Update src/seismic_hazard_forecasting.py

Reviewed-on: #21

gsim/lasocki_2013.py

@@ -0,0 +1,142 @@
+# -*- coding: utf-8 -*-
+# vim: tabstop=4 shiftwidth=4 softtabstop=4
+#
+# Copyright (C) 2013-2023 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 exports :class:`Lasocki2013`.
+"""
+import numpy as np
+from scipy.constants import g
+
+from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
+from openquake.hazardlib import const
+from openquake.hazardlib.imt import PGA, PGV, SA
+
+def get_magnitude_energy(mag):
+    """
+    Converts magnitude to energy-based magnitude term.
+    """
+    return 1.15 + 1.96 * mag
+
+def get_distance_term(coeffs, repi):
+    """
+    Computes the distance term using the given GMPE coefficients.
+    """
+    R_h = np.sqrt(repi ** 2 + coeffs["c7"] ** 2)
+    return np.log10(R_h)
+
+def get_standard_deviation(coeffs, coeffs_cov, magE, repi):
+    """
+    Computes the standard deviation term.
+    """
+    Cb = np.array(list(coeffs_cov)).reshape(3,3)
+    R_h = np.sqrt(repi ** 2 + coeffs["c7"] ** 2)
+    X0 = np.array([1, magE[0], np.log10(R_h[0]**2)])
+    variance_term = np.sqrt(X0 @ Cb @ X0 + coeffs["sigma"]**2)
+    return variance_term
+
+class Lasocki2013(GMPE):
+    """
+    Implement equation developed by Lasocki in "REPORT ON THE ATTENUATION 
+    RELATIONS OF PEAK GROUND ACCELERATION AND SPECTRAL ORDINATES OF GROUND 
+    MOTION FOR MINING-INDUCED SEISMIC EVENTS IN THE REGION OF THE ŻELAZNY 
+    MOST REPOSITORY", 2009.
+    Equation coefficients provided for the random horizontal component
+    """
+    #: Supported tectonic region type is induced, given
+    #: that the equations have been derived for the LGCD mining area
+    DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.INDUCED
+
+    #: Supported intensity measure types are spectral acceleration,
+    #: and peak ground acceleration
+    DEFINED_FOR_INTENSITY_MEASURE_TYPES = {PGA, SA}
+
+    #: Supported intensity measure component is random horizontal
+    #: :attr:`~openquake.hazardlib.const.IMC.RANDOM_HORIZONTAL`,
+    DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RANDOM_HORIZONTAL
+    # DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.VERTICAL
+
+    #: Supported standard deviation type is total
+    DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL}
+
+    #: site params are not required
+    REQUIRES_SITES_PARAMETERS = set()
+
+    #: Required rupture parameter is magnitude
+    REQUIRES_RUPTURE_PARAMETERS = {'mag'}
+
+    #: Required distance measure is epicentral distance
+    #: see paragraph 'Predictor Variables', page 6.
+    REQUIRES_DISTANCES = {'repi'}
+
+    def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi):
+        """
+        Computes mean ground motion values and standard deviations for Lasocki (2013) GMPE.
+        
+        Parameters:
+            repi (float): Epicentral distance (m)
+            mag (float): Earthquake magnitude
+            imts (list of str): List of intensity measure types (e.g., 'PHA', 'PVA')
+            mean (np.array): Array to store computed mean values
+            sig (np.array): Array to store computed standard deviations
+        """
+        # Loop through each IMT and compute values
+        for i, imt in enumerate(imts):            
+            C = self.COEFFS[imt]
+            C_cov = self.COEFFS_COV[imt]
+            mag = ctx.mag
+            repi = ctx.repi*1000.0 # Convert distance from km to m
+            # Compute magnitude energy term
+            magE = get_magnitude_energy(mag)
+
+            # Compute GMPE terms
+            mag_term = C['c1'] + C['c2'] * magE
+            dist_term = C['c5'] * get_distance_term(C, repi)
+            
+            # Compute mean ground motion
+            imean = mag_term + dist_term
+            mean_value = np.log((10 ** imean) / g)  # Convert to natural log scale and divide by g
+            mean[i] = mean_value
+
+            # Compute standard deviation
+            sigma = get_standard_deviation(C, C_cov, magE, repi)
+            sig[i] = sigma
+
+    #: coefficient table provided by report
+    COEFFS = CoeffsTable(sa_damping=5, table="""\
+    IMT  c1     c2      c5      c7    sigma     N        
+    pga  1.25   0.31    -1.34   558   0.196     1196
+    0.6  -3.86  0.55    -0.65   183   0.242     1194
+    1.0  -3.94  0.62    -0.66   308   0.262     1197
+    2.0  -1.14  0.47    -1.02   741   0.235     1195
+    5.0  0.99   0.31    -1.15   690   0.234     1206
+    10.0 3.06   0.27    -1.65   906   0.203     1192
+    20.0 2.62   0.27    -1.60   435   0.196     1191
+    50.0 2.09   0.27    -1.48   375   0.204     1191
+    """)
+    
+    COEFFS_COV = CoeffsTable(sa_damping=5, table="""\
+    IMT  Cb00      Cb01      Cb02      Cb10      Cb11      Cb12      Cb20      Cb21      Cb22        
+    pga  0.005586  -0.000376 -0.000752 -0.000376 0.000103  -0.000111 -0.000752 -0.000111 0.000440
+    0.6  0.007509  -0.000662 -0.000688 -0.000662 0.000161  -0.000154 -0.000688 -0.000154 0.000516
+    1.0  0.009119  -0.00075  -0.000948 -0.00075  0.000189  -0.000187 -0.000948 -0.000187 0.000657                 
+    2.0  0.008563  -0.000514 -0.001282 -0.000514 0.000147  -0.000164 -0.001282 -0.000164 0.000696         
+    5.0  0.008283  -0.000516 -0.001202 -0.000516 0.000145  -0.000161 -0.001202 -0.000161 0.000668
+   10.0  0.006904  -0.00036  -0.001145 -0.00036  0.000108  -0.000126 -0.001145 -0.000126 0.000578
+   20.0  0.005389  -0.000396 -0.000658 -0.000396 0.000104  -0.000107 -0.000658 -0.000107 0.000408
+   50.0  0.005874  -0.000449 -0.000678 -0.000449 0.000114  -0.000114 -0.000678 -0.000114 0.000428
+    """)

