# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2018 GEM Foundation
#
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"""
Module
:mod:`openquake.hazardlib.gsim.nath_2012`
exports
:class:`NathEtAl2012Lower`
:class:`NathEtAl2012Upper`
"""
import numpy as np
from openquake.hazardlib import const
from openquake.hazardlib.imt import SA, PGA, PGV
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
[docs]class NathEtAl2012Lower(GMPE):
# pylint: disable=too-few-public-methods, no-init
"""
Implements GMPE of Nath et. al (2012) for intraplate margin seismicity in
the Shillong Plateau of India at 25-45 km deph.
This model is based on stochastic simulation with a mean stress drop of 150
bars.
Verification of mean value data was done by digitizing Figure 11 using
http://arohatgi.info/WebPlotDigitizer/app/. Note that this independent
verification did not include magnitude dependence or standard deviations.
**Reference**
Page number citations in this documentation refer to:
Nath, S. K., Thingbaijam, K. K. S., Maiti, S. K., and Nayak, A. (2012).
Ground-motion predictions in Shillong region, northeast India. *Journal of
Seismology*, 16(3):475–488.
"""
#: "studies on micro-earthquakes indicated that reverse faulting is
#: predominant in the region" (p. 476)
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.SUBDUCTION_INTERFACE
#: Set of :mod:`intensity measure types <openquake.hazardlib.imt>`
#: this GSIM can calculate. A set should contain classes from
#: module :mod:`openquake.hazardlib.imt`.
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([SA, PGA, PGV])
#: In simulations only the vertical component is estimated (see p. 479)
#: and the stochastic dataset is what the GMPE is based on, so this model
#: effectively predicts vertical motions.
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.VERTICAL
#: The only sigma is reported in the main coefficient table, Table 5 on
#: p. 483, and must be the total standard deviation.
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([const.StdDev.TOTAL])
#: Required site parameter Vs30 is used to determing the NEHRP
#: site class, and thus to choose site amplification coefficients
#: and site amplification stanard error from Table 5 on p. 208.
REQUIRES_SITES_PARAMETERS = set()
#: Sole required rupture parameter is magnitude, since faulting
#: style is not addressed.
REQUIRES_RUPTURE_PARAMETERS = set(('mag',))
#: It is noted that "r_rup is the fault-rupture distance in kilometers"
#: following equation (11) on p. 484.
REQUIRES_DISTANCES = set(('rrup',))
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
# pylint: disable=too-many-arguments
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for specification of input and result values.
Implements equation (11) on p. 484:
``ln(P) = c1 + c2*M + c3*(10 - M)^3 + c4*ln(R + c5*exp(c6*M)``
"""
# obtain coefficients for required intensity measure type (IMT)
coeffs = self.COEFFS_BEDROCK[imt].copy()
# obtain IMT-independent coefficients
coeffs.update(self.CONSTS)
# compute bedrock motion, equation (11)
ln_mean = self._compute_mean(rup, dists, coeffs)
# obtain standard deviation
ln_stddev = self._get_stddevs(coeffs, stddev_types)
return ln_mean, [ln_stddev]
def _compute_mean(self, rup, dists, coeffs):
"""
Evaluate equation (11) on p. 484.
"""
ln_p = coeffs['c1'] + coeffs['c2']*rup.mag + \
coeffs['c3']*(self.CONSTS['ref_mag'] - rup.mag)**3 +\
coeffs['c4']*np.log(dists.rrup +
coeffs['c5']*np.exp(coeffs['c6']*rup.mag))
return ln_p
def _get_stddevs(self, coeffs, stddev_types):
"""
Look up values from Table 5 on p. 483 and convert to natural logarithm.
Interpretation of "sigma_log(Y)" as the common logarithm is based on
the order of magnitude of the values and consistent use of "log" and
"ln" to denote common and natural logarithm elsewhere in the paper.
"""
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
log_stddev = coeffs['sigma']
ln_stddev = log_stddev*np.log(10)
return ln_stddev
#: Coefficients taken from Table 5, p. 483.
COEFFS_BEDROCK = CoeffsTable(sa_damping=5., table="""\
IMT c1 c2 c3 c4 c5 c6 sigma
pga 9.1430 0.2470 -0.0140 -2.6700 32.9458 0.0663 0.3300
pgv -8.1069 1.2225 -0.0072 -1.1962 0.0004 1.4474 0.3807
0.1 4.8183 0.3531 -0.0152 -1.7445 5.0087 0.2973 0.3441
0.2 4.0396 0.3376 -0.0148 -1.6820 3.9925 0.2764 0.3117
0.5 2.6536 0.5799 -0.0162 -1.8479 1.9570 0.3919 0.3384
1 2.9150 0.1758 -0.0235 -1.3546 7.1026 0.1700 0.3762
2 -0.8191 0.4950 -0.0247 -1.1763 1.4407 0.3910 0.4536
4 -6.9619 0.9977 -0.0270 -0.8393 0.0000 1.9613 0.4614
""")
CONSTS = {
'ref_mag': 10.,
}
[docs]class NathEtAl2012Upper(NathEtAl2012Lower):
# pylint: disable=too-few-public-methods, no-init
"""
Implements GMPE of Nath et. al (2012) for intraplate margin seismicity in
the Shillong Plateau of India above 25 km deph.
This model is based on stochastic simulation with a mean stress drop of 40
bars.
"""
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
# pylint: disable=too-many-arguments
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for specification of input and result values.
Implements the correction factor for the upper crust, equation (12) on
p. 484:
``P' = P x Correction_factor``
"""
ln_mean, [ln_stddev] = super().get_mean_and_stddevs(
sites, rup, dists, imt, stddev_types)
# compute site corrections, equation (9)
coeffs = self.COEFFS_UPPER[imt]
ln_mean += np.log(coeffs['correction'])
return ln_mean, [ln_stddev]
#: Coefficients taken from Table 6, p. 485.
COEFFS_UPPER = CoeffsTable(sa_damping=5., table="""\
IMT correction
pga 0.6169
pgv 0.8781
0.1 0.6249
0.2 0.6584
0.5 0.8355
1 0.8704
2 0.9109
4 0.9292
""")