# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2013-2023 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,
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
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# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Module exports :class:`AfshariStewart2016`,
:class:`AfshariStewart2016Japan`
"""
import numpy as np
from openquake.baselib.general import CallableDict
from openquake.hazardlib.gsim.base import CoeffsTable, GMPE
from openquake.hazardlib import const
from openquake.hazardlib.imt import RSD595, RSD575, RSD2080
CONSTANTS = {"mstar": 6.0,
"r1": 10.0,
"r2": 50.0,
"v1": 600.0,
"dz1ref": 200.0}
_get_lnmu_z1 = CallableDict()
@_get_lnmu_z1.add("CAL")
def _get_lnmu_z1_1(region, vs30):
"""
Returns the z1.0 normalisation term for California (equation 11)
"""
return (-7.15 / 4.) * np.log(
(vs30 ** 4. + 570.94 ** 4) / (1360.0 ** 4. + 570.94 ** 4.)) -\
np.log(1000.0)
@_get_lnmu_z1.add("JPN")
def _get_lnmu_z1_2(region, vs30):
"""
Returns the z1.0 normalisation term for Japan (equation 12)
"""
return (-5.23 / 2.) * np.log(
(vs30 ** 2. + 412.39 ** 2) / (1360.0 ** 2. + 412.39 ** 2.)) -\
np.log(1000.0)
def _get_phi(C, mag):
"""
Returns the magnitude dependent intra-event standard deviation (phi)
(equation 15)
"""
phi = C["phi1"] + (C["phi2"] - C["phi1"]) * ((mag - 5.5) / 0.25)
phi[mag < 5.5] = C["phi1"]
phi[mag >= 5.75] = C["phi2"]
return phi
def _get_sof_terms(C, rake):
"""
Returns the style-of-faulting scaling parameters
"""
# Strike-slip faulting
b0 = np.full_like(rake, C["b0SS"])
b1 = np.full_like(rake, C["b1SS"])
# Reverse faulting
rev = (rake >= 45.) & (rake <= 135.)
b0[rev] = C["b0R"]
b1[rev] = C["b1R"]
# Normal faulting
nor = (rake <= -45.) & (rake >= -135.)
b0[nor] = C["b0N"]
b1[nor] = C["b1N"]
return b0, b1
def _get_tau(C, mag):
"""
Returns magnitude dependent inter-event standard deviation (tau)
(equation 14)
"""
tau = C["tau1"] + (C["tau2"] - C["tau1"]) * ((mag - 6.5) / 0.5)
tau[mag < 6.5] = C["tau1"]
tau[mag >= 7.] = C["tau2"]
return tau
[docs]def get_distance_term(C, rrup):
"""
Returns the distance scaling term in equation 7
"""
f_p = C["c1"] * rrup
idx = np.logical_and(rrup > CONSTANTS["r1"],
rrup <= CONSTANTS["r2"])
f_p[idx] = (C["c1"] * CONSTANTS["r1"]) +\
C["c2"] * (rrup[idx] - CONSTANTS["r1"])
idx = rrup > CONSTANTS["r2"]
f_p[idx] = C["c1"] * CONSTANTS["r1"] +\
C["c2"] * (CONSTANTS["r2"] - CONSTANTS["r1"]) +\
C["c3"] * (rrup[idx] - CONSTANTS["r2"])
return f_p
[docs]def get_magnitude_term(C, ctx):
"""
Returns the magnitude scaling term in equation 3
"""
b0, b1 = _get_sof_terms(C, ctx.rake)
# Calculate moment (equation 5)
m_0 = 10.0 ** (1.5 * ctx.mag + 16.05)
# Get stress-drop scaling (equation 6)
idx1 = ctx.mag > C["m2"]
b1[idx1] += (C["b2"] * (C["m2"] - CONSTANTS["mstar"]) +
(C["b3"] * (ctx.mag[idx1] - C["m2"])))
idx2 = ctx.mag <= C["m2"]
b1[idx2] += C["b2"] * (ctx.mag[idx2] - CONSTANTS["mstar"])
stress_drop = np.exp(b1)
# Get corner frequency (equation 4)
f0 = 4.9 * 1.0E6 * 3.2 * (stress_drop / m_0) ** (1. / 3.)
