# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2024 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:`CampbellBozorgnia2014`
:class:`CampbellBozorgnia2014HighQ`
:class:`CampbellBozorgnia2014LowQ`
:class:`CampbellBozorgnia2019`
:class:`CampbellBozorgnia2019HighQ`
:class:`CampbellBozorgnia2019LowQ`
"""
import numpy as np
from numpy import exp, radians, cos
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable, add_alias
from openquake.hazardlib.gsim.abrahamson_2014 import get_epistemic_sigma
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, PGV, SA, IA, CAV
from openquake.hazardlib.gsim.utils_usgs_basin_scaling import \
_get_z2pt5_usgs_basin_scaling
CONSTS = {"h4": 1.0, "c": 1.88, "n": 1.18}
# CyberShake basin adjustments for CB14 (only applied above
# 1.9 seconds so don't provide dummy values listed below 2 s)
# Taken from https://code.usgs.gov/ghsc/nshmp/nshmp-lib/-/blob/main/src/main/resources/gmm/coeffs/CB14.csv?ref_type=heads
COEFFS_CY = CoeffsTable(sa_damping=5, table="""\
IMT slope_cy
2.0 0.764
3.0 1.279
4.0 1.726
5.0 2.080
7.5 3.000
10 3.000
""")
def _get_alpha(C, vs30, pga_rock):
"""
Returns the alpha, the linearised functional relationship between the
site amplification and the PGA on rock. Equation 31.
"""
alpha = np.zeros(len(pga_rock))
idx = vs30 < C["k1"]
if np.any(idx):
af1 = pga_rock[idx] +\
CONSTS["c"] * ((vs30[idx] / C["k1"]) ** CONSTS["n"])
af2 = pga_rock[idx] + CONSTS["c"]
alpha[idx] = C["k2"] * pga_rock[idx] * ((1.0 / af1) - (1.0 / af2))
return alpha
def _get_anelastic_attenuation_term(C, rrup):
"""
Returns the anelastic attenuation term defined in equation 25
"""
f_atn = np.zeros(len(rrup))
idx = rrup >= 80.0
f_atn[idx] = (C["c20"] + C["Dc20"]) * (rrup[idx] - 80.0)
return f_atn
def _basin_term(C, imt, z2pt5, SJ, cy):
"""
Calculate the basin term
"""
# Get CyberShake adjustment if required and SA(T > 1.9)
if cy and imt.period > 1.9:
basin_coeffs = COEFFS_CY[imt]['slope_cy']
# Otherwise use regular coefficients
else:
basin_coeffs = C["c16"] * C["k3"]
fb = np.zeros(len(z2pt5))
idx = z2pt5 < 1.0
fb[idx] = (C["c14"] + C["c15"] * SJ) * (z2pt5[idx] - 1.0)
idx = z2pt5 > 3.0
fb[idx] = basin_coeffs * exp(-0.75) * (
1. - np.exp(-0.25 * (z2pt5[idx] - 3.)))
return fb
def _select_basin_model(SJ, vs30):
"""
Select the preferred basin model (California or Japan) to scale
basin depth with respect to Vs30
"""
if SJ:
# Japan Basin Model - Equation 34 of Campbell & Bozorgnia (2014)
return np.exp(5.359 - 1.102 * np.log(vs30))
else:
# California Basin Model - Equation 33 of
# Campbell & Bozorgnia (2014)
return np.exp(7.089 - 1.144 * np.log(vs30))
def _get_basin_term(C, ctx, region, imt, SJ, a1100,
usgs_bs=False, cy=False):
"""
Returns the basin response term defined in equation 20 and
apply any required adjustments.
