# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2021 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:`ChiouYoungs2014`
:class:`ChiouYoungs2014Japan`
:class:`ChiouYoungs2014Italy`
:class:`ChiouYoungs2014Wenchuan`
:class:`ChiouYoungs2014PEER`
:class:`ChiouYoungs2014NearFaultEffect`
"""
import numpy as np
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, PGV, SA
[docs]class ChiouYoungs2014(GMPE):
"""
Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs
Chiou, B. S.-J. and Youngs, R. R. (2014), "Updated of the Chiou and Youngs
NGA Model for the Average Horizontal Component of Peak Ground Motion and
Response Spectra, Earthquake Spectra, 30(3), 1117 - 1153,
DOI: 10.1193/072813EQS219M
"""
#: 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,
#: peak ground velocity and peak ground acceleration
DEFINED_FOR_INTENSITY_MEASURE_TYPES = {PGA, PGV, SA}
#: Supported intensity measure component is orientation-independent
#: measure :attr:`~openquake.hazardlib.const.IMC.RotD50`,
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RotD50
#: Supported standard deviation types are inter-event, intra-event
#: and total, see chapter "Variance model".
DEFINED_FOR_STANDARD_DEVIATION_TYPES = {
const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT}
#: Required site parameters are Vs30, Vs30 measured flag
#: and Z1.0.
REQUIRES_SITES_PARAMETERS = {'vs30', 'vs30measured', 'z1pt0'}
#: Required rupture parameters are magnitude, rake,
#: dip and ztor.
REQUIRES_RUPTURE_PARAMETERS = {'dip', 'rake', 'mag', 'ztor'}
#: Required distance measures are RRup, Rjb and Rx.
REQUIRES_DISTANCES = {'rrup', 'rjb', 'rx'}
#: Reference shear wave velocity
DEFINED_FOR_REFERENCE_VELOCITY = 1130
[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.
"""
C = self.COEFFS[imt]
# Get ground motion on reference rock
ln_y_ref = self.get_ln_y_ref(C, rup, dists)
y_ref = np.exp(ln_y_ref)
# Get the site amplification
# Get basin depth
dz1pt0 = self._get_centered_z1pt0(sites)
# In the case that Z1.0 = 0.0 then no deep soil correction is applied
dz1pt0[sites.z1pt0 <= 0.0] = 0.0
f_z1pt0 = self.get_basin_depth_term(C, dz1pt0)
# Get linear amplification term
f_lin = self.get_linear_site_term(C, sites)
# Get nonlinear amplification term
f_nl, f_nl_scaling = self.get_nonlinear_site_term(C, sites, y_ref)
# Add on the site amplification
mean = ln_y_ref + (f_lin + f_nl + f_z1pt0)
# Get standard deviations
stddevs = self.get_stddevs(C, sites, rup.mag, y_ref, f_nl_scaling,
stddev_types)
return mean, stddevs
[docs] def get_ln_y_ref(self, C, rup, dists):
"""
Returns the ground motion on the reference rock, described fully by
Equation 11
"""
delta_ztor = self._get_centered_ztor(rup)
return (self.get_stress_scaling(C) +
self.get_magnitude_scaling(C, rup.mag) +
self.get_source_scaling_terms(C, rup, delta_ztor) +
self.get_hanging_wall_term(C, rup, dists) +
self.get_geometric_spreading(C, rup.mag, dists.rrup) +
self.get_far_field_distance_scaling(C, rup.mag, dists.rrup) +
self.get_directivity(C, rup, dists))
[docs] def get_stddevs(self, C, sites, mag, y_ref, f_nl_scaling, stddev_types):
"""
Returns the standard deviation model described in equation 13
"""
# Determines the nonlinear term described in equation 13, line 4
nl0 = f_nl_scaling * (y_ref / (y_ref + C["phi4"]))
# Get between and within-event variability
tau = self.get_tau(C, mag)
phi_nl0 = self.get_phi(C, mag, sites, nl0)
# Get total standard deviation propagating the uncertainty in the
# nonlinear amplification term
sigma = np.sqrt(((1.0 + nl0) ** 2.) * (tau ** 2.) + phi_nl0 ** 2.)
