Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece)

Abstract

Prevailing patterns of seismicity dynamics, like the evolution of main shock–aftershock sequences and swarms, along with periods of seismic quiescence, are explored through the temporal analysis of the earthquake clustering in the area of Corinth Gulf, Greece. The clusters are unveiled by the implementation of a new algorithm, whose robustness is verified on simulated catalogs. The method is based on the application of a bivariate stochastic point process, the Markovian arrival process (MAP), (Nt,Jt)t∈R+, whose intensity function, λt, is driven by the underlying Markov process, Jt. In particular, each hidden state corresponds to a distinct occurrence rate of the counting process, Nt, that enables the modeling of changes in the earthquake dynamics. With the proposed algorithm, known as local decoding algorithm, the hidden states, i.e. seismicity rates, are revealed at each occurrence time.The study area is divided in the eastern and western subareas based on seismotectonic criteria, for enabling any given event to interact with the following ones. The performance of the model is evaluated on subcatalogs of short time intervals that include small earthquakes, as well as to catalogs of longer duration associated with well studied destructive events and swarms. A complete cluster analysis for the 1964–2017 seismicity catalog with magnitudes M≥4.5 is given, in a period that covers a sufficient number of well studied seismic sequences for both subareas. The identified earthquake clusters are consistent with those obtained from previous analyses of selected seismic sequences and the hidden states are associated mainly with main shock–aftershock and swarm-like sequences. The seismicity dynamics in the two subareas differ significantly, since in the eastern part earthquake frequency is remarkably low leading to more episodic transitions from periods of high seismic activity to ones of seismic quiescence.