This paper deals with kriging-based interpolation of dosimetric data. Such data typically show some inhomogeneities that are difficult to take into account by means of the usual non-stationary models available in geostatistics. By including the knowledge of suspected potential sources (such as pipes or reservoirs) better estimates can be obtained, even when no hard data are available on these sources. The proposed method decomposes the measured dose rates into a diffuse homogeneous term and the contribution from the sources. Accordingly, two random function models are introduced, the first associated with the diffuse term, and the second with the unknown and unmeasured source contribution. Estimation of the model parameters is based on cross-validation quadratic error. As a bonus, the resulting model makes it possible to estimate the source activity. The efficiency of this approach is demonstrated on data simulated according to the physical equations of the system. The method is available to researchers through an R-package provided by the authors upon request.

本文讨论了基于克里格法的剂量学数据插值。这样的数据通常显示出一些不均匀性，这些很难通过地统计学中常用的非平稳模型加以考虑。通过包括可疑潜在来源（例如管道或水库）的知识，即使在这些来源上没有可用的硬数据时，也可以获得更好的估计。所提出的方法将测得的剂量率分解成一个弥散的均值项，并从源中分解出来。因此，引入了两个随机函数模型，第一个与扩散项相关联，第二个与未知和未测源贡献相关。模型参数的估计基于交叉验证的二次误差。作为奖励，结果模型使估算源活动成为可能。根据系统的物理方程式在模拟的数据上证明了这种方法的效率。研究人员可以根据要求通过作者提供的R包使用该方法。