# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2017 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:`ZhaoEtAl2016Asc`,
:class:`ZhaoEtAl2016AscSiteSigma`,
:class:`ZhaoEtAl2016UpperMantle`,
:class:`ZhaoEtAl2016UpperMantleSiteSigma`,
:class:`ZhaoEtAl2016SInter`,
:class:`ZhaoEtAl2016SInterSiteSigma`,
:class:`ZhaoEtAl2016SSlab`,
:class:`ZhaoEtAl2016SSlabSiteSigma`
"""
from __future__ import division
import numpy as np
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, SA
[docs]class ZhaoEtAl2016Asc(GMPE):
"""
Implements the GMPE of Zhao et al (2016a) for shallow crustal and upper
mantle events from Japan. Only the shallow crustal version is implemented
here.
Zhao, J. X., Zhou, S., Zhou, J., Zhao, C., Zhang, H., Zhang, Y., Gao, P.,
Lan, X., Rhoades, D. A., Fukushima, Y., Somerville, P., Irikura, K.,
(2016c), "Ground-Motion Prediction Equations for Shallow Crustal and
Uppe-Mantle Earthquakes in Japan Using Site Class and Simple Geometric
Attenuation Functions", Bulletin of the Seismological Society of America,
106(4), 1518-1534
Main version with standard deviations independent of site term
"""
#: Supported tectonic region type is active shallow crust
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST
#: Supported intensity measure types are spectral acceleration,
#: and peak ground acceleration
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([
PGA,
SA
])
#: Supported intensity measure component is geometric mean
#: of two horizontal components :
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.AVERAGE_HORIZONTAL
#: Supported standard deviation types are inter-event, intra-event
#: and total
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([
const.StdDev.TOTAL,
const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT
])
#: Required site parameters is Vs30 (converted to site class)
REQUIRES_SITES_PARAMETERS = set(('vs30', ))
#: Required rupture parameters are magnitude, top-of-rupture depth and
#: style of faulting (rake)
REQUIRES_RUPTURE_PARAMETERS = set(('mag', 'rake', 'ztor'))
#: Required distance measure is Rrup and Rvolc
REQUIRES_DISTANCES = set(('rrup', 'rvolc'))
[docs] 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.
"""
# extracting dictionary of coefficients specific to required
# intensity measure type.
C = self.COEFFS[imt]
C_SITE = self.SITE_COEFFS[imt]
s_c, idx = self._get_site_classification(sites.vs30)
sa_rock = (self.get_magnitude_scaling_term(C, rup) +
self.get_sof_term(C, rup) +
self.get_depth_term(C, rup) +
self.get_distance_term(C, dists, rup))
sa_soil = self.add_site_amplification(C, C_SITE, sites,
sa_rock, idx, rup)
stddevs = self.get_stddevs(C, sites.vs30.shape, idx, stddev_types)
return sa_soil, stddevs
[docs] def get_magnitude_scaling_term(self, C, rup):
"""
Returns the magnitude scaling term in equations 1 and 2
"""
if rup.mag <= self.CONSTANTS["m_c"]:
return C["ccr"] * rup.mag
else:
return (C["ccr"] * self.CONSTANTS["m_c"]) +\
(C["dcr"] * (rup.mag - self.CONSTANTS["m_c"]))
[docs] def get_sof_term(self, C, rup):
"""
Shallow crustal faults have a style-of-faulting dependence as
normal faulting is found to produce higher ground motion (equation 1)
"""
if rup.rake <= -45.0 and rup.rake >= -135.0:
# Normal faulting
return C["FN_CR"]
else:
# No adjustment for strike-slip or reverse faulting
return 0.0
[docs] def get_depth_term(self, C, rup):
"""
Returns the top-of-rupture depth scaling (equation 1)
"""
return C["bcr"] * rup.ztor
[docs] def get_distance_term(self, C, dists, rup):
"""
Returns the distance scaling term defined in equation 3
"""
x_ij = dists.rrup
gn_exp = np.exp(C["c1"] + 6.5 * C["c2"])
# Geometric attenuation scaling described in equation 6
g_n = C["gcrN"] * np.log(self.CONSTANTS["xcro"] + 30. + gn_exp) *\
np.ones_like(x_ij)
idx = x_ij <= 30.0
if np.any(idx):
g_n[idx] = C["gcrN"] * np.log(self.CONSTANTS["xcro"] +
x_ij[idx] + gn_exp)
# equation 5
c_m = min(rup.mag, self.CONSTANTS["m_c"])
# equation 4
r_ij = self.CONSTANTS["xcro"] + x_ij + np.exp(C["c1"] + C["c2"] * c_m)
return C["gcr"] * np.log(r_ij) + C["gcrL"] * np.log(x_ij + 200.0) +\
g_n + C["ecr"] * x_ij + C["ecrV"] * dists.rvolc + C["gamma_S"]
[docs] def add_site_amplification(self, C, C_SITE, sites, sa_rock, idx, rup):
"""
Applies the site amplification scaling defined in equations from 10
to 15
"""
n_sites = sites.vs30.shape
# Convert from reference rock to hard rock
hard_rock_sa = sa_rock - C["lnSC1AM"]
# Gets the elastic site amplification ratio
ln_a_n_max = self._get_ln_a_n_max(C, n_sites, idx, rup)
# Retrieves coefficients needed to determine smr
sreff, sreffc, f_sr = self._get_smr_coeffs(C, C_SITE, idx, n_sites,
hard_rock_sa)
snc = np.zeros(n_sites)
alpha = self.CONSTANTS["alpha"]
beta = self.CONSTANTS["beta"]
smr = np.zeros(n_sites)
sa_soil = hard_rock_sa + ln_a_n_max
# Get lnSF
ln_sf = self._get_ln_sf(C, C_SITE, idx, n_sites, rup)
lnamax_idx = np.exp(ln_a_n_max) < 1.25
not_lnamax_idx = np.logical_not(lnamax_idx)
for i in range(1, 5):
idx_i = idx[i]
if not np.any(idx_i):
# No sites of the given site class
continue
idx2 = np.logical_and(lnamax_idx, idx_i)
if np.any(idx2):
# Use the approximate method for SRC and SNC
c_a = C_SITE["LnAmax1D{:g}".format(i)] /\
(np.log(beta) - np.log(sreffc[idx2] ** alpha + beta))
c_b = -c_a * np.log(sreffc[idx2] ** alpha + beta)
snc[idx2] = np.exp((c_a * (alpha - 1.) *
np.log(beta) * np.log(10.0 * beta) -
np.log(10.0) * (c_b + ln_sf[idx2])) /
(c_a * (alpha * np.log(10.0 * beta) -
np.log(beta))))
# For the cases when ln_a_n_max >= 1.25
idx2 = np.logical_and(not_lnamax_idx, idx_i)
if np.any(idx2):
snc[idx2] = (np.exp((ln_a_n_max[idx2] *
np.log(sreffc[idx2] ** alpha + beta) -
ln_sf[idx2] * np.log(beta)) /
C_SITE["LnAmax1D{:g}".format(i)]) - beta) **\
(1.0 / alpha)
smr[idx_i] = sreff[idx_i] * (snc[idx_i] / sreffc[idx_i]) *\
f_sr[idx_i]
# For the cases when site class = i and SMR != 0
idx2 = np.logical_and(idx_i, np.fabs(smr) > 0.0)
if np.any(idx2):
sa_soil[idx2] += (-C_SITE["LnAmax1D{:g}".format(i)] *
(np.log(smr[idx2] ** alpha + beta) -
np.log(beta)) /
(np.log(sreffc[idx2] ** alpha + beta) -
np.log(beta)))
return sa_soil
def _get_smr_coeffs(self, C, C_SITE, idx, n_sites, sa_rock):
"""
Returns the SReff and SReffC terms needed for equation 14 and 15
"""
# Get SR
sreff = np.zeros(n_sites)
sreffc = np.zeros(n_sites)
f_sr = np.zeros(n_sites)
for i in range(1, 5):
sreff[idx[i]] += (np.exp(sa_rock[idx[i]]) * self.IMF[i])
sreffc[idx[i]] += (C_SITE["Src1D{:g}".format(i)] * self.IMF[i])
# Get f_SR
f_sr[idx[i]] += C_SITE["fsr{:g}".format(i)]
return sreff, sreffc, f_sr
def _get_ln_a_n_max(self, C, n_sites, idx, rup):
"""
Defines the rock site amplification defined in equations 10a and 10b
"""
ln_a_n_max = C["lnSC1AM"] * np.ones(n_sites)
for i in [2, 3, 4]:
if np.any(idx[i]):
ln_a_n_max[idx[i]] += C["S{:g}".format(i)]
return ln_a_n_max
def _get_ln_sf(self, C, C_SITE, idx, n_sites, rup):
"""
Returns the log SF term required for equation 12
"""
ln_sf = np.zeros(n_sites)
for i in range(1, 5):
ln_sf_i = (C["lnSC1AM"] - C_SITE["LnAmax1D{:g}".format(i)])
if i > 1:
ln_sf_i += C["S{:g}".format(i)]
ln_sf[idx[i]] += ln_sf_i
return ln_sf
def _get_site_classification(self, vs30):
"""
Define the site class categories based on Vs30. Returns a
vector of site class values and a dictionary containing logical
vectors for each of the site classes
"""
site_class = np.ones(vs30.shape, dtype=int)
idx = {}
idx[1] = vs30 > 600.
idx[2] = np.logical_and(vs30 > 300., vs30 <= 600.)
idx[3] = np.logical_and(vs30 > 200., vs30 <= 300.)
idx[4] = vs30 <= 200.
