Source code for openquake.hmtk.seismicity.smoothing.kernels.isotropic_gaussian

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Module :mod:`openquake.hmtk.seismicity.smoothing.kernels.isotropic_gaussian`
imports :class:`openquake.hmtk.seismicity.smoothing.kernels.isotropic_gaussian.IsotropicGaussian`
the simple isotropic Gaussian smoothing kernel as described by Frankel (1995)

Frankel, A. (1995) Mapping Seismic Hazard in the Central and Eastern United
States. Seismological Research Letters. 66(4) 8 - 21

import numpy as np
from openquake.hmtk.seismicity.utils import haversine
from openquake.hmtk.seismicity.smoothing.kernels.base import (

[docs]class IsotropicGaussian(BaseSmoothingKernel): ''' Applies a simple isotropic Gaussian smoothing using an Isotropic Gaussian Kernel - taken from Frankel (1995) approach '''
[docs] def smooth_data(self, data, config, is_3d=False): ''' Applies the smoothing kernel to the data :param np.ndarray data: Raw earthquake count in the form [Longitude, Latitude, Depth, Count] :param dict config: Configuration parameters must contain: * BandWidth: The bandwidth of the kernel (in km) (float) * Length_Limit: Maximum number of standard deviations :returns: * smoothed_value: np.ndarray vector of smoothed values * Total (summed) rate of the original values * Total (summed) rate of the smoothed values ''' max_dist = config['Length_Limit'] * config['BandWidth'] smoothed_value = np.zeros(len(data), dtype=float) for iloc in range(0, len(data)): dist_val = haversine(data[:, 0], data[:, 1], data[iloc, 0], data[iloc, 1]) if is_3d: dist_val = np.sqrt(dist_val.flatten() ** 2.0 + (data[:, 2] - data[iloc, 2]) ** 2.0) id0 = np.where(dist_val <= max_dist)[0] w_val = (np.exp(-(dist_val[id0] ** 2.0) / (config['BandWidth'] ** 2.))).flatten() smoothed_value[iloc] = np.sum(w_val * data[id0, 3]) / np.sum(w_val) return smoothed_value, np.sum(data[:, -1]), np.sum(smoothed_value)