Utilizing Bayesian Modeling and MCMC for Accurate Characterization of Naturally Occurring Radionuclides Reference Background Levels in Mining Areas  

作　　者：Djicknack Dione Papa Macoumba Faye Nogaye Ndiaye Moussa Hamady Sy Oumar Ndiaye Alassane Traoré Ababacar Sadikhe Ndao Djicknack Dione;Papa Macoumba Faye;Nogaye Ndiaye;Moussa Hamady Sy;Oumar Ndiaye;Alassane Traoré;Ababacar Sadikhe Ndao(Institute Technologies of Nuclear Applied, Cheikh Anta Diop University of Dakar, Dakar, Senegal;Department of Physics, Faculty of Sciences and Techniques, Cheikh Anta Diop University of Dakar, Dakar, Senegal)

机构地区：[1]Institute Technologies of Nuclear Applied, Cheikh Anta Diop University of Dakar, Dakar, Senegal [2]Department of Physics, Faculty of Sciences and Techniques, Cheikh Anta Diop University of Dakar, Dakar, Senegal

出　　处：《World Journal of Nuclear Science and Technology》2024年第4期179-187,共9页核科学与技术国际期刊（英文）

摘　　要：Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.

关 键 词：Radionuclides Bayesian Modeling MCMC HDI 40K 232Th 238U 

分 类 号：O57[理学—粒子物理与原子核物理]