"""
# Get reference basin depth
z_ref = _select_basin_model(SJ, 1100.0) * np.ones_like(ctx.vs30)
z_ref_term = _basin_term(C, imt, z_ref, SJ, False)
# Get basin term
if isinstance(a1100, np.ndarray): # Site model defined
z2pt5 = ctx.z2pt5
else:
z2pt5 = z_ref
z2pt5_term = _basin_term(C, imt, z2pt5, SJ, cy)
# Apply USGS basin scaling model if required
if usgs_bs:
# Get the scaling factor per site
usgs_baf = _get_z2pt5_usgs_basin_scaling(ctx.z2pt5, imt.period)
z_scaled = z_ref_term * (1.0 - usgs_baf) + z2pt5_term * usgs_baf
# Apply additional CyberShake (CY_CSIM) adjustment if required
if cy and imt.period > 1.9:
z_scaled += 0.1
# If period is 0.075 seconds scale by factor of 0.585
if imt.period == 0.075:
return z_scaled * 0.585
# Otherwise return basin term adjusted except for sites shallower than
# upper z2pt5 value in USGS basin model (use z_ref_term instead here)
if imt.period > 0.5:
idx = z2pt5 > 1.0 # Upper z2pt5 depth in USGS basin model is 1 km
z_scaled[idx] = z_ref_term[idx]
return z_scaled
return z2pt5_term
def _get_f1rx(C, r_x, r_1):
"""
Defines the f1 scaling coefficient defined in equation 9
"""
rxr1 = r_x / r_1
return C["h1"] + C["h2"] * rxr1 + C["h3"] * rxr1 ** 2
def _get_f2rx(C, r_x, r_1, r_2):
"""
Defines the f2 scaling coefficient defined in equation 10
"""
drx = (r_x - r_1) / (r_2 - r_1)
return CONSTS["h4"] + C["h5"] * drx + C["h6"] * drx ** 2
def _get_fault_dip_term(C, ctx):
"""
Returns the fault dip term, defined in equation 24
"""
res = C["c19"] * (5.5 - ctx.mag) * ctx.dip
res[ctx.mag < 4.5] = C["c19"] * ctx.dip[ctx.mag < 4.5]
res[ctx.mag > 5.5] = 0.0
return res
def _get_geometric_attenuation_term(C, mag, rrup):
"""
Returns the geometric attenuation term defined in equation 3
"""
return (C["c5"] + C["c6"] * mag) * np.log(
np.sqrt(rrup ** 2 + C["c7"] ** 2))
def _get_hanging_wall_coeffs_dip(dip):
"""
Returns the hanging wall dip term defined in equation 16
"""
return (90.0 - dip) / 45.0
def _get_hanging_wall_coeffs_mag(C, mag):
"""
Returns the hanging wall magnitude term defined in equation 14
"""
res = (mag - 5.5) * (1.0 + C["a2"] * (mag - 6.5))
res[mag < 5.5] = 0.0
res[mag > 6.5] = 1.0 + C["a2"] * (mag[mag > 6.5] - 6.5)
return res
def _get_hanging_wall_coeffs_rrup(ctx):
"""
Returns the hanging wall rrup term defined in equation 13
"""
fhngrrup = np.ones(len(ctx.rrup))
idx = ctx.rrup > 0.0
fhngrrup[idx] = (ctx.rrup[idx] - ctx.rjb[idx]) / ctx.rrup[idx]
return fhngrrup
def _get_hanging_wall_coeffs_rx(C, ctx):
"""
Returns the hanging wall r-x caling term defined in equation 7 to 12
"""
r_x = ctx.rx
# Define coefficients R1 and R2
r_1 = ctx.width * cos(radians(ctx.dip))
r_2 = 62.0 * ctx.mag - 350.0
fhngrx = np.zeros(len(r_x))
# Case when 0 <= Rx <= R1
idx = np.logical_and(r_x >= 0., r_x < r_1)
fhngrx[idx] = _get_f1rx(C, r_x[idx], r_1[idx])
# Case when Rx > R1
idx = r_x >= r_1
f2rx = _get_f2rx(C, r_x[idx], r_1[idx], r_2[idx])
f2rx[f2rx < 0.0] = 0.0
fhngrx[idx] = f2rx
return fhngrx
def _get_hanging_wall_coeffs_ztor(ztor):
"""
Returns the hanging wall ztor term defined in equation 15
"""
res = 1. - 0.06 * ztor
res[ztor > 16.66] = 0.
return res
def _get_hanging_wall_term(C, ctx):
"""
Returns the hanging wall scaling term defined in equations 7 to 16
"""
return (C["c10"] *
_get_hanging_wall_coeffs_rx(C, ctx) *
_get_hanging_wall_coeffs_rrup(ctx) *
_get_hanging_wall_coeffs_mag(C, ctx.mag) *
_get_hanging_wall_coeffs_ztor(ctx.ztor) *
_get_hanging_wall_coeffs_dip(ctx.dip))
def _get_hypocentral_depth_term(C, ctx):
"""
Returns the hypocentral depth scaling term defined in equations 21 - 23
"""
fhyp_h = np.clip(ctx.hypo_depth - 7.0, 0., 13.)
