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个危害（例如，地面震动、滑坡和液化）和冲击（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，对于促进及时应对至关重要，例如及时疏散、搜索和救援以及有效分配有限资源。 研究人员一直在研究如何利用卫星图像提取广泛受影响地区的灾害和影响信息;然而，灾害和影响的同时发生和同时发生导致卫星图像中的信号混杂，使得直接对每种灾害和相关影响进行分类和估计非常困难。这个灾难恢复研究赠款（DRRG）项目旨在开发一个新的系统，通过利用它们之间的因果关系，提供快速，可扩展和联合评估级联地震灾害和影响。它将通过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，并利用联邦应急管理局的HAZUS工具建立脆弱性函数，来提高现有快速灾害信息系统的准确性、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对涉及连锁影响的其他自然灾害的研究。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，通过将来自卫星图像的信息与先前的地球物理知识相融合，并通过一个深层因果贝叶斯网络建立脆弱性函数，近实时地自适应提供区域尺度地震多重灾害和影响发生估计。首先，将建立一个新的范例，模拟复杂的和隐含的因果关系之间的级联地震多灾害和影响，作为一个基于流的因果贝叶斯网络整合信息从以前的灾害和影响模型与混合信号卫星图像。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地震多灾害和影响的估计，无论是否部分观察到地面实况。第三，当地的地理空间灾害模型和建筑脆弱性函数将通过一种新的不确定性感知先验模型更新计划，使用从因果贝叶斯网络的事件特定的模式进行更新。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损坏模式，从而提高区域对未来灾害事件的复原力。该框架将在七个中大型全球地震事件中得到验证。该奖项反映了NSF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。