Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
arxiv(2023)
摘要
Onsite disasters like earthquakes can trigger cascading hazards and impacts,
such as landslides and infrastructure damage, leading to catastrophic losses;
thus, rapid and accurate estimates are crucial for timely and effective
post-disaster responses. Interferometric Synthetic aperture radar (InSAR) data
is important in providing high-resolution onsite information for rapid hazard
estimation. Most recent methods using InSAR imagery signals predict a single
type of hazard and thus often suffer low accuracy due to noisy and complex
signals induced by co-located hazards, impacts, and irrelevant environmental
changes (e.g., vegetation changes, human activities). We introduce a novel
stochastic variational inference with normalizing flows derived to jointly
approximate posteriors of multiple unobserved hazards and impacts from noisy
InSAR imagery.
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