Source code for openquake.hazardlib.gsim.chiou_youngs_2014

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
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#
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"""
Module exports :class:`ChiouYoungs2014`
               :class:`ChiouYoungs2014Japan`
               :class:`ChiouYoungs2014Italy`
               :class:`ChiouYoungs2014Wenchuan`
               :class:`ChiouYoungs2014PEER`
               :class:`ChiouYoungs2014NearFaultEffect`
"""
import numpy as np

from openquake.baselib.general import CallableDict
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
from openquake.hazardlib.gsim.abrahamson_2014 import get_epistemic_sigma
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, PGV, SA

CONSTANTS = {"c2": 1.06, "c4": -2.1, "c4a": -0.5, "crb": 50.0,
             "c8a": 0.2695, "c11": 0.0, "phi6": 300.0, "phi6jp": 800.0}


def _get_centered_cdpp(clsname, ctx):
    """
    Returns the centred dpp term (zero by default)
    """
    if clsname.endswith("NearFaultEffect"):
        return ctx.rcdpp
    return np.zeros(ctx.rrup.shape)


def _get_centered_z1pt0(clsname, ctx):
    """
    Get z1pt0 centered on the Vs30- dependent average z1pt0(m)
    California and non-Japan regions
    """
    if clsname.endswith("Japan"):
        mean_z1pt0 = (-5.23 / 2.) * np.log(((ctx.vs30 ** 2.) + 412.39 ** 2.)
                                           / (1360 ** 2. + 412.39 ** 2.))
        return ctx.z1pt0 - np.exp(mean_z1pt0)

    #: California and non-Japan regions
    mean_z1pt0 = (-7.15 / 4.) * np.log(((ctx.vs30) ** 4. + 570.94 ** 4.)
                                       / (1360 ** 4. + 570.94 ** 4.))
    return ctx.z1pt0 - np.exp(mean_z1pt0)


def _get_centered_ztor(ctx):
    """
    Get ztor centered on the M- dependent avarage ztor(km)
    by different fault types.
    """
    # Strike-slip and normal faulting
    mean_ztor = np.clip(2.673 - 1.136 * np.clip(ctx.mag - 4.970, 0., None),
                        0., None) ** 2
    # Reverse and reverse-oblique faulting
    rev = (30. <= ctx.rake) & (ctx.rake <= 150.)
    mean_ztor[rev] = np.clip(2.704 - 1.226 * np.clip(
        ctx.mag[rev] - 5.849, 0.0, None), 0., None) ** 2
    return ctx.ztor - mean_ztor


def _get_ln_y_ref(ctx, C):
    """
    Get an intensity on a reference soil.
    Implements eq. 13a.
    """
    # Reverse faulting flag
    Frv = 1. if 30 <= ctx.rake <= 150 else 0.
    # Normal faulting flag
    Fnm = 1. if -120 <= ctx.rake <= -60 else 0.
    # A part in eq. 11
    mag_test1 = np.cosh(2. * np.clip(ctx.mag - 4.5, 0., None))
    # Centered DPP
    centered_dpp = 0
    # Centered Ztor
    centered_ztor = 0

    dist_taper = np.fmax(1 - (np.fmax(ctx.rrup - 40,
                              np.zeros_like(ctx)) / 30.),
                         np.zeros_like(ctx))
    dist_taper = dist_taper.astype(np.float64)
    ln_y_ref = (
        # first part of eq. 11
        C['c1']
        + (C['c1a'] + C['c1c'] / mag_test1) * Frv
        + (C['c1b'] + C['c1d'] / mag_test1) * Fnm
        + (C['c7'] + C['c7b'] / mag_test1) * centered_ztor
        + (C['c11'] + C['c11b'] / mag_test1) *
        np.cos(np.radians(ctx.dip)) ** 2
        # second part
        + C['c2'] * (ctx.mag - 6)
        + ((C['c2'] - C['c3']) / C['cn'])
        * np.log(1 + np.exp(C['cn'] * (C['cm'] - ctx.mag)))
        # third part
        + C['c4']
        * np.log(ctx.rrup + C['c5']
                 * np.cosh(C['c6'] * np.clip(ctx.mag - C['chm'], 0, None)))
        + (C['c4a'] - C['c4'])
        * np.log(np.sqrt(ctx.rrup ** 2 + C['crb'] ** 2))
        # forth part
        + (C['cg1'] + C['cg2'] / (
            np.cosh(np.clip(ctx.mag - C['cg3'], 0, None))))
        * ctx.rrup
        # fifth part
        + C['c8'] * dist_taper
        * np.clip((ctx.mag - 5.5, 0) / 0.8, 0., 1.)
        * np.exp(-1 * C['c8a'] * (ctx.mag - C['c8b']) ** 2) * centered_dpp
        # sixth part
        # + C['c9'] * Fhw * np.cos(math.radians(ctx.dip)) *
        # (C['c9a'] + (1 - C['c9a']) * np.tanh(ctx.rx / C['c9b']))
        # * (1 - np.sqrt(ctx.rjb ** 2 + ctx.ztor ** 2)
        #   / (ctx.rrup + 1.0))
    )
    return ln_y_ref


