Rapid, Scalable, and Joint Assessment of Seismic Multi-Hazards and Impacts: From Satellite Images to Causality-Informed Deep Bayesian Networks

地震事件通常涉及多种危害（例如，地面震动、滑坡和液化）和影响（例如，建筑物和基础设施的破坏）。在事件发生后立即以高分辨率了解此类危害和影响的位置和程度对于促进实时响应至关重要，例如及时疏散、搜索和救援，以及有效分配有限的资源。研究人员一直在调查使用卫星图像提取广泛受灾地区的危害和影响信息；然而，灾害和影响的共现和同址导致卫星图像中的信号混杂，这使得对每种灾害和相关影响进行直接分类和估计非常具有挑战性。该项目旨在开发一种新型系统，通过利用地震灾害之间的因果关系，对级联地震灾害和影响进行快速、可扩展和联合评估。它将通过整合卫星图像与美国地质调查局现有的地理空间灾害模型，以及联邦紧急事务管理局HAZUS工具的脆弱性功能，提高现有快速灾害信息系统的准确性、分辨率和及时性。揭示的区域因果机制旨在改进地震风险分析以及涉及级联影响的其他自然灾害的研究。这将提高整个社区对未来自然灾害的抵御能力。该项目将开发一个基于因果关系的变分贝叶斯网络建模框架，通过将卫星图像信息与先前的地球物理知识融合，并通过深层因果贝叶斯网络构建脆弱性函数，自适应地提供近实时的区域尺度地震多灾害和影响发生估计。首先，将建立一个新的范式来模拟级联地震多灾害和影响之间复杂和隐含的因果关系，作为一个基于流的因果贝叶斯网络，将来自先前灾害和影响模型的信息与混合信号卫星图像相结合。此外，将开发一个在线变分贝叶斯推理框架，以可扩展和有效的方式共同推断和更新地震多重灾害和影响的估计，无论是否部分观察到地面真相。第三，利用从因果贝叶斯网络中学习到的事件特定模式，通过一种新的不确定性感知先验模型更新方案，更新局部地理空间灾害模型和建筑物易损性函数。通过因果贝叶斯网络揭示的级联地震灾害和建筑破坏的定量因果机制，将在多个地震事件中进行表征，从而深入了解事件特定的地震灾害和破坏模式，从而提高区域对未来灾害事件的恢复能力。该框架将在七个全球中大型地震事件中进行演示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A seismic event often involves multiple hazards (e.g., ground shaking, landslide, and liquefaction) and impacts (e.g., building and infrastructure damage). Understanding the locations and extents of such hazards and impacts in high resolution immediately after an event is critical for facilitating real-time responses, such as timely evacuation, search and rescue, and effective allocations of limited resources. Researchers have been investigating using satellite imageries to extract hazard and impact information for wide affected areas; however, the co-occurrence and co-location of hazards and impacts result in mixed signals in satellite imagery, making it very challenging to directly categorize and estimate each hazard and associated impacts. This Disaster Resilience Research Grants (DRRG) project aims to develop a novel system to provide rapid, scalable, and joint assessments of cascading seismic hazards and impacts by leveraging the causal dependencies among them. It will enhance the accuracy, resolution, and timeliness of existing rapid disaster information systems by integrating satellite images with existing geospatial hazard models from The US Geological Survey and building fragility functions from the Federal Emergency Management Agency’s HAZUS tool. The revealed regional causal mechanisms aims to enable improved seismic risk analysis as well as the study of other natural disasters involving cascading impacts. This will improve overall community resilience to future natural disasters.This project will develop a causality-informed variational Bayesian network modeling framework to adaptively provide regional-scale seismic multi-hazard and impact occurrence estimates in near-real-time, by fusing information from satellite images with prior geophysical knowledge and building fragility functions through a deep causal Bayesian network. First, a novel paradigm will be established to model complex and implicit causal dependencies among cascading seismic multi-hazards and impacts as a flow-based causal Bayesian network to integrate information from prior hazards and impact models with mixed-signal satellite imagery. Further, an online variational Bayesian inference framework will be developed to jointly infer and update, in a scalable and efficient manner, the estimations of seismic multi-hazards and impacts, with or without partially observed ground truth. Third, local geospatial hazards model and building fragility functions will be updated through a novel uncertainty-aware prior model updating scheme using the event-specific patterns learned from the causal Bayesian network. The quantitative causal mechanisms of cascading seismic hazards and building damage, revealed by the causal Bayesian network, will be characterized in multiple earthquake events, to render an in-depth understanding of event-specific seismic hazards and damage patterns for improving regional resilience to future disaster events. The framework will be demonstrated on seven moderate-to-large global earthquake events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.