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，摇动，滑坡和液化）和撞击（例如，建筑物和基础设施损害）。在事件发生后立即了解此类危害的位置和范围，对于支持实时响应至关重要，例如及时撤离，搜救和救援以及对有限资源的有效分配。研究人员一直在研究使用卫星图像来提取危险和影响广泛地区的信息；但是，危害和影响的共发生和共同点导致卫星图像中的混合信号，因此直接对每种危害及相关影响直接分类和估计和估算的挑战非常挑战。这项灾难弹性研究补助金（DRRG）项目旨在开发一种新型系统，以通过利用其中的因果关系来提供层叠式地震危害和影响的快速，可扩展和联合评估。它将通过将卫星图像与美国地质调查局的现有地理空间危险模型相结合，并从联邦紧急事务机构的HAZUS工具中构建脆弱性功能，从而提高现有快速灾难信息系统的准确性，分辨率和及时性。揭示的区域因果机制旨在改善地震风险分析以及对其他自然灾害的研究涉及级联影响。这将提高社区对未来自然灾害的整体弹性。该项目将开发一个随意的变异贝叶斯网络建模框架，以适应性地提供区域尺度的地震多危险范围，并在近实时实时估算出来的估计，通过将卫星图像中的信息融合到以前的地球物理学知识和建立脆弱性的深层网络。首先，将建立一个新型的范式，以模拟级联的地震多危害中的复杂和隐式因果关系，并作为基于流动的因果贝叶斯网络的影响，以通过混合信号卫星图像从先前的危害和影响模型中的信息进行整合。此外，将开发一个在线变分推理框架，以可扩展有效的方式共同推断和更新地震多危险和影响的估计，无论是否部分观察到地面真理。第三，将使用从因果关系贝叶斯网络中学到的事件特定模式来更新本地的地理空间危害模型和构建脆性功能。因果贝叶斯网络所揭示的级联地震危害和建筑物损害的定量因果机制将在多个地震事件中进行表征，以使对事件特定的地震危害和损害模式有深入的了解，以改善未来灾害事件的区域弹性。该框架将在七个现代全球地震事件上展示。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的。