stddevs = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# Equation 13, line 1
stddevs.append(sigma)
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(phi_nl0)
elif stddev_type == const.StdDev.INTER_EVENT:
# This is implied by equation 13, line 1
stddevs.append(np.abs((1 + nl0) * tau))
return stddevs
[docs] def get_tau(self, C, mag):
"""
Returns the between-event variability described in equation 13, line 2
"""
# eq. 13 to calculate inter-event standard error
mag_test = min(max(mag, 5.0), 6.5) - 5.0
return C['tau1'] + ((C['tau2'] - C['tau1']) / 1.5) * mag_test
[docs] def get_phi(self, C, mag, sites, nl0):
"""
Returns the within-event variability described in equation 13, line 3
"""
phi = C["sig3"] * np.ones(sites.vs30.shape)
phi[sites.vs30measured] = 0.7
phi = np.sqrt(phi + ((1.0 + nl0) ** 2.))
mdep = C["sig1"] + (((C["sig2"] - C["sig1"]) / 1.5) *
(min(max(mag, 5.0), 6.5) - 5.0))
return mdep * phi
[docs] def get_stress_scaling(self, C):
"""
Returns the stress drop scaling factor
"""
return C["c1"]
[docs] def get_magnitude_scaling(self, C, mag):
"""
Returns the magnitude scaling
"""
f_m = np.log(1.0 + np.exp(C["cn"] * (C["cm"] - mag)))
f_m = self.CONSTANTS["c2"] * (mag - 6.0) +\
((self.CONSTANTS["c2"] - C["c3"]) / C["cn"]) * f_m
return f_m
[docs] def get_geometric_spreading(self, C, mag, rrup):
"""
Returns the near-field geometric spreading term
"""
# Get the near-field magnitude scaling
return self.CONSTANTS["c4"] * np.log(
rrup + C["c5"] * np.cosh(C["c6"] * max(mag - C["chm"], 0.0)))
[docs] def get_far_field_distance_scaling(self, C, mag, rrup):
"""
Returns the far-field distance scaling term - both magnitude and
distance
"""
# Get the attenuation distance scaling
f_r = (self.CONSTANTS["c4a"] - self.CONSTANTS["c4"]) * np.log(
np.sqrt(rrup ** 2. + self.CONSTANTS["crb"] ** 2.))
# Get the magnitude dependent term
f_rm = C["cg1"] + (C["cg2"] / np.cosh(max(mag - C["cg3"], 0.0)))
return f_r + f_rm * rrup
[docs] def get_source_scaling_terms(self, C, rup, delta_ztor):
"""
Returns additional source scaling parameters related to style of
faulting, dip and top of rupture depth
"""
f_src = 0.0
coshm = np.cosh(2.0 * max(rup.mag - 4.5, 0.0))
# Style of faulting term
if 30 <= rup.rake <= 150:
# reverse faulting flag
f_src += (C["c1a"] + (C["c1c"] / coshm))
elif -120 <= rup.rake <= -60:
# normal faulting flag
f_src += (C["c1b"] + (C["c1d"] / coshm))
# Top of rupture term
f_src += ((C["c7"] + (C["c7b"] / coshm)) * delta_ztor)
# Dip term
f_src += ((self.CONSTANTS["c11"] + (C["c11b"] / coshm)) *
np.cos(np.radians(rup.dip)) ** 2.0)
return f_src
[docs] def get_hanging_wall_term(self, C, rup, dists):
"""
Returns the hanging wall term
"""
fhw = np.zeros(dists.rrup.shape)
idx = dists.rx >= 0.0
if np.any(idx):
fdist = 1.0 - (np.sqrt(dists.rjb[idx] ** 2. + rup.ztor ** 2.) /
(dists.rrup[idx] + 1.0))
fdist *= (C["c9a"] + (1.0 - C["c9a"]) * np.tanh(dists.rx[idx] /
C["c9b"]))
fhw[idx] += (C["c9"] * np.cos(np.radians(rup.dip)) * fdist)
return fhw
[docs] def get_directivity(self, C, rup, dists):
"""
Returns the directivity term.
The directivity prediction parameter is centered on the average
directivity prediction parameter. Here we set the centered_dpp
equal to zero, since the near fault directivity effect prediction is
off by default in our calculation.