for i in [2, 3, 4]:
site_class[idx[i]] = i
return site_class, idx
[docs] def get_stddevs(self, C, n_sites, idx, stddev_types):
"""
Retuns the standard deviation
"""
stddevs = []
phi = C["sigma"] + np.zeros(n_sites)
tau = C["tau"] + np.zeros(n_sites)
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(phi ** 2. + tau ** 2.))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(tau)
return stddevs
# Coefficients taken from Excel spreadsheet provided by the author
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt c1 c2 ccr cum dcr FN_CR FRV_UM FN_UM FUM bcr gcr gUM gcrN gcrL ecr eum ecrV gamma_S lnSC1AM S2 S3 S4 sigma tau sigma_T sc1_sigma_S sc1_tau_S sc1_sigma_ST sc2_sigma_S sc2_tau_S sc2_sigma_ST sc3_sigma_S sc3_tau_S sc3_sigma_ST sc4_sigma_S sc4_tau_S sc4_sigma_ST
pga -3.224 0.900 1.07312 1.07312 0.20000 0.312820000000000 -0.20236 0.251935430429416 0.0 0.009070000000000 -1.26034 -1.09985 -0.49919 1.265640000000000 -0.00794 -0.010830000000000 -0.00628 -9.087242726439600 0.323 0.28877 0.12210 0.20813 0.556 0.391 0.680 0.4153 0.3581 0.5484 0.4363 0.3620 0.5669 0.4235 0.3486 0.5485 0.4426 0.2778 0.5225
0.005 -3.224 0.900 1.07312 1.07312 0.20000 0.312820000000000 -0.20236 0.251935430429416 0.0 0.009070000000000 -1.26034 -1.09985 -0.49919 1.265640000000000 -0.00794 -0.010830000000000 -0.00628 -9.087242726439600 0.323 0.28877 0.12210 0.20813 0.556 0.391 0.680 0.4153 0.3581 0.5484 0.4363 0.3620 0.5669 0.4235 0.3486 0.5485 0.4426 0.2778 0.5225
0.010 -3.357 0.909 1.07850 1.07850 0.20000 0.315680000000000 -0.21425 0.258372637504816 0.0 0.009070000000000 -1.26949 -1.10720 -0.48684 1.241487880676370 -0.00772 -0.010580000000000 -0.00629 -9.052058022318450 0.205 0.29991 0.10321 0.21925 0.556 0.390 0.679 0.4142 0.3581 0.5475 0.4356 0.3627 0.5668 0.4230 0.3482 0.5479 0.4419 0.2804 0.5233
0.020 -3.552 0.927 1.07254 1.07254 0.20000 0.318540000000000 -0.22125 0.260697166781076 0.0 0.009070000000000 -1.28775 -1.11766 -0.44645 1.198941940195550 -0.00756 -0.010505216376958 -0.00634 -8.857750749461700 0.083 0.29778 0.11109 0.21713 0.555 0.396 0.682 0.4123 0.3581 0.5460 0.4344 0.3646 0.5671 0.4226 0.3483 0.5477 0.4418 0.2823 0.5243
0.030 -3.640 0.937 1.05736 1.05736 0.20000 0.320220000000000 -0.22306 0.260150407731831 0.0 0.009070000000000 -1.29619 -1.11392 -0.42175 1.186537103020870 -0.00788 -0.011005243836384 -0.00647 -8.627942448779750 0.041 0.22535 0.08972 0.15948 0.553 0.408 0.687 0.4089 0.3625 0.5465 0.4307 0.3580 0.5601 0.4207 0.3544 0.5501 0.4415 0.2857 0.5259
0.040 -3.758 0.944 1.03568 1.03568 0.20000 0.321410000000000 -0.22334 0.258909018258196 0.0 0.009070000000000 -1.25147 -1.07971 -0.37622 1.142133802990220 -0.00863 -0.011487892527436 -0.00681 -8.398219253745690 0.034 0.15865 0.04037 0.07029 0.558 0.438 0.710 0.4072 0.3832 0.5591 0.4275 0.3666 0.5632 0.4208 0.3544 0.5501 0.4435 0.2881 0.5289
0.050 -3.826 0.948 1.00578 1.00578 0.20000 0.321850000000000 -0.22297 0.257458255516038 0.0 0.009070000000000 -1.14724 -0.98606 -0.43576 1.141373265957230 -0.00988 -0.012291799210266 -0.00710 -8.153070000000000 0.046 0.08257 -0.05934 -0.03529 0.564 0.460 0.728 0.4059 0.4052 0.5735 0.4294 0.3760 0.5708 0.4168 0.3508 0.5448 0.4446 0.2945 0.5332
0.060 -3.890 0.956 0.98413 0.98413 0.20000 0.324710000000000 -0.22229 0.255956496918906 0.0 0.009070000000000 -1.09126 -0.93901 -0.46789 1.157491249527060 -0.01075 -0.012739417109939 -0.00724 -8.003270000000000 0.069 0.05542 -0.10956 -0.09017 0.577 0.481 0.751 0.4066 0.4341 0.5948 0.4304 0.3911 0.5816 0.4156 0.3453 0.5404 0.4415 0.3082 0.5384
0.070 -3.965 0.967 0.98059 0.98059 0.20000 0.328600000000000 -0.22145 0.254464785542036 0.0 0.009070000000000 -1.04676 -0.90036 -0.50260 1.186791451212470 -0.01130 -0.013080135792042 -0.00734 -8.047830000000000 0.098 0.04177 -0.13262 -0.09583 0.599 0.488 0.772 0.4171 0.4517 0.6148 0.4329 0.4057 0.5933 0.4215 0.3605 0.5546 0.4407 0.3378 0.5552
0.080 -4.055 0.980 0.98634 0.98634 0.20000 0.329844812501483 -0.22053 0.253008191936176 0.0 0.009070000000000 -1.01364 -0.87064 -0.53802 1.223378486073060 -0.01164 -0.013312834482819 -0.00741 -8.196910000000000 0.132 0.06040 -0.13537 -0.07321 0.616 0.481 0.782 0.4255 0.4745 0.6374 0.4361 0.4247 0.6088 0.4220 0.3821 0.5693 0.4351 0.3557 0.5619
0.090 -4.153 0.995 0.99121 0.99121 0.20000 0.336373431249984 -0.21956 0.251596694854290 0.0 0.009070000000000 -0.99232 -0.85080 -0.57301 1.264040913700510 -0.01182 -0.013454810030999 -0.00746 -8.350790000000000 0.169 0.09304 -0.12513 -0.03629 0.629 0.477 0.789 0.4315 0.4884 0.6517 0.4417 0.4445 0.6266 0.4161 0.3958 0.5743 0.4374 0.3784 0.5784
0.100 -4.255 1.009 1.00033 1.00033 0.20000 0.340964949991461 -0.21858 0.250233442665255 0.0 0.009070000000000 -0.98315 -0.84175 -0.60839 1.305257984164180 -0.01184 -0.013478869951054 -0.00749 -8.524300000000000 0.208 0.15210 -0.07090 0.01110 0.641 0.460 0.789 0.4371 0.4932 0.6590 0.4478 0.4485 0.6338 0.4241 0.4229 0.5989 0.4402 0.4182 0.6072
0.120 -4.466 1.040 1.03440 1.03440 0.20000 0.345623419543235 -0.21661 0.247649908306841 0.0 0.009579251858235 -0.97311 -0.82925 -0.67012 1.392835221341830 -0.01174 -0.013487990193394 -0.00751 -9.054780000000000 0.288 0.26161 0.00261 0.10298 0.657 0.437 0.789 0.4515 0.4927 0.6683 0.4476 0.4641 0.6448 0.4332 0.4768 0.6442 0.4594 0.4216 0.6236
0.140 -4.677 1.070 1.08388 1.08388 0.20000 0.345964793275106 -0.21469 0.245243568556044 0.0 0.010548033973543 -0.98324 -0.83484 -0.72524 1.477695244453800 -0.01142 -0.013335243001700 -0.00748 -9.676400000000000 0.370 0.35467 0.11202 0.21434 0.663 0.414 0.782 0.4559 0.4906 0.6697 0.4553 0.4758 0.6586 0.4347 0.4592 0.6323 0.4697 0.3869 0.6085
0.150 -4.781 1.085 1.10602 1.10602 0.20000 0.344999481637875 -0.21374 0.244100676565134 0.0 0.011218017682426 -0.99261 -0.84153 -0.74977 1.518814263594270 -0.01123 -0.013231344388198 -0.00746 -9.965950000000000 0.412 0.39410 0.15670 0.25574 0.666 0.402 0.778 0.4594 0.4815 0.6655 0.4547 0.4817 0.6624 0.4380 0.4519 0.6294 0.4707 0.3867 0.6092
0.160 -4.883 1.100 1.12674 1.12674 0.20000 0.343465417412490 -0.21282 0.242994687815017 0.0 0.011700559401692 -1.00423 -0.85033 -0.77240 1.558838786529320 -0.01101 -0.013114569632387 -0.00743 -10.241600000000000 0.453 0.42523 0.19678 0.29811 0.671 0.384 0.773 0.4647 0.4725 0.6627 0.4607 0.4820 0.6668 0.4419 0.4617 0.6391 0.4744 0.3788 0.6071
0.180 -5.085 1.129 1.16455 1.16455 0.20000 0.339107164829684 -0.21102 0.240884343952180 0.0 0.012332909878059 -1.03299 -0.87328 -0.81285 1.634757732666460 -0.01054 -0.012840476069927 -0.00735 -10.752000000000000 0.535 0.47642 0.27414 0.36360 0.680 0.380 0.779 0.4721 0.4640 0.6619 0.4697 0.4830 0.6737 0.4514 0.4660 0.6488 0.4784 0.3903 0.6174
0.200 -5.233 1.151 1.19837 1.19837 0.20000 0.333545099152249 -0.20929 0.238895952015333 0.0 0.013457084876985 -1.06493 -0.89945 -0.84625 1.706045133394880 -0.01007 -0.012558176324852 -0.00725 -11.224920000000000 0.606 0.51169 0.33785 0.42568 0.692 0.359 0.780 0.4722 0.4708 0.6668 0.4800 0.4932 0.6882 0.4493 0.4452 0.6325 0.4825 0.4010 0.6274
0.250 -5.229 1.151 1.27000 1.27000 0.20000 0.316942763583834 -0.20526 0.234370987754789 0.0 0.016170000000000 -1.15136 -0.97276 -0.90575 1.862468560694100 -0.00890 -0.011816876971470 -0.00696 -12.262750000000000 0.670 0.55177 0.47868 0.55643 0.694 0.340 0.773 0.4780 0.4650 0.6669 0.4882 0.5436 0.7306 0.4626 0.4192 0.6243 0.4843 0.3942 0.6244
0.300 -5.226 1.151 1.32852 1.32852 0.20000 0.299124734648411 -0.20159 0.230357423625167 0.0 0.018310000000000 -1.23723 -1.04809 -0.93933 1.991624592628190 -0.00784 -0.011099993928613 -0.00661 -13.141810000000000 0.710 0.55325 0.57391 0.65844 0.688 0.344 0.770 0.4906 0.4595 0.6722 0.4991 0.5246 0.7241 0.4833 0.4356 0.6507 0.4930 0.3725 0.6179
0.350 -5.223 1.151 1.37801 1.37801 0.20000 0.281669557686284 -0.19822 0.226742010930494 0.0 0.019800000000000 -1.31736 -1.12034 -0.95403 2.098225191698050 -0.00691 -0.010436248715154 -0.00624 -13.902540000000000 0.719 0.53555 0.63824 0.73536 0.675 0.353 0.762 0.4918 0.4432 0.6621 0.5043 0.4872 0.7012 0.4783 0.4689 0.6698 0.4914 0.3421 0.5988
0.400 -5.221 1.151 1.42087 1.42087 0.20000 0.265209162746323 -0.19511 0.223445783566271 0.0 0.020780000000000 -1.38989 -1.18735 -0.95484 2.186300573524260 -0.00610 -0.009830759490494 -0.00586 -14.573550000000000 0.706 0.50855 0.67945 0.79334 0.667 0.363 0.759 0.4951 0.4296 0.6555 0.5074 0.4722 0.6931 0.4720 0.4821 0.6747 0.4783 0.3466 0.5907
0.450 -5.218 1.151 1.45868 1.45868 0.20000 0.249966529741596 -0.19221 0.220411614509675 0.0 0.021380000000000 -1.45461 -1.24859 -0.94513 2.259336928898360 -0.00541 -0.009285306710044 -0.00548 -15.173840000000000 0.693 0.47913 0.70517 0.83853 0.665 0.359 0.756 0.4927 0.4310 0.6546 0.5074 0.4651 0.6883 0.4944 0.4638 0.6779 0.4787 0.3594 0.5986
0.500 -5.216 1.151 1.49250 1.49250 0.19000 0.235980866829892 -0.18950 0.217596832776964 0.0 0.021680000000000 -1.51219 -1.30439 -0.92771 2.319726842585720 -0.00482 -0.008788091918160 -0.00511 -15.715670000000000 0.681 0.45094 0.72065 0.87505 0.664 0.361 0.756 0.4890 0.4323 0.6527 0.5149 0.4493 0.6834 0.4949 0.4327 0.6574 0.4764 0.3998 0.6220
0.600 -5.213 1.151 1.55102 1.55102 0.17800 0.211571038601388 -0.18454 0.212501492886060 0.0 0.021610000000000 -1.60834 -1.40097 -0.87661 2.410718513737190 -0.00387 -0.007921609069809 -0.00439 -16.662190000000000 0.658 0.39849 0.72820 0.92621 0.669 0.362 0.761 0.4792 0.4402 0.6507 0.5177 0.4472 0.6841 0.4848 0.4577 0.6668 0.4854 0.4454 0.6588
0.700 -5.210 1.151 1.60051 1.60051 0.16200 0.191334420072367 -0.18008 0.207971451817460 0.0 0.020940000000000 -1.68374 -1.48074 -0.81164 2.471787807358160 -0.00315 -0.007193263566848 -0.00374 -17.467280000000000 0.637 0.35254 0.71582 0.95627 0.670 0.370 0.765 0.4665 0.4390 0.6406 0.5044 0.4515 0.6770 0.4816 0.4712 0.6737 0.4707 0.4982 0.6854
