analysis.APositiveAValueEstimator.calculate#

APositiveAValueEstimator.calculate(magnitudes: ndarray, mc: float, delta_m: float, times: ndarray, scaling_factor: float | None = None, m_ref: float | None = None, b_value: float | None = None, dmc: float | None = None) → float#
Parameters:

  • magnitudes – Array of magnitudes.

  • mc – Completeness magnitude.

  • delta_m – Bin size of discretized magnitudes.

  • times – Array of times of the events, in any format (datetime, float, etc.).

  • scaling_factor – Scaling factor. If given, this is used to normalize the number of observed events. For example: Volume or area of the region considered or length of the time interval, given in the unit of interest.

  • m_ref – Reference magnitude for which the a-value is estimated.

  • b_value – b-value of the Gutenberg-Richter law. Only relevant when m_ref is not None.

  • dmc – Minimum magnitude difference between consecutive events. If None, the default value is delta_m.

Returns:

a_pos – a-value of the Gutenberg-Richter law.

Examples

>>> import numpy as np
>>> from seismostats.analysis import APositiveAValueEstimator

>>> magnitudes = np.array([2.1, 2.3, 2.0, 2.0, 2.1, 2.2, 2.1,
...                        2.3, 2.0, 2.0])
>>> times = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

>>> my_estimator = APositiveAValueEstimator()
>>> a_value = my_estimator.calculate(magnitudes=magnitudes,
...     mc=2.0, delta_m=0.1, times=times)

>>> a_value

0.9542425094393249