"""
cdpp = self._get_centered_cdpp(dists)
if not np.any(cdpp > 0.0):
# No directivity term
return 0.0
f_dir = np.exp(-C["c8a"] * ((rup.mag - C["c8b"]) ** 2.)) * cdpp
f_dir *= min((max(rup.mag - 5.5, 0.0) / 0.8), 1.)
rrup_max = dists.rrup - 40.
rrup_max[rrup_max < 0.0] = 0.0
rrup_max = 1.0 - (rrup_max / 30.)
rrup_max[rrup_max < 0.0] = 0.0
return C["c8"] * rrup_max * f_dir
[docs] def get_linear_site_term(self, C, sites):
"""
Returns the linear site scaling term
"""
return C["phi1"] * np.log(sites.vs30 / 1130).clip(-np.inf, 0.0)
[docs] def get_basin_depth_term(self, C, centered_z1pt0):
"""
Returns the basin depth scaling
"""
return C["phi5"] * (1.0 - np.exp(-centered_z1pt0 /
self.CONSTANTS["phi6"]))
[docs] def get_nonlinear_site_term(self, C, sites, y_ref):
"""
Returns the nonlinear site term and the Vs-scaling factor (to be
used in the standard deviation model
"""
vs = sites.vs30.clip(-np.inf, 1130.0)
f_nl_scaling = C["phi2"] * (np.exp(C["phi3"] * (vs - 360.)) -
np.exp(C["phi3"] * (1130. - 360.)))
f_nl = np.log((y_ref + C["phi4"]) / C["phi4"]) * f_nl_scaling
return f_nl, f_nl_scaling
def _get_centered_z1pt0(self, sites):
"""
Get z1pt0 centered on the Vs30- dependent average z1pt0(m)
California and non-Japan regions
"""
#: California and non-Japan regions
mean_z1pt0 = (-7.15 / 4.) * np.log(((sites.vs30) ** 4. + 570.94 ** 4.)
/ (1360 ** 4. + 570.94 ** 4.))
return sites.z1pt0 - np.exp(mean_z1pt0)
def _get_centered_ztor(self, rup):
"""
Get ztor centered on the M- dependent avarage ztor(km)
by different fault types.
"""
if 30 <= rup.rake <= 150:
# Reverse and reverse-oblique faulting
mean_ztor = max(2.704 - 1.226 * max(rup.mag - 5.849, 0.0), 0.) ** 2
else:
# Strike-slip and normal faulting
mean_ztor = max(2.673 - 1.136 * max(rup.mag - 4.970, 0.0), 0.) ** 2
return rup.ztor - mean_ztor
def _get_centered_cdpp(self, dists):
"""
Returns the centred dpp term - in this case this is turned off by
default
"""
return np.zeros(dists.rrup.shape)
#: Coefficient tables are constructed from values in tables 1 - 5
COEFFS = CoeffsTable(sa_damping=5, table="""\
IMT c1 c1a c1b c1c c1d cn cm c2 c3 c4 c4a crb c5 chm c6 c7 c7b c8 c8a c8b c9 c9a c9b c11 c11b cg1 cg2 cg3 phi1 phi2 phi3 phi4 phi5 phi6 gjpit gwn phi1jp phi5jp phi6jp tau1 tau2 sig1 sig2 sig3 sig2jp
pga -1.5065 0.165 -0.255 -0.165 0.255 16.0875 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0956 0.4908 0.0352 0.0462 0. 0.2695 0.4833 0.9228 0.1202 6.8607 0. -0.4536 -0.007146 -0.006758 4.2542 -0.521 -0.1417 -0.00701 0.102151 0. 300 1.5817 0.7594 -0.6846 0.459 800. 0.4 0.26 0.4912 0.3762 0.8 0.4528
