Source code for openquake.hazardlib.gsim.faccioli_cauzzi_2006

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2018 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
# 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 <>.

Module exports :

import numpy as np

from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
from openquake.hazardlib import const
from openquake.hazardlib.imt import MMI

[docs]class FaccioliCauzzi2006(GMPE): """ Implements "Macroseismic Intensities for seismic scenarios estimated from instrumentally based correlations" by E. Faccioli and C. Cauzzi First European Conference on Earthquake Engineering and Seismology Geneva, Switzerland, 3-8 September 2006 Paper Number: 569 Implemented by """ DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST DEFINED_FOR_INTENSITY_MEASURE_TYPES = {MMI} DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.HORIZONTAL DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL} REQUIRES_SITES_PARAMETERS = set() REQUIRES_RUPTURE_PARAMETERS = {'mag'} REQUIRES_DISTANCES = {'repi'}
[docs] 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 dictionaries of coefficients specific to required # intensity measure type C = self.COEFFS[imt] mean = self._compute_mean(C, rup, dists) stddevs = self._get_stddevs( C, stddev_types, num_sites=dists.repi.shape) return mean, stddevs
def _compute_mean(self, C, rup, dists): """ Compute mean value defined by equation 1/page 414 no amplification factor is applied to the equation hence the S-factor = 0 """ d = np.sqrt(dists.repi**2+C['h']**2) term01 = C['c3'] * (np.log(d)) mean = C['c1'] + C['c2'] * rup.mag + term01 return mean def _get_stddevs(self, C, stddev_types, num_sites): """ Return total standard deviation. """ stddevs = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES stddevs.append((C['sigma']) + np.zeros(num_sites)) return stddevs #: Coefficient table constructed from the electronic suplements of the #: original paper - coeff in the same order as in Table 4/page 703 #: for Maw only (read last paragraph on page 701 - #: expains what Maw should be used) COEFFS = CoeffsTable(table="""\ IMT c1 c2 c3 h sigma MMI 1.0157 1.2566 -0.6547 2 0.5344 """)