0.800 -5.208 1.151 1.64337 1.64337 0.14800 0.174557580328734 -0.17602 0.203882937318070 0.0 0.019870000000000 -1.74306 -1.54711 -0.73894 2.511003526994950 -0.00261 -0.006567782184498 -0.00315 -18.164530000000000 0.618 0.31707 0.69660 0.97619 0.674 0.378 0.773 0.4600 0.4491 0.6429 0.4954 0.4504 0.6696 0.4751 0.4622 0.6628 0.4837 0.5183 0.7089
0.900 -5.206 1.151 1.68118 1.68118 0.13600 0.160616866070260 -0.17229 0.200149921480523 0.0 0.018540000000000 -1.78967 -1.60263 -0.66214 2.534178788957430 -0.00220 -0.006025164356083 -0.00261 -18.777980000000000 0.600 0.28896 0.67366 0.98819 0.673 0.386 0.775 0.4541 0.4579 0.6449 0.4834 0.4559 0.6645 0.4554 0.4781 0.6603 0.4729 0.5358 0.7146
1.000 -5.204 1.151 1.71500 1.71500 0.12500 0.149000000000001 -0.16883 0.196710000000000 0.0 0.017040000000000 -1.82603 -1.64914 -0.58347 2.545383253429940 -0.00189 -0.005550701319875 -0.00214 -19.325620000000000 0.583 0.26685 0.64985 0.99485 0.670 0.390 0.776 0.4475 0.4663 0.6463 0.4720 0.4593 0.6586 0.4415 0.4743 0.6480 0.4709 0.5240 0.7045
1.250 -5.200 1.151 1.78663 1.78663 0.10100 0.127608506221474 -0.16113 0.189110834221069 0.0 0.012920000000000 -1.88672 -1.73810 -0.40237 2.547066045238380 -0.00139 -0.004579681056724 -0.00119 -20.467050000000000 0.545 0.22889 0.59394 0.99639 0.660 0.399 0.771 0.4404 0.4644 0.6400 0.4595 0.4606 0.6506 0.4380 0.4495 0.6276 0.4627 0.4932 0.6763
1.500 -5.196 1.151 1.84515 1.84515 0.08300 0.113816631375633 -0.15447 0.182585464558957 0.0 0.008630000000000 -1.91705 -1.79802 -0.24053 2.525806060977580 -0.00116 -0.003841842878586 -0.00054 -21.388220000000000 0.511 0.20778 0.55120 0.98814 0.656 0.397 0.767 0.4284 0.4546 0.6247 0.4499 0.4688 0.6497 0.4219 0.4633 0.6266 0.4564 0.4942 0.6727
2.000 -5.191 1.151 1.93750 1.93750 0.05300 0.099176091595634 -0.14326 0.171710489423896 0.0 0.000420000000000 -1.93178 -1.86912 -0.03590 2.497512890223570 -0.00109 -0.002838451894609 0.00000 -22.811700000000000 0.454 0.18675 0.50247 0.96004 0.631 0.381 0.737 0.4148 0.4190 0.5896 0.4374 0.4530 0.6297 0.4172 0.4718 0.6298 0.4323 0.5195 0.6758
2.500 -5.187 1.151 2.00913 2.00913 0.02967 0.093289958829313 -0.13399 0.162787337116202 0.0 -0.006870000000000 -1.91787 -1.90393 0.08743 2.478890119830560 -0.00125 -0.002226291691253 0.00000 -23.905120000000000 0.406 0.17749 0.48879 0.92903 0.605 0.376 0.713 0.4003 0.4084 0.5718 0.4343 0.4310 0.6119 0.3955 0.4530 0.6014 0.4128 0.5059 0.6529
3.000 -5.183 1.151 2.06765 2.06765 0.01078 0.091055639803074 -0.12604 0.155180191692808 0.0 -0.013150000000000 -1.89632 -1.92241 0.17408 2.464735244149810 -0.00144 -0.001844374770841 0.00000 -24.794480000000000 0.364 0.16807 0.48245 0.89351 0.591 0.363 0.694 0.4033 0.4052 0.5717 0.4328 0.4152 0.5997 0.3995 0.4030 0.5674 0.4170 0.4886 0.6424
3.500 -5.181 1.151 2.12357 2.12357 0.00000 0.090028774676100 -0.11905 0.148526449306405 0.0 -0.018490000000000 -1.87695 -1.93474 0.24501 2.452674190639510 -0.00159 -0.001603938312670 0.00000 -25.588720000000000 0.327 0.15988 0.47612 0.85603 0.578 0.363 0.682 0.4026 0.3861 0.5578 0.4214 0.3953 0.5778 0.4131 0.3803 0.5615 0.4265 0.4928 0.6517
4.000 -5.178 1.151 2.13734 2.13734 0.00000 0.088965758728213 -0.11280 0.142598301048652 0.0 -0.022980000000000 -1.86168 -1.94360 0.31173 2.442705939276230 -0.00171 -0.001487903801684 0.00000 -26.046350000000000 0.294 0.15433 0.46978 0.81676 0.556 0.376 0.671 0.3804 0.3756 0.5346 0.4264 0.3681 0.5633 0.4060 0.3228 0.5187 0.4259 0.4510 0.6203
4.500 -5.176 1.151 2.13734 2.13734 0.00000 0.087216625027152 -0.10713 0.137242613236394 0.0 -0.026720000000000 -1.85415 -1.95343 0.37949 2.433909230947000 -0.00176 -0.001451647632538 0.00000 -26.370080000000000 0.263 0.15116 0.46344 0.77379 0.542 0.377 0.661 0.3736 0.3595 0.5185 0.4290 0.3589 0.5593 0.3921 0.3069 0.4979 0.4256 0.4386 0.6111
5.000 -5.174 1.151 2.13734 2.13734 0.00000 0.084444226520033 -0.10194 0.132351162212196 0.0 -0.029800000000000 -1.85292 -1.96352 0.45204 2.426728340621800 -0.00177 -0.001498616447348 0.00000 -26.677970000000000 0.235 0.15275 0.45711 0.72802 0.538 0.395 0.667 0.3831 0.3594 0.5253 0.4378 0.3298 0.5481 0.3779 0.3076 0.4873 0.4195 0.3774 0.5643
""")
# Coefficients specific to the site amplification
SITE_COEFFS = CoeffsTable(sa_damping=5, table="""\
imt LnAmax1D1 LnAmax1D2 LnAmax1D3 LnAmax1D4 Src1D1 Src1D2 Src1D3 Src1D4 fsr1 fsr2 fsr3 fsr4
pga 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.000000 1.000000 1.000000 1.000000
0.005 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.000000 1.000000 1.000000 1.000000
0.010 0.651810 0.706790 0.646240 0.404280 8.090000 1.882560 1.114440 0.836440 1.000000 1.200000 1.000000 1.000000
0.020 0.653620 0.694650 0.638650 0.387890 6.992000 1.778610 1.124370 0.830000 1.000000 1.300000 1.000000 0.949000
0.030 0.654670 0.687550 0.634210 0.378300 6.350000 1.717810 1.130170 0.826240 1.000000 1.253000 1.000000 0.550000
0.040 0.652850 0.698920 0.606040 0.317370 4.883000 2.052340 1.150800 0.767580 1.000000 1.064000 1.000000 0.477000
0.050 0.672640 0.701370 0.617160 0.309340 5.043000 2.387130 1.239710 0.786320 1.000000 1.120000 1.000000 0.492000
0.060 0.699660 0.724450 0.637970 0.325300 6.271000 2.833990 1.348190 0.837750 1.000000 1.207000 1.000000 0.531000
0.070 0.717130 0.743430 0.654370 0.354120 7.667000 3.294470 1.451810 0.926160 1.000000 1.238000 1.000000 0.613000
0.080 0.716030 0.785980 0.680190 0.392820 9.034000 3.990910 1.583150 1.022280 1.000000 1.360000 1.000000 0.693000
0.090 0.725610 0.797210 0.708890 0.421840 11.251000 4.465760 1.732920 1.118020 1.000000 1.355000 1.000000 0.780000
0.100 0.742000 0.816680 0.718810 0.437360 14.817000 5.045610 1.841340 1.165780 1.000000 1.341000 1.080000 0.816000
0.120 0.762360 0.845230 0.725810 0.472080 14.817000 5.899600 2.030290 1.285510 0.000000 1.195000 1.093000 0.998000
0.140 0.752150 0.782960 0.745250 0.512780 14.817000 5.053530 2.281330 1.398080 0.000000 0.835000 0.948000 0.954000
0.150 0.738190 0.794800 0.761030 0.534320 14.817000 5.204900 2.444130 1.443270 0.000000 0.781000 0.908000 0.942000
0.160 0.719110 0.808610 0.768130 0.550220 14.817000 5.386940 2.580170 1.471770 0.000000 0.738000 0.862000 0.927000
0.180 0.654080 0.843310 0.756900 0.572790 14.817000 5.871650 2.741610 1.546940 0.000000 0.684000 0.745000 0.927000
0.200 0.583950 0.877700 0.717850 0.596740 14.817000 6.573910 2.825870 1.644010 0.000000 0.654000 0.623000 0.949000
0.250 0.583950 0.937670 0.654700 0.611360 14.817000 8.500000 2.718930 1.790130 0.000000 0.683000 0.436000 0.970000
0.300 0.583950 0.950000 0.696190 0.626380 14.817000 10.670300 2.417590 1.823450 0.000000 0.691000 0.400000 0.961000
0.350 0.583950 1.000000 0.779070 0.630120 14.817000 10.670300 2.303750 1.790370 0.000000 0.800000 0.433000 0.948000
0.400 0.583950 1.000000 0.827760 0.647730 14.817000 10.670300 2.236250 1.768440 0.000000 0.876000 0.460000 0.965000
0.450 0.583950 1.000000 0.876450 0.641520 14.817000 10.670300 2.216780 1.675390 0.000000 0.966000 0.496000 0.958000
0.500 0.583950 1.000000 0.925140 0.655820 14.817000 10.670300 2.243380 1.625390 0.000000 1.034000 0.529000 0.984000
0.600 0.583950 1.000000 0.973830 0.686680 14.817000 10.670300 2.805350 1.524530 0.000000 1.206000 0.578000 1.055000
0.700 0.583950 1.000000 1.022520 0.705600 14.817000 10.670300 6.658390 1.397240 0.000000 1.314000 0.578000 1.114000
0.800 0.583950 1.000000 1.071220 0.714290 14.817000 10.670300 30.000000 1.320290 0.000000 1.357000 0.578000 1.175000
0.900 0.583950 1.000000 1.119910 0.703880 14.817000 10.670300 30.000000 1.266370 0.000000 1.357000 0.578000 1.216000
1.000 0.583950 1.000000 1.168600 0.678130 14.817000 10.670300 30.000000 1.226800 0.000000 1.357000 0.000000 1.230000
1.250 0.583950 1.000000 1.217290 0.611190 14.817000 10.670300 30.000000 1.220650 0.000000 0.000000 0.000000 1.192000
1.500 0.583950 1.000000 1.265980 0.547360 14.817000 10.670300 30.000000 1.318050 0.000000 0.000000 0.000000 0.942000
2.000 0.583950 1.000000 1.314670 0.459440 14.817000 10.670300 30.000000 2.124850 0.000000 0.000000 0.000000 0.000000
2.500 0.583950 1.000000 1.363360 0.408460 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
3.000 0.583950 1.000000 1.412050 0.364210 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
3.500 0.583950 1.000000 1.460750 0.329840 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
4.000 0.583950 1.000000 1.509440 0.309120 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
4.500 0.583950 1.000000 1.558130 0.292510 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
5.000 0.583950 1.000000 1.606820 0.547360 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
""")
CONSTANTS = {"m_c": 7.1,
"xcro": 2.0,
"xinto": 10.0, # Used in subduction Interface class
"alpha": 2.0,
"beta": 0.6,
"Imin": 1.0,
"Imax": 12.0,
"m_sc": 6.3} # Used in Subduction Slab class
# Impedence ratio factors for each site class
IMF = {1: (1. + 0.8 * 2.73) / 3.5, # IMF 0.91
2: 3.07 / 3.0,
3: (1. + 0.9 * 1.76) / 2.5,
4: (1. + 0.6 * 2.02) / 3.0}
[docs]class ZhaoEtAl2016AscSiteSigma(ZhaoEtAl2016Asc):
"""
Adaption of the Zhao et al (2016a) GMPE for active shallow crust
events for the case when within-event variability is dependent on site
class
"""
[docs] def get_stddevs(self, C, n_sites, idx, stddev_types):
"""
Returns site class specific standard deviation
"""
stddevs = []
tau = C["tau"] + np.zeros(n_sites)
phi = np.zeros(n_sites)
for i in range(1, 5):
phi[idx[i]] += C["sc{:g}_sigma_S".format(i)]
