Aftershock identification

TL;DR

This paper introduces a statistical clustering method based on space-time-magnitude analysis to distinguish aftershocks from other earthquakes, validated on synthetic models and Southern California seismic data.

Contribution

It extends previous work by developing a Weibull distribution-based approach for identifying earthquake clusters and aftershocks in seismicity data.

Findings

The Weibull distribution models the space-time-magnitude distances effectively.

The method successfully identifies aftershock clusters in synthetic and real seismic data.

Application to Southern California seismicity demonstrates practical utility.

Abstract

Earthquake aftershock identification is closely related to the question "Are aftershocks different from the rest of earthquakes?" We give a positive answer to this question and introduce a general statistical procedure for clustering analysis of seismicity that can be used, in particular, for aftershock detection. The proposed approach expands the analysis of Baiesi and Paczuski [PRE, 69, 066106 (2004)] based on the space-time-magnitude nearest-neighbor distance $\eta$ between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance $\eta$ has Weibull distribution, which bridges our results with classical correlation analysis for unmarked point fields. We introduce a 2D distribution of spatial and temporal components of $\eta$, which allows us to identify the clustered part of a point field. The proposed technique is applied to several synthetic seismicity models and to the observed seismicity of Southern California.