具有信任意识的多模态数据融合用于产量估计：贝鲁特2020爆炸的案例研究
Trust-Aware Multimodal Data Fusion for Yield Estimation: A Case Study of the 2020 Beirut Explosion
AI总结：
本文提出一种基于贝叶斯分数后验框架的多模态数据融合方法，用于估计爆炸产量，通过信任权重校准不同观测数据，提升不确定性量化和抗偏差能力。
摘要：
从异质观测数据估计爆炸产量在反问题中提出了根本性的挑战，尤其是在将传统物理测量与现代人工智能解释的模态相结合时。我们提出了一种新颖的贝叶斯分数后验框架，融合地震波、陨石坑尺寸、合成孔径雷达图像和由视觉-语言模型解释的地面图像，以估计2020年贝鲁特爆炸的产量。与传统方法可能平等对待数据源不同，我们的方法通过狄利克雷先验学习每个模态的信任权重，自动校准不同观测的相对信息含量。应用于贝鲁特爆炸，该框架得出0.34-0.48千吨TNT当量的估计值，代表相对于爆炸储存的硝酸铵理论最大值2.75千吨的12至17%的爆炸效率。分数后验方法在不确定性量化方面优于单模态估计，同时提供对系统偏差的鲁棒性。这项工作建立了一个将定性评估与定量物理测量相结合的原理性框架，应用于爆炸监测、灾难响应和法医分析。
英文摘要：
The estimation of explosive yield from heterogeneous observational data presents fundamental challenges in inverse problems, particularly when combining traditional physical measurements with modern artificial intelligence-interpreted modalities. We present a novel Bayesian fractional posterior framework that fuses seismic waves, crater dimensions, synthetic aperture radar imagery, and vision-language model interpreted ground-level images to estimate the yield of the 2020 Beirut explosion. Unlike conventional approaches that may treat data sources equally, our method learns trust weights for each modality through a Dirichlet prior, automatically calibrating the relative information content of disparate observations. Applied to the Beirut explosion, the framework yields an estimate of 0.34--0.48 kt TNT equivalent, representing 12 to 17 percent detonation efficiency relative to the 2.75 kt theoretical maximum from the blast's stored ammonium nitrate. The fractional posterior approach demonstrates superior uncertainty quantification compared to single-modality estimates while providing robustness against systematic biases. This work establishes a principled framework for integrating qualitative assessments with quantitative physical measurements, with applications to explosion monitoring, disaster response, and forensic analysis.
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