IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v79y2015i2p1039-1058.html
   My bibliography  Save this article

Seismicity rate modeling for prospective stochastic forecasting: the case of 2014 Kefalonia, Greece, seismic excitation

Author

Listed:
  • D. Gospodinov
  • V. Karakostas
  • E. Papadimitriou

Abstract

We examined the January–February 2014 earthquake doublet (M w = 6.1 and M w = 6.0) and the associated aftershocks which form a seismic excitation adequately well recorded by a dense local seismological network. It started on January 26 with the main shock, causing a lot of panic and followed by numerous aftershocks. The second main shock with M w = 6.0 occurred 7 days later on an along-strike adjacent fault segment. The close proximity of the two main shocks, in both space and time and the intense aftershock sequence, triggered the investigation of the occurrence probability evolution for the stronger aftershocks and possibly a third main shock in the seismic excitation. This purpose was further motivated by the potential of the area for hosting a stronger (M w ≥ 6.0) earthquake based upon both historical information and instrumental data. Aftershock rate modeling was done on subsequent data samples by the restricted epidemic-type aftershock sequence stochastic model, and probabilities for the occurrence of strong (M w ≥ 5.0) earthquakes were calculated during the progress of the aftershock sequence. We executed daily model simulations and probability forecasts for 30 days focusing in more detail on the impact of some model parameters on the prospective forecasting. Trying to be near to a real-time case, all forecasts were done on data up to the moment of forecasting. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • D. Gospodinov & V. Karakostas & E. Papadimitriou, 2015. "Seismicity rate modeling for prospective stochastic forecasting: the case of 2014 Kefalonia, Greece, seismic excitation," 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. 79(2), pages 1039-1058, November.
  • Handle: RePEc:spr:nathaz:v:79:y:2015:i:2:p:1039-1058
    DOI: 10.1007/s11069-015-1890-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-015-1890-8
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11069-015-1890-8?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. Yosihiko Ogata, 1998. "Space-Time Point-Process Models for Earthquake Occurrences," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 379-402, June.
    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. Dewei Wang & Chendi Jiang & Chanseok Park, 2019. "Reliability analysis of load-sharing systems with memory," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 341-360, April.
    2. Jamie Olson & Kathleen Carley, 2013. "Exact and approximate EM estimation of mutually exciting hawkes processes," Statistical Inference for Stochastic Processes, Springer, vol. 16(1), pages 63-80, April.
    3. van den Hengel, G. & Franses, Ph.H.B.F., 2018. "Forecasting social conflicts in Africa using an Epidemic Type Aftershock Sequence model," Econometric Institute Research Papers EI2018-31, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Chenlong Li & Zhanjie Song & Wenjun Wang, 2020. "Space–time inhomogeneous background intensity estimators for semi-parametric space–time self-exciting point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 945-967, August.
    5. Sebastian Meyer & Johannes Elias & Michael Höhle, 2012. "A Space–Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence," Biometrics, The International Biometric Society, vol. 68(2), pages 607-616, June.
    6. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
    7. Møller, Jesper & Torrisi, Giovanni Luca, 2007. "The pair correlation function of spatial Hawkes processes," Statistics & Probability Letters, Elsevier, vol. 77(10), pages 995-1003, June.
    8. Giada Adelfio & Arianna Agosto & Marcello Chiodi & Paolo Giudici, 2021. "Financial contagion through space-time point processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 665-688, June.
    9. Fama, Yuchen & Pozdnyakov, Vladimir, 2011. "A test for self-exciting clustering mechanism," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1541-1546, October.
    10. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Exploiting Spillovers to Forecast Crashes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 936-955, December.
    11. Jiménez, Abigail, 2011. "Comparison of the Hurst and DEA exponents between the catalogue and its clusters: The California case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2146-2154.
    12. Sobin Joseph & Shashi Jain, 2024. "Non-Parametric Estimation of Multi-dimensional Marked Hawkes Processes," Papers 2402.04740, arXiv.org.
    13. Martin Magris, 2019. "On the simulation of the Hawkes process via Lambert-W functions," Papers 1907.09162, arXiv.org.
    14. Markéta Zikmundová & Kateřina Staňková Helisová & Viktor Beneš, 2012. "Spatio-Temporal Model for a Random Set Given by a Union of Interacting Discs," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 883-894, September.
    15. Md. Asaduzzaman & A. Latif, 2014. "A parametric Markov renewal model for predicting tropical cyclones in Bangladesh," 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. 73(2), pages 597-612, September.
    16. Vipul Aggarwal & Elina H. Hwang & Yong Tan, 2021. "Learning to Be Creative: A Mutually Exciting Spatiotemporal Point Process Model for Idea Generation in Open Innovation," Information Systems Research, INFORMS, vol. 32(4), pages 1214-1235, December.
    17. Jesper Møller & Jakob G. Rasmussen, 2006. "Approximate Simulation of Hawkes Processes," Methodology and Computing in Applied Probability, Springer, vol. 8(1), pages 53-64, March.
    18. Frederic Paik Schoenberg & Marc Hoffmann & Ryan J. Harrigan, 2019. "A recursive point process model for infectious diseases," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1271-1287, October.
    19. Weijin Xu & Jian Wu & Mengtan Gao, 2023. "Temporal distribution model and occurrence probability of M ≥ 6.5 earthquakes in North China Seismic Zone," 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. 119(1), pages 125-141, October.
    20. Hainaut, Donatien, 2021. "Moment generating function of non-Markov self-excited claims processes," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 406-424.

    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:spr:nathaz:v:79:y:2015:i:2:p:1039-1058. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.