
# term calculation
pga1000 = np.exp(
self._compute_pga_rock(C_PGA, dc1_pga, sites, rup, dists))
mean = (self._compute_magnitude_term(C, C["DC1"], rup.mag) +
self._compute_distance_term(C, rup.mag, dists) +
self._compute_focal_depth_term(C, rup) +
self._compute_forearc_backarc_term(C, sites, dists) +
self._compute_site_response_term(C, sites, pga1000))
stddevs = self._get_stddevs(C, stddev_types, len(sites.vs30))
return mean, stddevs
def _compute_magnitude_term(self, C, dc1, mag):
"""
Computes the magnitude scaling term given by equation (2)
"""
base = C['theta1'] + (C['theta4'] * dc1)
dmag = self.CONSTS["C1"] + dc1
if mag > dmag:
f_mag = (C['theta5'] * (mag - dmag)) +\
C['theta13'] * ((10. - mag) ** 2.)
else:
f_mag = (C['theta4'] * (mag - dmag)) +\
C['theta13'] * ((10. - mag) ** 2.)
return base + f_mag
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + C['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) + (C['theta6'] * dists.rrup)
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt DC1 vlin b theta1 theta2 theta3 theta4 theta5 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma phi_s2s
pga 0.200000000 865.1 -1.186 4.421043841 -1.235838621 0.260594468 -1.187387838 -0.626477898 -0.000269572 1.0988 -1.42 2.949670501 -0.003104918 0.957459320 -0.157989551 -0.292014223 0.9969 -1.00 0.491926896 0.487829138 0.692798195 0.349471043
0.020 0.200000000 865.1 -1.186 3.874508669 -1.129927615 0.303210981 -1.016431740 -0.856042843 -0.001156550 1.0988 -1.42 2.651058217 -0.001679006 0.981962173 -0.109534873 -0.223302533 0.9969 -1.00 0.504685072 0.499148597 0.709828390 0.354392334
0.050 0.200000000 1053.5 -1.346 8.521361235 -2.153485903 0.216539087 -0.892069941 -0.441095957 0.004014115 1.2536 -1.65 3.914113094 -0.002306697 1.218408927 -0.152272689 -0.423692594 1.1030 -1.18 0.516824000 0.530025557 0.740293278 0.376100230
0.075 0.200000000 1085.7 -1.471 9.507016024 -2.303567593 0.168972703 -0.756637072 -0.160138054 0.004278902 1.4175 -1.80 3.368267955 -0.002944423 1.466426198 -0.162075167 -0.305179119 1.2732 -1.36 0.534946941 0.575119919 0.785449648 0.415034755
0.100 0.200000000 1032.5 -1.624 9.174229254 -2.131225355 0.092702626 -0.771497698 0.297092771 0.002695241 1.3997 -1.80 3.477428636 -0.002798884 1.599181772 -0.210800665 -0.335654541 1.3042 -1.36 0.575331833 0.544835290 0.792371259 0.466376358
0.150 0.200000000 877.6 -1.931 7.207831450 -1.664408639 0.223043970 -1.061288150 -0.310593429 0.000966240 1.3582 -1.69 1.624196362 -0.003116267 1.855741636 -0.165407775 -0.014220063 1.2600 -1.30 0.552832400 0.546997931 0.777708427 0.426323266
0.200 0.200000000 748.2 -2.188 4.561677306 -1.041788833 0.226168552 -0.807295794 -0.363817426 -0.002510223 1.1648 -1.49 2.627179088 -0.003119800 1.998898516 -0.132808155 -0.240953984 1.2230 -1.25 0.565973072 0.466786338 0.733631382 0.405296582
0.250 0.200000000 654.3 -2.381 4.810358393 -1.110859712 0.227009222 -0.768610256 -0.419759393 -0.001236571 0.9940 -1.30 3.239867866 -0.000243437 2.207236399 -0.131514239 -0.398923534 1.1600 -1.17 0.539262168 0.503796884 0.737980343 0.375292128
0.300 0.200000000 587.1 -2.518 3.006548966 -0.673431383 0.281246059 -0.954544501 -0.728037681 -0.003404116 0.8821 -1.18 3.728941071 0.002175644 2.355466769 -0.122198212 -0.548990755 1.0500 -1.06 0.575397737 0.454036959 0.732961197 0.415387646
0.400 0.143682921 503.0 -2.657 1.453570870 -0.297213443 0.306654104 -1.064440434 -1.006169987 -0.004863754 0.7046 -0.98 3.773638124 0.000146167 2.364457383 -0.130406377 -0.582630195 0.8000 -0.78 0.558531802 0.436491292 0.708859945 0.372736555
0.500 0.100000000 456.6 -2.669 3.307586194 -0.815724589 0.341411999 -1.407863235 -0.896819300 -0.000615682 0.5799 -0.82 3.335502796 -0.000200400 2.377150769 -0.155356690 -0.512422157 0.6620 -0.62 0.533055290 0.476332422 0.714870980 0.331627116
0.600 0.073696559 430.3 -2.599 5.327549932 -1.343581321 0.311768348 -1.180697661 -0.749471035 0.002513322 0.5021 -0.70 4.343207653 0.000605325 2.318952991 -0.149976235 -0.730883194 0.5800 -0.50 0.527077142 0.465105269 0.702946104 0.331169660