def _get_mean(ctx, C, ln_y_ref, exp1, exp2):
    """
    Add site effects to an intensity. Implements eq. 13b.
    """
    eta = epsilon = 0.
    ln_y = (
        # first line of eq. 12
        ln_y_ref + eta
        # second line
        + C['phi1'] * np.log(ctx.vs30 / 1130).clip(-np.inf, 0)
        # third line
        + C['phi2'] * (exp1 - exp2)
        * np.log((np.exp(ln_y_ref) * np.exp(eta) + C['phi4']) / C['phi4'])
        # fourth line - removed
        # fifth line
        + epsilon)
    return ln_y


[docs]def get_basin_depth_term(clsname, C, centered_z1pt0):
    """
    Returns the basin depth scaling
    """
    if clsname.endswith("Japan"):
        return C["phi5jp"] * (1.0 - np.exp(-centered_z1pt0 /
                                           CONSTANTS["phi6jp"]))
    return C["phi5"] * (1.0 - np.exp(-centered_z1pt0 /
                                     CONSTANTS["phi6"]))


[docs]def get_directivity(clsname, C, ctx):
    """
    Returns the directivity term.

    The directivity prediction parameter is centered on the average
    directivity prediction parameter. Here we set the centered_dpp
    equal to zero, since the near fault directivity effect prediction is
    off by default in our calculation.
    """
    cdpp = _get_centered_cdpp(clsname, ctx)
    if not np.any(cdpp > 0.0):
        # No directivity term
        return 0.0
    f_dir = np.exp(-C["c8a"] * ((ctx.mag - C["c8b"]) ** 2.)) * cdpp
    f_dir *= np.clip((ctx.mag - 5.5) / 0.8, 0., 1.)
    rrup_max = ctx.rrup - 40.
    rrup_max[rrup_max < 0.0] = 0.0
    rrup_max = 1.0 - (rrup_max / 30.)
    rrup_max[rrup_max < 0.0] = 0.0
    return C["c8"] * rrup_max * f_dir


get_far_field_distance_scaling = CallableDict()


[docs]@get_far_field_distance_scaling.add("CAL")
def get_far_field_distance_scaling_1(region, C, mag, rrup):
    """
    Returns the far-field distance scaling term - both magnitude and
    distance - for California and other regions
    """
    # Get the attenuation distance scaling
    f_r = (CONSTANTS["c4a"] - CONSTANTS["c4"]) * np.log(
        np.sqrt(rrup ** 2. + CONSTANTS["crb"] ** 2.))
    # Get the magnitude dependent term
    f_rm = C["cg1"] + C["cg2"] / np.cosh(np.clip(mag - C["cg3"], 0.0, None))
    return f_r + f_rm * rrup


[docs]@get_far_field_distance_scaling.add("JPN")
def get_far_field_distance_scaling_2(region, C, mag, rrup):
    """
    Returns the far-field distance scaling term - both magnitude and
    distance - for Japan
    """
    # Get the attenuation distance scaling
    f_r = (CONSTANTS["c4a"] - CONSTANTS["c4"]) * np.log(
        np.sqrt(rrup ** 2. + CONSTANTS["crb"] ** 2.))

    # Get the magnitude dependent term
    f_rm = (C["cg1"] + C["cg2"] /
            np.cosh(np.clip(mag - C["cg3"], 0.0, None))) * rrup
    # Apply adjustment factor for Japan
    f_rm[(mag > 6.0) & (mag < 6.9)] *= C["gjpit"]
    return f_r + f_rm


[docs]@get_far_field_distance_scaling.add("ITA")
def get_far_field_distance_scaling_3(region, C, mag, rrup):
    """
    Returns the far-field distance scaling term - both magnitude and
    distance - for Italy
    """
    # Get the attenuation distance scaling
    f_r = (CONSTANTS["c4a"] - CONSTANTS["c4"]) * np.log(
        np.sqrt(rrup ** 2. + CONSTANTS["crb"] ** 2.))