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(phi ** 2. + tau ** 2.))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(tau)
return stddevs
[docs]class ZhaoEtAl2016UpperMantle(ZhaoEtAl2016Asc):
"""
Adaptation of the Zhao et al. (2016a) GMPE for the upper mantle events
"""
#: Supported tectonic region type is upper mantle
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.UPPER_MANTLE
[docs] def get_sof_term(self, C, rup):
"""
In the case of the upper mantle events separate coefficients
are considered for normal, reverse and strike-slip
"""
if rup.rake <= -45.0 and rup.rake >= -135.0:
# Normal faulting
return C["FN_UM"]
elif rup.rake > 45.0 and rup.rake < 135.0:
# Reverse faulting
return C["FRV_UM"]
else:
# No adjustment for strike-slip faulting
return 0.0
[docs] def get_depth_term(self, C, rup):
"""
No top of rupture depth is considered for upper mantle events
"""
return 0.0
[docs] def get_distance_term(self, C, dists, rup):
"""
Returns the distance attenuation term
"""
x_ij = dists.rrup
gn_exp = np.exp(C["c1"] + 6.5 * C["c2"])
g_n = C["gcrN"] * np.log(self.CONSTANTS["xcro"] + 30. + gn_exp) *\
np.ones_like(x_ij)
idx = x_ij <= 30.0
if np.any(idx):
g_n[idx] = C["gcrN"] * np.log(self.CONSTANTS["xcro"] +
x_ij[idx] + gn_exp)
c_m = min(rup.mag, self.CONSTANTS["m_c"])
r_ij = self.CONSTANTS["xcro"] + x_ij + np.exp(C["c1"] + C["c2"] * c_m)
return C["gUM"] * np.log(r_ij) +\
C["gcrL"] * np.log(x_ij + 200.0) +\
g_n + C["eum"] * x_ij + C["ecrV"] * dists.rvolc + C["gamma_S"]
# For Upper Mantle
SITE_COEFFS = CoeffsTable(sa_damping=5, table="""\
imt LnAmax1D1 LnAmax1D2 LnAmax1D3 LnAmax1D4 Src1D1 Src1D2 Src1D3 Src1D4 fsr1 fsr2 fsr3 fsr4
pga 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.000000 1.000000 1.000000 1.000000
0.005 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.000000 1.000000 1.000000 1.000000
0.010 0.651810 0.706790 0.646240 0.404280 8.090000 1.882560 1.114440 0.836440 1.000000 1.000000 1.000000 1.000000
0.020 0.653620 0.694650 0.638650 0.387890 6.992000 1.778610 1.124370 0.830000 1.000000 1.000000 1.000000 1.150000
0.030 0.654670 0.687550 0.634210 0.378300 6.350000 1.717810 1.130170 0.826240 1.000000 1.000000 1.000000 0.800000
0.040 0.652850 0.698920 0.606040 0.317370 4.883000 2.052340 1.150800 0.767580 1.000000 1.000000 1.000000 0.613000
0.050 0.672640 0.701370 0.617160 0.309340 5.043000 2.387130 1.239710 0.786320 1.000000 1.000000 1.000000 0.542000
0.060 0.699660 0.724450 0.637970 0.325300 6.271000 2.833990 1.348190 0.837750 1.000000 1.000000 1.000000 0.534000
0.070 0.717130 0.743430 0.654370 0.354120 7.667000 3.294470 1.451810 0.926160 1.000000 1.000000 1.000000 0.583000
0.080 0.716030 0.785980 0.680190 0.392820 9.034000 3.990910 1.583150 1.022280 1.000000 1.000000 0.830000 0.643000
0.090 0.725610 0.797210 0.708890 0.421840 11.251000 4.465760 1.732920 1.118020 1.000000 1.000000 0.706000 0.674000
0.100 0.742000 0.816680 0.718810 0.437360 14.817000 5.045610 1.841340 1.165780 1.000000 1.000000 0.800000 0.694000
0.120 0.762360 0.845230 0.725810 0.472080 14.817000 5.899600 2.030290 1.285510 0.000000 1.000000 0.759000 0.763000
0.140 0.752150 0.782960 0.745250 0.512780 14.817000 5.053530 2.281330 1.398080 0.000000 0.767000 0.715000 0.684000
0.150 0.738190 0.794800 0.761030 0.534320 14.817000 5.204900 2.444130 1.443270 0.000000 0.708000 0.686000 0.645000
0.160 0.719110 0.808610 0.768130 0.550220 14.817000 5.386940 2.580170 1.471770 0.000000 0.657000 0.644000 0.610000
0.180 0.654080 0.843310 0.756900 0.572790 14.817000 5.871650 2.741610 1.546940 0.000000 0.568000 0.549000 0.532000
0.200 0.583950 0.877700 0.717850 0.596740 14.817000 6.573910 2.825870 1.644010 0.000000 0.509000 0.434000 0.468000
0.250 0.583950 0.937670 0.654700 0.611360 14.817000 8.500000 2.718930 1.790130 0.000000 0.433000 0.292000 0.291000
0.300 0.583950 0.950000 0.696190 0.626380 14.817000 10.670300 2.417590 1.823450 0.000000 0.337000 0.275000 0.275000
0.350 0.583950 1.000000 0.779070 0.630120 14.817000 10.670300 2.303750 1.790370 0.000000 0.337000 0.295000 0.295000
0.400 0.583950 1.000000 0.827760 0.647730 14.817000 10.670300 2.236250 1.768440 0.000000 0.337000 0.293000 0.293000
0.450 0.583950 1.000000 0.876450 0.641520 14.817000 10.670300 2.216780 1.675390 0.000000 0.000000 0.000000 0.000000
0.500 0.583950 1.000000 0.925140 0.655820 14.817000 10.670300 2.243380 1.625390 0.000000 0.000000 0.000000 0.000000
0.600 0.583950 1.000000 0.973830 0.686680 14.817000 10.670300 2.805350 1.524530 0.000000 0.000000 0.000000 0.000000
0.700 0.583950 1.000000 1.022520 0.705600 14.817000 10.670300 6.658390 1.397240 0.000000 0.000000 0.000000 0.000000
0.800 0.583950 1.000000 1.071220 0.714290 14.817000 10.670300 30.000000 1.320290 0.000000 0.000000 0.000000 0.000000
0.900 0.583950 1.000000 1.119910 0.703880 14.817000 10.670300 30.000000 1.266370 0.000000 0.000000 0.000000 0.000000
1.000 0.583950 1.000000 1.168600 0.678130 14.817000 10.670300 30.000000 1.226800 0.000000 0.000000 0.000000 0.000000
1.250 0.583950 1.000000 1.217290 0.611190 14.817000 10.670300 30.000000 1.220650 0.000000 0.000000 0.000000 0.000000
1.500 0.583950 1.000000 1.265980 0.547360 14.817000 10.670300 30.000000 1.318050 0.000000 0.000000 0.000000 0.000000
2.000 0.583950 1.000000 1.314670 0.459440 14.817000 10.670300 30.000000 2.124850 0.000000 0.000000 0.000000 0.000000
2.500 0.583950 1.000000 1.363360 0.408460 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
3.000 0.583950 1.000000 1.412050 0.364210 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
3.500 0.583950 1.000000 1.460750 0.329840 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
4.000 0.583950 1.000000 1.509440 0.309120 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
4.500 0.583950 1.000000 1.558130 0.292510 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
5.000 0.583950 1.000000 1.606820 0.547360 14.817000 10.670300 30.000000 14.381810 0.000000 0.000000 0.000000 0.000000
""")
[docs]class ZhaoEtAl2016UpperMantleSiteSigma(ZhaoEtAl2016UpperMantle):
"""
Adaption of the Zhao et al (2016a) GMPE for upper mantle events for the
case when within-event variability is dependent on site class
"""
[docs] def get_stddevs(self, C, n_sites, idx, stddev_types):
"""
Returns site class specific standard deviation
"""
stddevs = []
tau = C["tau"] + np.zeros(n_sites)
phi = np.zeros(n_sites)
for i in range(1, 5):
phi[idx[i]] += C["sc{:g}_sigma_S".format(i)]
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(phi ** 2. + tau ** 2.))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(tau)
return stddevs
[docs]class ZhaoEtAl2016SInter(ZhaoEtAl2016Asc):
"""
Implements the subduction interface GMPE of Zhao et al (2016b)
Zhao, J. X., Liang, X., Jiang, F., Xing, H., Zhu, M., Hou, R., Zhang, Y.,
Lan, X., Rhoades, D. A., Irikura, K., Fukushima, Y.,
Somerville, P. (2016b), "Ground-Motion Prediction Equations for
Subduction Interface Earthquakes in Japan Using Site Class and Simple
Geometric Attenuation Functions", Bulletin of the Seismological
Society of America, 106(4), 1518-1534
Main version with standard deviations independent of site term
"""
#: Supported tectonic region type is subduction interface
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.SUBDUCTION_INTERFACE
#: Required rupture parameters are magnitude and top-of-rupture depth
REQUIRES_RUPTURE_PARAMETERS = set(('mag', 'ztor'))
[docs] def get_magnitude_scaling_term(self, C, rup):
"""
Returns magnitude scaling term, which is dependent on top of rupture
depth - as described in equations 1 and 2
"""
if rup.ztor > 25.0:
# Deep interface events
c_int = C["cint"]
else:
c_int = C["cintS"]
if rup.mag <= self.CONSTANTS["m_c"]:
return c_int * rup.mag
else:
return (c_int * self.CONSTANTS["m_c"]) +\
(C["dint"] * (rup.mag - self.CONSTANTS["m_c"]))
[docs] def get_sof_term(self, C, rup):
"""
No style of faulting dependence here
"""
return 0.0
[docs] def get_depth_term(self, C, rup):
"""
Returns depth term (dependent on top of rupture depth) as given
in equations 1 and 2
"""
return (C["bint"] * rup.ztor)
[docs] def get_distance_term(self, C, dists, rup):
"""
Returns distance scaling term, dependent on top of rupture depth,
as described in equation 6
"""
x_ij = dists.rrup
# Get r_ij - distance for geometric spreading (equations 4 & 5)
c_m = min(rup.mag, self.CONSTANTS["m_c"])
r_ij = self.CONSTANTS["xinto"] + x_ij +\
np.exp(C["alpha"] + C["beta"] * c_m)
# Get factors common to both shallow and deep
dist_term = C["gint"] * np.log(r_ij) + C["eintV"] * dists.rvolc +\
C["gammaint"]
if rup.ztor < 25.:
# Shallow events have geometric and anelastic attenuation term
dist_term += (C["gintLS"] * np.log(x_ij + 200.0)
+ C["eintS"] * x_ij) + C["gamma_ints"]
else:
# Deep events do not have an anelastic attenuation term
dist_term += (C["gintLD"] * np.log(x_ij + 200.0))
return dist_term
def _get_ln_a_n_max(self, C, n_sites, idx, rup):
"""
Defines the rock site amplification defined in equations 7a and 7b
For events deeper than 25 km the rock-site factor is slightly different
for site classes SCII, SCIII, SCIV
"""
ln_a_n_max = C["lnSC1AM"] * np.ones(n_sites)
for i in [2, 3, 4]:
if np.any(idx[i]):
# For deep events site classes 5, 6 and 7 are used
if rup.ztor > 25.0:
loc = i + 3
else:
loc = i
ln_a_n_max[idx[i]] += C["S{:g}".format(loc)]
return ln_a_n_max
def _get_ln_sf(self, C, C_SITE, idx, n_sites, rup):
"""
Returns the log SF term required for equation 9
For events deeper than 25 km the rock-site factor is slightly different
for site classes SCII, SCIII, SCIV
"""
ln_sf = np.zeros(n_sites)
for i in range(1, 5):
ln_sf_i = (C["lnSC1AM"] - C_SITE["LnAmax1D{:g}".format(i)])
