A Bayesian source-term algorithm recently published by Eslinger et al. (2019) extended previous models by including the ability to discriminate between classes of releases such as nuclear explosions, nuclear power plants, or medical isotope production facilities when multiple isotopes are measured. Using 20 release cases from a synthetic data set previously published by Haas et al. (2017), algorithm performance was demonstrated on the transport scale (400–1000 km) associated with the radionuclide samplers in the International Monitoring System. Inclusion of multiple isotopes improves release location and release time estimates over analyses using only a single isotope. The ability to discriminate between classes of releases does not depend on the accuracy of the location or time of release estimates. For some combinations of isotopes, the ability to confidently discriminate between classes of releases requires only a few samples.

Eslinger等人最近发表的一种贝叶斯源项算法。（2019）扩展了以前的模型，包括当测量多种同位素时能够区分核爆炸，核电厂或医学同位素生产设施等排放类别的能力。使用先前由Haas等人发布的合成数据集中的20个释放案例。（2017年），在国际监测系统中与放射性核素采样器相关的运输规模（400-1000公里）上证明了算法的性能。与仅使用单个同位素的分析相比，包含多个同位素可改善释放位置和释放时间估计。区分发布类别的能力不取决于发布估计的位置或时间的准确性。对于同位素的某些组合，