fhyp_m = C["c17"] + (C["c18"] - C["c17"]) * (ctx.mag - 5.5)
fhyp_m[ctx.mag <= 5.5] = C["c17"]
fhyp_m[ctx.mag > 6.5] = C["c18"]
return fhyp_h * fhyp_m
def _get_magnitude_term(C, mag):
"""
Returns the magnitude scaling term defined in equation 2
"""
f_mag = C["c0"] + C["c1"] * mag
around5 = (mag > 4.5) & (mag <= 5.5)
around6 = (mag > 5.5) & (mag <= 6.5)
beyond = mag > 6.5
f_mag[around5] += C["c2"] * (mag[around5] - 4.5)
f_mag[around6] += (C["c2"] * (mag[around6] - 4.5) +
C["c3"] * (mag[around6] - 5.5))
f_mag[beyond] += (C["c2"] * (mag[beyond] - 4.5) +
C["c3"] * (mag[beyond] - 5.5) +
C["c4"] * (mag[beyond] - 6.5))
return f_mag
def _get_philny(C, mag):
"""
Returns the intra-event random effects coefficient (phi)
Equation 28.
"""
res = C["phi2"] + (C["phi1"] - C["phi2"]) * (5.5 - mag)
res[mag <= 4.5] = C["phi1"]
res[mag >= 5.5] = C["phi2"]
return res
def _get_shallow_site_response_term(SJ, C, vs30, pga_rock):
"""
Returns the shallow site response term defined in equations 17, 18 and
19
"""
vs_mod = vs30 / C["k1"]
# Get linear global site response term
f_site_g = C["c11"] * np.log(vs_mod)
idx = vs30 > C["k1"]
f_site_g[idx] = f_site_g[idx] + (C["k2"] * CONSTS["n"] *
np.log(vs_mod[idx]))
# Get nonlinear site response term
idx = np.logical_not(idx)
if np.any(idx):
f_site_g[idx] = f_site_g[idx] + C["k2"] * (
np.log(pga_rock[idx] +
CONSTS["c"] * (vs_mod[idx] ** CONSTS["n"])) -
np.log(pga_rock[idx] + CONSTS["c"]))
if SJ:
fsite_j = (C["c13"] + C["k2"] * CONSTS["n"]) * \
np.log(vs_mod)
# additional term activated for soft ctx (Vs30 <= 200m/s)
# in Japan data
idx = vs30 <= 200.0
add_soft = (C["c12"] + C["k2"] * CONSTS["n"]) * \
(np.log(vs_mod) - np.log(200.0 / C["k1"]))
# combine terms
fsite_j[idx] += add_soft[idx]
return f_site_g + fsite_j
else:
return f_site_g
def _get_style_of_faulting_term(C, ctx):
"""
Returns the style-of-faulting scaling term defined in equations 4 to 6
"""
frv = np.zeros_like(ctx.rake)
fnm = np.zeros_like(ctx.rake)
frv[(ctx.rake > 30.) & (ctx.rake < 150.)] = 1.
fnm[(ctx.rake > -150.) & (ctx.rake < -30.)] = 1.
fflt_f = C["c8"] * frv + C["c9"] * fnm
fflt_m = ctx.mag - 4.5
fflt_m[ctx.mag <= 4.5] = 0.
fflt_m[ctx.mag > 5.5] = 1.
return fflt_f * fflt_m
def _get_taulny(C, mag):
"""
Returns the inter-event random effects coefficient (tau)
Equation 28.
"""
res = C["tau2"] + (C["tau1"] - C["tau2"]) * (5.5 - mag)
res[mag <= 4.5] = C["tau1"]
res[mag >= 5.5] = C["tau2"]
return res
[docs]def get_mean_values(SJ, C, ctx, imt, usgs_bs=False, cy=False, a1100=None):
"""
Returns the mean values for a specific IMT
"""
if isinstance(a1100, np.ndarray):
# Site model defined
temp_vs30 = ctx.vs30
else:
# Default site and basin model
temp_vs30 = 1100.0 * np.ones(len(ctx))
return (_get_magnitude_term(C, ctx.mag) +
_get_geometric_attenuation_term(C, ctx.mag, ctx.rrup) +
_get_style_of_faulting_term(C, ctx) +
_get_hanging_wall_term(C, ctx) +
_get_shallow_site_response_term(SJ, C, temp_vs30, a1100) +
_get_basin_term(C, ctx, None, imt, SJ, a1100, usgs_bs, cy) +
_get_hypocentral_depth_term(C, ctx) +
_get_fault_dip_term(C, ctx) +
_get_anelastic_attenuation_term(C, ctx.rrup))
def _update_ctx(gsim, ctx):
"""
Use the ztor, width and hypo_depth formula to estimate
if the estimate attribute is set.
