def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extract faulting style and rock adjustment coefficients for the
# given imt
C_ADJ = self.COEFFS_FS_ROCK[imt]
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
# apply faulting style and rock adjustment factor for mean and std
mean = np.log(
np.exp(mean) * _compute_faulting_style_term(
C_ADJ['Frss'], self.CONSTS_FS['pR'], self.CONSTS_FS['Fnss'],
self.CONSTS_FS['pN'], rup.rake) * C_ADJ['AFrock'])
stddevs = np.array(stddevs)
return mean, stddevs
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] = _compute_mean(C, ctx.mag, ctx.rjb)
# Compute total standard deviation, equations 5 and 6, page 48
# aleatory uncertainty
sigma_ale_m = np.interp(ctx.mag, [5.0, 5.5, 8.0],
[C['m50'], C['m55'], C['m80']])
sigma_ale_rjb = np.interp(ctx.rjb, [5.0, 20.0],
[C['r5'], C['r20']])
sigma_ale = np.sqrt(sigma_ale_m**2 + sigma_ale_rjb**2)
# epistemic uncertainty
if imt.period < 1:
sigma_epi = 0.36 + 0.07 * (ctx.mag - 6)
else:
sigma_epi = 0.34 + 0.06 * (ctx.mag - 6)
sig[m] = np.sqrt(sigma_ale**2 + sigma_epi**2)
# apply decay factor for 3 and 4 seconds (not originally supported
# by the equations)
if imt.period == 3.0:
mean[m] /= 0.612
if imt.period == 4.0:
mean[m] /= 0.559
if self.CONSTS_FS: # fault style and rock adjustement in SHARE
C_ADJ = self.COEFFS_FS_ROCK[imt]
mean[m] = np.log(
np.exp(mean[m]) * (_compute_faulting_style_term(
C_ADJ['Frss'], self.CONSTS_FS['pR'],
self.CONSTS_FS['Fnss'], self.CONSTS_FS['pN'], ctx.rake)
* C_ADJ['AFrock']))
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extract faulting style and rock adjustment coefficients for the
# given imt
C_ADJ = self.COEFFS_FS_ROCK[imt]
mean, stddevs = super(ToroEtAl2002SHARE, self).\
get_mean_and_stddevs(sites, rup, dists, imt, stddev_types)
# apply faulting style and rock adjustment factor for mean and std
mean = np.log(np.exp(mean) *
_compute_faulting_style_term(C_ADJ['Frss'],
self.CONSTS_FS['pR'],
self.CONSTS_FS['Fnss'],
self.CONSTS_FS['pN'],
rup.rake) * C_ADJ['AFrock'])
stddevs = np.array(stddevs)
return mean, stddevs
