用卫星雷达数据高效精准判断地震后建筑损毁等级
Multi-class Seismic Building Damage Assessment from InSAR Imagery using Quadratic Variational Causal Bayesian Inference
- 基于因果贝叶斯推理构建新型分析框架，融合多源数据提升准确性
- 五次大地震测试中准确率AUC达0.94-0.96，较现有方法提升35.7%
- 计算效率提升超40%，适合无大量实地数据的快速灾情评估
干涉合成孔径雷达（InSAR）技术利用卫星雷达探测地表形变，监测地震对建筑的影响。尽管对应急响应至关重要，从InSAR数据中提取多类建筑损毁分类仍面临挑战：损毁信号与环境噪声重叠、多类别场景下计算复杂度高，以及需实现区域级快速处理。本文提出一种带二次变分界的新颖多类变分因果贝叶斯推断框架，在保证严格近似的同时确保高效性。通过整合InSAR观测、美国地质调查局地面失效模型及建筑易损性函数，该方法有效分离建筑损毁信号，并通过策略性剪枝降低计算开销。在海地2021年、波多黎各2020年、萨格勒布2020年、意大利2016年和里奇克雷斯特2019年五次重大地震中评估显示，损伤分类准确率（AUC: 0.94–0.96）相较现有方法最高提升35.7%。该方法在所有损毁类别上均保持高精度（AUC > 0.93），同时计算开销减少超40%，且无需大量真实标注数据。
原文摘要 · Abstract (English)
Interferometric Synthetic Aperture Radar (InSAR) technology uses satellite radar to detect surface deformation patterns and monitor earthquake impacts on buildings. While vital for emergency response planning, extracting multi-class building damage classifications from InSAR data faces challenges: overlapping damage signatures with environmental noise, computational complexity in multi-class scenarios, and the need for rapid regional-scale processing. Our novel multi-class variational causal Bayesian inference framework with quadratic variational bounds provides rigorous approximations while ensuring efficiency. By integrating InSAR observations with USGS ground failure models and building fragility functions, our approach separates building damage signals while maintaining computational efficiency through strategic pruning. Evaluation across five major earthquakes (Haiti 2021, Puerto Rico 2020, Zagreb 2020, Italy 2016, Ridgecrest 2019) shows improved damage classification accuracy (AUC: 0.94-0.96), achieving up to 35.7% improvement over existing methods. Our approach maintains high accuracy (AUC > 0.93) across all damage categories while reducing computational overhead by over 40% without requiring extensive ground truth data.
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