Source code for openquake.hazardlib.gsim.nshmp_2014

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"""
Module exports :class:`AtkinsonMacias2009NSHMP2014` and :class:`NSHMP2014`
"""
import numpy as np
from openquake.hazardlib import const
from openquake.hazardlib.gsim import base
from openquake.hazardlib.gsim.atkinson_macias_2009 import AtkinsonMacias2009
from openquake.hazardlib.gsim.can15.sinter import SInterCan15Mid


[docs]class AtkinsonMacias2009NSHMP2014(AtkinsonMacias2009): """ Implements an adjusted version of the Atkinson and Macias (2009) GMPE. The motion is scaled B/C conditions following the approach described in Atkinson and Adams (2013) and implemented in :mod:`openquake.hazardlib.gsim.can15.sinter`. """ #: Shear-wave velocity for reference soil conditions in [m s-1] DEFINED_FOR_REFERENCE_VELOCITY = 760. #: GMPE not tested against independent implementation so raise #: not verified warning non_verified = True
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # Get original mean and standard deviations mean, stddevs = super().get_mean_and_stddevs( sctx, rctx, dctx, imt, stddev_types) mean += np.log(SInterCan15Mid.SITE_COEFFS[imt]['mf']) return mean, stddevs
[docs]def nga_west2_epistemic_adjustment(magnitude, distance): """ 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 """ if magnitude < 6.0: adjustment = 0.22 * np.ones_like(distance) adjustment[distance < 10.0] = 0.37 elif magnitude >= 7.0: adjustment = 0.36 * np.ones_like(distance) adjustment[distance < 10.0] = 0.40 adjustment[distance >= 30.0] = 0.33 else: adjustment = 0.23 * np.ones_like(distance) adjustment[distance < 10.0] = 0.25 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 get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ mean, stddevs = self.gsim.get_mean_and_stddevs( sctx, rctx, dctx, imt, stddev_types) # return mean increased by the adjustment factor and standard deviation self.adjustment = nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup) return mean + self.sgn * self.adjustment, stddevs
# 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.gsim_aliases[a] = f'[NSHMP2014]\ngmpe_name="{name}"\nsgn={sgn}'