# -*- coding: utf-8 -*-
# LICENSE
#
# Copyright (C) 2010-2018 GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli.
#
# The Hazard Modeller's Toolkit 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 <http://www.gnu.org/licenses/>
#
# DISCLAIMER
#
# The software Hazard Modeller's Toolkit (openquake.hmtk) 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
# (https://www.globalquakemodel.org/tools-products) 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 Hazard Modeller's Toolkit (openquake.hmtk) 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.
# -*- coding: utf-8 -*-
'''
Module :mod: openquake.hmtk.seismicity.smoothing.smoothed_seismicity implements the
:class: openquake.hmtk.seismicity.smoothing.smoothed_seismicity.SmoothedSeismicity,
a general class for implementing seismicity smoothing algorithms
'''
import csv
import collections
from math import fabs, log
import numpy as np
from openquake.hazardlib.geo.point import Point
from openquake.hazardlib.geo.polygon import Polygon
from openquake.hmtk.seismicity.smoothing import utils
from openquake.hmtk.seismicity.smoothing.kernels.isotropic_gaussian import \
IsotropicGaussian
from openquake.hmtk.registry import CatalogueFunctionRegistry
[docs]class Grid(collections.OrderedDict):
[docs] @classmethod
def make_from_list(cls, grid_limits):
new = cls()
new.update({'xmin': grid_limits[0],
'xmax': grid_limits[1],
'xspc': grid_limits[2],
'ymin': grid_limits[3],
'ymax': grid_limits[4],
'yspc': grid_limits[5],
'zmin': grid_limits[6],
'zmax': grid_limits[7],
'zspc': grid_limits[8]})
return new
[docs] @classmethod
def make_from_catalogue(cls, catalogue, spacing, dilate):
'''
Defines the grid on the basis of the catalogue
'''
new = cls()
cat_bbox = get_catalogue_bounding_polygon(catalogue)
if dilate > 0:
cat_bbox = cat_bbox.dilate(dilate)
# Define Grid spacing
new.update({'xmin': np.min(cat_bbox.lons),
'xmax': np.max(cat_bbox.lons),
'xspc': spacing,
'ymin': np.min(cat_bbox.lats),
'ymax': np.max(cat_bbox.lats),
'yspc': spacing,
'zmin': 0.,
'zmax': np.max(catalogue.data['depth']),
'zspc': np.max(catalogue.data['depth'])})
if new['zmin'] == new['zmax'] == new['zspc'] == 0:
new['zmax'] = new['zspc'] = 1
return new
[docs] def as_list(self):
return [self['xmin'], self['xmax'], self['xspc'],
self['ymin'], self['ymax'], self['yspc'],
self['zmin'], self['zmax'], self['zspc']]
[docs] def as_polygon(self):
return Polygon([
Point(self['xmin'], self['ymax']),
Point(self['xmax'], self['ymax']),
Point(self['xmax'], self['ymin']),
Point(self['xmin'], self['ymin'])])
[docs] def dilate(self, width):
polygon = self.as_polygon().dilate(width)
self.update({'xmin': np.min(polygon.lons),
'xmax': np.max(polygon.lons),
'ymin': np.min(polygon.lats),
'ymax': np.max(polygon.lats)})
return self
def _get_adjustment(mag, year, mmin, completeness_year, t_f, mag_inc=0.1):
'''
If the magnitude is greater than the minimum in the completeness table
and the year is greater than the corresponding completeness year then
return the Weichert factor
:param float mag:
Magnitude of an earthquake
:param float year:
Year of earthquake
:param np.ndarray completeness_table:
Completeness table
:param float mag_inc:
Magnitude increment
:param float t_f:
Weichert adjustment factor
:returns:
Weichert adjustment factor is event is in complete part of catalogue
(0.0 otherwise)
'''
if len(completeness_year) == 1:
if (mag >= mmin) and (year >= completeness_year[0]):
# No adjustment needed - event weight == 1
return 1.0
else:
# Event should not be counted
return False
kval = int(((mag - mmin) / mag_inc)) + 1
if (kval >= 1) and (year >= completeness_year[kval - 1]):
return t_f
else:
return False
[docs]def get_catalogue_bounding_polygon(catalogue):
'''
Returns a polygon containing the bounding box of the catalogue
'''
upper_lon = np.max(catalogue.data['longitude'])
upper_lat = np.max(catalogue.data['latitude'])
lower_lon = np.min(catalogue.data['longitude'])
lower_lat = np.min(catalogue.data['latitude'])
return Polygon([Point(lower_lon, upper_lat), Point(upper_lon, upper_lat),
Point(upper_lon, lower_lat), Point(lower_lon, lower_lat)])
[docs]class SmoothedSeismicity(object):
'''
Class to implement an analysis of Smoothed Seismicity, including the
grid counting of data and the smoothing.
