This paper addresses a long-standing gap in natural hazard modeling by unifying physics-based fragility functions with real-time post-disaster observations. It introduces a Bayesian framework that continuously refines regional vulnerability estimates as new data emerges. The framework reformulates physics-informed fragility estimates into a Probit-Normal (PN) representation that captures aleatory variability and epistemic uncertainty in an analytically tractable form. Stage 1 performs local Bayesian updating by moment-matching PN marginals to Beta surrogates that preserve their probability shapes, enabling conjugate Beta-Bernoulli updates with soft, multi-fidelity observations. Fidelity weights encode source reliability, and the resulting Beta posteriors are re-projected into PN form, producing heteroscedastic fragility estimates whose variances reflect data quality and coverage. Stage 2 assimilates these heteroscedastic observations within a probit-warped Gaussian Process (GP), which propagates information from high-fidelity sites to low-fidelity and unobserved regions through a composite kernel that links space, archetypes, and correlated damage states. The framework is applied to the 2011 Joplin tornado, where wind-field priors and computer-vision damage assessments are fused under varying assumptions about tornado width, sampling strategy, and observation completeness. Results show that the method corrects biased priors, propagates information spatially, and produces uncertainty-aware exceedance probabilities that support real-time situational awareness.

翻译：本文通过将基于物理的易损性函数与实时灾后观测数据相统一，弥补了自然灾害建模中长期存在的空白。该研究提出了一种贝叶斯框架，能够随着新数据的出现持续优化区域脆弱性估计。该框架将物理信息驱动的易损性估计重构为概率单位-正态（PN）表示形式，以解析可处理的方式捕捉偶然变异性和认知不确定性。第一阶段通过矩匹配将PN边缘分布拟合为保持其概率形状的Beta代理分布，实现局部贝叶斯更新，从而能够利用软性多保真度观测数据进行共轭Beta-Bernoulli更新。保真度权重编码了数据源的可靠性，所得Beta后验分布被重新投影为PN形式，生成异方差易损性估计，其方差反映了数据质量和覆盖范围。第二阶段在概率单位扭曲高斯过程（GP）中融合这些异方差观测数据，通过连接空间、原型和相关损伤状态的复合核，将信息从高保真度站点传播到低保真度和未观测区域。该框架应用于2011年乔普林龙卷风案例，在龙卷风宽度、采样策略和观测完整性等不同假设下，融合了风场先验信息和计算机视觉损伤评估。结果表明，该方法能够修正有偏先验，实现空间信息传播，并生成支持实时态势感知的不确定性感知超越概率。