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Finite data-size scaling of clustering in earthquake networks

Author

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  • Abe, Sumiyoshi
  • Pastén, Denisse
  • Suzuki, Norikazu

Abstract

An earthquake network is known to be of the small-world type. The values of the network’s characteristics, however, depend not only on the cell size (i.e., the scale of coarse graining needed for constructing the network) but also on the size of a seismic data set. Here, discovery of a scaling law for the clustering coefficient in terms of the data size, which is referred to here as finite data-size scaling, is reported. Its universality is shown to be supported by the detailed analysis of the data taken from California, Japan and Iran. Effects of setting a threshold of magnitude are also discussed.

Suggested Citation

  • Abe, Sumiyoshi & Pastén, Denisse & Suzuki, Norikazu, 2011. "Finite data-size scaling of clustering in earthquake networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1343-1349.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:7:p:1343-1349
    DOI: 10.1016/j.physa.2010.11.033
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    Citations

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    Cited by:

    1. Xu, Yanjie & Ren, Tao & Liu, Yiyang & Li, Zhe, 2018. "Earthquake prediction based on community division," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 969-974.
    2. Ferreira, Douglas S.R. & Ribeiro, Jennifer & Oliveira, Paulo S.L. & Pimenta, André R. & Freitas, Renato P. & Papa, Andrés R.R., 2020. "Long-range correlation studies in deep earthquakes global series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    3. Rezaei, Soghra & Moghaddasi, Hanieh & Darooneh, Amir Hossein, 2018. "Preferential attachment in evolutionary earthquake networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 172-179.
    4. Rezaei, Soghra & Darooneh, Amir Hossein & Lotfi, Nastaran & Asaadi, Nazila, 2017. "The earthquakes network: Retrieving the empirical seismological laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 80-87.
    5. Deyasi, Krishanu & Chakraborty, Abhijit & Banerjee, Anirban, 2017. "Network similarity and statistical analysis of earthquake seismic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 224-234.
    6. Razdan, Ashok, 2013. "Networks in extensive air showers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 982-986.
    7. He, Xuan & Zhao, Hai & Cai, Wei & Liu, Zheng & Si, Shuai-Zong, 2014. "Earthquake networks based on space–time influence domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 175-184.
    8. Lotfi, Nastaran & Darooneh, Amir H., 2013. "Nonextensivity measure for earthquake networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3061-3065.
    9. Chiao, Ling-Yun, 2012. "Variation dynamics of the complex topology of a seismicity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 497-507.

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