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Seismic multi-hazard and impact estimation via causal inference from satellite imagery

Author

Listed:
  • Susu Xu

    (Stony Brook University
    Stony Brook University)

  • Joshua Dimasaka

    (Stanford University)

  • David J. Wald

    (U.S. Geological Survey)

  • Hae Young Noh

    (Stanford University)

Abstract

Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.

Suggested Citation

  • Susu Xu & Joshua Dimasaka & David J. Wald & Hae Young Noh, 2022. "Seismic multi-hazard and impact estimation via causal inference from satellite imagery," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35418-8
    DOI: 10.1038/s41467-022-35418-8
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    References listed on IDEAS

    as
    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. M. Budimir & P. Atkinson & H. Lewis, 2014. "Earthquake-and-landslide events are associated with more fatalities than earthquakes alone," 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. 72(2), pages 895-914, June.
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    Cited by:

    1. Olalekan R. Sodeinde & Magaly Koch & Babak Moaveni & Laurie G. Baise, 2024. "One versus all: identifiability with a multi-hazard and multiclass building damage imagery dataset and a deep learning neural network," 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. 120(9), pages 8337-8366, July.

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