Source code for openquake.hazardlib.gsim.ecos_2009

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"""
Module exports:
class:`ECOS2009`,
class:`ECOS2009Highest`
"""
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 ECOS2009(GMPE): """ Implements the Intensity Prediction Equation of "Calibration of historical earthquakes for the earthquake catalogue of Switzerland (ECOS-09)": Appendix D This class implements the version using "all intensity levels", fixed depth (h=10km) and the weighting scheme "no weighting". See page 18 for general equation (8) - needs to be solved for I_obs - and equation (9) for estimating coefficients c0,c1,c2,c3. Coefficients a,b are taken from Table 4 on page 19. Coefficients alpha,beta are taken from Table 5 on page 19. Implemented by laurentiu.danciu@sed.ethz.ch """ 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', 'hypo_depth'} REQUIRES_DISTANCES = {'rhypo'}
[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, imt) stddevs = self._get_stddevs(C, stddev_types, num_sites=dists.rhypo.shape) return mean, stddevs
def _compute_mean(self, C, rup, dists, num_sites): """ Compute mean value. """ c0 = C['alpha'] * (C['a']*np.log(30/C['hypo_depth']) + C['b']*(30-C['hypo_depth'])) + C['beta'] c1 = C['alpha'] c2 = -(C['a']) * C['alpha'] c3 = -(C['b']) * C['alpha'] log_term = np.log(dists.rhypo / C['hypo_depth']) dist_term = c3 * (dists.rhypo-C['hypo_depth']) return (rup.mag - c2 * log_term - dist_term - c0) / c1 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. COEFFS = CoeffsTable(table="""\ IMT a b alpha beta hypo_depth sigma MMI -0.67755 -0.00174 0.7725 1.0363 10.0 0.4073 """)
[docs]class ECOS2009Highest(ECOS2009): """ This class implements the version using "three highest intensity levels", fixed depth (h=10km) and the weighting scheme "no weighting". See page 18 for general equation (8) - needs to be solved for I_obs - and equation (9) for estimating coefficients c0,c1,c2,c3. Coefficients a,b are taken from Table 4 on page 19. Coefficients alpha,beta are taken from Table 5 on page 19. """ DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL}
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] mean = self._compute_mean(C, rup, dists, imt) stddevs = self._get_stddevs(C, stddev_types, num_sites=dists.rhypo.shape) return mean, stddevs
COEFFS = CoeffsTable(table="""\ IMT a b alpha beta hypo_depth sigma MMI -0.4834 -0.00179 0.732 1.132 10.0 0.36474 """)