src/seismic_hazard_forecasting.py

@@ -4,6 +4,27 @@
 def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max,
          m_kde_method, xy_select, grid_dim, xy_win_method, rate_select, time_win_duration,
          forecast_select, custom_rate, forecast_len, time_unit, model, products_string, verbose):
+    #catalog_file = 'LGCD_catalog_2024.mat'
+    mc_file = None
+    pdf_file = None
+    m_file = None
+    m_select = True
+    mag_label = 'Mw'
+    mc = 1.8
+    m_max = 4.5
+    m_kde_method = 'arviz-silverman'
+    xy_select = True
+    grid_dim = 5000
+    xy_win_method = False
+    rate_select = True
+    time_win_duration = 100
+    forecast_select = True
+    custom_rate = None
+    forecast_len = 100
+    time_unit = 'days'
+    model = 'Lasocki2013'
+    products_string = 'PGA'
+    verbose = True
     """
     Python application that reads an earthquake catalog and performs seismic hazard forecasting.
     Arguments:
@@ -52,7 +73,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     from math import ceil, floor, isnan
     import numpy as np
     import dask
-    from dask.diagnostics import ProgressBar  # use Dask progress bar
+    # from dask.diagnostics import ProgressBar  # use Dask progress bar
     import kalepy as kale
     import utm
     from skimage.transform import resize
@@ -69,6 +90,7 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
     from matplotlib.contour import ContourSet
     import xml.etree.ElementTree as ET
     import json
+    from concurrent.futures import ThreadPoolExecutor
 
     logger = getDefaultLogger('igfash')
 
@@ -111,7 +133,7 @@ verbose: {verbose}")
     logger.debug(f"Rbeast version {version('rbeast')}")
     logger.debug(f"Dask version {version('dask')}")
 
-    dask.config.set(scheduler='processes')
+    dask.config.set(scheduler='threads', pool=ThreadPoolExecutor(16))
 
     # run magnitude distribution modeling if selected by user and no magnitude pdf file provided
     if m_select and m_range[0] == None and m_pdf[0] == None:
@@ -366,7 +388,9 @@ verbose: {verbose}")
 
         # Assign probabilities 
         lambdas, lambdas_perc = lambda_probs(act_rate, dates_calc, bin_edges)
-
+        lambdas = np.array(lambdas, dtype='d')
+        lambdas_perc = np.array(lambdas_perc, dtype='d')
+        
         # print("Forecasted activity rates: ", lambdas, "events per", time_unit[:-1])
         logger.info(f"Forecasted activity rates: {lambdas} events per {time_unit} with percentages {lambdas_perc}")
         np.savetxt('activity_rate.csv', np.vstack((lambdas, lambdas_perc)).T, header="lambda, percentage",
@@ -452,11 +476,11 @@ verbose: {verbose}")
 
         start = timer()
 
-        use_pp = False
+        use_pp = True
 
         if use_pp:         # use dask parallel computing
-            pbar = ProgressBar()
+            # pbar = ProgressBar()
-            pbar.register()
+            # pbar.register()
             # iter = range(0,len(distances))
             iter = indices
             iml_grid_raw = []  # raw ground motion grids