"""
if gsim.estimate_ztor:
# Equation 4 and 5 of Chiou & Youngs 2014
ctx.ztor = np.where(
(ctx.rake > 30.) & (ctx.rake < 150.),
np.maximum(2.704-1.226 * np.maximum(ctx.mag-5.849, 0), 0)**2,
np.maximum(2.673-1.136 * np.maximum(ctx.mag-4.970, 0), 0)**2)
if gsim.estimate_width:
# width estimation requires Zbot
# where Zbot is the depth to the bottom of the seismogenic crust
if not hasattr(ctx, "zbot"):
raise KeyError('Zbot is required if width is unknown.')
# Equation 39 of Campbell & Bozorgnia 2014
mask = np.absolute(np.sin(np.radians(ctx.dip))) > 0
ctx.width = np.sqrt(10**((ctx.mag - 4.07) / 0.98))
ctx.width[mask] = np.minimum(
ctx.width[mask], (ctx.zbot[mask] - ctx.ztor[mask]) /
np.sin(np.radians(ctx.dip[mask])))
if gsim.estimate_hypo_depth:
# Equation 36 of Campbell & Bozorgnia 2014
fdz_m = np.where(
ctx.mag < 6.75, -4.317 + 0.984 * ctx.mag, 2.325)
# Equation 37 of Campbell & Bozorgnia 2014
fdz_d = np.where(
ctx.dip <= 40, 0.0445 * (ctx.dip - 40), 0)
# The depth to the bottom of the rupture plane
zbor = ctx.ztor + ctx.width * np.sin(np.radians(ctx.dip))
# Equation 35 of Campbell & Bozorgnia 2014
mask = zbor > ctx.ztor
dz = np.zeros_like(ctx.ztor)
dz[mask] = np.exp(
np.minimum(
fdz_m[mask] + fdz_d[mask],
np.log(0.9 * (zbor[mask] - ctx.ztor[mask]))))
ctx.hypo_depth = ctx.ztor + dz
def _get_rholnpga(C, mag):
"""
Returns the magnitude-dependent correlation coefficient (rho) —
Equation 5 of CB19.
"""
rho_ln_pga = C["rho2pga"] + (C["rho1pga"] - C["rho2pga"]) * (5.5 - mag)
rho_ln_pga[mag <= 4.5] = C["rho1pga"]
rho_ln_pga[mag >= 5.5] = C["rho2pga"]
return rho_ln_pga
[docs]class CampbellBozorgnia2014(GMPE):
"""
Implements NGA-West 2 GMPE developed by Kenneth W. Campbell and Yousef
Bozorgnia, published as "NGA-West2 Ground Motion Model for the Average
Horizontal Components of PGA, PGV, and 5 % Damped Linear Acceleration
Response Spectra" (2014, Earthquake Spectra, Volume 30, Number 3,
pages 1087 - 1115).
"""
#: 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, IA, CAV}
#: Supported intensity measure component is orientation-independent
#: average horizontal :attr:`~openquake.hazardlib.const.IMC.GMRotI50`
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RotD50
#: Supported standard deviation types are inter-event, intra-event
#: and total, see section "Aleatory Variability Model", page 1094.
DEFINED_FOR_STANDARD_DEVIATION_TYPES = {
const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT}
#: Required site parameters are Vs30, Vs30 type (measured or inferred),
#: and depth (km) to the 2.5 km/s shear wave velocity layer (z2pt5)
REQUIRES_SITES_PARAMETERS = {'vs30', 'z2pt5'}
#: Required rupture parameters are magnitude, rake, dip, ztor, rupture
#: width and hypocentral depth
REQUIRES_RUPTURE_PARAMETERS = {
'mag', 'rake', 'dip', 'ztor', 'width', 'hypo_depth'}
#: Required distance measures are Rrup, Rjb and Rx
REQUIRES_DISTANCES = {'rrup', 'rjb', 'rx'}
def __init__(self, coeffs={}, SJ=False, sigma_mu_epsilon=0.0,
estimate_ztor=False, estimate_width=False,
estimate_hypo_depth=False, usgs_basin_scaling=False,
cybershake_basin_adj=False):
# tested in logictree/case_71
if coeffs: # extra coefficients by IMT
self.COEFFS |= CoeffsTable.fromdict(coeffs)
self.SJ = SJ # flag for Japan
self.sigma_mu_epsilon = sigma_mu_epsilon
self.estimate_ztor = estimate_ztor
self.estimate_width = estimate_width
self.estimate_hypo_depth = estimate_hypo_depth
if self.estimate_width:
# To estimate a width, the GMPE needs Zbot
self.REQUIRES_RUPTURE_PARAMETERS |= {"zbot"}
self.usgs_basin_scaling = usgs_basin_scaling
self.cybershake_basin_adj = cybershake_basin_adj
[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.
