IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119320230.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119320230
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.123630?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    3. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
    4. 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.
    5. 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.
    6. 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).
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brice B. Hanberry, 2021. "Timing of Tree Density Increases, Influence of Climate Change, and a Land Use Proxy for Tree Density Increases in the Eastern United States," Land, MDPI, vol. 10(11), pages 1-17, October.
    2. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    3. Nora M. Villanueva & Marta Sestelo & Miguel M. Fonseca & Javier Roca-Pardiñas, 2023. "seq2R: An R Package to Detect Change Points in DNA Sequences," Mathematics, MDPI, vol. 11(10), pages 1-20, May.
    4. Laha, A. K. & Putatunda, Sayan, 2017. "Travel Time Prediction for Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    6. Michael A Ruderman & Deirdra F Wilson & Savanna Reid, 2015. "Does Prison Crowding Predict Higher Rates of Substance Use Related Parole Violations? A Recurrent Events Multi-Level Survival Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    7. Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    8. Malte Willmes & Katherine M Ransom & Levi S Lewis & Christian T Denney & Justin J G Glessner & James A Hobbs, 2018. "IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    9. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    10. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    11. Chen, Zhanshou & Xu, Qiongyao & Li, Huini, 2019. "Inference for multiple change points in heavy-tailed time series via rank likelihood ratio scan statistics," Economics Letters, Elsevier, vol. 179(C), pages 53-56.
    12. repec:irf:wpaper:010 is not listed on IDEAS
    13. Hui Zhang & Minna Väliranta & Graeme T. Swindles & Marco A. Aquino-López & Donal Mullan & Ning Tan & Matthew Amesbury & Kirill V. Babeshko & Kunshan Bao & Anatoly Bobrov & Viktor Chernyshov & Marissa , 2022. "Recent climate change has driven divergent hydrological shifts in high-latitude peatlands," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    14. Subhashis Chatterjee & Ankur Shukla, 2016. "Change point–based software reliability model under imperfect debugging with revised concept of fault dependency," Journal of Risk and Reliability, , vol. 230(6), pages 579-597, December.
    15. Andreas Anastasiou & Piotr Fryzlewicz, 2022. "Detecting multiple generalized change-points by isolating single ones," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 141-174, February.
    16. Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
    17. Stefan Albert & Michael Messer & Julia Schiemann & Jochen Roeper & Gaby Schneider, 2017. "Multi-Scale Detection of Variance Changes in Renewal Processes in the Presence of Rate Change Points," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 1028-1052, November.
    18. 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).
    19. Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    20. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    21. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119320230. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.