:param np.ndarray grid:
Observed count in each cell [Long., Lat., Depth., Count]
:param catalogue:
Valid instance of the :class: openquake.hmtk.seismicity.catalogue.Catalogue
:param bool use_3d:
Decide if analysis is 2-D (False) or 3-D (True). If 3-D then distances
will use hypocentral distance, otherwise epicentral distance
:param float bval:
b-value
:param float beta:
Beta value for exponential form (beta = bval * log(10.))
:param np.ndarray data:
Smoothed seismicity output
:param dict grid_limits:
Limits ot the grid used for defining the cells
'''
def __init__(self, grid_limits, use_3d=False, bvalue=None):
'''
Instatiate class with a set of grid limits
:param grid_limits:
It could be a float (in that case the grid is computed from the
catalogue with the given spacing).
Or an array of the form:
[xmin, xmax, spcx, ymin, ymax, spcy, zmin, spcz]
:param bool use_3d:
Choose whether to use hypocentral distances for smoothing or only
epicentral
:param float bval:
b-value for analysis
'''
self.grid = None
self.catalogue = None
self.use_3d = use_3d
self.bval = bvalue
if self.bval:
self.beta = self.bval * log(10.)
else:
self.beta = None
self.data = None
self.grid_limits = grid_limits
self.kernel = None
[docs] def run_analysis(self, catalogue, config, completeness_table=None,
smoothing_kernel=None):
'''
Runs an analysis of smoothed seismicity in the manner
originally implemented by Frankel (1995)
:param catalogue:
Instance of the openquake.hmtk.seismicity.catalogue.Catalogue class
catalogue.data dictionary containing the following -
'year' - numpy.ndarray vector of years
'longitude' - numpy.ndarray vector of longitudes
'latitude' - numpy.ndarray vector of latitudes
'depth' - numpy.ndarray vector of depths
:param dict config:
Configuration settings of the algorithm:
* 'Length_Limit' - Maximum number of bandwidths for use in
smoothing (Float)
* 'BandWidth' - Bandwidth (km) of the Smoothing Kernel (Float)
* 'increment' - Output incremental (True) or cumulative a-value
(False)
:param np.ndarray completeness_table:
Completeness of the catalogue assuming evenly spaced magnitudes
from most recent bin to oldest bin [year, magnitude]
:param smoothing_kernel:
Smoothing kernel as instance of :class:
`openquake.hmtk.seismicity.smoothing.kernels.base.BaseSmoothingKernel`
:returns:
Full smoothed seismicity data as np.ndarray, of the form
[Longitude, Latitude, Depth, Observed, Smoothed]
'''
self.catalogue = catalogue
if smoothing_kernel:
self.kernel = smoothing_kernel
else:
self.kernel = IsotropicGaussian()
# If no grid limits are specified then take from catalogue
if isinstance(self.grid_limits, list):
self.grid_limits = Grid.make_from_list(self.grid_limits)
assert self.grid_limits['xmax'] >= self.grid_limits['xmin']
assert self.grid_limits['xspc'] > 0.0
assert self.grid_limits['ymax'] >= self.grid_limits['ymin']
assert self.grid_limits['yspc'] > 0.0
elif isinstance(self.grid_limits, float):
self.grid_limits = Grid.make_from_catalogue(
self.catalogue, self.grid_limits,
config['Length_Limit'] * config['BandWidth'])
completeness_table, mag_inc = utils.get_even_magnitude_completeness(
completeness_table,
self.catalogue)
end_year = self.catalogue.end_year
# Get Weichert factor
t_f, _ = utils.get_weichert_factor(self.beta,
completeness_table[:, 1],
completeness_table[:, 0],
end_year)
# Get the grid
self.create_3D_grid(self.catalogue, completeness_table, t_f, mag_inc)
if config['increment']:
# Get Hermann adjustment factors
fval, fival = utils.hermann_adjustment_factors(
self.bval,
completeness_table[0, 1], config['increment'])
self.data[:, -1] = fval * fival * self.data[:, -1]
# Apply smoothing
smoothed_data, sum_data, sum_smooth = self.kernel.smooth_data(
