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 backgro...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.展开更多
【目的】在“双碳”战略目标下,中国天然气消费需求正快速增长,但天然气具有易燃易爆性,一旦天然气管道发生泄漏事故,易造成人员伤亡、环境污染以及经济损失等,天然气管道泄漏检测的研究显得尤为重要。【方法】以高斯烟羽模型与加装甲...【目的】在“双碳”战略目标下,中国天然气消费需求正快速增长,但天然气具有易燃易爆性,一旦天然气管道发生泄漏事故,易造成人员伤亡、环境污染以及经济损失等,天然气管道泄漏检测的研究显得尤为重要。【方法】以高斯烟羽模型与加装甲烷浓度传感器的无人机为基础,采用基于贝叶斯推理的马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)法获取天然气管道泄漏源的泄漏位置、泄漏速率;采用基于概率统计的气体源强反算方法,计算概率最高的泄漏参数区间。利用设置的天然气架空管道连续泄漏事故场景进行气体泄漏模拟,验证MCMC算法确定天然气管道泄漏源的有效性。【结果】MCMC算法通过计算得到天然气管道泄漏位置和泄漏速率,总误差的增大使得MCMC算法的成功率降低,但数据清洗会增强算法误差适应性,未经过数据处理的算法成功率则逐渐降低,而经过数据清洗的算法成功率超过90%;将危险气体源强反算的思想应用于天然气管道泄漏检测中,有助于更加准确地获得管道泄漏位置与泄漏速率;初始点远离真实泄漏源会降低MCMC算法的性能,因此合理地选择初始点有利于算法的运行。【结论】基于MCMC算法与加装甲烷浓度传感器的无人机相结合的检测方法,可同时确定天然气管道的泄漏位置与泄漏速率,对泄漏事故发生后的应急处理具有重要意义。(图7,表5,参24)展开更多
文摘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.
文摘【目的】在“双碳”战略目标下,中国天然气消费需求正快速增长,但天然气具有易燃易爆性,一旦天然气管道发生泄漏事故,易造成人员伤亡、环境污染以及经济损失等,天然气管道泄漏检测的研究显得尤为重要。【方法】以高斯烟羽模型与加装甲烷浓度传感器的无人机为基础,采用基于贝叶斯推理的马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)法获取天然气管道泄漏源的泄漏位置、泄漏速率;采用基于概率统计的气体源强反算方法,计算概率最高的泄漏参数区间。利用设置的天然气架空管道连续泄漏事故场景进行气体泄漏模拟,验证MCMC算法确定天然气管道泄漏源的有效性。【结果】MCMC算法通过计算得到天然气管道泄漏位置和泄漏速率,总误差的增大使得MCMC算法的成功率降低,但数据清洗会增强算法误差适应性,未经过数据处理的算法成功率则逐渐降低,而经过数据清洗的算法成功率超过90%;将危险气体源强反算的思想应用于天然气管道泄漏检测中,有助于更加准确地获得管道泄漏位置与泄漏速率;初始点远离真实泄漏源会降低MCMC算法的性能,因此合理地选择初始点有利于算法的运行。【结论】基于MCMC算法与加装甲烷浓度传感器的无人机相结合的检测方法,可同时确定天然气管道的泄漏位置与泄漏速率,对泄漏事故发生后的应急处理具有重要意义。(图7,表5,参24)