term = 1. / f0
term[ctx.mag <= C["m1"]] = b0[ctx.mag <= C["m1"]]
return term
[docs]def get_site_amplification(region, C, ctx):
"""
Returns the site amplification term
"""
# Gets delta normalised z1
dz1 = ctx.z1pt0 - np.exp(_get_lnmu_z1(region, ctx.vs30))
f_s = C["c5"] * dz1
# Calculates site amplification term
f_s[dz1 > CONSTANTS["dz1ref"]] = (C["c5"] * CONSTANTS["dz1ref"])
idx = ctx.vs30 > CONSTANTS["v1"]
f_s[idx] += (C["c4"] * np.log(CONSTANTS["v1"] / C["vref"]))
idx = np.logical_not(idx)
f_s[idx] += (C["c4"] * np.log(ctx.vs30[idx] / C["vref"]))
return f_s
[docs]def get_stddevs(C, mag):
"""
Returns the standard deviations
"""
tau = _get_tau(C, mag)
phi = _get_phi(C, mag)
return [np.sqrt(tau ** 2. + phi ** 2.), tau, phi]
[docs]class AfshariStewart2016(GMPE):
"""
Implements the GMPE of Afshari & Stewart (2016) for relative significant
duration for 5 - 75 %, 5 - 95 % and 20 - 80 % Arias Intensity.
Afshari, K. and Stewart, J. P. (2016) "Physically Parameterized Prediction
Equations for Signficant Duration in Active Crustal Regions", Earthquake
Spectra, 32(4), 2057 - 2081
"""
region = "CAL"
#: Supported tectonic region type is active shallow crust
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST
#: Supported intensity measure types are 5 - 95 % Arias and 5 - 75 % Arias
#: significant duration
DEFINED_FOR_INTENSITY_MEASURE_TYPES = {RSD595, RSD575, RSD2080}
#: Supported intensity measure component is the geometric mean horizontal
#: component
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.GEOMETRIC_MEAN
#: Supported standard deviation type is only total, see table 7, page 35
DEFINED_FOR_STANDARD_DEVIATION_TYPES = {
const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT}
#: Requires vs30
REQUIRES_SITES_PARAMETERS = {'vs30', 'z1pt0'}
#: Required rupture parameters are magnitude and top of rupture depth
REQUIRES_RUPTURE_PARAMETERS = {'mag', 'rake'}
#: Required distance measure is closest distance to rupture
REQUIRES_DISTANCES = {'rrup'}
[docs] def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.compute>`
for spec of input and result values.
"""
for m, imt in enumerate(imts):
C = self.COEFFS[imt]
mean[m] = (np.log(get_magnitude_term(C, ctx) +
get_distance_term(C, ctx.rrup)) +
get_site_amplification(self.region, C, ctx))
sig[m], tau[m], phi[m] = get_stddevs(C, ctx.mag)
COEFFS = CoeffsTable(sa_damping=5, table="""\
imt m1 m2 b0N b0R b0SS b0U b1N b1R b1SS b1U b2 b3 c1 c2 c3 c4 c5 vref tau1 tau2 phi1 phi2
rsd575 5.35 7.15 1.555 0.7806 1.2790 1.280 4.992 7.061 5.578 5.576 0.9011 -1.684 0.1159 0.1065 0.0682 -0.2246 0.0006 368.2 0.28 0.25 0.54 0.41
rsd595 5.2 7.40 2.541 1.6120 2.3020 2.182 3.170 4.536 3.467 3.628 0.9443 -3.911 0.3165 0.2539 0.0932 -0.3183 0.0006 369.9 0.25 0.19 0.43 0.35
rsd2080 5.2 7.40 1.409 0.7729 0.8804 0.8822 4.778 6.579 6.188 6.182 0.7414 -3.164 0.0646 0.0865 0.0373 -0.4237 0.0005 369.6 0.30 0.19 0.56 0.45
""")
[docs]class AfshariStewart2016Japan(AfshariStewart2016):
"""
Adaption of the Afshari & Stewart (2016) GMPE for relative significant
duration for the case when the Japan basin model is preferred
"""
region = "JPN"