Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929282836594688 |
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| author | Li, Xuechun Burgi, Paula M. Ma, Wei Noh, Hae Young Wald, David J. Xu, Susu |
| author_facet | Li, Xuechun Burgi, Paula M. Ma, Wei Noh, Hae Young Wald, David J. Xu, Susu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13805 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery Li, Xuechun Burgi, Paula M. Ma, Wei Noh, Hae Young Wald, David J. Xu, Susu Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.13805 |