Stochastic event set generation in openquake.hazardlib

LICENSE Copyright (c) 2014, GEM Foundation, G. Weatherill, M. Pagani, D. Monelli. The notebook 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. You should have received a copy of the GNU Affero General Public License along with OpenQuake. If not, see DISCLAIMER The notebook provided herein is released as a prototype implementation on behalf of scientists and engineers working within the GEM Foundation (Global Earthquake Model). It is distributed for the purpose of open collaboration and in the hope that it will be useful to the scientific, engineering, disaster risk and software design communities. The software is NOT distributed as part of GEM's OpenQuake suite (http://www.globalquakemodel.org/openquake) and must be considered as a separate entity. The software provided herein is designed and implemented by scientific staff. It is not developed to the design standards, nor subject to same level of critical review by professional software developers, as GEM's OpenQuake software suite. Feedback and contribution to the software is welcome, and can be directed to the hazard scientific staff of the GEM Model Facility (hazard@globalquakemodel.org). The notebook is therefore distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GEM Foundation, and the authors of the software, assume no liability for use of the software.



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%load_ext autoreload
%autoreload 2
import warnings; warnings.filterwarnings("ignore")



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%matplotlib inline

from openquake.hazardlib.source import AreaSource
from openquake.hazardlib.mfd import TruncatedGRMFD
from openquake.hazardlib.scalerel import WC1994
from openquake.hazardlib.geo import Point, NodalPlane, Polygon
from openquake.hazardlib.pmf import PMF
from openquake.hazardlib.tom import PoissonTOM
from openquake.hazardlib.calc import stochastic_event_set

import matplotlib
import numpy
from matplotlib import pyplot
from matplotlib import patches
from mpl_toolkits.basemap import Basemap
from obspy.imaging.beachball import Beach



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# beach ball reference width
BB_WIDTH = 40000

# magnitude bins
MAG_BINS = numpy.array([4., 5., 6., 7., 8., 9.])
MAG_BB_WIDTHS = numpy.array([BB_WIDTH * 0.5, BB_WIDTH, BB_WIDTH * 1.5, BB_WIDTH * 2., BB_WIDTH * 2.5])
MAG_BB_COLORS = numpy.array(['r', 'g', 'b', 'c', 'm'])



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def get_map_projection(src):
    """
    Return map projection specific to source.
    """
    # extract rupture enclosing polygon (considering a buffer of 10 km)
    rup_poly = src.get_rupture_enclosing_polygon(10.)
    min_lon = numpy.min(rup_poly.lons)
    max_lon = numpy.max(rup_poly.lons)
    min_lat = numpy.min(rup_poly.lats)
    max_lat = numpy.max(rup_poly.lats)
    
    # create map projection
    m = Basemap(projection='merc', llcrnrlat=min_lat, urcrnrlat=max_lat,
                llcrnrlon=min_lon, urcrnrlon=max_lon, resolution='l')

    return min_lon, max_lon, min_lat, max_lat, m



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def create_ses_legend():
    """
    Create legend for ses plot.
    """
    bb1 = matplotlib.patches.Circle((0, 0), color=MAG_BB_COLORS[0])
    bb2 = matplotlib.patches.Circle((0, 0), color=MAG_BB_COLORS[1])
    bb3 = matplotlib.patches.Circle((0, 0), color=MAG_BB_COLORS[2])
    bb4 = matplotlib.patches.Circle((0, 0), color=MAG_BB_COLORS[3])
    bb5 = matplotlib.patches.Circle((0, 0), color=MAG_BB_COLORS[4])
    matplotlib.pyplot.legend([bb1, bb2, bb3, bb4, bb5],
        ['Mw < 5','5 <= Mw < 6', '6 <= Mw < 7', '7 <= Mw <= 8', 'Mw > 8'], numpoints=1)

Stochastic event set generated by an area source



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# time span for stochastic event set generation
time_span = 1000.

# define area source
src = AreaSource(
    source_id='1',
    name='area',
    tectonic_region_type='Active Shallow Crust',
    mfd=TruncatedGRMFD(min_mag=5., max_mag=6.5, bin_width=0.2, a_val=4.45, b_val=0.98),
    rupture_mesh_spacing=2.,
    magnitude_scaling_relationship=WC1994(),
    rupture_aspect_ratio=1.,
    temporal_occurrence_model=PoissonTOM(time_span),
    upper_seismogenic_depth=2.,
    lower_seismogenic_depth=12.,
    nodal_plane_distribution=PMF([(0.125, NodalPlane(strike=0, dip=90, rake=0)),
                                  (0.125, NodalPlane(strike=45, dip=90, rake=0)),
                                  (0.125, NodalPlane(strike=90, dip=90, rake=0)),
                                  (0.125, NodalPlane(strike=135, dip=90, rake=0)),
                                  (0.0625, NodalPlane(strike=0, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=45, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=90, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=135, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=180, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=225, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=270, dip=50, rake=90)),
                                  (0.0625, NodalPlane(strike=315, dip=50, rake=90))]),
    hypocenter_distribution=PMF([(1, 7.)]),
    polygon=Polygon([Point(133.5, -22.5), Point(133.5, -23.0), Point(130.75, -23.75), Point(130.75, -24.5),
                     Point(133.5, -26.0), Point(133.5, -27.0), Point(130.75, -27.0), Point(128.977, -25.065),
                     Point(128.425, -23.436), Point(126.082, -23.233), Point(125.669, -22.351), Point(125.4, -20.5),
                     Point(125.75, -20.25), Point(126.7, -21.25), Point(128.5, -21.25), Point(129.25, -20.6),
                     Point(130.0, -20.6), Point(130.9, -22.25), Point(133.0, -22.0), Point(133.5, -22.5)]),
    area_discretization=20.
)



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# generate two different stochastic event sets from the area source
numpy.random.seed(123)

# generate event and store them in lists
ses1 = list(stochastic_event_set([src]))
ses2 = list(stochastic_event_set([src]))



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# define map projection
min_lon, max_lon, min_lat, max_lat, m = get_map_projection(src)



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# plot events for event set 1
for i, ses in enumerate([ses1, ses2]):
    fig = pyplot.figure(figsize=(10, 10), dpi=160)
    
    # m.drawparallels(numpy.arange(min_lat, max_lat, 1.), labels=[True, False, False, True])
    # m.drawmeridians(numpy.arange(min_lon, max_lon, 1.), labels=[True, False, False, True])
    m.drawcoastlines()
    m.drawcountries()
    
    x, y = m(src.polygon.lons, src.polygon.lats)
    m.plot(x, y, color='black', linewidth=2)
    
    for rup in ses:
        strike = rup.surface.get_strike()
        dip = rup.surface.get_dip()
        rake = rup.rake
        x, y = m(rup.hypocenter.longitude, rup.hypocenter.latitude)
        [bb_width] = MAG_BB_WIDTHS[numpy.digitize([rup.mag], MAG_BINS) - 1]
        [bb_color] = MAG_BB_COLORS[numpy.digitize([rup.mag], MAG_BINS) - 1]
        beach = Beach([strike, dip, rake], linewidth=1, xy=(x, y),
                      width=bb_width, zorder=bb_width, facecolor=bb_color)
        pyplot.gca().add_collection(beach)
    
    create_ses_legend()
    pyplot.title('Stochastic Event Set %s for time span of %s years' % (i, time_span), fontsize=20)