if i > 1:
if rup.ztor > 25.0:
# For deep events site classes 5, 6, and 7 are used
loc = i + 3
else:
# For shallow events the conventional approach applies
loc = i
ln_sf_i += C["S{:g}".format(loc)]
ln_sf[idx[i]] += ln_sf_i
return ln_sf
# Coefficients table taken from spreadsheet supplied by the author
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt alpha beta cint cintS dint gamma_ints bint gint gintLD gintLS eintV eintS gammaint S2 S3 S4 S5 S6 S7 lnSC1AM sigma tau sigma_T sc1_sigma_S sc1_tau_S sc1_sigma_ST sc2_sigma_S sc2_tau_T sc2_sigma_ST sc3_sigma_S sc3_tau_S sc3_sigma_ST sc4_sigma_S sc4_tau_S sc4_sigma_ST
pga -5.3011890395444800 1.151 1.09973 1.31479 0.553 -3.89528 0.01999 -2.05587 0.545410000000000 1.133644136349760 -0.01123 -0.00628 -4.49858 0.31288 -0.0043099999999998 0.22838 0.31288 -0.0043099999999998 0.22838 0.3059054757248630 0.553 0.377 0.669 0.398 0.419 0.571 0.417 0.424 0.597 0.409 0.376 0.544 0.415 0.346 0.574
0.005 -5.3011890395444800 1.151 1.09973 1.31479 0.553 -3.89528 0.01999 -2.05587 0.545410000000000 1.133644136349760 -0.01123 -0.00628 -4.49858 0.31288 -0.0043099999999998 0.22838 0.31288 -0.0043099999999998 0.22838 0.3059054757248630 0.553 0.377 0.669 0.398 0.419 0.571 0.417 0.424 0.597 0.409 0.376 0.544 0.415 0.346 0.574
0.010 -5.2884351314221800 1.151 1.09848 1.31739 0.553 -3.89528 0.01999 -2.06565 0.549750000000000 1.133647532106160 -0.01125 -0.00625 -4.45894 0.30850 -0.0111500000000002 0.22305 0.30850 -0.0111500000000002 0.22305 0.2211189175034500 0.554 0.377 0.670 0.397 0.420 0.571 0.417 0.424 0.598 0.409 0.377 0.545 0.415 0.348 0.543
0.020 -5.2756812232998800 1.151 1.09227 1.31919 0.553 -3.89528 0.01999 -2.10231 0.561715000000000 1.133645539711160 -0.01127 -0.00616 -4.25807 0.29297 -0.0216599999999998 0.20892 0.29297 -0.0216599999999998 0.20892 0.1391818272825550 0.553 0.384 0.673 0.395 0.425 0.574 0.417 0.425 0.598 0.408 0.376 0.544 0.416 0.352 0.544
0.030 -5.2682206653106900 1.151 1.10690 1.34096 0.553 -3.89528 0.02066 -2.19232 0.578870000000000 1.133638051189290 -0.01158 -0.00572 -3.91800 0.22869 -0.1129091455512500 0.13314 0.22869 -0.0829091455512501 0.16314 0.0928148352701154 0.555 0.397 0.682 0.389 0.444 0.586 0.418 0.422 0.595 0.409 0.386 0.553 0.417 0.355 0.546
0.040 -5.2629273151775800 1.151 1.11578 1.38051 0.553 -3.89528 0.02308 -2.24640 0.493330000000000 0.988105377906839 -0.01203 -0.00532 -3.11423 0.16316 -0.1887005077743300 0.06959 0.16316 -0.1887005077743300 0.05959 0.0626979621246270 0.565 0.428 0.709 0.387 0.474 0.611 0.420 0.434 0.604 0.413 0.393 0.557 0.420 0.362 0.555
0.050 -5.2588214738333900 1.151 1.10234 1.43246 0.553 -3.89528 0.02709 -2.29341 0.491000000000000 0.904439734658832 -0.01256 -0.00503 -2.76035 0.12129 -0.2283118706921090 0.02845 0.12129 -0.2283118706921090 0.00845 0.0360112352938003 0.570 0.463 0.734 0.387 0.486 0.620 0.422 0.440 0.608 0.409 0.384 0.554 0.422 0.375 0.562
0.060 -5.2554667571883900 1.151 1.08611 1.46238 0.553 -3.89528 0.02972 -2.31172 0.508475000000000 0.887664211721920 -0.01312 -0.00528 -2.64088 0.12345 -0.2192024788530120 0.00070 0.12345 -0.2192024788530120 0.00070 0.0372315296271544 0.583 0.488 0.760 0.397 0.500 0.632 0.416 0.449 0.614 0.401 0.379 0.550 0.423 0.388 0.571
0.070 -5.2526303846795600 1.151 1.07291 1.47120 0.553 -3.89528 0.03207 -2.31101 0.527500000000000 0.904912893427476 -0.01359 -0.00569 -2.65622 0.13970 -0.1900877585085220 -0.00949 0.13970 -0.1900877585085220 0.00551 0.0491648511532895 0.602 0.501 0.784 0.403 0.531 0.658 0.412 0.466 0.628 0.394 0.377 0.558 0.420 0.410 0.587
0.080 -5.2501734070552700 1.151 1.06383 1.46432 0.553 -3.89461 0.03196 -2.28778 0.545953146186990 0.942062229717542 -0.01382 -0.00619 -2.75266 0.16385 -0.1549929496094120 -0.00485 0.16385 -0.1549929496094120 0.01415 0.0983337731804003 0.614 0.501 0.793 0.413 0.553 0.681 0.412 0.489 0.647 0.389 0.374 0.552 0.423 0.416 0.591
0.090 -5.2480061991992000 1.151 1.05863 1.44695 0.553 -3.90179 0.02972 -2.24680 0.563067703920420 0.986519531877540 -0.01393 -0.00673 -2.89920 0.20501 -0.1125034957053890 0.03047 0.20501 -0.1125034957053890 0.04047 0.1753764826899090 0.625 0.495 0.797 0.422 0.559 0.688 0.412 0.521 0.672 0.391 0.381 0.557 0.427 0.435 0.604
0.100 -5.2460675657110900 1.151 1.05673 1.42323 0.553 -3.90765 0.02789 -2.20410 0.576153228856880 1.035532649999650 -0.01395 -0.00718 -3.07698 0.24449 -0.0750832172171707 0.06080 0.24449 -0.0750832172171707 0.07080 0.2447184227049870 0.637 0.478 0.796 0.429 0.562 0.693 0.418 0.540 0.687 0.394 0.409 0.574 0.426 0.469 0.632
0.120 -5.2427128490660800 1.151 1.06051 1.36833 0.553 -3.91644 0.02470 -2.12011 0.592575859588849 1.135292002539040 -0.01381 -0.00793 -3.48283 0.32284 0.0150002886394358 0.14231 0.32284 0.0150002886394358 0.14031 0.3366973661131230 0.646 0.453 0.789 0.440 0.564 0.697 0.429 0.537 0.684 0.428 0.480 0.635 0.445 0.517 0.670
0.140 -5.2398764765572600 1.151 1.07135 1.31558 0.553 -3.92273 0.02117 -2.04337 0.609834200714791 1.234237180364630 -0.01351 -0.00853 -3.91612 0.40120 0.0969875152845021 0.22696 0.40120 0.0969875152845021 0.20196 0.4219918629306410 0.654 0.412 0.773 0.441 0.556 0.692 0.435 0.560 0.712 0.433 0.440 0.599 0.442 0.507 0.670
0.150 -5.2386070077219000 1.151 1.07856 1.29277 0.553 -3.92526 0.01951 -2.01088 0.619605756588544 1.281341268532020 -0.01333 -0.00879 -4.13481 0.43622 0.1458772263727260 0.25758 0.43622 0.1358772263727260 0.23258 0.4561999113437010 0.659 0.404 0.773 0.450 0.552 0.690 0.440 0.574 0.724 0.420 0.427 0.592 0.440 0.504 0.663
0.160 -5.2374194989329700 1.151 1.08664 1.27319 0.553 -3.92753 0.01793 -1.98301 0.630846000686806 1.326585227368230 -0.01312 -0.00902 -4.35243 0.46736 0.1879291503650610 0.30748 0.46736 0.1729291503650610 0.26248 0.4857829542311720 0.663 0.398 0.774 0.452 0.548 0.688 0.444 0.573 0.726 0.424 0.429 0.589 0.437 0.496 0.661
0.180 -5.2352522910768900 1.151 1.10470 1.24828 0.553 -3.93130 0.01505 -1.94607 0.661957417503245 1.411336369650280 -0.01269 -0.00927 -4.78030 0.51201 0.2515429577444550 0.35974 0.51201 0.2415429577444550 0.31974 0.5337861886971570 0.672 0.387 0.776 0.454 0.534 0.681 0.447 0.557 0.719 0.446 0.436 0.600 0.436 0.488 0.667
0.200 -5.2333136575887900 1.151 1.12443 1.23715 0.553 -3.93446 0.01255 -1.92702 0.699775273836698 1.488543999616950 -0.01223 -0.00942 -5.19439 0.53926 0.3029826435245730 0.40313 0.53926 0.3029826435245730 0.37313 0.5700442187746950 0.678 0.382 0.778 0.462 0.528 0.679 0.450 0.532 0.710 0.441 0.437 0.602 0.432 0.481 0.663
0.250 -5.2292078162446100 1.151 1.17689 1.22387 0.553 -3.94068 0.00769 -1.89876 0.784470723616457 1.652089063569910 -0.01108 -0.00959 -6.15803 0.58602 0.4269231521787280 0.50765 0.58602 0.4269231521787280 0.48765 0.6247924955873390 0.659 0.365 0.753 0.474 0.493 0.650 0.470 0.551 0.723 0.459 0.398 0.572 0.432 0.448 0.637
0.300 -5.2258530995996000 1.151 1.22970 1.22846 0.553 -3.94547 0.00438 -1.89141 0.859387831257168 1.781266639239090 -0.00998 -0.00952 -7.02003 0.60465 0.5161970850888910 0.57779 0.60465 0.5161970850888910 0.57779 0.6505259707420690 0.640 0.348 0.729 0.472 0.471 0.632 0.475 0.509 0.695 0.437 0.421 0.579 0.432 0.442 0.623
0.350 -5.2230167270907800 1.151 1.28058 1.24219 0.553 -3.94943 0.00215 -1.89299 0.923381083222217 1.884375114633360 -0.00898 -0.00933 -7.79153 0.60640 0.5695125616089470 0.63821 0.60640 0.5795125616089470 0.64821 0.6617360212939630 0.634 0.360 0.729 0.468 0.474 0.629 0.478 0.485 0.683 0.444 0.457 0.607 0.432 0.442 0.618
0.400 -5.2205597494664900 1.151 1.32873 1.26077 0.553 -3.95273 0.00000 -1.89531 0.980106570565885 1.967625670226430 -0.00808 -0.00911 -8.49551 0.60277 0.6236634605365610 0.70323 0.60277 0.6336634605365610 0.71323 0.6649532313027650 0.627 0.354 0.720 0.457 0.449 0.610 0.484 0.496 0.683 0.453 0.463 0.607 0.416 0.435 0.607
0.450 -5.2183925416104100 1.151 1.37394 1.28190 0.553 -3.95558 0.00000 -1.90578 1.022203929823320 2.035537053673680 -0.00727 -0.00888 -9.11351 0.58043 0.6581416274761160 0.75082 0.58043 0.6581416274761160 0.75082 0.6654266030879300 0.620 0.363 0.719 0.450 0.423 0.595 0.477 0.495 0.679 0.477 0.447 0.593 0.408 0.418 0.592
0.500 -5.2164539081223000 1.151 1.41630 1.30432 0.553 -3.95795 0.00000 -1.91467 1.058737908474810 2.091423339372400 -0.00656 -0.00866 -9.68515 0.55692 0.6867116556863830 0.79378 0.55692 0.6867116556863830 0.79378 0.6635290829305090 0.612 0.364 0.712 0.445 0.415 0.583 0.470 0.473 0.658 0.472 0.435 0.579 0.409 0.432 0.603
0.600 -5.2130991914773000 1.151 1.49310 1.35019 0.553 -3.96181 0.00000 -1.92743 1.118034334349720 2.176403198118720 -0.00534 -0.00824 -10.68946 0.50969 0.7122000042516460 0.84953 0.50969 0.7122000042516460 0.84953 0.6564608147533650 0.613 0.379 0.720 0.448 0.419 0.576 0.460 0.450 0.636 0.459 0.434 0.580 0.407 0.442 0.598
0.700 -5.2102628189684700 1.151 1.56069 1.39523 0.560 -3.96483 0.00000 -1.93452 1.163018824473690 2.235996913698260 -0.00437 -0.00787 -11.54596 0.46501 0.7123896083711760 0.87979 0.46501 0.7123896083711760 0.87979 0.6475225398028140 0.625 0.393 0.739 0.440 0.429 0.583 0.460 0.454 0.634 0.461 0.448 0.591 0.403 0.454 0.613
0.800 -5.2078058413441800 1.151 1.62055 1.43824 0.580 -3.96729 0.00000 -1.93739 1.197347867256100 2.278310911386920 -0.00359 -0.00755 -12.28722 0.42440 0.6993827558539090 0.89539 0.42440 0.6993827558539090 0.89539 0.6378151725112400 0.628 0.396 0.743 0.441 0.427 0.580 0.460 0.448 0.631 0.457 0.440 0.590 0.407 0.468 0.627
0.900 -5.2056386334881000 1.151 1.67390 1.47881 0.602 -3.96962 0.00000 -1.93725 1.223616172806850 2.308465259401480 -0.00296 -0.00726 -12.93630 0.38841 0.6799630664371980 0.90256 0.38841 0.6799630664371980 0.90256 0.6275299463931780 0.628 0.397 0.743 0.435 0.433 0.586 0.456 0.446 0.622 0.449 0.445 0.593 0.408 0.485 0.633
