异常放射性氙浓度的检测：一种无分布的方法

IF 1.5 3区环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Environmetrics Pub Date : 2023-04-24 DOI:10.1002/env.2804

Michele Scagliarini, Rosanna Gualdi, Giuseppe Ottaviano, Antonietta Rizzo

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引用次数: 0

摘要

异常大气放射性氙浓度的探测在探测地下核爆炸以及核电站和医用同位素生产设施的放射性排放方面发挥着关键作用。为此，禁核试组织的国际数据中心采用了一种基于描述性阈值的程序。为了用基于统计推断的方法补充这一过程，我们比较了几种非参数变化点控制图，用于检测自然放射性氙背景以上的偏移。结果表明，所提出的方法可以为负责异常放射性氙浓度验证和分类的机构提供有价值的工具。

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Detection of anomalous radioxenon concentrations: A distribution-free approach

The detection of anomalous atmospheric radioxenon concentrations plays a key role in detecting both underground nuclear explosions and radioactive emissions from nuclear power plants and medical isotope production facilities. For this purpose, the CTBTO's International Data Centre uses a procedure based on descriptive thresholds. In order to supplement this procedure with a statistical inference-based method, we compared several non-parametric change-point control charts for detecting shifts above the natural radioxenon background. The results indicate that the proposed methods can provide valuable tools for the institutions responsible for the verification and classification of anomalous radioxenon concentrations.

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来源期刊

Environmetrics 环境科学-环境科学

CiteScore

2.90

自引率

17.60%

发文量

审稿时长

18-36 weeks

期刊介绍： Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.

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