Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery

• URL: http://arxiv.org/abs/2310.13805v2
• Date: Wed, 20 Mar 2024 16:23:20 GMT
• Title: Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
• Authors: Xuechun Li, Paula M. Burgi, Wei Ma, Hae Young Noh, David J. Wald, Susu Xu,
• Abstract summary: Interferometric Synthetic aperture radar (InSAR) data is important in providing high-resolution onsite information for rapid hazard estimation. We introduce a novel variational inference with normalizing flows derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy InSAR imagery.
• Score: 11.753558942419545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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