In decommissioning projects of nuclear facilities, radiological characterisation aims to estimate the quantity and spatial distribution of different radionuclides. To carry out the estimation, measurements are performed on site to obtain preliminary information. The usual industrial practice consists of applying spatial interpolation tools (as the ordinary kriging method) on these data to predict the value of interest for the contamination (radionuclide concentration, radioactivity, etc.) at unobserved positions. This paper questions the ordinary kriging tool on the well-known problem of the overoptimistic prediction variances due to not taking into account uncertainties on the estimation of the kriging parameters (variance and range). To overcome this issue, the practical use of the Bayesian kriging method, where the model parameters are considered as random variables, is deepened. The usefulness of Bayesian kriging, whilst comparing its performance to that of ordinary kriging, is demonstrated in the small data context (which is often the case in decommissioning projects). This result is obtained via several numerical tests on different toy models and using complementary validation criteria: the predictivity coefficient (\(Q^2\)), the predictive variance adequacy, the \(\alpha \) confidence interval plot (and its associated mean squared error \(\alpha \)), and the predictive interval adequacy. The latter is a new criterion adapted to Bayesian kriging results. Finally, the same comparison is performed on a real data set coming from the decommissioning project of the CEA Marcoule G3 reactor. It illustrates the practical interest of Bayesian kriging in industrial radiological characterisation.

在核设施退役项目中，放射性表征旨在估计不同放射性核素的数量和空间分布。为了进行估计，需要在现场进行测量以获得初步信息。通常的工业实践包括对这些数据应用空间插值工具（如普通克里金法）来预测未观测位置的污染（放射性核素浓度、放射性等）的感兴趣值。本文对普通克里金工具因未考虑克里金参数（方差和范围）估计的不确定性而导致预测方差过于乐观这一众所周知的问题提出了质疑。为了克服这个问题，贝叶斯克里金方法的实际使用，其中模型参数被视为随机变量，是深化的。贝叶斯克里金法的实用性，同时将其性能与普通克里金法的性能进行比较，在小数据环境中得到了证明（退役项目中经常出现这种情况）。该结果是通过对不同玩具模型进行多次数值测试并使用补充验证标准获得的：预测系数（\(Q^2\)）、预测方差充分性、\(\alpha \)置信区间图（及其相关的均方误差\(\alpha \)）和预测区间充分性。后者是适应贝叶斯克里金结果的新准则。最后，对来自 CEA Marcoule G3 反应堆退役项目的真实数据集进行了相同的比较。它说明了贝叶斯克里金法在工业放射学表征中的实际意义。