    # Get the magnitude dependent term
    f_rm = (C["cg1"] + C["cg2"] /
            np.cosh(np.clip(mag - C["cg3"], 0.0, None))) * rrup
    # Apply adjustment factor for Italy
    f_rm[(mag > 6.0) & (mag < 6.9)] *= C["gjpit"]
    return f_r + f_rm


[docs]@get_far_field_distance_scaling.add("WEN")
def get_far_field_distance_scaling_4(region, C, mag, rrup):
    """
    Returns the far-field distance scaling term - both magnitude and
    distance - for Wenchuan
    """
    # Get the attenuation distance scaling
    f_r = (CONSTANTS["c4a"] - CONSTANTS["c4"]) * np.log(
        np.sqrt(rrup ** 2. + CONSTANTS["crb"] ** 2.))

    # Get the magnitude dependent term
    f_rm = (C["cg1"] + C["cg2"] /
            np.cosh(np.clip(mag - C["cg3"], 0.0, None))) * rrup
    # Apply adjustment factor for Wenchuan
    return f_r + (f_rm * C["gwn"])


[docs]def get_geometric_spreading(C, mag, rrup):
    """
    Returns the near-field geometric spreading term
    """
    # Get the near-field magnitude scaling
    return CONSTANTS["c4"] * np.log(
        rrup + C["c5"] * np.cosh(C["c6"] * np.clip(mag - C["chm"], 0.0, None)))


[docs]def get_hanging_wall_term(C, ctx):
    """
    Returns the hanging wall term
    """
    fhw = np.zeros(ctx.rrup.shape)
    idx = ctx.rx >= 0.0
    if np.any(idx):
        fdist = 1.0 - (np.sqrt(ctx.rjb[idx] ** 2. + ctx.ztor[idx] ** 2.) /
                       (ctx.rrup[idx] + 1.0))
        fdist *= C["c9a"] + (1.0 - C["c9a"]) * np.tanh(ctx.rx[idx] / C["c9b"])
        fhw[idx] += C["c9"] * np.cos(np.radians(ctx.dip[idx])) * fdist
    return fhw


[docs]def get_linear_site_term(clsname, C, ctx):
    """
    Returns the linear site scaling term
    """
    if clsname.endswith("Japan"):
        return C["phi1jp"] * np.log(ctx.vs30 / 1130).clip(-np.inf, 0.0)
    return C["phi1"] * np.log(ctx.vs30 / 1130).clip(-np.inf, 0.0)


[docs]def get_region(clsname):
    if clsname.endswith("Italy"):
        return "ITA"
    elif clsname.endswith("Japan"):
        return "JPN"
    elif clsname.endswith("Wenchuan"):
        return "WEN"
    else:
        return "CAL"


[docs]def get_ln_y_ref(clsname, C, ctx):
    """
    Returns the ground motion on the reference rock, described fully by
    Equation 11
    """
    region = get_region(clsname)
    delta_ztor = _get_centered_ztor(ctx)
    return (get_stress_scaling(C) +
            get_magnitude_scaling(C, ctx.mag) +
            get_source_scaling_terms(C, ctx, delta_ztor) +
            get_hanging_wall_term(C, ctx) +
            get_geometric_spreading(C, ctx.mag, ctx.rrup) +
            get_far_field_distance_scaling(region, C, ctx.mag, ctx.rrup) +
            get_directivity(clsname, C, ctx))


[docs]def get_magnitude_scaling(C, mag):
    """
    Returns the magnitude scaling
    """
    f_m = np.log(1.0 + np.exp(C["cn"] * (C["cm"] - mag)))
    f_m = CONSTANTS["c2"] * (mag - 6.0) +\
        ((CONSTANTS["c2"] - C["c3"]) / C["cn"]) * f_m
    return f_m


[docs]def get_nonlinear_site_term(C, ctx, y_ref):
    """
    Returns the nonlinear site term and the Vs-scaling factor (to be
    used in the standard deviation model
    """
    vs = ctx.vs30.clip(-np.inf, 1130.0)
    f_nl_scaling = C["phi2"] * (np.exp(C["phi3"] * (vs - 360.)) -
                                np.exp(C["phi3"] * (1130. - 360.)))
    f_nl = np.log((y_ref + C["phi4"]) / C["phi4"]) * f_nl_scaling
    return f_nl, f_nl_scaling


[docs]def get_phi(C, mag, ctx, nl0):
    """
    Returns the within-event variability described in equation 13, line 3
    """
    phi = C["sig3"] * np.ones(ctx.vs30.shape)
    phi[ctx.vs30measured] = 0.7
    phi = np.sqrt(phi + ((1.0 + nl0) ** 2.))
    mdep = C["sig1"] + (
        C["sig2"] - C["sig1"]) * np.clip(mag - 5., 0., 1.5) / 1.5
    return mdep * phi