pgv 2.3549 0.165 -0.0626 -0.165 0.0626 3.3024 5.423 1.06 2.3152 -2.1 -0.5 50 5.8096 3.0514 0.4407 0.0324 0.0097 0.2154 0.2695 5. 0.3079 0.1 6.5 0 -0.3834 -0.001852 -0.007403 4.3439 -0.7936 -0.0699 -0.008444 5.41 0.0202 300. 2.2306 0.335 -0.7966 0.9488 800. 0.3894 0.2578 0.4785 0.3629 0.7504 0.3918
0.01 -1.5065 0.165 -0.255 -0.165 0.255 16.0875 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0956 0.4908 0.0352 0.0462 0. 0.2695 0.4833 0.9228 0.1202 6.8607 0. -0.4536 -0.007146 -0.006758 4.2542 -0.521 -0.1417 -0.00701 0.102151 0. 300 1.5817 0.7594 -0.6846 0.459 800. 0.4 0.26 0.4912 0.3762 0.8 0.4528
0.02 -1.4798 0.165 -0.255 -0.165 0.255 15.7118 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0963 0.4925 0.0352 0.0472 0. 0.2695 1.2144 0.9296 0.1217 6.8697 0. -0.4536 -0.007249 -0.006758 4.2386 -0.5055 -0.1364 -0.007279 0.10836 0. 300 1.574 0.7606 -0.6681 0.458 800. 0.4026 0.2637 0.4904 0.3762 0.8 0.4551
0.03 -1.2972 0.165 -0.255 -0.165 0.255 15.8819 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0974 0.4992 0.0352 0.0533 0. 0.2695 1.6421 0.9396 0.1194 6.9113 0. -0.4536 -0.007869 -0.006758 4.2519 -0.4368 -0.1403 -0.007354 0.119888 0. 300 1.5544 0.7642 -0.6314 0.462 800. 0.4063 0.2689 0.4988 0.3849 0.8 0.4571
0.04 -1.1007 0.165 -0.255 -0.165 0.255 16.4556 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0988 0.5037 0.0352 0.0596 0. 0.2695 1.9456 0.9661 0.1166 7.0271 0. -0.4536 -0.008316 -0.006758 4.296 -0.3752 -0.1591 -0.006977 0.133641 0. 300 1.5502 0.7676 -0.5855 0.453 800. 0.4095 0.2736 0.5049 0.391 0.8 0.4642
0.05 -0.9292 0.165 -0.255 -0.165 0.255 17.6453 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.1011 0.5048 0.0352 0.0639 0. 0.2695 2.181 0.9794 0.1176 7.0959 0. -0.4536 -0.008743 -0.006758 4.3578 -0.3469 -0.1862 -0.006467 0.148927 0. 300 1.5391 0.7739 -0.5457 0.436 800. 0.4124 0.2777 0.5096 0.3957 0.8 0.4716
0.075 -0.658 0.165 -0.254 -0.165 0.254 20.1772 5.0031 1.06 1.9636 -2.1 -0.5 50 6.4551 3.1094 0.5048 0.0352 0.063 0. 0.2695 2.6087 1.026 0.1171 7.3298 0. -0.4536 -0.009537 -0.00619 4.5455 -0.3747 -0.2538 -0.005734 0.190596 0. 300 1.4804 0.7956 -0.4685 0.383 800. 0.4179 0.2855 0.5179 0.4043 0.8 0.5022
0.1 -0.5613 0.165 -0.253 -0.165 0.253 19.9992 5.0172 1.06 1.9636 -2.1 -0.5 50 6.8305 3.2381 0.5048 0.0352 0.0532 0. 0.2695 2.9122 1.0177 0.1146 7.2588 0. -0.4536 -0.00983 -0.005332 4.7603 -0.444 -0.2943 -0.005604 0.230662 0. 300 1.4094 0.7932 -0.4985 0.375 800. 0.4219 0.2913 0.5236 0.4104 0.8 0.523
0.12 -0.5342 0.165 -0.252 -0.165 0.252 18.7106 5.0315 1.06 1.9795 -2.1 -0.5 50 7.1333 3.3407 0.5048 0.0352 0.0452 0. 0.2695 3.1045 1.0008 0.1128 7.2372 0. -0.4536 -0.009913 -0.004732 4.8963 -0.4895 -0.3077 -0.005696 0.253169 0. 300 1.3682 0.7768 -0.5603 0.377 800. 0.4244 0.2949 0.527 0.4143 0.8 0.5278
