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

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  • Bountzis, P.
  • Papadimitriou, E.
  • Tsaklidis, G.

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.

Suggested Citation

  • Bountzis, P. & Papadimitriou, E. & Tsaklidis, G., 2020. "Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320394
    DOI: 10.1016/j.physa.2019.123655
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    References listed on IDEAS

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    1. Vassilios Karakostas & Eleni Karagianni & Parthena Paradisopoulou, 2012. "Space–time analysis, faulting and triggering of the 2010 earthquake doublet in western Corinth Gulf," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 1181-1202, September.
    2. P. Bountzis & E. Papadimitriou & G. Tsaklidis, 2019. "Estimating the earthquake occurrence rates in Corinth Gulf (Greece) through Markovian arrival process modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(6), pages 995-1020, April.
    3. Votsi, I. & Limnios, N. & Tsaklidis, G. & Papadimitriou, E., 2013. "Hidden Markov models revealing the stress field underlying the earthquake generation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(13), pages 2868-2885.
    4. Ting Wang & Mark Bebbington & David Harte, 2012. "Markov-modulated Hawkes process with stepwise decay," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 521-544, June.
    5. Marcel F. Neuts, 1978. "Renewal processes of phase type," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 25(3), pages 445-454, September.
    6. C. E. Pertsinidou & G. Tsaklidis & E. Papadimitriou & N. Limnios, 2017. "Application of hidden semi-Markov models for the seismic hazard assessment of the North and South Aegean Sea, Greece," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(6), pages 1064-1085, April.
    7. Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    8. Shaochuan Lu, 2017. "A continuous-time HMM approach to modeling the magnitude-frequency distribution of earthquakes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 71-88, January.
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    Cited by:

    1. Bountzis, P. & Tsaklidis, G. & Papadimitriou, E., 2022. "Pseudo-prospective forecasting of large earthquakes full distribution in circum-Pacific belt incorporating non-stationary modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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