0.750 0.041503750 410.5 -2.401 5.589219343 -1.441383553 0.301580764 -1.382196596 -0.704555670 0.003818032 0.3687 -0.54 5.158520076 0.006268798 2.083206269 -0.192499135 -0.938076275 0.4800 -0.34 0.544745579 0.462604736 0.714668376 0.341107592
1.000 0.000000000 400.0 -1.955 6.833915630 -1.839918841 0.139211855 -0.615908920 0.018849747 0.006807460 0.1746 -0.34 7.583151773 0.008447436 1.627620072 -0.204966366 -1.443206849 0.3300 -0.14 0.560830181 0.480690122 0.738643003 0.366107585
1.500 -0.058496250 400.0 -1.025 6.698635097 -1.958789333 0.148294995 -0.951008651 -0.092476579 0.008414586 -0.0820 -0.05 7.640205186 0.006957710 0.829497462 -0.254484399 -1.457578682 0.3100 0.00 0.534843737 0.430881175 0.686816140 0.349243635
2.000 -0.100000000 400.0 -0.299 5.116920971 -1.696151120 0.208255744 -0.965679548 -0.372756399 0.007452801 -0.2821 0.12 8.474441461 0.006094473 0.045703748 -0.233394032 -1.609662697 0.3000 0.00 0.523010126 0.373031102 0.642410923 0.306715602
2.500 -0.155033971 400.0 0.000 2.952223207 -1.247457793 0.330951170 -1.364354124 -1.034194183 0.004668068 -0.4108 0.25 6.539745117 0.002886707 -0.277064931 -0.216626666 -1.193108681 0.3000 0.00 0.531096964 0.352462534 0.637411816 0.322722003
3.000 -0.200000000 400.0 0.000 2.151641944 -1.081513741 0.348370635 -1.499087128 -1.250128425 0.003296688 -0.4466 0.30 5.935473694 0.002722442 -0.256283303 -0.229623442 -1.077893684 0.3000 0.00 0.527722253 0.354614799 0.635800622 0.347317253
4.000 -0.200000000 400.0 0.000 1.868673247 -1.053014667 0.328124701 -1.752940627 -1.430457238 0.002985483 -0.4344 0.30 6.055671528 0.002430183 -0.362763376 -0.286285202 -1.092252871 0.3000 0.00 0.529919168 0.357819355 0.639413025 0.335891892
5.000 -0.200000000 400.0 0.000 1.534511025 -1.096577020 0.338646912 -1.426486307 -1.363581787 0.002951891 -0.4368 0.30 5.461045784 0.003143436 -0.391044896 -0.238484997 -0.966497823 0.3000 0.00 0.542113572 0.368657975 0.655588154 0.356525842
6.000 -0.200000000 400.0 0.000 1.937770195 -1.224379809 0.262085586 -1.023980198 -1.138124238 0.002748822 -0.4586 0.30 5.249951895 0.004509412 -0.431170579 -0.238032714 -0.939031684 0.3000 0.00 0.548589140 0.373138146 0.663462222 0.356247776
7.500 -0.200000000 400.0 0.000 2.535966020 -1.385887157 0.194894324 -0.905332513 -0.987484435 0.003310315 -0.4433 0.30 5.894671637 0.004853253 -0.312098228 -0.271717995 -1.087338293 0.3000 0.00 0.592245922 0.395033858 0.711903773 0.431311836
10.00 -0.200000000 400.0 0.000 2.882913552 -1.578520114 0.161250323 -1.042710824 -0.492859538 0.004355431 -0.4828 0.30 5.793722700 0.004376783 -0.261759431 -0.302832003 -1.079092370 0.3000 0.00 0.593304693 0.379251210 0.704160450 0.438551963
""")
CONSTS = {
# Period-Independent Coefficients (Table 2)
'n': 1.18,
'c': 1.88,
'c4': 10.0,
'C1': 7.8,
'theta9': 0.4
}
[docs]class MontalvaEtAl2015SSlab(AbrahamsonEtAl2015SSlab):
"""
Adaptation of the Abrahamson et al. (2015) BC Hydro subduction in-slab
GMPE, calibrated to Chilean strong motion data
"""
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extract dictionaries of coefficients specific to required
# intensity measure type and for PGA
C = self.COEFFS[imt]
# For inslab GMPEs the correction term is fixed at -0.3
dc1 = -0.3
C_PGA = self.COEFFS[PGA()]
# compute median pga on rock (vs30=1000), needed for site response
# term calculation
pga1000 = np.exp(
self._compute_pga_rock(C_PGA, dc1, sites, rup, dists))
mean = (self._compute_magnitude_term(C, dc1, rup.mag) +
self._compute_distance_term(C, rup.mag, dists) +
self._compute_focal_depth_term(C, rup) +
self._compute_forearc_backarc_term(C, sites, dists) +
self._compute_site_response_term(C, sites, pga1000))
stddevs = self._get_stddevs(C, stddev_types, len(sites.vs30))
return mean, stddevs
def _compute_magnitude_term(self, C, dc1, mag):
"""
Computes the magnitude scaling term given by equation (2)
corrected by a local adjustment factor
"""
base = C['theta1'] + (C['theta4'] * dc1)
dmag = self.CONSTS["C1"] + dc1
if mag > dmag:
f_mag = (C['theta5'] * (mag - dmag)) +\
C['theta13'] * ((10. - mag) ** 2.)
