Statistical Distributions of Earthquake Numbers: Consequence of Branching Process

TL;DR

This paper analyzes the statistical distributions, especially the negative binomial distribution, of earthquake numbers using a branching process model, highlighting their applications and limitations in earthquake forecasting.

Contribution

It introduces a branching process model that explains earthquake count distributions and compares different statistical representations of the negative binomial distribution.

Findings

Negative binomial distribution effectively models earthquake counts.

Parameters of the NBD vary with magnitude thresholds and spatial-temporal windows.

The model explains clustering and over-dispersion in earthquake data.

Abstract

We discuss various statistical distributions of earthquake numbers. Previously we derived several discrete distributions to describe earthquake numbers for the branching model of earthquake occurrence: these distributions are the Poisson, geometric, logarithmic, and the negative binomial (NBD). The theoretical model is the `birth and immigration' population process. The first three distributions above can be considered special cases of the NBD. In particular, a point branching process along the magnitude (or log seismic moment) axis with independent events (immigrants) explains the magnitude/moment-frequency relation and the NBD of earthquake counts in large time/space windows, as well as the dependence of the NBD parameters on the magnitude threshold (magnitude of an earthquake catalog completeness). We discuss applying these distributions, especially the NBD, to approximate event numbers in earthquake catalogs. There are many different representations of the NBD. Most can be traced either to the Pascal distribution or to the mixture of the Poisson distribution with the gamma law. We discuss advantages and drawbacks of both representations for statistical analysis of earthquake catalogs. We also consider applying the NBD to earthquake forecasts and describe the limits of the application for the given equations. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrence, the NBD has two parameters. The second parameter can be used to characterize clustering or over-dispersion of a process. We determine the parameter values and their uncertainties for several local and global catalogs, and their subdivisions in various time intervals, magnitude thresholds, spatial windows, and tectonic categories.