Eslinger Paul W, Lowrey Justin D, Miley Harry S, Rosenthal W Steven, Schrom Brian T
Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA.
J Environ Radioact. 2019 Aug;204:111-116. doi: 10.1016/j.jenvrad.2019.04.004. Epub 2019 Apr 17.
Algorithms that estimate the location and magnitude of an atmospheric release using remotely sampled air concentrations typically involve a single chemical or radioactive isotope. A new Bayesian algorithm is presented that makes discrimination between possible types of releases (e.g., nuclear explosion, nuclear power plant, or medical isotope production facility) an integral part of the analysis for samples that contain multiple isotopes. Algorithm performance is demonstrated using synthetic data and correctly discriminated between most release-type hypotheses, with higher accuracy when data are available on three or more isotopes.
使用远程采样空气浓度来估计大气释放位置和规模的算法通常涉及单一化学物质或放射性同位素。本文提出了一种新的贝叶斯算法，该算法将对可能的释放类型（例如核爆炸、核电站或医用同位素生产设施）进行区分作为包含多种同位素样本分析的一个组成部分。使用合成数据展示了算法性能，该算法能正确区分大多数释放类型假设，当有三种或更多同位素的数据时，准确率更高。