else:
f_mag = (C['theta4'] * (mag - dmag)) +\
C['theta13'] * ((10. - mag) ** 2.)
return base + f_mag
def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1b)
"""
return ((C['theta2'] + C['theta14'] + C['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
(C['theta6'] * dists.rhypo)) + C["theta10"]
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt DC1 vlin b theta1 theta2 theta3 theta4 theta5 theta6 theta7 theta8 theta10 theta11 theta12 theta13 theta14 theta15 theta16 phi tau sigma phi_s2s
pga -0.300000000 865.1 -1.186 4.421043841 -1.235838621 0.260594468 -1.187387838 -0.626477898 -0.000269572 1.0988 -1.42 2.949670501 -0.003104918 0.957459320 -0.157989551 -0.292014223 0.9969 -1.00 0.491926896 0.487829138 0.692798195 0.349471043
0.020 -0.300000000 865.1 -1.186 3.874508669 -1.129927615 0.303210981 -1.016431740 -0.856042843 -0.001156550 1.0988 -1.42 2.651058217 -0.001679006 0.981962173 -0.109534873 -0.223302533 0.9969 -1.00 0.504685072 0.499148597 0.709828390 0.354392334
0.050 -0.300000000 1053.5 -1.346 8.521361235 -2.153485903 0.216539087 -0.892069941 -0.441095957 0.004014115 1.2536 -1.65 3.914113094 -0.002306697 1.218408927 -0.152272689 -0.423692594 1.1030 -1.18 0.516824000 0.530025557 0.740293278 0.376100230
0.075 -0.300000000 1085.7 -1.471 9.507016024 -2.303567593 0.168972703 -0.756637072 -0.160138054 0.004278902 1.4175 -1.80 3.368267955 -0.002944423 1.466426198 -0.162075167 -0.305179119 1.2732 -1.36 0.534946941 0.575119919 0.785449648 0.415034755
0.100 -0.300000000 1032.5 -1.624 9.174229254 -2.131225355 0.092702626 -0.771497698 0.297092771 0.002695241 1.3997 -1.80 3.477428636 -0.002798884 1.599181772 -0.210800665 -0.335654541 1.3042 -1.36 0.575331833 0.544835290 0.792371259 0.466376358
0.150 -0.300000000 877.6 -1.931 7.207831450 -1.664408639 0.223043970 -1.061288150 -0.310593429 0.000966240 1.3582 -1.69 1.624196362 -0.003116267 1.855741636 -0.165407775 -0.014220063 1.2600 -1.30 0.552832400 0.546997931 0.777708427 0.426323266
0.200 -0.300000000 748.2 -2.188 4.561677306 -1.041788833 0.226168552 -0.807295794 -0.363817426 -0.002510223 1.1648 -1.49 2.627179088 -0.003119800 1.998898516 -0.132808155 -0.240953984 1.2230 -1.25 0.565973072 0.466786338 0.733631382 0.405296582
0.250 -0.300000000 654.3 -2.381 4.810358393 -1.110859712 0.227009222 -0.768610256 -0.419759393 -0.001236571 0.9940 -1.30 3.239867866 -0.000243437 2.207236399 -0.131514239 -0.398923534 1.1600 -1.17 0.539262168 0.503796884 0.737980343 0.375292128
0.300 -0.300000000 587.1 -2.518 3.006548966 -0.673431383 0.281246059 -0.954544501 -0.728037681 -0.003404116 0.8821 -1.18 3.728941071 0.002175644 2.355466769 -0.122198212 -0.548990755 1.0500 -1.06 0.575397737 0.454036959 0.732961197 0.415387646
0.400 -0.300000000 503.0 -2.657 1.453570870 -0.297213443 0.306654104 -1.064440434 -1.006169987 -0.004863754 0.7046 -0.98 3.773638124 0.000146167 2.364457383 -0.130406377 -0.582630195 0.8000 -0.78 0.558531802 0.436491292 0.708859945 0.372736555
