An algorithm for earthquakes clustering based on maximum likelihood

Abstract

A new diagnostic method for space-time point process is here introduced and applied to seismic data. It is based on the interpretation of some second-order statistics, that alow to analyze clustering features of data. In particular, this method is used to assess the goodness of fit of ETAS model, estimated by a nonparametric approach; the goal of this paper is to interpret space-time variations of seismic activity of a fixed area of Japan and to focus on clustering features that are useful for prediction purposes