0.15 -0.5462 0.165 -0.25 -0.165 0.25 16.6246 5.0547 1.06 2.0362 -2.1 -0.5 50 7.3621 3.43 0.5045 0.0352 0.0345 0. 0.2695 3.3399 0.9801 0.1106 7.2109 0. -0.4536 -0.009896 -0.003806 5.0644 -0.5477 -0.3113 -0.005845 0.266468 0. 300 1.3241 0.7437 -0.6451 0.379 800. 0.4275 0.2993 0.5308 0.4191 0.8 0.5304
0.17 -0.5858 0.165 -0.248 -0.165 0.248 15.3709 5.0704 1.06 2.0823 -2.1 -0.5 50 7.4365 3.4688 0.5036 0.0352 0.0283 0. 0.2695 3.4719 0.9652 0.115 7.2491 0. -0.4536 -0.009787 -0.00328 5.1371 -0.5922 -0.3062 -0.005959 0.26506 0. 300 1.3071 0.7219 -0.6981 0.38 800. 0.4292 0.3017 0.5328 0.4217 0.8 0.531
0.2 -0.6798 0.165 -0.2449 -0.165 0.2449 13.7012 5.0939 1.06 2.1521 -2.1 -0.5 50 7.4972 3.5146 0.5016 0.0352 0.0202 0. 0.2695 3.6434 0.9459 0.1208 7.2988 0. -0.444 -0.009505 -0.00269 5.188 -0.6693 -0.2927 -0.006141 0.255253 0. 300 1.2931 0.6922 -0.7653 0.384 800. 0.4313 0.3047 0.5351 0.4252 0.8 0.5312
0.25 -0.8663 0.165 -0.2382 -0.165 0.2382 11.2667 5.1315 1.06 2.2574 -2.1 -0.5 50 7.5416 3.5746 0.4971 0.0352 0.009 0. 0.2695 3.8787 0.9196 0.1208 7.3691 0. -0.3539 -0.008918 -0.002128 5.2164 -0.7766 -0.2662 -0.006439 0.231541 0. 300 1.315 0.6579 -0.8469 0.393 800. 0.4341 0.3087 0.5377 0.4299 0.7999 0.5309
0.3 -1.0514 0.165 -0.2313 -0.165 0.2313 9.1908 5.167 1.06 2.344 -2.1 -0.5 50 7.56 3.6232 0.4919 0.0352 -0.0004 0. 0.2695 4.0711 0.8829 0.1175 6.8789 0. -0.2688 -0.008251 -0.001812 5.1954 -0.8501 -0.2405 -0.006704 0.207277 0.001 300 1.3514 0.6362 -0.8999 0.408 800. 0.4363 0.3119 0.5395 0.4338 0.7997 0.5307
0.4 -1.3794 0.165 -0.2146 -0.165 0.2146 6.5459 5.2317 1.06 2.4709 -2.1 -0.5 50 7.5735 3.6945 0.4807 0.0352 -0.0155 0. 0.2695 4.3745 0.8302 0.106 6.5334 0. -0.1793 -0.007267 -0.001274 5.0899 -0.9431 -0.1975 -0.007125 0.165464 0.004 300 1.4051 0.6049 -0.9618 0.462 800. 0.4396 0.3165 0.5422 0.4399 0.7988 0.531
0.5 -1.6508 0.165 -0.1972 -0.165 0.1972 5.2305 5.2893 1.06 2.5567 -2.1 -0.5 50 7.5778 3.7401 0.4707 0.0352 -0.0278 0.0991 0.2695 4.6099 0.7884 0.1061 6.526 0. -0.1428 -0.006492 -0.001074 4.7854 -1.0044 -0.1633 -0.007435 0.133828 0.01 300 1.4402 0.5507 -0.9945 0.524 800. 0.4419 0.3199 0.5433 0.4446 0.7966 0.5313
0.75 -2.1511 0.165 -0.162 -0.165 0.162 3.7896 5.4109 1.06 2.6812 -2.1 -0.5 50 7.5808 3.7941 0.4575 0.0352 -0.0477 0.1982 0.2695 5.0376 0.6754 0.1 6.5 0. -0.1138 -0.005147 -0.001115 4.3304 -1.0602 -0.1028 -0.00812 0.085153 0.034 300 1.528 0.3582 -1.0225 0.658 800. 0.4459 0.3255 0.5294 0.4533 0.7792 0.5309
1 -2.5365 0.165 -0.14 -0.165 0.14 3.3024 5.5106 1.06 2.7474 -2.1 -0.5 50 7.5814 3.8144 0.4522 0.0352 -0.0559 0.2154 0.2695 5.3411 0.6196 0.1 6.5 0. -0.1062 -0.004277 -0.001197 4.1667 -1.0941 -0.0699 -0.008444 0.058595 0.067 300 1.6523 0.2003 -1.0002 0.78 800. 0.4484 0.3291 0.5105 0.4594 0.7504 0.5302
