Trust-Aware Multimodal Data Fusion for Yield Estimation: A Case Study of the 2020 Beirut Explosion

TL;DR

This paper introduces a Bayesian fractional posterior framework that fuses diverse observational data sources to accurately estimate explosive yield, demonstrating improved uncertainty quantification and robustness in the case of the Beirut explosion.

Contribution

The novel Bayesian fractional posterior method automatically calibrates trust in heterogeneous data sources for yield estimation, integrating physical and AI-interpreted modalities.

Findings

Estimated Beirut explosion yield: 0.34--0.48 kt TNT.

Framework outperforms single-modality estimates in uncertainty quantification.

Provides robust estimates against systematic biases.

Abstract

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.