Source code for openquake.hmtk.seismicity.occurrence.aki_maximum_likelihood

# -*- coding: utf-8 -*-
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import warnings
import numpy as np
from openquake.hmtk.seismicity.occurrence.base import (
    SeismicityOccurrence, OCCURRENCE_METHODS)
from openquake.hmtk.seismicity.occurrence.utils import recurrence_table, input_checks


@OCCURRENCE_METHODS.add('calculate', completeness=True)
class AkiMaxLikelihood(SeismicityOccurrence):

    def calculate(self, catalogue, config=None, completeness=None):
        """
        Calculation of b-value and its uncertainty for a given
        catalogue, using the maximum likelihood method of Aki (1965),
        with a correction for discrete bin width (Bender, 1983).

        :param catalogue:
            See :class:`openquake.hmtk.seismicity.occurrence.base.py`
            for further explanation
        :param config:
            The configuration in this case do not contains specific
            information
        :keyword float completeness:
            Completeness magnitude

        :return float bval:
            b-value of the Gutenberg-Richter relationship
        :return float sigma_b:
            Standard deviation of the GR b-value
        """
        # Input checks
        _cmag, _ctime, _ref_mag, dmag, config = input_checks(catalogue, config,
                                                             completeness)
        rt = recurrence_table(
            catalogue.data['magnitude'], dmag, catalogue.data['year'])
        bval, sigma_b = self._aki_ml(rt[:, 0], rt[:, 1])
        return bval, sigma_b

    def _aki_ml(self, mval, number_obs, dmag=0.1, m_c=0.0):
        """
        :param numpy.ndarray mval:
            array of reference magnitudes (column 0 from recurrence
            table)
        :param numpy.ndarray number_obs:
            number of observations in magnitude bin (column 1 from
            recurrence table)
        :keyword float dmag:
            magnitude interval
        :keyword float m_c:
            completeness magnitude

        :return float bval:
            b-value of the Gutenberg-Richter relationship
        :return float sigma_b:
            Standard deviation of the GR b-value
        """
        # Exclude data below Mc
        id0 = mval >= m_c
        mval = mval[id0]
        number_obs = number_obs[id0]
        # Get Number of events, minimum magnitude and mean magnitude
        neq = np.sum(number_obs)
        if neq <= 1:
            # Cannot determine b-value (too few event) return NaNs
            warnings.warn('Too few events (<= 1) to calculate b-value')
            return np.nan, np.nan

        m_min = np.min(mval)
        m_ave = np.sum(mval * number_obs) / neq
        # Calculate b-value
        bval = np.log10(np.exp(1.0)) / (m_ave - m_min + (dmag / 2.))
        # Calculate sigma b from Bender estimator
        sigma_b = np.sum(number_obs * ((mval - m_ave) ** 2.0)) /\
            (neq * (neq - 1))
        sigma_b = np.log(10.) * (bval ** 2.0) * np.sqrt(sigma_b)
        return bval, sigma_b