[docs]def get_source_scaling_terms(C, ctx, delta_ztor):
    """
    Returns additional source scaling parameters related to style of
    faulting, dip and top of rupture depth
    """
    f_src = np.zeros_like(ctx.mag)
    coshm = np.cosh(2.0 * np.clip(ctx.mag - 4.5, 0., None))
    # Style of faulting term
    pos = (30 <= ctx.rake) & (ctx.rake <= 150)
    neg = (-120 <= ctx.rake) & (ctx.rake <= -60)
    # reverse faulting flag
    f_src[pos] += C["c1a"] + (C["c1c"] / coshm[pos])
    # normal faulting flag
    f_src[neg] += C["c1b"] + (C["c1d"] / coshm[neg])
    # Top of rupture term
    f_src += (C["c7"] + (C["c7b"] / coshm)) * delta_ztor
    # Dip term
    f_src += ((CONSTANTS["c11"] + (C["c11b"] / coshm)) *
              np.cos(np.radians(ctx.dip)) ** 2.0)
    return f_src


[docs]def get_stddevs(clsname, C, ctx, mag, y_ref, f_nl_scaling):
    """
    Returns the standard deviation model described in equation 13
    """
    if clsname == 'ChiouYoungs2014PEER':
        # the standard deviation, which is fixed at 0.65 for every site
        return [0.65 * np.ones_like(ctx.vs30), 0, 0]

    # Determines the nonlinear term described in equation 13, line 4
    nl0 = f_nl_scaling * (y_ref / (y_ref + C["phi4"]))
    # Get between and within-event variability
    tau = get_tau(C, mag)
    phi_nl0 = get_phi(C, mag, ctx, nl0)
    # Get total standard deviation propagating the uncertainty in the
    # nonlinear amplification term
    sigma = np.sqrt(((1.0 + nl0) ** 2.) * (tau ** 2.) + phi_nl0 ** 2.)
    return [sigma, np.abs((1 + nl0) * tau), phi_nl0]


[docs]def get_stress_scaling(C):
    """
    Returns the stress drop scaling factor
    """
    return C["c1"]


[docs]def get_tau(C, mag):
    """
    Returns the between-event variability described in equation 13, line 2
    """
    # eq. 13 to calculate inter-event standard error
    mag_test = np.clip(mag - 5.0, 0., 1.5)
    return C['tau1'] + (C['tau2'] - C['tau1']) / 1.5 * mag_test


[docs]def get_mean_stddevs(name, C, ctx):
    """
    Return mean and standard deviation values
    """
    # Get ground motion on reference rock
    ln_y_ref = get_ln_y_ref(name, C, ctx)
    y_ref = np.exp(ln_y_ref)
    # Get the site amplification
    # Get basin depth
    dz1pt0 = _get_centered_z1pt0(name, ctx)
    # for Z1.0 = 0.0 no deep soil correction is applied
    dz1pt0[ctx.z1pt0 <= 0.0] = 0.0
    f_z1pt0 = get_basin_depth_term(name, C, dz1pt0)
    # Get linear amplification term
    f_lin = get_linear_site_term(name, C, ctx)
    # Get nonlinear amplification term
    f_nl, f_nl_scaling = get_nonlinear_site_term(C, ctx, y_ref)

    # Add on the site amplification
    mean = ln_y_ref + (f_lin + f_nl + f_z1pt0)
    # Get standard deviations
    sig, tau, phi = get_stddevs(
        name, C, ctx, ctx.mag, y_ref, f_nl_scaling)

    return mean, sig, tau, phi


[docs]class ChiouYoungs2014(GMPE):
    """
    Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs

    Chiou, B. S.-J. and Youngs, R. R. (2014), "Updated of the Chiou and Youngs
    NGA Model for the Average Horizontal Component of Peak Ground Motion and
    Response Spectra, Earthquake Spectra, 30(3), 1117 - 1153,
    DOI: 10.1193/072813EQS219M
    """
    adapted = False  # overridden in acme_2019

    #: Supported tectonic region type is active shallow crust
    DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST

    #: Supported intensity measure types are spectral acceleration,
    #: peak ground velocity and peak ground acceleration
    DEFINED_FOR_INTENSITY_MEASURE_TYPES = {PGA, PGV, SA}

    #: Supported intensity measure component is orientation-independent
    #: measure :attr:`~openquake.hazardlib.const.IMC.RotD50`,
    DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RotD50

    #: Supported standard deviation types are inter-event, intra-event
    #: and total, see chapter "Variance model".
    DEFINED_FOR_STANDARD_DEVIATION_TYPES = {
        const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT}

    #: Required site parameters are Vs30, Vs30 measured flag
    #: and Z1.0.
    REQUIRES_SITES_PARAMETERS = {'vs30', 'vs30measured', 'z1pt0'}

    #: Required rupture parameters are magnitude, rake,
    #: dip and ztor.
    REQUIRES_RUPTURE_PARAMETERS = {'dip', 'rake', 'mag', 'ztor'}

