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

P. Bountzis, E. Papadimitriou and G. Tsaklidis

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

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.

Keywords: Hidden Markov models; Forward–backward equations; Cluster analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320394

DOI: 10.1016/j.physa.2019.123655

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