1.000 -5.2037000000000000 1.151 1.72171 1.51685 0.622 -3.97202 0.00000 -1.93505 1.243670000000000 2.329852618176160 -0.00244 -0.00700 -13.51000 0.35703 0.6582841883037910 0.90528 0.35703 0.6582841883037910 0.90528 0.6166948032035810 0.633 0.403 0.750 0.427 0.453 0.606 0.448 0.445 0.617 0.442 0.422 0.578 0.408 0.498 0.642
1.250 -5.1995941586558200 1.151 1.82188 1.60148 0.667 -3.97949 0.00000 -1.92471 1.272450000000000 2.358529248389570 -0.00153 -0.00644 -14.69034 0.29673 0.6192828473123780 0.91793 0.29673 0.6192828473123780 0.91793 0.5871136982629510 0.636 0.404 0.753 0.413 0.457 0.613 0.442 0.457 0.624 0.428 0.428 0.587 0.412 0.507 0.657
1.500 -5.1962394420108100 1.151 1.90081 1.67280 0.705 -3.99048 0.00000 -1.91189 1.285410000000000 2.366526445593360 -0.00097 -0.00597 -15.60298 0.25785 0.5828781633806380 0.92130 0.25785 0.5828781633806380 0.92130 0.5541350591719840 0.644 0.392 0.754 0.416 0.464 0.619 0.448 0.448 0.628 0.418 0.434 0.604 0.418 0.505 0.653
2.000 -5.1909460918777000 1.151 2.01482 1.78372 0.768 -4.02652 0.00000 -1.88859 1.288300000000000 2.355357695068850 -0.00043 -0.00518 -16.90010 0.22262 0.5262044723719100 0.91709 0.22262 0.5262044723719100 0.91709 0.4823188293988830 0.635 0.382 0.741 0.409 0.464 0.623 0.437 0.458 0.633 0.406 0.470 0.625 0.413 0.524 0.665
2.500 -5.1868402505335200 1.151 2.08892 1.86241 0.820 -4.08299 0.00000 -1.87252 1.277270000000000 2.331055616102490 -0.00023 -0.00451 -17.73658 0.21840 0.4871991042586300 0.90551 0.21840 0.4871991042586300 0.90551 0.4108485356913110 0.619 0.393 0.734 0.398 0.441 0.599 0.429 0.443 0.623 0.387 0.446 0.612 0.414 0.529 0.676
3.000 -5.1834855338885100 1.151 2.13574 1.91711 0.863 -4.15939 0.00000 -1.86345 1.260490000000000 2.304087350833550 -0.00016 -0.00393 -18.27135 0.21595 0.4569807448342530 0.88668 0.21595 0.4569807448342530 0.88668 0.3476970039189440 0.599 0.385 0.712 0.390 0.412 0.572 0.423 0.441 0.623 0.369 0.400 0.565 0.412 0.524 0.668
3.500 -5.1806491613796900 1.151 2.16254 1.95322 0.902 -4.25418 0.00000 -1.85969 1.241065000000000 2.277938604223720 0.00000 -0.00344 -18.59264 0.21595 0.4280893607115190 0.85880 0.21595 0.4280893607115190 0.85880 0.2981089167617070 0.581 0.376 0.692 0.386 0.394 0.553 0.410 0.424 0.608 0.376 0.353 0.536 0.402 0.535 0.672
4.000 -5.1781921837553900 1.151 2.17393 1.97450 0.935 -4.36584 0.00000 -1.85953 1.220255000000000 2.253713119173820 0.00000 -0.00302 -18.75474 0.21595 0.3952406635315720 0.82025 0.21595 0.3952406635315720 0.82025 0.2652398018598770 0.568 0.377 0.682 0.377 0.376 0.541 0.410 0.409 0.603 0.362 0.365 0.540 0.394 0.542 0.671
4.500 -5.1760249758993200 1.151 2.17301 1.98361 0.966 -4.49267 0.00000 -1.86151 1.198585000000000 2.231608900335380 0.00000 -0.00267 -18.79351 0.21595 0.3548841036361290 0.76990 0.21595 0.3548841036361290 0.76990 0.2508917433151500 0.551 0.376 0.667 0.360 0.363 0.528 0.412 0.393 0.591 0.368 0.349 0.519 0.385 0.530 0.652
5.000 -5.1740863424112100 1.151 2.16199 1.98255 0.994 -4.63313 0.00000 -1.86451 1.176285000000000 2.211490595470470 0.00000 -0.00240 -18.73393 0.21595 0.3046830024209600 0.70705 0.21595 0.3046830024209600 0.70705 0.2365436847704230 0.563 0.374 0.675 0.361 0.346 0.525 0.447 0.408 0.615 0.381 0.293 0.476 0.380 0.514 0.633
""")
# Coefficients specific to the site amplification
SITE_COEFFS = CoeffsTable(sa_damping=5, table="""\
imt LnAmax1D1 LnAmax1D2 LnAmax1D3 LnAmax1D4 Src1D1 Src1D2 Src1D3 Src1D4 fsr1 fsr2 fsr3 fsr4
pga 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.0000 1.0000 1.1650 1.0000
0.005 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.0000 1.0000 1.1650 1.0000
0.010 0.651810 0.706790 0.646240 0.404280 8.090000 1.882560 1.114440 0.836440 1.0000 1.0000 0.9440 1.0000
0.020 0.653620 0.694650 0.638650 0.387890 6.992000 1.778610 1.124370 0.830000 1.0000 1.0000 1.0120 1.0000
0.030 0.654670 0.687550 0.634210 0.378300 6.350000 1.717810 1.130170 0.826240 1.0000 0.9990 1.1000 1.0000
0.040 0.652850 0.698920 0.606040 0.317370 4.883000 2.052340 1.150800 0.767580 1.0000 0.8430 0.9590 0.5570
0.050 0.672640 0.701370 0.617160 0.309340 5.043000 2.387130 1.239710 0.786320 1.0000 0.6630 0.8890 0.5430
0.060 0.699660 0.724450 0.637970 0.325300 6.271000 2.833990 1.348190 0.837750 1.0000 0.8410 0.9460 0.5740
0.070 0.717130 0.743430 0.654370 0.354120 7.667000 3.294470 1.451810 0.926160 1.0000 1.0290 1.0060 0.6480
0.080 0.716030 0.785980 0.680190 0.392820 9.034000 3.990910 1.583150 1.022280 1.0000 1.2350 1.0650 0.7210
0.090 0.725610 0.797210 0.708890 0.421840 11.251000 4.465760 1.732920 1.118020 1.0000 1.1440 1.0930 0.8090
0.100 0.742000 0.816680 0.718810 0.437360 14.817000 5.045610 1.841340 1.165780 1.0000 1.0920 1.0770 1.0150
0.120 0.762360 0.845230 0.725810 0.472080 14.817000 5.899600 2.030290 1.285510 0.0000 0.9450 1.0360 0.9720
0.140 0.752150 0.782960 0.745250 0.512780 14.817000 5.053530 2.281330 1.398080 0.0000 0.6240 0.8950 0.9670
0.150 0.738190 0.794800 0.761030 0.534320 14.817000 5.204900 2.444130 1.443270 0.0000 0.5770 0.8600 0.9630
0.160 0.719110 0.808610 0.768130 0.550220 14.817000 5.386940 2.580170 1.471770 0.0000 0.5450 0.8220 0.9530
0.180 0.654080 0.843310 0.756900 0.572790 14.817000 5.871650 2.741610 1.546940 0.0000 0.5270 0.7430 0.9670
0.200 0.583950 0.877700 0.717850 0.596740 14.817000 6.573910 2.825870 1.644010 0.0000 0.5460 0.6630 1.0050
0.250 0.583950 0.937670 0.654700 0.611360 14.817000 8.500000 2.718930 1.790130 0.0000 0.5960 0.4870 1.0450
0.300 0.583950 0.950000 0.696190 0.626380 14.817000 10.670300 2.417590 1.823450 0.0000 0.6230 0.4470 1.0350
0.350 0.583950 1.000000 0.779070 0.630120 14.817000 10.670300 2.303750 1.790370 0.0000 0.7010 0.4730 1.0080
0.400 0.583950 1.000000 0.827760 0.647730 14.817000 10.670300 2.236250 1.768440 0.0000 0.7080 0.4870 1.0070
0.450 0.583950 1.000000 0.876450 0.641520 14.817000 10.670300 2.216780 1.675390 0.0000 0.7370 0.5110 0.9810
0.500 0.583950 1.000000 0.925140 0.655820 14.817000 10.670300 2.243380 1.625390 0.0000 0.7480 0.5360 0.9900
0.600 0.583950 1.000000 0.973830 0.686680 14.817000 10.670300 2.805350 1.524530 0.0000 0.7280 0.5400 1.0160
0.700 0.583950 1.000000 1.022520 0.705600 14.817000 10.670300 6.658390 1.397240 0.0000 0.6340 0.4770 1.0220
0.800 0.583950 1.000000 1.071220 0.714290 14.817000 10.670300 30.000000 1.320290 0.0000 0.0000 0.0000 1.0230
0.900 0.583950 1.000000 1.119910 0.703880 14.817000 10.670300 30.000000 1.266370 0.0000 0.0000 0.0000 0.9970
1.000 0.583950 1.000000 1.168600 0.678130 14.817000 10.670300 30.000000 1.226800 0.0000 0.0000 0.0000 0.9480
1.250 0.583950 1.000000 1.217290 0.611190 14.817000 10.670300 30.000000 1.220650 0.0000 0.0000 0.0000 0.8020
1.500 0.583950 1.000000 1.265980 0.547360 14.817000 10.670300 30.000000 1.318050 0.0000 0.0000 0.0000 0.0000
2.000 0.583950 1.000000 1.314670 0.459440 14.817000 10.670300 30.000000 2.124850 0.0000 0.0000 0.0000 0.0000
2.500 0.583950 1.000000 1.363360 0.408460 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
3.000 0.583950 1.000000 1.412050 0.364210 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
3.500 0.583950 1.000000 1.460750 0.329840 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
4.000 0.583950 1.000000 1.509440 0.309120 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
4.500 0.583950 1.000000 1.558130 0.292510 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
5.000 0.583950 1.000000 1.606820 0.547360 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
""")
[docs]class ZhaoEtAl2016SInterSiteSigma(ZhaoEtAl2016SInter):
"""
Subclass of the Zhao et al. (2016b) subduction interface GMPE for the
case of site-dependent within-event variability
"""
[docs] def get_stddevs(self, C, n_sites, idx, stddev_types):
"""
"""
stddevs = []
tau = C["tau"] + np.zeros(n_sites)
phi = np.zeros(n_sites)
for i in range(1, 5):
phi[idx[i]] += C["sc{:g}_sigma_S".format(i)]
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(phi ** 2. + tau ** 2.))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(tau)
return stddevs
[docs]class ZhaoEtAl2016SSlab(ZhaoEtAl2016Asc):
"""
Implements the subduction slab GMPE of Zhao et al (2016c)
Zhao, J. X., Jiang, F., Shi, P., Xing, H., Huang, H., Hou, R.,
Zhang, Y., Yu, P., Lan, X., Rhoades, D. A., Somerville, P. G., Irikura, K.,
Fukushima, Y. (2016c), "Ground-Motion Prediction Equations for
Subduction Slab Earthquakes in Japan Using Site Class and Simple
Geometric Attenuation Functions", Bulletin of the Seismological
Society of America, 106(4), 1535-1551
Main version with standard deviations independent of site term
"""
#: Supported tectonic region type is subduction inslab
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.SUBDUCTION_INTRASLAB
#: Required rupture parameters are magnitude and top of rupture depth.
REQUIRES_RUPTURE_PARAMETERS = set(('mag', 'ztor'))
[docs] def get_magnitude_scaling_term(self, C, rup):
"""
Returns the magnitude scaling defined in equation 1
"""
m_c = self.CONSTANTS["m_c"]
if rup.mag <= m_c:
return C["cSL"] * rup.mag +\
C["cSL2"] * ((rup.mag - self.CONSTANTS["m_sc"]) ** 2.)
else:
return C["cSL"] * m_c +\
C["cSL2"] * ((m_c - self.CONSTANTS["m_sc"]) ** 2.) +\
C["dSL"] * (rup.mag - m_c)
[docs] def get_sof_term(self, C, rup):
"""
No style of faulting dependence here
"""
return 0.0
[docs] def get_depth_term(self, C, rup):
"""
Returns depth term (dependent on top of rupture depth) as given
in equations 1
Note that there is a ztor cap of 100 km that is introduced in the
Fortran code but not mentioned in the original paper!
