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Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China

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  • Dongping Li
  • Qingquan Tan
  • Zhiyi Tong
  • Jingfei Yin
  • Min Li
  • Huanyu Li
  • Haiqing Sun

Abstract

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

Suggested Citation

  • Dongping Li & Qingquan Tan & Zhiyi Tong & Jingfei Yin & Min Li & Huanyu Li & Haiqing Sun, 2024. "Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0298236
    DOI: 10.1371/journal.pone.0298236
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    References listed on IDEAS

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    1. Lirong Yin & Lei Wang & Jianqiang Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Shan Liu & Wenfeng Zheng, 2023. "YOLOV4_CSPBi: Enhanced Land Target Detection Model," Land, MDPI, vol. 12(9), pages 1-17, September.
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