    #: Required distance measures are RRup, Rjb and Rx.
    REQUIRES_DISTANCES = {'rrup', 'rjb', 'rx'}

    #: Reference shear wave velocity
    DEFINED_FOR_REFERENCE_VELOCITY = 1130

    def __init__(self, sigma_mu_epsilon=0.0, **kwargs):
        super().__init__(sigma_mu_epsilon=sigma_mu_epsilon, **kwargs)
        self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
        """
        See :meth:`superclass method
        <.base.GroundShakingIntensityModel.compute>`
        for spec of input and result values.
        """
        name = self.__class__.__name__
        # reference to page 1144, PSA might need PGA value
        pga_mean, pga_sig, pga_tau, pga_phi = get_mean_stddevs(name, self.COEFFS[PGA()], ctx)
        for m, imt in enumerate(imts):
            if repr(imt) == "PGA":
                mean[m] = pga_mean
                mean[m] += (self.sigma_mu_epsilon*get_epistemic_sigma(ctx))
                sig[m], tau[m], phi[m] = pga_sig, pga_tau, pga_phi
            else:
                imt_mean, imt_sig, imt_tau, imt_phi = \
                    get_mean_stddevs(name, self.COEFFS[imt], ctx)
                # reference to page 1144
                # Predicted PSA value at T ≤ 0.3s should be set equal to the value of PGA
                # when it falls below the predicted PGA
                mean[m] = np.where(imt_mean < pga_mean, pga_mean, imt_mean) \
                    if repr(imt).startswith("SA") and imt.period <= 0.3 \
                    else imt_mean

                mean[m] += (self.sigma_mu_epsilon*get_epistemic_sigma(ctx))