"""
if rup.ztor > 100.0:
return C["bSLH"] * 100.0
else:
return C["bSLH"] * rup.ztor
[docs] def get_distance_term(self, C, dists, rup):
"""
Returns the distance scaling term in equation 2a
Note that the paper describes a lower and upper cap on Rvolc that
is not found in the Fortran code, and is thus neglected here.
"""
x_ij = dists.rrup
# Get anelastic scaling term in quation 5
if rup.ztor >= 50.:
qslh = C["eSLH"] * (0.02 * rup.ztor - 1.0)
else:
qslh = 0.0
# r_volc = np.copy(dists.rvolc)
# r_volc[np.logical_and(r_volc > 0.0, r_volc <= 12.0)] = 12.0
# r_volc[r_volc >= 80.0] = 80.0
# Get r_ij - distance for geometric spreading (equations 3 and 4)
c_m = min(rup.mag, self.CONSTANTS["m_c"])
r_ij = x_ij + np.exp(C["alpha"] + C["beta"] * c_m)
return C["gSL"] * np.log(r_ij) + \
C["gLL"] * np.log(x_ij + 200.) +\
C["eSL"] * x_ij + qslh * x_ij +\
C["eSLV"] * dists.rvolc + C["gamma"]
# Coefficients table taken from spreadsheet supplied by the author
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt alpha beta cSL cSL2 dSL bSLH gSL gLL eSLV eSL eSLH gamma S2 S3 S4 lnSC1AM sigma tau sigma_T sc1_sigma_S sc1_tau_S sc1_sigma_ST sc2_sigma_S sc2_tau_S sc2_sigma_ST sc3_sigma_S sc3_tau_S sc3_sigma_ST sc4_sigma_S sc4_tau_S sc4_sigma_ST
pga -5.30118903954448 1.151 1.44758 0.37625 0.42646 0.01825668401347 -1.98471 1.12071 -0.01499 -0.00340 -0.000501 -9.879560 0.232000 0.143711 0.147037 0.3230000000000000 0.5870 0.4570 0.7440 0.3981 0.5107 0.6475 0.4174 0.4494 0.6133 0.4091 0.4306 0.5940 0.4152 0.4217 0.5918
0.005 -5.30118903954448 1.151 1.44758 0.37625 0.42646 0.01825668401347 -1.98471 1.12071 -0.01499 -0.00340 -0.000501 -9.879560 0.232000 0.143711 0.147037 0.3230000000000000 0.5870 0.4570 0.7440 0.3981 0.5107 0.6475 0.4174 0.4494 0.6133 0.4091 0.4306 0.5940 0.4152 0.4217 0.5918
0.010 -5.28843513142218 1.151 1.45400 0.38099 0.42075 0.01825668401347 -1.96360 1.03278 -0.01503 -0.00331 -0.000501 -9.512690 0.228860 0.139780 0.132840 0.2050000000000000 0.5870 0.4590 0.7460 0.3968 0.5166 0.6514 0.4173 0.4499 0.6136 0.4086 0.4314 0.5941 0.4149 0.4183 0.5892
0.020 -5.27568122329988 1.151 1.46625 0.39101 0.40055 0.01825668401347 -1.91839 0.94715 -0.01517 -0.00345 -0.000501 -9.266260 0.218250 0.126000 0.144310 0.0830000000000000 0.5870 0.4650 0.7490 0.3951 0.5180 0.6515 0.4172 0.4488 0.6128 0.4082 0.4313 0.5938 0.4158 0.4245 0.5942
0.030 -5.26822066531069 1.151 1.49246 0.41976 0.36433 0.01825668401347 -1.89271 0.93420 -0.01567 -0.00391 -0.000501 -9.331500 0.187370 0.061640 0.066000 0.0410000000000000 0.5880 0.4800 0.7590 0.3894 0.5369 0.6633 0.4179 0.4492 0.6135 0.4088 0.4300 0.5933 0.4166 0.4224 0.5933
0.040 -5.26292731517758 1.151 1.50129 0.45746 0.32072 0.01825668401347 -1.87260 0.97168 -0.01616 -0.00454 -0.000501 -9.507980 0.123320 -0.017050 -0.017140 0.0340000000000000 0.6000 0.5200 0.7940 0.3873 0.5716 0.6905 0.4201 0.4559 0.6200 0.4125 0.4281 0.5945 0.4201 0.4308 0.6017
0.050 -5.25882147383339 1.151 1.51051 0.48601 0.30000 0.01825668401347 -1.85351 1.01492 -0.01676 -0.00510 -0.000501 -9.728580 0.072070 -0.063310 -0.073120 0.0460000000000000 0.6070 0.5550 0.8230 0.3874 0.5857 0.7022 0.4219 0.4794 0.6386 0.4089 0.4286 0.5924 0.4217 0.4387 0.6085
0.060 -5.25546675718839 1.151 1.51380 0.50311 0.31147 0.01825668401347 -1.83395 1.06854 -0.01722 -0.00552 -0.000501 -9.966280 0.027010 -0.101030 -0.119550 0.0690000000000000 0.6230 0.5840 0.8540 0.3970 0.6131 0.7304 0.4160 0.5074 0.6561 0.4012 0.4485 0.6018 0.4227 0.4450 0.6138
0.070 -5.25263038467956 1.151 1.51111 0.50704 0.32673 0.01825668401347 -1.81345 1.13401 -0.01752 -0.00588 -0.000487 -10.225830 -0.006210 -0.146840 -0.160060 0.0980000000000000 0.6380 0.5980 0.8750 0.4027 0.6326 0.7499 0.4121 0.5317 0.6727 0.3942 0.4556 0.6025 0.4203 0.4732 0.6329
0.080 -5.25017340705527 1.151 1.50406 0.50004 0.34289 0.01825668401347 -1.79189 1.20364 -0.01768 -0.00615 -0.000479 -10.551110 0.015650 -0.144790 -0.124340 0.1320000000000000 0.6510 0.5980 0.8840 0.4130 0.6434 0.7646 0.4124 0.5554 0.6918 0.3887 0.4554 0.5987 0.4230 0.4905 0.6477
0.090 -5.24800619919920 1.151 1.49423 0.48071 0.35921 0.01825668401347 -1.76931 1.25808 -0.01772 -0.00635 -0.000476 -10.807210 0.050890 -0.126660 -0.072930 0.1690000000000000 0.6630 0.5850 0.8840 0.4221 0.6307 0.7589 0.4122 0.5677 0.7015 0.3905 0.4752 0.6151 0.4269 0.5177 0.6710
0.100 -5.24606756571109 1.151 1.48300 0.45759 0.37000 0.01825668401347 -1.74581 1.30112 -0.01768 -0.00652 -0.000478 -11.021900 0.095600 -0.093190 -0.014580 0.2080000000000000 0.6740 0.5670 0.8810 0.4286 0.6258 0.7585 0.4177 0.5662 0.7036 0.3936 0.5067 0.6416 0.4263 0.5593 0.7033
0.120 -5.24271284906608 1.151 1.45559 0.41355 0.40606 0.01825668401347 -1.73746 1.39137 -0.01742 -0.00660 -0.000489 -11.365300 0.200370 -0.008780 0.082520 0.2880000000000000 0.6900 0.5340 0.8720 0.4402 0.6135 0.7551 0.4285 0.5695 0.7127 0.4279 0.5479 0.6951 0.4445 0.5762 0.7277
0.140 -5.23987647655726 1.151 1.44277 0.37828 0.43450 0.01825668401347 -1.74463 1.47084 -0.01700 -0.00652 -0.000508 -11.730390 0.303720 0.089260 0.171510 0.3700000000000000 0.6920 0.5040 0.8560 0.4411 0.6126 0.7549 0.4347 0.5971 0.7386 0.4326 0.5072 0.6666 0.4424 0.5608 0.7143
0.150 -5.23860700772190 1.151 1.43314 0.36308 0.45000 0.01825668401347 -1.74972 1.50784 -0.01676 -0.00647 -0.000520 -11.880130 0.342840 0.136020 0.209320 0.4120000000000000 0.6960 0.4860 0.8500 0.4495 0.6023 0.7515 0.4399 0.5985 0.7428 0.4200 0.4896 0.6450 0.4401 0.5546 0.7080
0.160 -5.23741949893297 1.151 1.43253 0.34919 0.46055 0.01825668401347 -1.76259 1.54326 -0.01649 -0.00636 -0.000532 -12.056370 0.374000 0.177510 0.241170 0.4530000000000000 0.6970 0.4650 0.8380 0.4518 0.5987 0.7500 0.4443 0.5970 0.7442 0.4239 0.4939 0.6509 0.4369 0.5517 0.7037
0.180 -5.23525229107689 1.151 1.43710 0.32464 0.48439 0.01825668401347 -1.78989 1.60985 -0.01594 -0.00614 -0.000559 -12.420440 0.427000 0.253090 0.298960 0.5350000000000000 0.7040 0.4300 0.8250 0.4540 0.5990 0.7516 0.4474 0.6005 0.7489 0.4463 0.5003 0.6704 0.4361 0.5534 0.7046
0.200 -5.23331365758879 1.151 1.44781 0.30358 0.50900 0.01825668401347 -1.82110 1.67146 -0.01537 -0.00590 -0.000588 -12.785420 0.462970 0.320050 0.345910 0.6060000000000000 0.7130 0.4060 0.8210 0.4622 0.5897 0.7493 0.4499 0.5962 0.7469 0.4411 0.4907 0.6598 0.4322 0.5617 0.7088
0.250 -5.22920781624461 1.151 1.48260 0.26174 0.55500 0.01825668401347 -1.90412 1.80738 -0.01395 -0.00526 -0.000667 -13.635370 0.508560 0.453040 0.442310 0.6700000000000000 0.7110 0.3850 0.8080 0.4743 0.5545 0.7297 0.4702 0.6010 0.7631 0.4587 0.4500 0.6426 0.4319 0.5065 0.6657
0.300 -5.22585309959960 1.151 1.51881 0.23036 0.59300 0.01825668401347 -1.98439 1.92242 -0.01261 -0.00468 -0.000749 -14.380860 0.507760 0.548750 0.517820 0.7100000000000000 0.6830 0.3650 0.7750 0.4723 0.5320 0.7114 0.4749 0.5574 0.7322 0.4369 0.4989 0.6631 0.4323 0.4889 0.6526
0.350 -5.22301672709078 1.151 1.55291 0.20580 0.62500 0.01825668401347 -2.05756 2.02102 -0.01139 -0.00415 -0.000831 -15.035110 0.497100 0.617130 0.575960 0.7189367707551090 0.6650 0.3730 0.7620 0.4677 0.5050 0.6883 0.4780 0.5178 0.7047 0.4435 0.5315 0.6922 0.4318 0.4630 0.6331
0.400 -5.22055974946649 1.151 1.58443 0.18597 0.65200 0.01825668401347 -2.12282 2.10642 -0.01029 -0.00369 -0.000912 -15.615990 0.480650 0.666340 0.622390 0.7056101598849490 0.6570 0.3830 0.7610 0.4574 0.4792 0.6625 0.4836 0.4878 0.6868 0.4530 0.5458 0.7093 0.4155 0.4661 0.6244
0.450 -5.21839254161041 1.151 1.61360 0.16960 0.67500 0.01825668401347 -2.18047 2.18097 -0.00931 -0.00327 -0.000993 -16.138300 0.461590 0.701110 0.659760 0.6928932372909810 0.6470 0.3910 0.7560 0.4500 0.4608 0.6441 0.4772 0.4790 0.6761 0.4771 0.5294 0.7127 0.4079 0.4613 0.6158
0.500 -5.21645390812230 1.151 1.64075 0.15585 0.69500 0.01825668401347 -2.23118 2.24651 -0.00843 -0.00290 -0.001071 -16.613239 0.442240 0.725580 0.690660 0.6807534705053220 0.6400 0.4030 0.7560 0.4452 0.4506 0.6334 0.4696 0.4713 0.6653 0.4718 0.4947 0.6836 0.4087 0.4665 0.6202
0.600 -5.21309919147730 1.151 1.69020 0.13405 0.72900 0.01825668401347 -2.31475 2.35602 -0.00694 -0.00227 -0.001239 -17.452980 0.405370 0.752940 0.737960 0.6580415130288750 0.6330 0.4120 0.7550 0.4480 0.4355 0.6248 0.4597 0.4671 0.6553 0.4590 0.4701 0.6570 0.4065 0.4335 0.5943
0.700 -5.21026281896847 1.151 1.73450 0.11757 0.75600 0.01825668401347 -2.37885 2.44331 -0.00574 -0.00178 -0.001393 -18.180950 0.373420 0.762450 0.772260 0.6371531353146750 0.6330 0.4320 0.7660 0.4396 0.4299 0.6149 0.4595 0.4642 0.6532 0.4611 0.4727 0.6604 0.4031 0.4300 0.5894
