A cluster identification framework illustrated by a filtering model for earthquake occurrences

TL;DR

This paper introduces a general framework for identifying clusters in earthquake data, combining modeling and computational techniques, and demonstrates its application through a stochastic declustering model that distinguishes clusters from single events.

Contribution

The paper presents a novel dynamical cluster identification framework and a stochastic earthquake declustering model that incorporates filtering techniques and maximum likelihood estimation.

Findings

Effective filtering-based declustering criteria

Successful application to earthquake occurrence data

Enhanced understanding of earthquake clustering patterns

Abstract

A general dynamical cluster identification framework including both modeling and computation is developed. The earthquake declustering problem is studied to demonstrate how this framework applies. A stochastic model is proposed for earthquake occurrences that considers the sequence of occurrences as composed of two parts: earthquake clusters and single earthquakes. We suggest that earthquake clusters contain a ``mother quake'' and her ``offspring.'' Applying the filtering techniques, we use the solution of filtering equations as criteria for declustering. A procedure for calculating maximum likelihood estimations (MLE's) and the most likely cluster sequence is also presented.