"""
if (self.estimate_ztor or self.estimate_width or
self.estimate_hypo_depth):
ctx = ctx.copy()
_update_ctx(self, ctx)
C_PGA = self.COEFFS[PGA()]
# Get mean and standard deviation of PGA on rock (Vs30 1100 m/s^2)
pga1100 = np.exp(get_mean_values(self.SJ, C_PGA, ctx, PGA(),
self.usgs_basin_scaling,
self.cybershake_basin_adj))
for m, imt in enumerate(imts):
C = self.COEFFS[imt]
# Get mean and standard deviations for IMT
mean[m] = get_mean_values(self.SJ, C, ctx, imt,
self.usgs_basin_scaling,
self.cybershake_basin_adj,
pga1100)
mean[m] += (self.sigma_mu_epsilon*get_epistemic_sigma(ctx))
if imt.string[:2] == "SA" and imt.period < 0.25:
# According to Campbell & Bozorgnia (2013) [NGA West 2 Report]
# If Sa (T) < PGA for T < 0.25 then set mean Sa(T) to mean PGA
# Get PGA on soil
pga = get_mean_values(self.SJ, C_PGA, ctx, imt,
self.usgs_basin_scaling,
self.cybershake_basin_adj,
pga1100)
idx = mean[m] <= pga
mean[m, idx] = pga[idx]
mean[m] += self.sigma_mu_epsilon * get_epistemic_sigma(ctx)
# Get stddevs for PGA on basement rock
tau_lnpga_b = _get_taulny(C_PGA, ctx.mag)
phi_lnpga_b = np.sqrt(_get_philny(C_PGA, ctx.mag) ** 2. -
C_PGA["philnAF"] ** 2.)
# Get tau_lny on the basement rock
tau_lnyb = _get_taulny(C, ctx.mag)
# Get phi_lny on the basement rock
phi_lnyb = np.sqrt(_get_philny(C, ctx.mag) ** 2. -
C["philnAF"] ** 2.)
# Get site scaling term
alpha = _get_alpha(C, ctx.vs30, pga1100)
if imt.string in ['CAV', 'IA']:
# Use formula in CB19 supplementary spreadsheet
t = np.sqrt(tau_lnyb**2 + alpha**2 * tau_lnpga_b**2 +
2. * alpha * _get_rholnpga(C, ctx.mag) * tau_lnyb * tau_lnpga_b)
p = np.sqrt(
phi_lnyb**2 + C["philnAF"]**2 + alpha**2 * phi_lnpga_b**2
+ 2.0 * alpha * _get_rholnpga(C, ctx.mag) * phi_lnyb * phi_lnpga_b)
else:
# Use formula in CB14 supplementary spreadsheet
t = np.sqrt(tau_lnyb**2 + alpha**2 * tau_lnpga_b**2 +
2.0 * alpha * C["rholny"] * tau_lnyb * tau_lnpga_b)
p = np.sqrt(
phi_lnyb**2 + C["philnAF"]**2 + alpha**2 * phi_lnpga_b**2
+ 2.0 * alpha * C["rholny"] * phi_lnyb * phi_lnpga_b)
sig[m] = np.sqrt(t**2 + p**2)
tau[m] = t
phi[m] = p
COEFFS = CoeffsTable(sa_damping=5, table="""\
IMT c0 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 c16 c17 c18 c19 c20 Dc20 a2 h1 h2 h3 h5 h6 k1 k2 k3 phi1 phi2 tau1 tau2 rho1pga rho2pga philnAF phiC rholny
0.01 -4.365 0.977 0.533 -1.485 -0.499 -2.773 0.248 6.753 0 -0.214 0.72 1.094 2.191 1.416 -0.007 -0.207 0.39 0.0981 0.0334 0.00755 -0.0055 0 0.168 0.242 1.471 -0.714 -0.336 -0.27 865 -1.186 1.839 0.734 0.492 0.404 0.325 1 1 0.3 0.19 1