0.800 -5.20780584134418 1.151 1.77474 0.10476 0.77800 0.01825668401347 -2.42769 2.51391 -0.00477 -0.00139 -0.001535 -18.824989 0.346230 0.761190 0.797360 0.6178103254041100 0.6360 0.4360 0.7710 0.4412 0.4269 0.6139 0.4596 0.4559 0.6474 0.4573 0.4565 0.6461 0.4067 0.4543 0.6097
0.900 -5.20563863348810 1.151 1.81162 0.09458 0.79600 0.01825668401347 -2.46450 2.57166 -0.00398 -0.00109 -0.001664 -19.403130 0.323640 0.753840 0.816160 0.5997867391697460 0.6360 0.4370 0.7720 0.4345 0.4314 0.6123 0.4559 0.4659 0.6519 0.4490 0.4391 0.6280 0.4082 0.4556 0.6117
1.000 -5.20370000000000 1.151 1.84561 0.08636 0.81200 0.01825668401347 -2.49170 2.61931 -0.00333 -0.00086 -0.001781 -19.927660 0.304790 0.742790 0.830090 0.5829000000000000 0.6370 0.4360 0.7720 0.4270 0.4400 0.6131 0.4479 0.4663 0.6466 0.4419 0.4415 0.6246 0.4080 0.4619 0.6163
1.250 -5.19959415865582 1.151 1.92015 0.07173 0.84100 0.01807627932941 -2.52758 2.70638 -0.00215 -0.00052 -0.001989 -21.058180 0.270260 0.708330 0.850360 0.5447531267038370 0.6350 0.4440 0.7750 0.4126 0.4401 0.6032 0.4422 0.4755 0.6493 0.4279 0.4321 0.6081 0.4123 0.4437 0.6057
1.500 -5.19623944201081 1.151 1.98274 0.06258 0.86100 0.01785908731221 -2.53359 2.76244 -0.00142 -0.00043 -0.002134 -21.996330 0.248310 0.672560 0.857320 0.5111822983011660 0.6450 0.4480 0.7850 0.4157 0.4485 0.6115 0.4477 0.4730 0.6513 0.4177 0.4605 0.6217 0.4178 0.4432 0.6090
2.000 -5.19094609187770 1.151 2.08214 0.05327 0.88400 0.01717728835933 -2.49565 2.82205 -0.00067 -0.00070 -0.002245 -23.488390 0.225290 0.610670 0.849910 0.4538170835899950 0.6330 0.4240 0.7620 0.4089 0.4409 0.6013 0.4371 0.4624 0.6362 0.4061 0.4720 0.6227 0.4133 0.4392 0.6031
2.500 -5.18684025053352 1.151 2.15841 0.05036 0.90000 0.01628389643418 -2.42623 2.84475 -0.00039 -0.00127 -0.002188 -24.647409 0.215400 0.564030 0.827570 0.4056165742693500 0.6080 0.4130 0.7350 0.3979 0.4247 0.5820 0.4293 0.4273 0.6057 0.3868 0.4841 0.6197 0.4139 0.4267 0.5945
3.000 -5.18348553388851 1.151 2.22046 0.04536 0.90000 0.01549254642056 -2.34726 2.84988 -0.00030 -0.00198 -0.002068 -25.597130 0.211540 0.526120 0.799110 0.3638313271186210 0.5820 0.4070 0.7110 0.3901 0.3963 0.5561 0.4227 0.4071 0.5869 0.3688 0.4470 0.5795 0.4117 0.4309 0.5959
3.500 -5.18064916137969 1.151 2.27406 0.04536 0.90000 0.01489171351523 -2.27002 2.84667 -0.00026 -0.00271 -0.001926 -26.409969 0.209756 0.497660 0.767820 0.3268167127622250 0.5620 0.3940 0.6870 0.3858 0.3856 0.5454 0.4104 0.4011 0.5738 0.3763 0.4339 0.5743 0.4016 0.4127 0.5758
4.000 -5.17819218375539 1.151 2.32307 0.04536 0.90000 0.01458020631717 -2.19947 2.83992 -0.00021 -0.00341 -0.001798 -27.131809 0.208748 0.476850 0.735940 0.2935047212753080 0.5400 0.3810 0.6600 0.3766 0.3728 0.5299 0.4104 0.3862 0.5636 0.3621 0.4064 0.5443 0.3943 0.3906 0.5550
4.500 -5.17602497589932 1.151 2.37009 0.04536 0.90000 0.01458710652043 -2.12528 2.82802 -0.00021 -0.00421 -0.001701 -27.792990 0.207737 0.462230 0.704070 0.2630000000000000 0.5250 0.3650 0.6400 0.3604 0.3635 0.5119 0.4116 0.3731 0.5555 0.3675 0.3831 0.5309 0.3853 0.3664 0.5317
5.000 -5.17408634241121 1.151 2.37009 0.04536 0.90000 0.01458710652043 -2.02646 2.82521 -0.00021 -0.00500 -0.001575 -28.313459 0.206721 0.452670 0.672200 0.2350000000000000 0.5220 0.3780 0.6450 0.3612 0.3588 0.5091 0.4469 0.3219 0.5508 0.3806 0.3163 0.4949 0.3799 0.3235 0.4990
""")
# Coefficients specific to the site amplification
SITE_COEFFS = CoeffsTable(sa_damping=5, table="""\
imt LnAmax1D1 LnAmax1D2 LnAmax1D3 LnAmax1D4 Src1D1 Src1D2 Src1D3 Src1D4 fsr1 fsr2 fsr3 fsr4
pga 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.0000 1.0000 1.0000 1.0000
0.005 0.650220 0.709730 0.644340 0.404280 8.429000 1.913680 1.117140 0.836440 1.0000 1.0000 1.0000 1.0000
0.010 0.651810 0.706790 0.646240 0.404280 8.090000 1.882560 1.114440 0.836440 1.0000 1.0000 1.0000 1.0000
0.020 0.653620 0.694650 0.638650 0.387890 6.992000 1.778610 1.124370 0.830000 1.0000 1.0000 1.0000 1.0500
0.030 0.654670 0.687550 0.634210 0.378300 6.350000 1.717810 1.130170 0.826240 1.0000 1.0000 1.0000 0.5800
0.040 0.652850 0.698920 0.606040 0.317370 4.883000 2.052340 1.150800 0.767580 1.0000 1.0060 1.0000 0.4820
0.050 0.672640 0.701370 0.617160 0.309340 5.043000 2.387130 1.239710 0.786320 1.0000 0.8510 1.0000 0.4720
0.060 0.699660 0.724450 0.637970 0.325300 6.271000 2.833990 1.348190 0.837750 1.0000 0.8030 1.0440 0.5060
0.070 0.717130 0.743430 0.654370 0.354120 7.667000 3.294470 1.451810 0.926160 1.0000 0.9180 0.9750 0.5870
0.080 0.716030 0.785980 0.680190 0.392820 9.034000 3.990910 1.583150 1.022280 1.0000 1.0620 0.9640 0.6830
0.090 0.725610 0.797210 0.708890 0.421840 11.251000 4.465760 1.732920 1.118020 1.0000 1.1060 0.9800 0.7820
0.100 0.742000 0.816680 0.718810 0.437360 14.817000 5.045610 1.841340 1.165780 1.0000 1.0710 0.9700 0.8230
0.120 0.762360 0.845230 0.725810 0.472080 14.817000 5.899600 2.030290 1.285510 0.0000 0.9515 1.0220 1.0290
0.140 0.752150 0.782960 0.745250 0.512780 14.817000 5.053530 2.281330 1.398080 0.0000 0.6720 0.8890 0.9910
0.150 0.738190 0.794800 0.761030 0.534320 14.817000 5.204900 2.444130 1.443270 0.0000 0.6310 0.8610 0.9830
0.160 0.719110 0.808610 0.768130 0.550220 14.817000 5.386940 2.580170 1.471770 0.0000 0.6000 0.8310 0.9730
0.180 0.654080 0.843310 0.756900 0.572790 14.817000 5.871650 2.741610 1.546940 0.0000 0.5710 0.7480 0.9790
0.200 0.583950 0.877700 0.717850 0.596740 14.817000 6.573910 2.825870 1.644010 0.0000 0.5650 0.6500 1.0060
0.250 0.583950 0.937670 0.654700 0.611360 14.817000 8.500000 2.718930 1.790130 0.0000 0.6010 0.4790 1.0270
0.300 0.583950 0.950000 0.696190 0.626380 14.817000 10.670300 2.417590 1.823450 0.0000 0.5790 0.4490 1.0210
0.350 0.583950 1.000000 0.779070 0.630120 14.817000 10.670300 2.303750 1.790370 0.0000 0.6790 0.4820 1.0030
0.400 0.583950 1.000000 0.827760 0.647730 14.817000 10.670300 2.236250 1.768440 0.0000 0.6550 0.4990 1.0100
0.450 0.583950 1.000000 0.876450 0.641520 14.817000 10.670300 2.216780 1.675390 0.0000 0.6150 0.5150 0.9850
0.500 0.583950 1.000000 0.925140 0.655820 14.817000 10.670300 2.243380 1.625390 0.0000 0.5500 0.5300 0.9900
0.600 0.583950 1.000000 0.973830 0.686680 14.817000 10.670300 2.805350 1.524530 0.0000 0.0000 0.5300 1.0060
0.700 0.583950 1.000000 1.022520 0.705600 14.817000 10.670300 6.658390 1.397240 0.0000 0.0000 0.4990 1.0000
0.800 0.583950 1.000000 1.071220 0.714290 14.817000 10.670300 30.000000 1.320290 0.0000 0.0000 0.3690 1.0000
0.900 0.583950 1.000000 1.119910 0.703880 14.817000 10.670300 30.000000 1.266370 0.0000 0.0000 0.3000 0.9600
1.000 0.583950 1.000000 1.168600 0.678130 14.817000 10.670300 30.000000 1.226800 0.0000 0.0000 0.2000 0.9040
1.250 0.583950 1.000000 1.217290 0.611190 14.817000 10.670300 30.000000 1.220650 0.0000 0.0000 0.0000 0.7380
1.500 0.583950 1.000000 1.265980 0.547360 14.817000 10.670300 30.000000 1.318050 0.0000 0.0000 0.0000 0.5350
2.000 0.583950 1.000000 1.314670 0.459440 14.817000 10.670300 30.000000 2.124850 0.0000 0.0000 0.0000 0.3580
2.500 0.583950 1.000000 1.363360 0.408460 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
3.000 0.583950 1.000000 1.412050 0.364210 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
3.500 0.583950 1.000000 1.460750 0.329840 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
4.000 0.583950 1.000000 1.509440 0.309120 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
4.500 0.583950 1.000000 1.558130 0.292510 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
5.000 0.583950 1.000000 1.606820 0.547360 14.817000 10.670300 30.000000 14.381810 0.0000 0.0000 0.0000 0.0000
""")
[docs]class ZhaoEtAl2016SSlabSiteSigma(ZhaoEtAl2016SSlab):
"""
Subclass of the Zhao et al. (2016c) subduction in-slab GMPE for the
case of site-dependent within-event variability
"""
[docs] def get_stddevs(self, C, n_sites, idx, stddev_types):
"""
Returns the intra-event standard deviation calibrated for the
specific site class
"""
stddevs = []
tau = C["tau"] + np.zeros(n_sites)
phi = np.zeros(n_sites)
for i in range(1, 5):
phi[idx[i]] += C["sc{:g}_sigma_S".format(i)]
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(phi ** 2. + tau ** 2.))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi)
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(tau)
return stddevs