Title: Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation Show Chinese title

Title: 空间可变因果贝叶斯推理在快速地震地面失效及其影响评估中的应用

Abstract: Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses. Progression in remote sensing technologies has paved the way for rapid acquisition of detailed, localized data, enabling swift hazard estimation through analysis of correlation deviations between pre- and post-quake satellite imagery. However, discerning seismic hazards and their impacts is challenged by overlapping satellite signals from ground failures, building damage, and environmental noise. Previous advancements introduced a novel causal graph-based Bayesian network that continually refines seismic ground failure and building damage estimates derived from satellite imagery, accounting for the intricate interplay among geospatial elements, seismic activity, ground failures, building structures, damages, and satellite data. However, this model's neglect of spatial heterogeneity across different locations in a seismic region limits its precision in capturing the spatial diversity of seismic effects. In this study, we pioneer an approach that accounts for spatial intricacies by introducing a spatial variable influenced by the bilateral filter to capture relationships from surrounding hazards. The bilateral filter considers both spatial proximity of neighboring hazards and their ground shaking intensity values, ensuring refined modeling of spatial relationships. This integration achieves a balance between site-specific characteristics and spatial tendencies, offering a comprehensive representation of the post-disaster landscape. Our model, tested across multiple earthquake events, demonstrates significant improvements in capturing spatial heterogeneity in seismic hazard estimation. The results highlight enhanced accuracy and efficiency in post-earthquake large-scale multi-impact estimation, effectively informing rapid disaster responses. Show Chinese abstract

Abstract: 地震后快速准确地估算地面失效和建筑物损坏对于有效的灾后响应至关重要。遥感技术的进步为快速获取详细的地方化数据铺平了道路，通过分析震前和震后卫星图像的相关性偏差，可以迅速估计危险程度。 然而，区分地震危害及其影响受到地面失效、建筑损害以及环境噪声重叠的卫星信号的挑战。 先前的研究引入了一种基于因果图的贝叶斯网络，该网络不断优化从卫星图像推导出的地震地面失效和建筑损害估算，考虑到了地理空间要素、地震活动、地面失效、建筑结构、损害和卫星数据之间的复杂相互作用。 然而，这种模型忽视了地震区域不同地点之间的空间异质性，这限制了其捕捉地震效应的空间多样性的精度。 在这项研究中，我们开创了一种方法，通过引入受双边滤波器影响的空间变量来捕捉周围危害的关系，从而考虑到空间复杂性。 双边滤波器同时考虑了相邻危害的空间接近性和它们的地面晃动强度值，确保了空间关系的精炼建模。 这种集成实现了特定场地特性和空间趋势之间的平衡，提供了灾后景观的全面表示。 我们的模型在多次地震事件中进行了测试，显示出了在捕捉地震危害估算中的空间异质性方面的显著改进。 结果显示，在地震后的大规模多影响估算中，准确性与效率得到了提高，有效地支持了快速的灾害响应。