0.02 -4.348 0.976 0.549 -1.488 -0.501 -2.772 0.247 6.502 0 -0.208 0.73 1.149 2.189 1.453 -0.0167 -0.199 0.387 0.1009 0.0327 0.00759 -0.0055 0 0.166 0.244 1.467 -0.711 -0.339 -0.263 865 -1.219 1.84 0.738 0.496 0.417 0.326 0.999 0.998 0.3 0.166 0.998
0.03 -4.024 0.931 0.628 -1.494 -0.517 -2.782 0.246 6.291 0 -0.213 0.759 1.29 2.164 1.476 -0.0422 -0.202 0.378 0.1095 0.0331 0.0079 -0.0057 0 0.167 0.246 1.467 -0.713 -0.338 -0.259 908 -1.273 1.841 0.747 0.503 0.446 0.344 0.987 0.987 0.3 0.166 0.986
0.05 -3.479 0.887 0.674 -1.388 -0.615 -2.791 0.24 6.317 0 -0.244 0.826 1.449 2.138 1.549 -0.0663 -0.339 0.295 0.1226 0.027 0.00803 -0.0063 0 0.173 0.251 1.449 -0.701 -0.338 -0.263 1054 -1.346 1.843 0.777 0.52 0.508 0.377 0.955 0.946 0.3 0.166 0.938
0.075 -3.293 0.902 0.726 -1.469 -0.596 -2.745 0.227 6.861 0 -0.266 0.815 1.535 2.446 1.772 -0.0794 -0.404 0.322 0.1165 0.0288 0.00811 -0.007 0 0.198 0.26 1.435 -0.695 -0.347 -0.219 1086 -1.471 1.845 0.782 0.535 0.504 0.418 0.943 0.897 0.3 0.165 0.887
0.1 -3.666 0.993 0.698 -1.572 -0.536 -2.633 0.21 7.294 0 -0.229 0.831 1.615 2.969 1.916 -0.0294 -0.416 0.384 0.0998 0.0325 0.00744 -0.0073 0 0.174 0.259 1.449 -0.708 -0.391 -0.201 1032 -1.624 1.847 0.769 0.543 0.445 0.426 0.942 0.883 0.3 0.162 0.87
0.15 -4.866 1.267 0.51 -1.669 -0.49 -2.458 0.183 8.031 0 -0.211 0.749 1.877 3.544 2.161 0.0642 -0.407 0.417 0.076 0.0388 0.00716 -0.0069 0 0.198 0.254 1.461 -0.715 -0.449 -0.099 878 -1.931 1.852 0.769 0.543 0.382 0.387 0.921 0.891 0.3 0.158 0.876
0.2 -5.411 1.366 0.447 -1.75 -0.451 -2.421 0.182 8.385 0 -0.163 0.764 2.069 3.707 2.465 0.0968 -0.311 0.404 0.0571 0.0437 0.00688 -0.006 0 0.204 0.237 1.484 -0.721 -0.393 -0.198 748 -2.188 1.856 0.761 0.552 0.339 0.338 0.874 0.881 0.3 0.17 0.87
0.25 -5.962 1.458 0.274 -1.711 -0.404 -2.392 0.189 7.534 0 -0.15 0.716 2.205 3.343 2.766 0.1441 -0.172 0.466 0.0437 0.0463 0.00556 -0.0055 0 0.185 0.206 1.581 -0.787 -0.339 -0.21 654 -2.381 1.861 0.744 0.545 0.34 0.316 0.809 0.861 0.3 0.18 0.85
0.3 -6.403 1.528 0.193 -1.77 -0.321 -2.376 0.195 6.99 0 -0.131 0.737 2.306 3.334 3.011 0.1597 -0.084 0.528 0.0323 0.0508 0.00458 -0.0049 0 0.164 0.21 1.586 -0.795 -0.447 -0.121 587 -2.518 1.865 0.727 0.568 0.34 0.3 0.741 0.824 0.3 0.186 0.819
0.4 -7.566 1.739 -0.02 -1.594 -0.426 -2.303 0.185 7.012 0 -0.159 0.738 2.398 3.544 3.203 0.141 0.085 0.54 0.0209 0.0432 0.00401 -0.0037 0 0.16 0.226 1.544 -0.77 -0.525 -0.086 503 -2.657 1.874 0.69 0.593 0.356 0.264 0.635 0.738 0.3 0.191 0.743
0.5 -8.379 1.872 -0.121 -1.577 -0.44 -2.296 0.186 6.902 0 -0.153 0.718 2.355 3.016 3.333 0.1474 0.233 0.638 0.0092 0.0405 0.00388 -0.0027 0 0.184 0.217 1.554 -0.77 -0.407 -0.281 457 -2.669 1.883 0.663 0.611 0.379 0.263 0.553 0.661 0.3 0.198 0.684
