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Change point analysis on the Corinth Gulf (Greece) seismicity

Author

Listed:
  • Lykou, R.
  • Tsaklidis, G.
  • Papadimitriou, E.

Abstract

Change point analysis is performed on the seismicity in Gulf of Corinth (Greece), an extensional graben which constitutes one of the most seismically active areas in Greece. Seismicity appears intense and strongly clustered and therefore analysis on mean and variance is appropriate. Sample autocorrelation function of the data is non-zero even for bigger lags, indicating long-range correlations. This phenomenon can be justified by possible changes in the mean of the observations. Non-parametric multiple change point analysis is applied to both the sequence of the earthquakes from a set of observations and its detrended data considering the earthquake occurrence frequency. The results of the analysis on the initial data set are compared to those of its detrended residuals. This procedure employs both online and offline methods providing different perspectives. Promising patterns are defined offline and most of them are detectable online.

Suggested Citation

  • Lykou, R. & Tsaklidis, G. & Papadimitriou, E., 2020. "Change point analysis on the Corinth Gulf (Greece) seismicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
  • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119320230
    DOI: 10.1016/j.physa.2019.123630
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    References listed on IDEAS

    as
    1. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    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. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    4. Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    5. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
    6. Jin Zhang, 2002. "Powerful goodness‐of‐fit tests based on the likelihood ratio," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 281-294, May.
    7. Renata Rotondi, 1999. "Statistical Analysis of Temporal Variations of Seismicity Level in Some Italian Regions," 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. 19(2), pages 139-150, May.
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