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

arXiv: https://arxiv.org/abs/2310.13805 · HTML: https://mlsanity.com/paper/2310.13805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)

## Built on by (most cited first, 3 of 3)

- [Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows](https://mlsanity.com/paper/2504.04013/) (2025)
- [Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference](https://mlsanity.com/paper/2504.04015/) (2025)
- [Decision-focused Sensing and Forecasting for Adaptive and Rapid Flood Response: An Implicit Learning Approach](https://mlsanity.com/paper/2510.16015/) (2025)

## Abstract (arXiv)

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.