Earthquake Clustering and Declustering: Model-based Methods and Their Uses

Abstract

Earthquake clustering, including but by no means limited to aftershock sequences, is one of the most pervasive features of earthquake occurrence. Quantitative physical models for this phenomenon remain elusive, but a number of statistical models, notably Ogata's ETAS model, fit the main features of earthquake clustering remarkably well. Such models commonly include a base component of independent events, and a second component of associated events. When fitted to real data, however, they provide only estimates of the probability that a given event is independent or associated. These probabilities can be made the basis of a stochastic declustering simulation procedure, whereby events are randomly allocated to one or other category according to the specified probabilities. Examples of stochastically declustered catalogues will be shown, and their uses discussed.