Current efforts to detect nuclear detonations and correctly categorize explosion sources with ground- and space-collected discriminants presents challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller events (lower yield explosions) often include only sparse observations among few modalities and can therefore lack a complete set of discriminants. The covariance structures can also vary significantly between such observations of event (source-type) categories. Both obstacles are problematic for ``classic'' ECM. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed B-ECM, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment ECM with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates with B-ECM, we compare an array of B-ECM and classic ECM models with multiple performance metrics that leverage Monte Carlo experiments. We use both synthetic and real data. Our B-ECM models show consistent gains in overall accuracy and a lower false negative rates relative to the classic ECM model. We propose future avenues to improve B-ECM that expand its decision-making and predictive capability.

翻译：当前利用地面和空间收集的判别因子探测核爆炸并正确分类爆炸源的工作，面临着传统事件分类矩阵（ECM）模型尚未解决的挑战。较小当量爆炸事件往往仅包含少数观测模态的稀疏数据，因此可能缺乏完整的判别因子集。不同事件类别（源类型）观测数据的协方差结构也可能存在显著差异。这两大障碍对"经典"ECM模型构成了难题。本研究通过提出经典ECM模型的贝叶斯改进版本（称为B-ECM）来填补这一空白，该模型能够基于部分观测数据进行训练，且不依赖于合并协方差结构。我们进一步将贝叶斯决策理论融入ECM框架，从而以直观方式降低事件分类的假阴性或假阳性率。为验证B-ECM在分类准确率上的提升，我们通过蒙特卡洛实验采用多维度性能指标，系统比较了B-ECM与经典ECM模型系列。实验同时使用了合成数据与真实数据。结果表明，相较于经典ECM模型，B-ECM模型在整体准确率上持续提升，同时保持更低的假阴性率。最后，我们提出了扩展B-ECM决策与预测能力的未来改进方向。