                sig[m], tau[m], phi[m] = imt_sig, imt_tau, imt_phi

    #: Coefficient tables are constructed from values in tables 1 - 5
    COEFFS = CoeffsTable(sa_damping=5, table="""\
IMT     c1      c1a     c1b     c1c     c1d     cn      cm    c2      c3    c4     c4a  crb   c5      chm     c6      c7      c7b     c8     c8a    c8b       c9     c9a    c9b     c11      c11b        cg1        cg2       cg3     phi1       phi2      phi3     phi4     phi5   phi6  gjpit  gwn      phi1jp  phi5jp   phi6jp     tau1    tau2    sig1    sig2    sig3    sig2jp
pga   -1.5065  0.165  -0.255  -0.165  0.255  16.0875  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.0956  0.4908  0.0352   0.0462  0.     0.2695  0.4833  0.9228  0.1202  6.8607  0.      -0.4536    -0.007146  -0.006758  4.2542  -0.521   -0.1417   -0.00701   0.102151  0.     300  1.5817  0.7594  -0.6846  0.459    800.        0.4     0.26    0.4912  0.3762  0.8     0.4528
pgv    2.3549  0.165  -0.0626 -0.165  0.0626  3.3024  5.423   1.06  2.3152  -2.1  -0.5  50  5.8096  3.0514  0.4407  0.0324   0.0097  0.2154 0.2695  5.      0.3079  0.1     6.5     0       -0.3834    -0.001852  -0.007403  4.3439  -0.7936  -0.0699   -0.008444  5.41      0.0202 300. 2.2306  0.335   -0.7966  0.9488   800.        0.3894  0.2578  0.4785  0.3629  0.7504  0.3918
0.01  -1.5065  0.165  -0.255  -0.165  0.255  16.0875  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.0956  0.4908  0.0352   0.0462  0.     0.2695  0.4833  0.9228  0.1202  6.8607  0.      -0.4536    -0.007146  -0.006758  4.2542  -0.521   -0.1417   -0.00701   0.102151  0.     300  1.5817  0.7594  -0.6846  0.459    800.        0.4     0.26    0.4912  0.3762  0.8     0.4528
0.02  -1.4798  0.165  -0.255  -0.165  0.255  15.7118  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.0963  0.4925  0.0352   0.0472  0.     0.2695  1.2144  0.9296  0.1217  6.8697  0.      -0.4536    -0.007249  -0.006758  4.2386  -0.5055  -0.1364   -0.007279  0.10836   0.     300  1.574   0.7606  -0.6681  0.458    800.        0.4026  0.2637  0.4904  0.3762  0.8     0.4551
0.03  -1.2972  0.165  -0.255  -0.165  0.255  15.8819  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.0974  0.4992  0.0352   0.0533  0.     0.2695  1.6421  0.9396  0.1194  6.9113  0.      -0.4536    -0.007869  -0.006758  4.2519  -0.4368  -0.1403   -0.007354  0.119888  0.     300  1.5544  0.7642  -0.6314  0.462    800.        0.4063  0.2689  0.4988  0.3849  0.8     0.4571
0.04  -1.1007  0.165  -0.255  -0.165  0.255  16.4556  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.0988  0.5037  0.0352   0.0596  0.     0.2695  1.9456  0.9661  0.1166  7.0271  0.      -0.4536    -0.008316  -0.006758  4.296   -0.3752  -0.1591   -0.006977  0.133641  0.     300  1.5502  0.7676  -0.5855  0.453    800.        0.4095  0.2736  0.5049  0.391   0.8     0.4642
0.05  -0.9292  0.165  -0.255  -0.165  0.255  17.6453  4.9993  1.06  1.9636  -2.1  -0.5  50  6.4551  3.1011  0.5048  0.0352   0.0639  0.     0.2695  2.181   0.9794  0.1176  7.0959  0.      -0.4536    -0.008743  -0.006758  4.3578  -0.3469  -0.1862   -0.006467  0.148927  0.     300  1.5391  0.7739  -0.5457  0.436    800.        0.4124  0.2777  0.5096  0.3957  0.8     0.4716
0.075 -0.658   0.165  -0.254  -0.165  0.254  20.1772  5.0031  1.06  1.9636  -2.1  -0.5  50  6.4551  3.1094  0.5048  0.0352   0.063   0.     0.2695  2.6087  1.026   0.1171  7.3298  0.      -0.4536    -0.009537  -0.00619   4.5455  -0.3747  -0.2538   -0.005734  0.190596  0.     300  1.4804  0.7956  -0.4685  0.383    800.        0.4179  0.2855  0.5179  0.4043  0.8     0.5022
0.1   -0.5613  0.165  -0.253  -0.165  0.253  19.9992  5.0172  1.06  1.9636  -2.1  -0.5  50  6.8305  3.2381  0.5048  0.0352   0.0532  0.     0.2695  2.9122  1.0177  0.1146  7.2588  0.      -0.4536    -0.00983   -0.005332  4.7603  -0.444   -0.2943   -0.005604  0.230662  0.     300  1.4094  0.7932  -0.4985  0.375    800.        0.4219  0.2913  0.5236  0.4104  0.8     0.523
0.12  -0.5342  0.165  -0.252  -0.165  0.252  18.7106  5.0315  1.06  1.9795  -2.1  -0.5  50  7.1333  3.3407  0.5048  0.0352   0.0452  0.     0.2695  3.1045  1.0008  0.1128  7.2372  0.      -0.4536    -0.009913  -0.004732  4.8963  -0.4895  -0.3077   -0.005696  0.253169  0.     300  1.3682  0.7768  -0.5603  0.377    800.        0.4244  0.2949  0.527   0.4143  0.8     0.5278
0.15  -0.5462  0.165  -0.25   -0.165  0.25   16.6246  5.0547  1.06  2.0362  -2.1  -0.5  50  7.3621  3.43    0.5045  0.0352   0.0345  0.     0.2695  3.3399  0.9801  0.1106  7.2109  0.      -0.4536    -0.009896  -0.003806  5.0644  -0.5477  -0.3113   -0.005845  0.266468  0.     300  1.3241  0.7437  -0.6451  0.379    800.        0.4275  0.2993  0.5308  0.4191  0.8     0.5304
0.17  -0.5858  0.165  -0.248  -0.165  0.248  15.3709  5.0704  1.06  2.0823  -2.1  -0.5  50  7.4365  3.4688  0.5036  0.0352   0.0283  0.     0.2695  3.4719  0.9652  0.115   7.2491  0.      -0.4536    -0.009787  -0.00328   5.1371  -0.5922  -0.3062   -0.005959  0.26506   0.     300  1.3071  0.7219  -0.6981  0.38     800.        0.4292  0.3017  0.5328  0.4217  0.8     0.531
