# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2020, 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# 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 <http://www.gnu.org/licenses/>.
"""
Module :mod:`openquake.hazardlib.gsim.sgobba_2020` implements
:class:`~openquake.hazardlib.gsim.sgobba_2020.SgobbaEtAl2020`
"""
import os
import copy
import numpy as np
import pandas as pd
from scipy.constants import g as gravity_acc
from scipy.spatial import cKDTree
from openquake.hazardlib.geo import Point, Polygon
from openquake.hazardlib import const
from openquake.hazardlib.geo.mesh import Mesh
from openquake.hazardlib.imt import PGA, SA
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
# From http://www.csgnetwork.com/degreelenllavcalc.html
LEN_1_DEG_LAT_AT_43pt5 = 111.10245
LEN_1_DEG_LON_AT_43pt5 = 80.87665
DATA_FOLDER = os.path.join(os.path.dirname(__file__), 'sgobba_2020')
REGIONS = {
'1': [[13.37, 42.13], [13.60, 42.24], [13.48, 42.51], [13.19, 42.36]],
'4': [[13.26, 42.41], [13.43, 42.49], [13.27, 43.02], [12.96, 42.86]],
'5': [[13.03, 42.90], [13.21, 42.99], [13.10, 43.13], [12.90, 43.06]]}
CONSTS = {'Mh': 5.0,
'Rref': 1.0,
'PseudoDepth': 6.0}
def _get_cluster_correction(dat, C, ctx, imt):
"""
Get cluster correction. The use can specify various options through
the cluster parameter. The available options are:
- cluster = None
In this case the code finds the most appropriate correction using
the rupture position
- cluster = 0
No cluster correction
- cluser = 1 or 4 or 5
The code uses the correction for the given cluster
"""
cluster = dat.cluster
shape = ctx.sids.shape
correction = np.zeros_like(shape)
# st.dev.
tau_L2L = np.zeros(shape)
Bp_model = np.zeros(shape)
phi_P2P = np.zeros(shape)
# No cluster correction
if cluster == 0:
tau_L2L = C['tau_L2L']
phi_P2P = C['phi_P2P']
return correction, tau_L2L, Bp_model, phi_P2P
# the code finds the most appropriate correction
if cluster is None:
mesh = Mesh(np.array([ctx.hypo_lon]), np.array([ctx.hypo_lat]))
# midp = ctx.surface.get_middle_point()
# mesh = Mesh(np.array([midp.longitude]),np.array([midp.latitude]))
for key in REGIONS:
coo = np.array(REGIONS[key])
pnts = [Point(lo, la) for lo, la in zip(coo[:, 0], coo[:, 1])]
poly = Polygon(pnts)
within = poly.intersects(mesh)
if within.all():
cluster = int(key)
break
# if OUT clusters do not apply corrections
if cluster is None:
tau_L2L = C['tau_L2L']
phi_P2P = C['phi_P2P']
return correction, tau_L2L, Bp_model, phi_P2P
else:
# if IN clusters apply corrections
# Cluster coefficients
fname = 'P_model_cluster{:d}.csv'.format(cluster)
fname = os.path.join(DATA_FOLDER, fname)
data = np.loadtxt(fname, delimiter=",", skiprows=1)
# for st.dev.
fname2 = 'beta_dP2P_cluster{:d}.csv'.format(cluster)
fname2 = os.path.join(DATA_FOLDER, fname2)
data2 = np.loadtxt(fname2, delimiter=",", skiprows=1)
# Compute the coefficients
correction = np.zeros(shape)
per = imt.period
for idx in np.unique(dat.idxs):
tmp = data[int(idx)]
correction[dat.idxs == idx] = np.interp(
per, dat.PERIODS, tmp[0:5])
# Adding L2L correction
label = "dL2L_cluster{:d}".format(cluster)
correction += C[label]