self.data, config, self.use_3d)
print('Smoothing Total Rate Comparison - '
'Observed: %.6g, Smoothed: %.6g' % (sum_data, sum_smooth))
self.data = np.column_stack([self.data, smoothed_data])
return self.data
[docs] def create_2D_grid_simple(self, longitude, latitude, year, magnitude,
completeness_table, t_f=1., mag_inc=0.1):
'''
Generates the grid from the limits using an approach closer to that of
Frankel (1995)
:param numpy.ndarray longitude:
Vector of earthquake longitudes
:param numpy.ndarray latitude:
Vector of earthquake latitudes
:param numpy.ndarray year:
Vector of earthquake years
:param numpy.ndarray magnitude:
Vector of earthquake magnitudes
:param numpy.ndarray completeness_table:
Completeness table
:param float t_f:
Weichert adjustment factor
:returns:
Two-dimensional spatial grid of observed rates
'''
assert mag_inc > 0.
xlim = np.ceil(
(self.grid_limits['xmax'] - self.grid_limits['xmin']) /
self.grid_limits['xspc'])
ylim = np.ceil(
(self.grid_limits['ymax'] - self.grid_limits['ymin']) /
self.grid_limits['yspc'])
ncolx = int(xlim)
ncoly = int(ylim)
grid_count = np.zeros(ncolx * ncoly, dtype=float)
for iloc in range(0, len(longitude)):
dlon = (longitude[iloc] - self.grid_limits['xmin']) /\
self.grid_limits['xspc']
if (dlon < 0.) or (dlon > xlim):
# Earthquake outside longitude limits
continue
xcol = int(dlon)
if xcol == ncolx:
# If longitude is directly on upper grid line then retain
xcol = ncolx - 1
dlat = fabs(self.grid_limits['ymax'] - latitude[iloc]) /\
self.grid_limits['yspc']
if (dlat < 0.) or (dlat > ylim):
# Earthquake outside latitude limits
continue
ycol = int(dlat) # Correct for floating precision
if ycol == ncoly:
# If latitude is directly on upper grid line then retain
ycol = ncoly - 1
kmarker = (ycol * int(xlim)) + xcol
adjust = _get_adjustment(magnitude[iloc],
year[iloc],
completeness_table[0, 1],
completeness_table[:, 0],
t_f,
mag_inc)
if adjust:
grid_count[kmarker] = grid_count[kmarker] + adjust
return grid_count
[docs] def create_3D_grid(self, catalogue, completeness_table, t_f=1.0,
mag_inc=0.1):
'''
Counts the earthquakes observed in a three dimensional grid
:param catalogue:
Instance of the openquake.hmtk.seismicity.catalogue.Catalogue class
catalogue.data dictionary containing the following -
'year' - numpy.ndarray vector of years
'longitude' - numpy.ndarray vector of longitudes
'latitude' - numpy.ndarray vector of latitudes
'depth' - numpy.ndarray vector of depths
:param np.ndarray completeness_table:
Completeness of the catalogue assuming evenly spaced magnitudes
from most recent bin to oldest bin [year, magnitude]
:param float t_f:
Weichert adjustment factor
:param float mag_inc:
Increment of the completeness magnitude (rendered 0.1)
:returns:
Three-dimensional spatial grid of observed rates (or two dimensional
if only one depth layer is considered)
'''
x_bins = np.arange(self.grid_limits['xmin'],
self.grid_limits['xmax'],
self.grid_limits['xspc'])
if x_bins[-1] < self.grid_limits['xmax']:
x_bins = np.hstack([x_bins, x_bins[-1] + self.grid_limits['xspc']])
y_bins = np.arange(self.grid_limits['ymin'],
self.grid_limits['ymax'],
self.grid_limits['yspc'])
if y_bins[-1] < self.grid_limits['ymax']:
y_bins = np.hstack([y_bins, y_bins[-1] + self.grid_limits['yspc']])
z_bins = np.arange(self.grid_limits['zmin'],
self.grid_limits['zmax'] + self.grid_limits['zspc'],
self.grid_limits['zspc'])
if z_bins[-1] < self.grid_limits['zmax']:
z_bins = np.hstack([z_bins, z_bins[-1] + self.grid_limits['zspc']])
# Define centre points of grid cells
gridx, gridy = np.meshgrid((x_bins[1:] + x_bins[:-1]) / 2.,
(y_bins[1:] + y_bins[:-1]) / 2.)