0.75 -9.841 2.021 -0.042 -1.757 -0.443 -2.232 0.186 5.522 0 -0.09 0.795 1.995 2.616 3.054 0.1764 0.411 0.776 -0.0082 0.042 0.0042 -0.0016 0 0.216 0.154 1.626 -0.78 -0.371 -0.285 410 -2.401 1.906 0.606 0.633 0.43 0.326 0.393 0.526 0.3 0.206 0.562
1 -11.011 2.18 -0.069 -1.707 -0.527 -2.158 0.169 5.65 0 -0.105 0.556 1.447 2.47 2.562 0.2593 0.479 0.771 -0.0131 0.0426 0.00409 -0.0006 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 -1.955 1.929 0.579 0.628 0.47 0.353 0.313 0.438 0.3 0.208 0.467
1.5 -12.469 2.27 0.047 -1.621 -0.63 -2.063 0.158 5.795 0 -0.058 0.48 0.33 2.108 1.453 0.2881 0.566 0.748 -0.0187 0.038 0.00424 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 -1.025 1.974 0.541 0.603 0.497 0.399 0.242 0.36 0.3 0.221 0.364
2 -12.969 2.271 0.149 -1.512 -0.768 -2.104 0.158 6.632 0 -0.028 0.401 -0.514 1.327 0.657 0.3112 0.562 0.763 -0.0258 0.0252 0.00448 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 -0.299 2.019 0.529 0.588 0.499 0.4 0.234 0.318 0.3 0.225 0.298
3 -13.306 2.15 0.368 -1.315 -0.89 -2.051 0.148 6.759 0 0 0.206 -0.848 0.601 0.367 0.3478 0.534 0.686 -0.0311 0.0236 0.00345 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0 2.11 0.527 0.578 0.5 0.417 0.236 0.295 0.3 0.222 0.234
4 -14.02 2.132 0.726 -1.506 -0.885 -1.986 0.135 7.978 0 0 0.105 -0.793 0.568 0.306 0.3747 0.522 0.691 -0.0413 0.0102 0.00603 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0 2.2 0.521 0.559 0.543 0.393 0.232 0.274 0.3 0.226 0.202
5 -14.558 2.116 1.027 -1.721 -0.878 -2.021 0.135 8.538 0 0 0 -0.748 0.356 0.268 0.3382 0.477 0.67 -0.0281 0.0034 0.00805 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0 2.291 0.502 0.551 0.534 0.421 0.182 0.247 0.3 0.229 0.184
7.5 -15.509 2.223 0.169 -0.756 -1.077 -2.179 0.165 8.468 0 0 0 -0.664 0.075 0.374 0.3754 0.321 0.757 -0.0205 0.005 0.0028 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0 2.517 0.457 0.546 0.523 0.438 0.142 0.203 0.3 0.237 0.176
10 -15.975 2.132 0.367 -0.8 -1.282 -2.244 0.18 6.564 0 0 0 -0.576 -0.027 0.297 0.3506 0.174 0.621 0.0009 0.0099 0.00458 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0 2.744 0.441 0.543 0.466 0.438 0.111 0.103 0.3 0.237 0.154
pga -4.416 0.984 0.537 -1.499 -0.496 -2.773 0.248 6.768 0 -0.212 0.72 1.09 2.186 1.42 -0.0064 -0.202 0.393 0.0977 0.0333 0.00757 -0.0055 0 0.167 0.241 1.474 -0.715 -0.337 -0.27 865 -1.186 1.839 0.734 0.492 0.409 0.322 1 1 0.3 0.271 1
pgv -2.895 1.51 0.27 -1.299 -0.453 -2.466 0.204 5.837 0 -0.168 0.305 1.713 2.602 2.457 0.106 0.332 0.585 0.0517 0.0327 0.00613 -0.0017 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 -1.955 1.929 0.655 0.494 0.317 0.297 0.877 0.654 0.3 0.29 0.684