# compute st.dev.
for idx in np.unique(dat.idxs):
tmp2 = data2[int(idx)]
Bp_model[dat.idxs == idx] = np.interp(per, dat.PERIODS, tmp2[0:5])
return correction, tau_L2L, Bp_model, phi_P2P
def _get_distance_term(C, mag, ctx):
"""
Eq.3 - page 3
"""
term1 = C['c1'] * (mag - C['mref']) + C['c2']
tmp = np.sqrt(ctx.rjb**2 + CONSTS['PseudoDepth']**2)
term2 = np.log10(tmp / CONSTS['Rref'])
term3 = C['c3'] * (tmp - CONSTS['Rref'])
return term1 * term2 + term3
def _get_magnitude_term(C, mag):
"""
Eq.2 - page 3
"""
return np.where(mag <= CONSTS['Mh'],
C['b1'] * (mag - CONSTS['Mh']),
C['b2'] * (mag - CONSTS['Mh']))
def _get_site_correction(data, shape, imt):
"""
Get site correction
"""
correction = np.zeros_like(shape)
# Compute the coefficients
correction = np.zeros(shape)
# stand.dev.
Bs_model = np.zeros(shape)
phi_S2Sref = np.zeros(shape)
per = imt.period
for idx in np.unique(data.idxs):
tmp = data.Smodel[int(idx)]
correction[data.idxs == idx] = np.interp(
per, data.PERIODS, tmp[0:5])
tmp2 = data.betaS2S[int(idx)]
Bs_model[data.idxs == idx] = np.interp(
per, data.PERIODS, tmp2[0:5])
return correction, Bs_model, phi_S2Sref
[docs]class Data(object):
"""Helper class"""
def __init__(self, smodel, cluster, periods, betaS2S, idxs):
self.Smodel = smodel
self.cluster = copy.copy(cluster)
self.PERIODS = periods
self.betaS2S = betaS2S
self.idxs = idxs
# NB: the implementation here is HORRIBLE performance-wise,
# because it is using KDTree and pandas!
[docs]class SgobbaEtAl2020(GMPE):
"""
Implements the GMM proposed by Sgobba et al. (2020).
Warning:
This GMM is not meant for national models where it would be too slow,
it is meant for scenario calculations.
:param event_id:
A string identifying an event amongst the ones comprised in the
list available in the file `event.csv`
:param directionality:
A boolean
:param cluster:
If set to 'None', the OQ Engine finds the corresponding cluster
using the rupture epicentral location.
If cluster=0, no cluster correction applied.
Otherwise, if an integer ID is provided, that
corresponds to the cluster id (available cluster indexes are 1, 4
and 5), the corresponding correction id applied.
"""
#: Supported tectonic region type is 'active shallow crust'
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST
#: 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 = {PGA, SA}
#: Supported intensity measure component is the geometric mean of two
#: horizontal components
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.GEOMETRIC_MEAN
#: Supported standard deviation types are inter-event, intra-event
#: and total
DEFINED_FOR_STANDARD_DEVIATION_TYPES = {
const.StdDev.TOTAL, const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT}
#: Required site parameters are lon and lat
REQUIRES_SITES_PARAMETERS = {'lon', 'lat'}
#: Required rupture parameters is magnitude, hypo_lat, hypo_lon
REQUIRES_RUPTURE_PARAMETERS = {'mag', 'hypo_lon', 'hypo_lat'}
#: Required distance measure is Rjb
REQUIRES_DISTANCES = {'rjb'}
PERIODS = np.array([0, 0.2, 0.50251256281407, 1.0, 2.0])
def __init__(self, event_id=None, directionality=False, cluster=None,
site=False, bedrock=False):
self.event_id = event_id
self.directionality = directionality
self.cluster = cluster
self.site = site
self.bedrock = bedrock
# Get site indexes. They are used for the site correction and the
# cluster (path) correction
# Load the coordinate of the grid
fname = os.path.join(DATA_FOLDER, 'grid.csv')