n_x, n_y = np.shape(gridx)
gridx = np.reshape(gridx, [n_x * n_y, 1])
gridy = np.reshape(np.flipud(gridy), [n_x * n_y, 1])
# Only one depth range
idx = np.logical_and(catalogue.data['depth'] >= z_bins[0],
catalogue.data['depth'] < z_bins[1])
mid_depth = (z_bins[0] + z_bins[1]) / 2.
data_grid = np.column_stack([
gridx,
gridy,
mid_depth * np.ones(n_x * n_y, dtype=float),
self.create_2D_grid_simple(catalogue.data['longitude'][idx],
catalogue.data['latitude'][idx],
catalogue.data['year'][idx],
catalogue.data['magnitude'][idx],
completeness_table,
t_f,
mag_inc)])
if len(z_bins) < 3:
# Only one depth range
self.data = data_grid
return
# Multiple depth layers - append to grid
for iloc in range(1, len(z_bins) - 1):
idx = np.logical_and(catalogue.data['depth'] >= z_bins[iloc],
catalogue.data['depth'] < z_bins[iloc + 1])
mid_depth = (z_bins[iloc] + z_bins[iloc + 1]) / 2.
temp_grid = np.column_stack([
gridx,
gridy,
mid_depth * np.ones(n_x * n_y, dtype=float),
self.create_2D_grid_simple(catalogue.data['longitude'][idx],
catalogue.data['latitude'][idx],
catalogue.data['year'][idx],
catalogue.data['magnitude'][idx],
completeness_table,
t_f,
mag_inc)])
data_grid = np.vstack([data_grid, temp_grid])
self.data = data_grid
[docs] def write_to_csv(self, filename):
'''
Exports to simple csv
:param str filename:
Path to file for export
'''
fid = open(filename, 'wt')
# Create header list
header_info = ['Longitude', 'Latitude', 'Depth', 'Observed Count',
'Smoothed Rate', 'b-value']
writer = csv.DictWriter(fid, fieldnames=header_info)
headers = dict((name0, name0) for name0 in header_info)
# Write to file
writer.writerow(headers)
for row in self.data:
# institute crude compression by omitting points with no seismicity
# and taking advantage of the %g format
if row[4] == 0:
continue
row_dict = {'Longitude': '%g' % row[0],
'Latitude': '%g' % row[1],
'Depth': '%g' % row[2],
'Observed Count': '%d' % row[3],
'Smoothed Rate': '%.6g' % row[4],
'b-value': '%g' % self.bval}
writer.writerow(row_dict)
fid.close()
SMOOTHED_SEISMICITY_METHODS = CatalogueFunctionRegistry()
[docs]@SMOOTHED_SEISMICITY_METHODS.add(
"run",
completeness=True,
b_value=np.float,
use_3d=bool,
grid_limits=Grid,
Length_Limit=np.float,
BandWidth=np.float,
increment=bool)
class IsotropicGaussianMethod(object):
[docs] def run(self, catalogue, config, completeness=None):
ss = SmoothedSeismicity(config['grid_limits'],
config['use_3d'],
config['b_value'])
return ss.run_analysis(
catalogue, config, completeness_table=completeness)