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

地震多重灾害和影响的快速、可扩展和联合评估：从卫星图像到因果关系深度贝叶斯网络

• 批准号: 2242590
• 负责人: SUSU XU
• 金额: $ 39.2万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242590&HistoricalAwards=false
• 关键词: Rapid; Scalable; Joint; Assessment; Seismic

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.

地震事件通常涉及多种危害（例如地面震动、山体滑坡和液化）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此类危害和影响的位置和程度对于促进地震至关重要。实时响应，例如及时疏散、搜索和救援以及有限资源的有效性，研究人员一直在研究利用卫星图像为广泛受影响的地区分配提取的危险和影响信息；灾害和影响的同时发生和共存导致卫星图像中出现混合信号，这使得直接对每种灾害和相关影响进行分类和估计变得非常具有挑战性。该灾难恢复研究补助金（DRRG）项目旨在开发一种新颖的系统来预测灾害和影响。通过利用它们之间的因果依赖性，提供快速、可扩展和联合的级联地震灾害和影响。它将通过将卫星图像与现有地理空间相结合，提高现有灾害信息系统快速评估的准确性、分辨率和及时性。所揭示的区域因果机制旨在改进地震风险分析以及涉及连锁影响的其他自然灾害的研究。抵御未来自然灾害的能力。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，通过融合信息，自适应地提供近乎实时的区域尺度地震多灾种和影响发生估计首先，将建立一种新的范式，将级联地震多重灾害和影响之间复杂且隐含的因果依赖性建模为基于流的因果贝叶斯网络。此外，将开发在线变分贝叶斯推理框架，以可扩展且高效的方式联合推断和更新对风险的估计。第三，将使用从因果贝叶斯网络学习到的特定事件模式，通过新颖的不确定性感知先验模型更新方案来更新当地地理空间灾害模型和建筑脆弱性函数。因果贝叶斯网络揭示的级联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进行表征，以深入了解特定事件的地震灾害和损坏模式。该框架将在七次中型至大型全球地震事件中进行演示。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。 。