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

Susu Xu (), Joshua Dimasaka, David J. Wald and Hae Young Noh
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Susu Xu: Stony Brook University
Joshua Dimasaka: Stanford University
David J. Wald: U.S. Geological Survey
Hae Young Noh: Stanford University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-35418-8

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