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Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China

Author

Listed:
  • Xinxin Guo

    (Institute of Geology, China Earthquake Administration, Beijing 100029, China)

  • Benyong Wei

    (Institute of Geology, China Earthquake Administration, Beijing 100029, China
    Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China)

  • Gaozhong Nie

    (Institute of Geology, China Earthquake Administration, Beijing 100029, China
    Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China)

  • Guiwu Su

    (Institute of Geology, China Earthquake Administration, Beijing 100029, China
    Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China)

Abstract

Seismic disasters are sudden and unpredictable, often causing massive damage, casualties and socioeconomic losses. Rapid and accurate determination of the scale and degree of destruction of the seismic influence field in an affected area can aid in timely emergency rescue work after an earthquake. In this study, the relationship between the changes in four types of mobile signaling data and the seismic influence field was explored in the 2017 Jiuzhaigou earthquake-hit area, China, by using the methods of comparative analysis, regression analysis and spatial autocorrelation analysis. The results revealed that after the earthquake, the number of mobile signaling significantly decreased. The higher the intensity, the more obvious the reduction of mobile signaling data and the later the recovery time. The Loginmac and WiFi data showed greater sensitivity than Gid and Station. There was a significant correlation between the changes in the mobile signaling numbers and the seismic intensity, which can more accurately reflect the approximate extent of the seismic influence field and the degree of actual damage. The changes in mobile signaling can provide a helpful reference for the rapid determination of seismic influence fields.

Suggested Citation

  • Xinxin Guo & Benyong Wei & Gaozhong Nie & Guiwu Su, 2022. "Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China," IJERPH, MDPI, vol. 19(17), pages 1-23, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10697-:d:899555
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    References listed on IDEAS

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    1. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 335-338, July.
    2. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 279-300, July.
    3. Kim, Jooho & Bae, Juhee & Hastak, Makarand, 2018. "Emergency information diffusion on online social media during storm Cindy in U.S," International Journal of Information Management, Elsevier, vol. 40(C), pages 153-165.
    4. Yates, Dave & Paquette, Scott, 2011. "Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake," International Journal of Information Management, Elsevier, vol. 31(1), pages 6-13.
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