1.5 -3.0686 0.165 -0.1184 -0.165 0.1184 2.8498 5.6705 1.06 2.8161 -2.1 -0.5 50 7.5817 3.8284 0.4501 0.0352 -0.063 0.2154 0.2695 5.7688 0.5101 0.1 6.5 0. -0.102 -0.002979 -0.001675 4.0029 -1.1142 -0.0425 -0.007707 0.031787 0.143 300 1.8872 0.0356 -0.9245 0.96 800. 0.4515 0.3335 0.4783 0.468 0.7136 0.5276
2 -3.4148 0.1645 -0.11 -0.1645 0.11 2.5417 5.7981 1.06 2.8514 -2.1 -0.5 50 7.5818 3.833 0.45 0.0352 -0.0665 0.2154 0.2695 6.0723 0.3917 0.1 6.5 0. -0.1009 -0.002301 -0.002349 3.8949 -1.1154 -0.0302 -0.004792 0.019716 0.203 300 2.1348 0. -0.8626 1.11 800. 0.4534 0.3363 0.4681 0.4681 0.7035 0.5167
3 -3.9013 0.1168 -0.104 -0.1168 0.104 2.1488 5.9983 1.06 2.8875 -2.1 -0.5 50 7.5818 3.8361 0.45 0.016 -0.0516 0.2154 0.2695 6.5 0.1244 0.1 6.5 0. -0.1003 -0.001344 -0.003306 3.7928 -1.1081 -0.0129 -0.001828 0.009643 0.277 300 3.5752 0. -0.7882 1.291 800. 0.4558 0.3398 0.4617 0.4617 0.7006 0.4917
4 -4.2466 0.0732 -0.102 -0.0732 0.102 1.8957 6.1552 1.06 2.9058 -2.1 -0.5 50 7.5818 3.8369 0.45 0.0062 -0.0448 0.2154 0.2695 6.8035 0.0086 0.1 6.5 0. -0.1001 -0.001084 -0.003566 3.7443 -1.0603 -0.0016 -0.001523 0.005379 0.309 300 3.8646 0. -0.7195 1.387 800. 0.4574 0.3419 0.4571 0.4571 0.7001 0.4682
5 -4.5143 0.0484 -0.101 -0.0484 0.101 1.7228 6.2856 1.06 2.9169 -2.1 -0.5 50 7.5818 3.8376 0.45 0.0029 -0.0424 0.2154 0.2695 7.0389 0. 0.1 6.5 0. -0.1001 -0.00101 -0.00364 3.709 -0.9872 0. -0.00144 0.003223 0.321 300 3.7292 0. -0.656 1.433 800. 0.4584 0.3435 0.4535 0.4535 0.7 0.4517
7.5 -5.0009 0.022 -0.101 -0.022 0.101 1.5737 6.5428 1.06 2.932 -2.1 -0.5 50 7.5818 3.838 0.45 0.0007 -0.0348 0.2154 0.2695 7.4666 0. 0.1 6.5 0. -0.1 -0.000964 -0.003686 3.6632 -0.8274 0. -0.001369 0.001134 0.329 300 2.3763 0. -0.5202 1.46 800. 0.4601 0.3459 0.4471 0.4471 0.7 0.4167
10 -5.3461 0.0124 -0.1 -0.0124 0.1 1.5265 6.7415 1.06 2.9396 -2.1 -0.5 50 7.5818 3.838 0.45 0.0003 -0.0253 0.2154 0.2695 7.77 0. 0.1 6.5 0. -0.1 -0.00095 -0.0037 3.623 -0.7053 0. -0.001361 0.000515 0.33 300 1.7679 0. -0.4068 1.464 800. 0.4612 0.3474 0.4426 0.4426 0.7 0.3755
""")
CONSTANTS = {"c2": 1.06, "c4": -2.1, "c4a": -0.5, "crb": 50.0,
"c8a": 0.2695, "c11": 0.0, "phi6": 300.0, "phi6jp": 800.0}
[docs]class ChiouYoungs2014Japan(ChiouYoungs2014):
"""
Regionalisation of the Chiou & Youngs (2014) GMPE for use with the
Japan far-field distance attuation scaling and site model
"""
[docs] def get_far_field_distance_scaling(self, C, mag, rrup):
"""
Returns the far-field distance scaling term - both magnitude and
distance
"""
# Get the attenuation distance scaling
f_r = (self.CONSTANTS["c4a"] - self.CONSTANTS["c4"]) * np.log(
np.sqrt(rrup ** 2. + self.CONSTANTS["crb"] ** 2.))