cav -4.75 1.397 0.282 -1.062 -0.17 -1.624 0.134 6.325 0.054 -0.1 0.469 1.015 1.208 1.777 0.1248 -0.191 1.087 0.0432 0.0127 0.00429 -0.0043 0 0.167 0.241 1.474 -0.715 -0.337 -0.27 400 -1.311 1 0.514 0.394 0.276 0.257 0.842 0.78 0.3 0 0
ia -10.272 2.318 0.88 -2.672 -0.837 -4.441 0.416 4.869 0.187 -0.196 1.165 1.596 2.829 2.76 0.108 -0.315 1.612 0.1311 0.0453 0.01242 -0.0103 0 0.167 0.241 1.474 -0.715 -0.337 -0.27 400 -1.982 1 1.174 0.809 0.614 0.435 0.948 0.911 0.615989848 0 0
""")
coeffs_high = CoeffsTable.fromtoml('''
"SA(0.01)".Dc20 = 0.0036
"SA(0.02)".Dc20 = 0.0036
"SA(0.03)".Dc20 = 0.0037
"SA(0.05)".Dc20 = 0.0040
"SA(0.075)".Dc20 = 0.0039
"SA(0.1)".Dc20 = 0.0042
"SA(0.15)".Dc20 = 0.0042
"SA(0.2)".Dc20 = 0.0041
"SA(0.25)".Dc20 = 0.0036
"SA(0.3)".Dc20 = 0.0031
"SA(0.4)".Dc20 = 0.0028
"SA(0.5)".Dc20 = 0.0025
"SA(0.75)".Dc20 = 0.0016
"SA(1.0)".Dc20 = 0.0006
PGA.Dc20 = 0.0036
PGV.Dc20 = 0.0017
CAV.Dc20 = 0.0027
IA.Dc20 = 0.0064
''').to_dict()
coeffs_low = CoeffsTable.fromtoml('''
"SA(0.01)".Dc20 = -0.0035
"SA(0.02)".Dc20 = -0.0035
"SA(0.03)".Dc20 = -0.0034
"SA(0.05)".Dc20 = -0.0037
"SA(0.075)".Dc20 = -0.0037
"SA(0.1)".Dc20 = -0.0034
"SA(0.15)".Dc20 = -0.0030
"SA(0.2)".Dc20 = -0.0031
"SA(0.25)".Dc20 = -0.0033
"SA(0.3)".Dc20 = -0.0035
"SA(0.4)".Dc20 = -0.0034
"SA(0.5)".Dc20 = -0.0034
"SA(0.75)".Dc20 = -0.0032
"SA(1.0)".Dc20 = -0.0030
"SA(1.5)".Dc20 = -0.0019
"SA(2.0)".Dc20 = -0.0005
PGA.Dc20 = -0.0035
PGV.Dc20 = -0.0006
CAV.Dc20 = -0.0024
IA.Dc20 = -0.0051
''').to_dict()
[docs]class CampbellBozorgnia2019(CampbellBozorgnia2014):
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.GEOMETRIC_MEAN
add_alias('CampbellBozorgnia2014HighQ', CampbellBozorgnia2014,
coeffs=coeffs_high)
add_alias('CampbellBozorgnia2014LowQ', CampbellBozorgnia2014,
coeffs=coeffs_low)
add_alias('CampbellBozorgnia2014JapanSite', CampbellBozorgnia2014,
SJ=True)
add_alias('CampbellBozorgnia2014HighQJapanSite', CampbellBozorgnia2014,
coeffs=coeffs_high, SJ=True)
add_alias('CampbellBozorgnia2014LowQJapanSite', CampbellBozorgnia2014,
coeffs=coeffs_low, SJ=True)
add_alias('CampbellBozorgnia2019HighQ', CampbellBozorgnia2019,
coeffs=coeffs_high)
add_alias('CampbellBozorgnia2019LowQ', CampbellBozorgnia2019,
coeffs=coeffs_low)
add_alias('CampbellBozorgnia2019JapanSite', CampbellBozorgnia2019,
SJ=True)
add_alias('CampbellBozorgnia2019HighQJapanSite', CampbellBozorgnia2019,
coeffs=coeffs_high, SJ=True)
add_alias('CampbellBozorgnia2019LowQJapanSite', CampbellBozorgnia2019,
coeffs=coeffs_low, SJ=True)
