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

地震事件往往涉及多种危害(例如，地面震动、滑坡和液化)和影响(例如，建筑物和基础设施损坏)。在事件发生后立即以高分辨率了解这种危险和影响的位置和程度，对于促进实时反应至关重要，例如及时疏散、搜索和救援，以及有效分配有限的资源。研究人员一直在利用卫星图像进行调查，以提取大范围受影响地区的危险和影响信息；然而，危险和影响的共同出现和共处导致卫星图像中的混合信号，这使得直接对每一种危险和相关影响进行分类和估计非常困难。灾难恢复研究补助金(DRRG)项目旨在开发一种新的系统，通过利用级联地震灾害和影响之间的因果关系，提供快速、可扩展和联合的评估。它将提高现有快速灾害信息系统的准确性、分辨率和及时性，方法是将卫星图像与美国地质调查局现有的地理空间灾害模型相结合，并从联邦紧急事务管理局的HAZUS工具中构建脆弱性函数。所揭示的区域因果机制旨在改进地震风险分析以及研究涉及连锁影响的其他自然灾害。这将提高整个社区对未来自然灾害的抵抗力。该项目将开发一个因果关系信息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，并通过深度因果贝叶斯网络建立脆弱性函数，自适应地提供区域规模的地震多灾害和影响发生估计。首先，将建立一个新的范例来模拟级联地震多种灾害和影响之间的复杂和隐含的因果依赖关系，作为一个基于流的因果贝叶斯网络，以将来自先前的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联合推断和更新对地震多重灾害和影响的估计，无论是否有部分观测到的地面真实情况。第三，局部地理空间灾害模型和建筑物脆弱性函数将通过一种新的不确定性感知先验模型更新方案，该方案使用从因果贝叶斯网络学习的特定事件模式。由因果贝叶斯网络揭示的连锁地震灾害和建筑物损坏的定量因果机制将在多个地震事件中表征，以深入了解特定事件的地震灾害和破坏模式，以提高区域对未来灾害事件的复原力。该框架将在七次中到大型的全球地震事件中得到展示。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.