# Get the magnitude dependent term
f_rm = (C["cg1"] +
(C["cg2"] / np.cosh(max(mag - C["cg3"], 0.0)))) * rrup
if (mag > 6.0) and (mag < 6.9):
# Apply adjustment factor for Japan
f_rm *= C["gjpit"]
return f_r + f_rm
[docs] def get_linear_site_term(self, C, sites):
"""
Returns the linear site scaling term
"""
return C["phi1jp"] * np.log(sites.vs30 / 1130).clip(-np.inf, 0.0)
[docs] def get_basin_depth_term(self, C, centered_z1pt0):
"""
Returns the basin depth scaling
"""
return C["phi5jp"] * (1.0 - np.exp(-centered_z1pt0 /
self.CONSTANTS["phi6jp"]))
def _get_centered_z1pt0(self, sites):
"""
Get z1pt0 centered on the Vs30- dependent average z1pt0(m) for Japan
"""
#: Japan
mean_z1pt0 = (-5.23 / 2.) * np.log(((sites.vs30 ** 2.) + 412.39 ** 2.)
/ (1360 ** 2. + 412.39 ** 2.))
return sites.z1pt0 - np.exp(mean_z1pt0)
[docs]class ChiouYoungs2014Italy(ChiouYoungs2014):
"""
Adaption of the Chiou & Youngs (2014) GMPE for the the Italy far-field
attenuation scaling, but assuming the California site amplification model
"""
[docs] def get_far_field_distance_scaling(self, C, mag, rrup):
"""
Returns the far-field distance scaling term - both magnitude and
distance
"""
# Get the attenuation distance scaling
f_r = (self.CONSTANTS["c4a"] - self.CONSTANTS["c4"]) * np.log(
np.sqrt(rrup ** 2. + self.CONSTANTS["crb"] ** 2.))
# Get the magnitude dependent term
f_rm = (C["cg1"] +
(C["cg2"] / np.cosh(max(mag - C["cg3"], 0.0)))) * rrup
if (mag > 6.0) and (mag < 6.9):
# Apply adjustment factor for Italy
f_rm *= C["gjpit"]
return f_r + f_rm
[docs]class ChiouYoungs2014Wenchuan(ChiouYoungs2014):
"""
Adaption of the Chiou & Youngs (2014) GMPE for the Wenchuan far-field
attenuation scaling, but assuming the California site amplification model.
It should be note that according to Chiou & Youngs (2014) this adjustment
is calibrated only for the M7.9 Wenchuan earthquake, so application to
other scenarios is at the user's own risk
"""
[docs] def get_far_field_distance_scaling(self, C, mag, rrup):
"""
Returns the far-field distance scaling term - both magnitude and
distance
"""
# Get the attenuation distance scaling
f_r = (self.CONSTANTS["c4a"] - self.CONSTANTS["c4"]) * np.log(
np.sqrt(rrup ** 2. + self.CONSTANTS["crb"] ** 2.))
# Get the magnitude dependent term
f_rm = (C["cg1"] +
(C["cg2"] / np.cosh(max(mag - C["cg3"], 0.0)))) * rrup
# Apply adjustment factor for Wenchuan
return f_r + (f_rm * C["gwn"])
[docs]class ChiouYoungs2014PEER(ChiouYoungs2014):
"""
This implements the Chiou & Youngs (2014) GMPE for use with the PEER
tests. In this version the total standard deviation is fixed at 0.65
"""
#: Only the total standars deviation is defined
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([
const.StdDev.TOTAL,
])
#: The PEER tests requires only PGA
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([
PGA,
])
[docs] def get_stddevs(self, C, sites, mag, y_ref, f_nl_scaling, stddev_types):
"""
Returns the standard deviation, which is fixed at 0.65 for every site
"""
ret = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# Fix total sigma to 0.65
ret.append(0.65 * np.ones_like(sites.vs30))
return ret
[docs]class ChiouYoungs2014NearFaultEffect(ChiouYoungs2014):
"""
This implements the Chiou & Youngs (2014) GMPE include the near fault
effect prediction. In this version, we add the distance measure, rcdpp
for directivity prediction.
"""
#: Required distance measures are RRup, Rjb, Rx, and Rcdpp
REQUIRES_DISTANCES = set(('rrup', 'rjb', 'rx', 'rcdpp'))
def _get_centered_cdpp(self, dists):
"""
Get directivity prediction parameter centered on the avgerage
directivity prediction parameter.
"""
return dists.rcdpp