Source code for openquake.hazardlib.gsim.nshmp_2014

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"""
Module exports :class:`AbrahamsonEtAl2014NSHMPUpper`
               :class:`AbrahamsonEtAl2014NSHMPLower`
               :class:`BooreEtAl2014NSHMPUpper`
               :class:`BooreEtAl2014NSHMPLower`
               :class:`CampbellBozorgnia2014NSHMPUpper`
               :class:`CampbellBozorgnia2014NSHMPLower`
               :class:`ChiouYoungs2014NSHMPUpper`
               :class:`ChiouYoungs2014NSHMPLower`
               :class:`Idriss2014NSHMPUpper`
               :class:`Idriss2014NSHMPLower`
"""
import numpy as np
from openquake.hazardlib.gsim.base import _norm_sf, _truncnorm_sf
from openquake.hazardlib import const
# NGA West 2 GMPEs
from openquake.hazardlib.gsim.abrahamson_2014 import AbrahamsonEtAl2014
from openquake.hazardlib.gsim.boore_2014 import BooreEtAl2014
from openquake.hazardlib.gsim.campbell_bozorgnia_2014 import \
    CampbellBozorgnia2014
from openquake.hazardlib.gsim.chiou_youngs_2014 import ChiouYoungs2014
from openquake.hazardlib.gsim.idriss_2014 import Idriss2014


[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
DEFAULT_WEIGHTING = [(0.185, -1.), (0.63, 0.), (0.185, 1.)]
[docs]def get_weighted_poes(gsim, sctx, rctx, dctx, imt, imls, truncation_level, weighting=DEFAULT_WEIGHTING): """ This function implements the NGA West 2 GMPE epistemic uncertainty adjustment factor without re-calculating the actual GMPE each time. :param gsim: Instance of the GMPE :param list weighting: Weightings as a list of tuples of (weight, number standard deviations of the epistemic uncertainty adjustment) """ if truncation_level is not None and truncation_level < 0: raise ValueError('truncation level must be zero, positive number ' 'or None') gsim._check_imt(imt) adjustment = nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup) adjustment = adjustment.reshape(adjustment.shape + (1, )) if truncation_level == 0: # zero truncation mode, just compare imls to mean imls = gsim.to_distribution_values(imls) mean, _ = gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt, []) mean = mean.reshape(mean.shape + (1, )) output = np.zeros([mean.shape[0], imls.shape[0]]) for (wgt, fct) in weighting: output += (wgt * (imls <= (mean + (fct * adjustment))).astype(float)) return output else: # use real normal distribution assert (const.StdDev.TOTAL in gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES) imls = gsim.to_distribution_values(imls) mean, [stddev] = gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt, [const.StdDev.TOTAL]) mean = mean.reshape(mean.shape + (1, )) stddev = stddev.reshape(stddev.shape + (1, )) output = np.zeros([mean.shape[0], imls.shape[0]]) for (wgt, fct) in weighting: values = (imls - (mean + (fct * adjustment))) / stddev if truncation_level is None: output += (wgt * _norm_sf(values)) else: output += (wgt * _truncnorm_sf(truncation_level, values)) return output
[docs]class AbrahamsonEtAl2014NSHMPUpper(AbrahamsonEtAl2014): """ Implements the positive NSHMP adjustment factor for the Abrahamson et al. (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class AbrahamsonEtAl2014NSHMPLower(AbrahamsonEtAl2014): """ Implements the negative NSHMP adjustment factor for the Abrahamson et al. (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class AbrahamsonEtAl2014NSHMPMean(AbrahamsonEtAl2014): """ Implements the Abrahamson et al (2014) GMPE for application to the weighted mean case """
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level): """ Adapts the original `get_poes()` from the :class: openquake.hazardlib.gsim.base.GMPE to call a function that take the weighted sum of the PoEs from the epistemic uncertainty adjustment """ return get_weighted_poes(self, sctx, rctx, dctx, imt, imls, truncation_level)
[docs]class BooreEtAl2014NSHMPUpper(BooreEtAl2014): """ Implements the positive NSHMP adjustment factor for the Boore et al. (2014) NGA West 2 GMPE """ # Originally Boore et al. (2014) requires only Rjb, but the epistemic # adjustment factors are given in terms of Rrup, so both are required here REQUIRES_DISTANCES = set(("rjb", "rrup"))
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean + nga_west2_epistemic_adjustment( rctx.mag, dctx.rrup), stddevs
[docs]class BooreEtAl2014NSHMPLower(BooreEtAl2014): """ Implements the negative NSHMP adjustment factor for the Boore et al. (2014) NGA West 2 GMPE """ # See similar comment above REQUIRES_DISTANCES = set(("rjb", "rrup"))
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class BooreEtAl2014NSHMPMean(BooreEtAl2014): """ Implements the Boore et al (2014) GMPE for application to the weighted mean case """ # See similar comment above REQUIRES_DISTANCES = set(("rjb", "rrup"))
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level): """ Adapts the original `get_poes()` from the :class: openquake.hazardlib.gsim.base.GMPE to call a function that take the weighted sum of the PoEs from the epistemic uncertainty adjustment """ return get_weighted_poes(self, sctx, rctx, dctx, imt, imls, truncation_level)
[docs]class CampbellBozorgnia2014NSHMPUpper(CampbellBozorgnia2014): """ Implements the positive NSHMP adjustment factor for the Campbell and Bozorgnia (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class CampbellBozorgnia2014NSHMPLower(CampbellBozorgnia2014): """ Implements the negative NSHMP adjustment factor for the Campbell and Bozorgnia (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class CampbellBozorgnia2014NSHMPMean(CampbellBozorgnia2014): """ Implements the Campbell & Bozorgnia (2014) GMPE for application to the weighted mean case """
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level): """ Adapts the original `get_poes()` from the :class: openquake.hazardlib.gsim.base.GMPE to call a function that take the weighted sum of the PoEs from the epistemic uncertainty adjustment """ return get_weighted_poes(self, sctx, rctx, dctx, imt, imls, truncation_level)
[docs]class ChiouYoungs2014NSHMPUpper(ChiouYoungs2014): """ Implements the positive NSHMP adjustment factor for the Chiou & Youngs (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class ChiouYoungs2014NSHMPLower(ChiouYoungs2014): """ Implements the negative NSHMP adjustment factor for the Chiou & Youngs (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class ChiouYoungs2014NSHMPMean(ChiouYoungs2014): """ Implements the Chiou & Youngs (2014) GMPE for application to the weighted mean case """
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level): """ Adapts the original `get_poes()` from the :class: openquake.hazardlib.gsim.base.GMPE to call a function that take the weighted sum of the PoEs from the epistemic uncertainty adjustment """ return get_weighted_poes(self, sctx, rctx, dctx, imt, imls, truncation_level)
[docs]class Idriss2014NSHMPUpper(Idriss2014): """ Implements the positive NSHMP adjustment factor for the Idriss (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class Idriss2014NSHMPLower(Idriss2014): """ Implements the negative NSHMP adjustment factor for the Idriss (2014) NGA West 2 GMPE """
[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) # Return mean, increased by the adjustment factor, # and standard devation return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\ stddevs
[docs]class Idriss2014NSHMPMean(Idriss2014): """ Implements the Idriss (2014) GMPE for application to the weighted mean case """
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level): """ Adapts the original `get_poes()` from the :class: openquake.hazardlib.gsim.base.GMPE to call a function that take the weighted sum of the PoEs from the epistemic uncertainty adjustment """ return get_weighted_poes(self, sctx, rctx, dctx, imt, imls, truncation_level)