# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2022 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:`AtkinsonMacias2009NSHMP2014` and :class:`NSHMP2014`
"""
import numpy as np
from openquake.hazardlib import const
from openquake.hazardlib.gsim import base
[docs]def nga_west2_epistemic_adjustment(mag, dist):
"""
Applies the "average" adjustment factor for epistemic uncertainty
as defined in Table 17 of Petersen et al., (2014)::
| R < 10. | 10.0 <= R < 30.0 | R >= 30.0
-----------------------------------------------------------
M < 6.0 | 0.37 | 0.22 | 0.22
6 <= M <7.0 | 0.25 | 0.23 | 0.23
M >= 7.0 | 0.40 | 0.36 | 0.33
"""
adjustment = 0.23 * np.ones_like(dist)
adjustment[(mag < 6.) & (dist < 10.)] = .37
adjustment[(mag < 6.) & (dist >= 10.)] = .22
adjustment[(mag >= 6.) & (mag < 7.) & (dist < 10.)] = .25
adjustment[(mag >= 7.) & (dist < 10.)] = .40
adjustment[(mag >= 7.) & (dist >= 10.) & (dist < 30.)] = .36
adjustment[(mag >= 7.) & (dist >= 30.)] = .33
return adjustment
[docs]class NSHMP2014(base.GMPE):
"""
Implements the NSHMP adjustment factors for the NGA West GMPEs.
Requires two parameters `gmpe_name` (one of Idriss2014, ChiouYoungs2014,
CampbellBozorgnia2014, BooreEtAl2014, AbrahamsonEtAl2014) and `sgn`
(one of -1, 0, +1).
"""
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = ()
DEFINED_FOR_INTENSITY_MEASURE_TYPES = ()
DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL}
DEFINED_FOR_TECTONIC_REGION_TYPE = ()
REQUIRES_DISTANCES = ()
REQUIRES_RUPTURE_PARAMETERS = ()
REQUIRES_SITES_PARAMETERS = ()
def __init__(self, **kwargs):
self.gmpe_name = kwargs['gmpe_name']
self.sgn = kwargs['sgn']
if self.sgn == 0:
# default weighting
self.weights_signs = [(0.185, -1.), (0.63, 0.), (0.185, 1.)]
cls = base.registry[self.gmpe_name]
for name in vars(cls):
if name.startswith(('DEFINED_FOR', 'REQUIRES_')):
setattr(self, name, getattr(cls, name))
# the gsim requires only Rjb, but the epistemic adjustment factors
# are given in terms of Rrup, so both are required in the subclass
self.REQUIRES_DISTANCES = frozenset(self.REQUIRES_DISTANCES | {'rrup'})
self.gsim = cls() # underlying gsim
super().__init__(**kwargs)
[docs] def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
"""
Compute mean, sig, tau, phi and returns the so called adjustment
"""
self.gsim.compute(ctx, imts, mean, sig, tau, phi)
adjustment = nga_west2_epistemic_adjustment(ctx.mag, ctx.rrup)
mean[:] += self.sgn * adjustment
return adjustment
# populate gsim_aliases
# for instance "AbrahamsonEtAl2014NSHMPMean" is associated to the TOML string
# [NSHMP2014]
# gmpe_name = "AbrahamsonEtAl2014"
# sgn = 0
SUFFIX = {0: 'Mean', -1: 'Lower', 1: 'Upper'}
for name in ('Idriss2014', 'ChiouYoungs2014', 'CampbellBozorgnia2014',
'BooreEtAl2014', 'AbrahamsonEtAl2014'):
for sgn in (1, -1, 0):
a = name + 'NSHMP' + SUFFIX[sgn]
base.add_alias(a, NSHMP2014, gmpe_name=name, sgn=sgn)