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Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes

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Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 943))

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Abstract

In this paper, we address the problem of automatically extracting several clusters consisting of spatio-temporally similar earthquakes whose average magnitudes are substantially different from the total average. For this purpose, we propose a new method consisting of two phases: tree construction and tree separation. In the former phase, we employ one of two different declustering algorithms called single-link and correlation-metric developed in the field of seismology, while in the later phase, we employ a variant of the change-point detection algorithm, developed in the field of data mining. In our empirical evaluation using earthquake catalog data covering the whole of Japan, we show that the proposed method employing the single-link algorithm can produce more desirable results for our purpose in terms of the improvement of weighted sums of variances and visualization results.

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Notes

  1. 1.

    https://www.data.jma.go.jp/svd/eqev/data/bulletin/hypo.html.

References

  1. Aki, K.: Maximum likelihood estimate of bin the formula \(\log (N) = a - bM\) and its confidence limits. Bull. Earthq. Res. Inst. 43, 237–239 (1965)

    Google Scholar 

  2. Baiesi, M., Paczuski, M.: Scale-free networks of earthquakes and aftershocks. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. 69, 066106 (2004)

    Google Scholar 

  3. Bottiglieri, M., Lippiello, E., Godano, C., De Arcangelis, L.: Identification and spatiotemporal organization of aftershocks. J. Geophys. Res. 114 (2009)

    Google Scholar 

  4. Davis, S.D., Frohlich, C.: Single-link cluster analysis, synthetic earthquake catalogues, and aftershock identification. Geophys. J. Int. 104(2), 289–306 (1991)

    Article  Google Scholar 

  5. Frohlich, C., Davis, S.: Identification of aftershocks of deep earthquakes by a new ratios method. Geophys. Res. Lett. 12, 713–716 (1985)

    Article  Google Scholar 

  6. Frohlich, C., Davis, S.D.: Single-link cluster analysis as a method to evaluate spatial and temporal properties of earthquake catalogues. Geophys. J. Int. 100(1), 19–32 (1990)

    Article  Google Scholar 

  7. Gardner, J.K., Knopoff, L.: Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian? Bull. Seismol. Soc. Am. 64(5), 1363–1367 (1974)

    Google Scholar 

  8. Hainzl, S., Scherbaum, F., Beauval, C.: Estimating background activity based on interevent-time distribution. Bull. Seismol. Soc. Am. 96, 313–320 (2006)

    Article  Google Scholar 

  9. Kagan, Y., Jackson, D.: Long-term earthquake clustering. Geophys. J. Int. 104, 117–133 (1991)

    Article  Google Scholar 

  10. Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002), pp. 91–101 (2002)

    Google Scholar 

  11. Knopoff, L., Gardner, J.K.: Higher seismic activity during local night on the raw worldwide earthquake catalogue. Geophys. J. Int. 28, 311–313 (1972)

    Article  Google Scholar 

  12. Marsan, D., Lengliné, O.: Extending earthquakes’ reach through cascading. Science 319(5866), 1076–1079 (2008)

    Article  Google Scholar 

  13. Marsan, D., Lengliné, O.: A new estimation of the decay of after shock density with distance to the mainshock. J. Geophys. Res.: Solid Earth, 115 (2010)

    Google Scholar 

  14. Molchan, G.M., Dmitrieva, O.E.: Aftershock identification: methods and new approaches. Geophys. J. Int. 109, 501–516 (1992)

    Article  Google Scholar 

  15. Ogata, Y.: Statistical models for earthquake occurrences and residual analysis for point processes. J. Am. Stat. Assoc. 83(401), 9–27 (1988)

    Article  Google Scholar 

  16. Ogata, Y.: Space-time point-process models for earthquake occurrences. Ann. Inst. Stat. Math. 50, 379–402 (1998)

    Article  Google Scholar 

  17. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  18. Reasenberg, P.: Second-order moment of Central California seismicity, 1969–1982. J. Geophys. Res. 90, 5479–5495 (1985)

    Article  Google Scholar 

  19. Savage, W.U.: Microearthquake clustering near Fairview Peak, Nevada, and in the Nevada seismic zone. J. Geophys. Res. 77, 7049–7056 (1972)

    Article  Google Scholar 

  20. van Stiphout, T., Zhuang, J., Marsan, D.: Seismicity declustering. Community Online Resource for Statistical Seismicity Analysis (2012)

    Google Scholar 

  21. Swan, R., Allan, J.: Automatic generation of overview timelines. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2000), pp. 49–56 (2000)

    Google Scholar 

  22. Uhrhammer, R.A.: Characteristics of Northern and Central California seismicity. Earthq. Notes 57(1), 21–37 (1986)

    Google Scholar 

  23. Yamagishi, Y., Okubo, S., Saito, K., Ohara, K., Kimura, M., Motoda, H.: A method to divide stream data of scores over review sites. In: PRICAI 2014: Trends in Artificial Intelligence - 13th Pacific Rim International Conference on Artificial Intelligence. Lecture Notes in Computer Science, vol. 8862, pp. 913–919. Springer (2014)

    Google Scholar 

  24. Yamagishi, Y., Saito, K.: Visualizing switching regimes based on multinomial distribution in buzz marketing sites. In: Foundations of Intelligent Systems - 23rd International Symposium, ISMIS 2017. Lecture Notes in Computer Science, vol. 10352, pp. 385–395. Springer (2017)

    Google Scholar 

  25. Zaliapin, I., Gabrielov, A., Keilis-Borok, V., Wong, H.: Clustering analysis of seismicity and aftershock identification. Phys. Rev. Lett. 101(1), 1–4 (2008)

    Article  Google Scholar 

  26. Zhuang, J.: Multi-dimensional second-order residual analysis of space-time point processes and its applications in modelling earthquake data. J. R. Stat. Soc. 68(4), 635–653 (2006)

    Article  MathSciNet  Google Scholar 

  27. Zhuang, J., Ogata, Y., Vere-Jones, D.: Stochastic declustering of space-time earthquake occurrences. J. Am. Stat. Assoc. 97(458), 369–380 (2002)

    Article  MathSciNet  Google Scholar 

  28. Zhuang, J., Ogata, Y., Vere-Jones, D.: Analyzing earthquake clustering features by using stochastic reconstruction. J. Geophys. Res. 109 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Informatics, Shizuoka Institute of Science and Technology, Fukuroi, Japan

    Yuki Yamagishi

  2. Center for Advanced Intelligence Project, RIKEN, Kyoto, Japan

    Kazumi Saito, Kazuro Hirahara & Naonori Ueda

Authors
  1. Yuki Yamagishi
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  2. Kazumi Saito
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  3. Kazuro Hirahara
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  4. Naonori Ueda
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Corresponding author

Correspondence to Yuki Yamagishi .

Editor information

Editors and Affiliations

  1. Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa M. Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LIB, UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matematicas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Yamagishi, Y., Saito, K., Hirahara, K., Ueda, N. (2021). Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_52

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  • DOI: https://doi.org/10.1007/978-3-030-65347-7_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65346-0

  • Online ISBN: 978-3-030-65347-7

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