coo = np.fliplr(np.loadtxt(fname, delimiter=";"))
self.kdt = cKDTree(coo)
# Load the table with the between event coefficients
if event_id is not None:
self.event_id = event_id
fname = os.path.join(DATA_FOLDER, 'event.csv')
# Create dataframe with events
df = pd.read_csv(fname, dtype={'id': str})
df.set_index('id', inplace=True)
self.df = df
assert event_id in df.index.values
# Site coefficients
fname = os.path.join(DATA_FOLDER, "S_model.csv")
self.Smodel = np.loadtxt(fname, delimiter=",", skiprows=1)
fname = os.path.join(DATA_FOLDER, "beta_dS2S.csv")
self.betaS2S = np.loadtxt(fname, delimiter=",", skiprows=1)
[docs] def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
"""
Eq.1 - page 2
"""
for m, imt in enumerate(imts):
# Ergodic coeffs
C = self.COEFFS[imt]
# between-event
if self.event_id is not None:
label = "dBe_{:s}".format(str(imt))
self.be = self.df.loc[self.event_id][label]
self.be_std = 0.0
else:
self.be_std = C['tau_ev']
self.be = 0.0
# Site correction
points = np.array([ctx.lon, ctx.lat]).T # shape (N, 2)
dsts, idxs = self.kdt.query(points)
dat = Data(self.Smodel, self.cluster, self.PERIODS,
self.betaS2S, idxs)
sc = 0
phi_S2Sref = C['phi_S2S_ref']
Bs_model = np.zeros(ctx.sids.shape)
if self.site and self.bedrock is False:
sc, Bs_model, phi_S2Sref = _get_site_correction(
dat, ctx.sids.shape, imt)
cc, tau_L2L, Bp_model, phi_P2P = _get_cluster_correction(
dat, C, ctx, imt)
# Get mean
mean[m] = (C['a'] + _get_magnitude_term(C, ctx.mag) +
_get_distance_term(C, ctx.mag, ctx) +
sc + cc + self.be)
# To natural logarithm and fraction of g
mean[m] = np.log(10.0**mean[m] / (gravity_acc*100))
# Get stds
std = np.sqrt(C['sigma_0'] ** 2 + self.be_std ** 2 + tau_L2L ** 2 +
Bs_model + phi_S2Sref ** 2 + Bp_model + phi_P2P ** 2)
sig[m] = np.log(10.0 ** std)
tau[m] = np.log(10.0 ** self.be_std)
phi[m] = np.log(10.0 ** np.sqrt((std ** 2 - self.be_std ** 2)))
COEFFS = CoeffsTable(sa_damping=5., table="""\
IMT a b1 b2 c1 c2 c3 mref tau_ev tau_L2L phi_S2S_ref phi_S2S phi_P2P sigma_0 dL2L_cluster1 dL2L_cluster4 dL2L_cluster5
pga 2.92178299969904 0.549352522898805 0.195787600661646 0.182324348626393 -1.56833817017883 -0.00277072348000775 3.81278167434967 0.148487352282019 0.0244729216674648 0.2011 0.249446934633114 0.129067143622624 0.194457660872416 -0.0144001959308384 -0.0141006684390188 -0.0814912207894038
0.2 3.23371734550753 0.718110435825521 0.330819511910566 0.101391376086178 -1.47499081134392 -0.00235944669544279 3.52085298608413 0.142257128473462 0.0142903160948886 0.2049 0.25356556670542 0.108002263739343 0.211634711188527 -0.0295313493684611 -0.0242995747709838 -0.0779761977475732
0.50251256281407 3.16050217205595 0.838494386998919 0.466787811642044 0.105723089676 -1.48056328666322 0 4.87194107479204 0.118125529818761 0.0131546256192079 0.1419 0.230442366433058 0.0959080118602377 0.191012833298469 0.00929098048437728 -0.00995372305434456 -0.00828100722167989
1 2.58227846237728 0.85911311807545 0.519131261495525 0.146088352194266 -1.28019118368202 0 5.42555199253122 0.124229747688977 2.39299038437967e-08 0.1153 0.212408309867869 0.118568732468557 0.176037658051544 0.000737173026444824 -0.00123578210338215 0.000181351036566464
2 1.88792168738756 0.727248116061721 0.47362977053987 0.244695132922949 -1.19816952711971 0 5.26896508895249 0.127711124129548 8.69064652723658e-09 0.1078 0.189154038588083 0.119572905421336 0.183045950697286 5.60984632803441e-15 -1.18288330352055e-14 9.31778101896791e-15
""")
