Joint Bayesian Inference for Near‐Surface Explosion Yield and Height‐of‐Burst

Abstract

AbstractForensic capabilities to understand chemical and nuclear explosions are greatly aided by an accurate estimate of explosive yield with uncertainty. The relationship between explosive size and geophysical observations of seismic, acoustic, and optical waves can be exploited to provide an estimate of yield. Any near‐surface yield estimate is complicated by the surface interaction, so an estimate for the explosion height‐of‐burst is necessarily included in the relationship. The relationship dictates a trade‐off between estimates of yield and height‐of‐burst. Fortunately, the surface interaction for each type of observation is different, which breaks the trade‐off, and the inclusion of height‐of‐burst with multiple data types improves yield estimation. We define simple parametric forward models to relate seismoäcoustoöptic observations from a data set of known explosive yields and height‐of‐bursts. The parameters of the models and a prediction for the yield and height‐of‐burst of a new event can then be estimated given new observations via Bayesian inference. We report posterior distribution estimates of the parametric models using a Markov chain Monte Carlo sampling technique. These models are then used to predict the yield and height‐of‐burst of SUGAR, a historical near‐surface nuclear explosion, using its reported historical observations. The reported yield of 1.2 ktonne Trinitrotoluene (TNT)‐equivalent (Department of Energy, 2015) is within the estimated posterior. Yield uncertainty can be estimated from the spread of the posterior, which is between 0.9 and 2.1 ktonne TNT‐equivalent. The posterior for height‐of‐burst has a wider range between 10 m below and 8 m above ground that includes the true height‐of‐burst of 1 m.