0.2   -0.6798  0.165  -0.2449 -0.165  0.2449 13.7012  5.0939  1.06  2.1521  -2.1  -0.5  50  7.4972  3.5146  0.5016  0.0352   0.0202  0.     0.2695  3.6434  0.9459  0.1208  7.2988  0.      -0.444     -0.009505  -0.00269   5.188   -0.6693  -0.2927   -0.006141  0.255253  0.     300  1.2931  0.6922  -0.7653  0.384    800.        0.4313  0.3047  0.5351  0.4252  0.8     0.5312
0.25  -0.8663  0.165  -0.2382 -0.165  0.2382 11.2667  5.1315  1.06  2.2574  -2.1  -0.5  50  7.5416  3.5746  0.4971  0.0352   0.009   0.     0.2695  3.8787  0.9196  0.1208  7.3691  0.      -0.3539    -0.008918  -0.002128  5.2164  -0.7766  -0.2662   -0.006439  0.231541  0.     300  1.315   0.6579  -0.8469  0.393    800.        0.4341  0.3087  0.5377  0.4299  0.7999  0.5309
0.3   -1.0514  0.165  -0.2313 -0.165  0.2313  9.1908  5.167   1.06  2.344   -2.1  -0.5  50  7.56    3.6232  0.4919  0.0352  -0.0004  0.     0.2695  4.0711  0.8829  0.1175  6.8789  0.      -0.2688    -0.008251  -0.001812  5.1954  -0.8501  -0.2405   -0.006704  0.207277  0.001  300  1.3514  0.6362  -0.8999  0.408    800.        0.4363  0.3119  0.5395  0.4338  0.7997  0.5307
0.4   -1.3794  0.165  -0.2146 -0.165  0.2146  6.5459  5.2317  1.06  2.4709  -2.1  -0.5  50  7.5735  3.6945  0.4807  0.0352  -0.0155  0.     0.2695  4.3745  0.8302  0.106   6.5334  0.      -0.1793    -0.007267  -0.001274  5.0899  -0.9431  -0.1975   -0.007125  0.165464  0.004  300  1.4051  0.6049  -0.9618  0.462    800.        0.4396  0.3165  0.5422  0.4399  0.7988  0.531
0.5   -1.6508  0.165  -0.1972 -0.165  0.1972  5.2305  5.2893  1.06  2.5567  -2.1  -0.5  50  7.5778  3.7401  0.4707  0.0352  -0.0278  0.0991 0.2695  4.6099  0.7884  0.1061  6.526   0.      -0.1428    -0.006492  -0.001074  4.7854  -1.0044  -0.1633   -0.007435  0.133828  0.01   300  1.4402  0.5507  -0.9945  0.524    800.        0.4419  0.3199  0.5433  0.4446  0.7966  0.5313
0.75  -2.1511  0.165  -0.162  -0.165  0.162   3.7896  5.4109  1.06  2.6812  -2.1  -0.5  50  7.5808  3.7941  0.4575  0.0352  -0.0477  0.1982 0.2695  5.0376  0.6754  0.1     6.5     0.      -0.1138    -0.005147  -0.001115  4.3304  -1.0602  -0.1028   -0.00812   0.085153  0.034  300  1.528   0.3582  -1.0225  0.658    800.        0.4459  0.3255  0.5294  0.4533  0.7792  0.5309
1     -2.5365  0.165  -0.14   -0.165  0.14    3.3024  5.5106  1.06  2.7474  -2.1  -0.5  50  7.5814  3.8144  0.4522  0.0352  -0.0559  0.2154 0.2695  5.3411  0.6196  0.1     6.5     0.      -0.1062    -0.004277  -0.001197  4.1667  -1.0941  -0.0699   -0.008444  0.058595  0.067  300  1.6523  0.2003  -1.0002  0.78     800.        0.4484  0.3291  0.5105  0.4594  0.7504  0.5302
1.5   -3.0686  0.165  -0.1184 -0.165  0.1184  2.8498  5.6705  1.06  2.8161  -2.1  -0.5  50  7.5817  3.8284  0.4501  0.0352  -0.063   0.2154 0.2695  5.7688  0.5101  0.1     6.5     0.      -0.102     -0.002979  -0.001675  4.0029  -1.1142  -0.0425   -0.007707  0.031787  0.143  300  1.8872  0.0356  -0.9245  0.96     800.        0.4515  0.3335  0.4783  0.468   0.7136  0.5276
2     -3.4148  0.1645 -0.11   -0.1645 0.11    2.5417  5.7981  1.06  2.8514  -2.1  -0.5  50  7.5818  3.833   0.45    0.0352  -0.0665  0.2154 0.2695  6.0723  0.3917  0.1     6.5     0.      -0.1009    -0.002301  -0.002349  3.8949  -1.1154  -0.0302   -0.004792  0.019716  0.203  300  2.1348  0.      -0.8626  1.11     800.        0.4534  0.3363  0.4681  0.4681  0.7035  0.5167
3     -3.9013  0.1168 -0.104  -0.1168 0.104   2.1488  5.9983  1.06  2.8875  -2.1  -0.5  50  7.5818  3.8361  0.45    0.016   -0.0516  0.2154 0.2695  6.5     0.1244  0.1     6.5     0.      -0.1003    -0.001344  -0.003306  3.7928  -1.1081  -0.0129   -0.001828  0.009643  0.277  300  3.5752  0.      -0.7882  1.291    800.        0.4558  0.3398  0.4617  0.4617  0.7006  0.4917
4     -4.2466  0.0732 -0.102  -0.0732 0.102  1.8957   6.1552  1.06  2.9058  -2.1  -0.5  50  7.5818  3.8369  0.45    0.0062  -0.0448  0.2154 0.2695  6.8035  0.0086  0.1     6.5     0.      -0.1001    -0.001084  -0.003566  3.7443  -1.0603  -0.0016   -0.001523  0.005379  0.309  300  3.8646  0.      -0.7195  1.387    800.        0.4574  0.3419  0.4571  0.4571  0.7001  0.4682
5     -4.5143  0.0484 -0.101  -0.0484 0.101  1.7228   6.2856  1.06  2.9169  -2.1  -0.5  50  7.5818  3.8376  0.45    0.0029  -0.0424  0.2154 0.2695  7.0389  0.      0.1     6.5     0.      -0.1001    -0.00101   -0.00364   3.709   -0.9872   0.       -0.00144   0.003223  0.321  300  3.7292  0.      -0.656   1.433    800.        0.4584  0.3435  0.4535  0.4535  0.7     0.4517
7.5   -5.0009  0.022  -0.101  -0.022  0.101  1.5737   6.5428  1.06  2.932   -2.1  -0.5  50  7.5818  3.838   0.45    0.0007  -0.0348  0.2154 0.2695  7.4666  0.      0.1     6.5     0.      -0.1       -0.000964  -0.003686  3.6632  -0.8274   0.       -0.001369  0.001134  0.329  300  2.3763  0.      -0.5202  1.46     800.        0.4601  0.3459  0.4471  0.4471  0.7     0.4167
10    -5.3461  0.0124 -0.1    -0.0124 0.1    1.5265   6.7415  1.06  2.9396  -2.1  -0.5  50  7.5818  3.838   0.45    0.0003  -0.0253  0.2154 0.2695  7.77    0.      0.1     6.5     0.      -0.1       -0.00095   -0.0037    3.623   -0.7053   0.       -0.001361  0.000515  0.33   300  1.7679  0.      -0.4068  1.464    800.        0.4612  0.3474  0.4426  0.4426  0.7     0.3755
""")