0.500 -0.300000000 456.6 -2.669 3.307586194 -0.815724589 0.341411999 -1.407863235 -0.896819300 -0.000615682 0.5799 -0.82 3.335502796 -0.000200400 2.377150769 -0.155356690 -0.512422157 0.6620 -0.62 0.533055290 0.476332422 0.714870980 0.331627116
0.600 -0.300000000 430.3 -2.599 5.327549932 -1.343581321 0.311768348 -1.180697661 -0.749471035 0.002513322 0.5021 -0.70 4.343207653 0.000605325 2.318952991 -0.149976235 -0.730883194 0.5800 -0.50 0.527077142 0.465105269 0.702946104 0.331169660
0.750 -0.300000000 410.5 -2.401 5.589219343 -1.441383553 0.301580764 -1.382196596 -0.704555670 0.003818032 0.3687 -0.54 5.158520076 0.006268798 2.083206269 -0.192499135 -0.938076275 0.4800 -0.34 0.544745579 0.462604736 0.714668376 0.341107592
1.000 -0.300000000 400.0 -1.955 6.833915630 -1.839918841 0.139211855 -0.615908920 0.018849747 0.006807460 0.1746 -0.34 7.583151773 0.008447436 1.627620072 -0.204966366 -1.443206849 0.3300 -0.14 0.560830181 0.480690122 0.738643003 0.366107585
1.500 -0.300000000 400.0 -1.025 6.698635097 -1.958789333 0.148294995 -0.951008651 -0.092476579 0.008414586 -0.0820 -0.05 7.640205186 0.006957710 0.829497462 -0.254484399 -1.457578682 0.3100 0.00 0.534843737 0.430881175 0.686816140 0.349243635
2.000 -0.300000000 400.0 -0.299 5.116920971 -1.696151120 0.208255744 -0.965679548 -0.372756399 0.007452801 -0.2821 0.12 8.474441461 0.006094473 0.045703748 -0.233394032 -1.609662697 0.3000 0.00 0.523010126 0.373031102 0.642410923 0.306715602
2.500 -0.300000000 400.0 0.000 2.952223207 -1.247457793 0.330951170 -1.364354124 -1.034194183 0.004668068 -0.4108 0.25 6.539745117 0.002886707 -0.277064931 -0.216626666 -1.193108681 0.3000 0.00 0.531096964 0.352462534 0.637411816 0.322722003
3.000 -0.300000000 400.0 0.000 2.151641944 -1.081513741 0.348370635 -1.499087128 -1.250128425 0.003296688 -0.4466 0.30 5.935473694 0.002722442 -0.256283303 -0.229623442 -1.077893684 0.3000 0.00 0.527722253 0.354614799 0.635800622 0.347317253
4.000 -0.300000000 400.0 0.000 1.868673247 -1.053014667 0.328124701 -1.752940627 -1.430457238 0.002985483 -0.4344 0.30 6.055671528 0.002430183 -0.362763376 -0.286285202 -1.092252871 0.3000 0.00 0.529919168 0.357819355 0.639413025 0.335891892
5.000 -0.300000000 400.0 0.000 1.534511025 -1.096577020 0.338646912 -1.426486307 -1.363581787 0.002951891 -0.4368 0.30 5.461045784 0.003143436 -0.391044896 -0.238484997 -0.966497823 0.3000 0.00 0.542113572 0.368657975 0.655588154 0.356525842
6.000 -0.300000000 400.0 0.000 1.937770195 -1.224379809 0.262085586 -1.023980198 -1.138124238 0.002748822 -0.4586 0.30 5.249951895 0.004509412 -0.431170579 -0.238032714 -0.939031684 0.3000 0.00 0.548589140 0.373138146 0.663462222 0.356247776
7.500 -0.300000000 400.0 0.000 2.535966020 -1.385887157 0.194894324 -0.905332513 -0.987484435 0.003310315 -0.4433 0.30 5.894671637 0.004853253 -0.312098228 -0.271717995 -1.087338293 0.3000 0.00 0.592245922 0.395033858 0.711903773 0.431311836
10.00 -0.300000000 400.0 0.000 2.882913552 -1.578520114 0.161250323 -1.042710824 -0.492859538 0.004355431 -0.4828 0.30 5.793722700 0.004376783 -0.261759431 -0.302832003 -1.079092370 0.3000 0.00 0.593304693 0.379251210 0.704160450 0.438551963
""")