Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography.
Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography.
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DOI：
10.3390/jimaging7100212
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发表时间：
2021-10-14
影响因子：
3.2
通讯作者：
Wiaux Y
中科院分区：
文献类型：
--
作者：
Eldaly AK;Fang M;Di Fulvio A;McLaughlin S;Davies ME;Altmann Y;Wiaux Y
在本文中，我们解决的问题，在被动γ发射断层扫描（PGET）的乏核燃料的活性估计。两种不同的噪声模型被认为是和比较，即各向同性高斯和泊松噪声模型。该问题是制定在贝叶斯框架内作为一个线性逆问题和先验分布分配给未知的模型参数。特别是，伯努利截断高斯先验模型被认为是促进稀疏引脚配置。马尔可夫链蒙特卡罗（MCMC）方法，基于分裂和增强吉布斯采样器，然后被用来采样未知参数的后验分布。所提出的算法首先验证使用合成数据进行的模拟，使用标称模型生成。然后，我们考虑更现实的数据模拟使用定制的模拟器，其正演模型是非线性的，无法解析。在这种情况下，所使用的线性模型是错误指定的，我们分析其活性估计的鲁棒性。结果表明，上级性能所提出的方法在估计引脚活动在不同的组装模式，除了能够量化他们的不确定性措施，与现有的方法相比。
In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.