[docs]class ChiouYoungs2014Japan(ChiouYoungs2014):
    """
    Regionalisation of the Chiou & Youngs (2014) GMPE for use with the
    Japan far-field distance attuation scaling and site model
    """


[docs]class ChiouYoungs2014Italy(ChiouYoungs2014):
    """
    Adaption of the Chiou & Youngs (2014) GMPE for the the Italy far-field
    attenuation scaling, but assuming the California site amplification model
    """


[docs]class ChiouYoungs2014Wenchuan(ChiouYoungs2014):
    """
    Adaption of the Chiou & Youngs (2014) GMPE for the Wenchuan far-field
    attenuation scaling, but assuming the California site amplification model.
    It should be note that according to Chiou & Youngs (2014) this adjustment
    is calibrated only for the M7.9 Wenchuan earthquake, so application to
    other scenarios is at the user's own risk
    """


[docs]class ChiouYoungs2014PEER(ChiouYoungs2014):
    """
    This implements the Chiou & Youngs (2014) GMPE for use with the PEER
    tests. In this version the total standard deviation is fixed at 0.65
    """
    #: Only the total standars deviation is defined
    DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL}
    #: The PEER tests requires only PGA
    DEFINED_FOR_INTENSITY_MEASURE_TYPES = {PGA}


[docs]class ChiouYoungs2014NearFaultEffect(ChiouYoungs2014):
    """
    This implements the Chiou & Youngs (2014) GMPE include the near fault
    effect prediction. In this version, we add the distance measure, rcdpp
    for directivity prediction.
    """
    #: Required distance measures are RRup, Rjb, Rx, and Rcdpp
    REQUIRES_DISTANCES = {'rrup', 'rjb', 'rx', 'rcdpp'}


[docs]class ChiouYoungs2014ACME2019(ChiouYoungs2014):
    """
    Implements a modified version of the CY2014 GMM. Main changes:
    - Hanging wall term excluded
    - Centered Ztor = 0
    - Centered Dpp = 0
    """
    adapted = True

[docs]    def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
        """
        See :meth:`superclass method
        <.base.GroundShakingIntensityModel.compute>`
        for spec of input and result values.
        """
        for m, imt in enumerate(imts):
            C = self.COEFFS[imt]
            # intensity on a reference soil is used for both mean
            # and stddev calculations.
            ln_y_ref = _get_ln_y_ref(self.__class__.__name__, ctx, C)
            # exp1 and exp2 are parts of eq. 12 and eq. 13,
            # calculate it once for both.
            exp1 = np.exp(C['phi3'] * (ctx.vs30.clip(-np.inf, 1130) - 360))
            exp2 = np.exp(C['phi3'] * (1130 - 360))
            mean[m] = _get_mean(ctx, C, ln_y_ref, exp1, exp2)