用空间异质因果模型提升地震多灾种预测精度
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
- 构建基于高斯过程与归一化流的时空因果网络，参数随地理位置变化
- 在三次地震测试中AUC最高提升35.2%，显著优于现有方法
- 适合灾害评估、应急资源调度等需要精准空间预测的场景
震后灾害与影响评估对有效救灾至关重要，但现有方法存在明显局限。传统模型采用固定参数，无法反映地震效应在不同地貌中的差异；遥感技术也难以区分共位灾害。本文提出一种空间感知的因果贝叶斯网络（Spatial-VCBN），通过位置相关的参数解耦共位灾害，建模其因果关系。框架结合传感观测、隐变量与空间异质性，创新性地融合高斯过程与归一化流，捕捉同一地震在不同地质与地形条件下产生的差异化影响。在三次地震事件上的评估显示，Spatial-VCBN在曲线下面积（AUC）上相比现有方法最高提升35.2%。结果表明，在因果机制中建模空间异质性对精准灾评至关重要，可直接用于优化应急资源调配。
原文摘要 · Abstract (English)
Post-earthquake hazard and impact estimation are critical for effective disaster response, yet current approaches face significant limitations. Traditional models employ fixed parameters regardless of geographical context, misrepresenting how seismic effects vary across diverse landscapes, while remote sensing technologies struggle to distinguish between co-located hazards. We address these challenges with a spatially-aware causal Bayesian network that decouples co-located hazards by modeling their causal relationships with location-specific parameters. Our framework integrates sensing observations, latent variables, and spatial heterogeneity through a novel combination of Gaussian Processes with normalizing flows, enabling us to capture how same earthquake produces different effects across varied geological and topographical features. Evaluations across three earthquakes demonstrate Spatial-VCBN achieves Area Under the Curve (AUC) improvements of up to 35.2% over existing methods. These results highlight the critical importance of modeling spatial heterogeneity in causal mechanisms for accurate disaster assessment, with direct implications for improving emergency response resource allocation.
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