A Non-Homogeneous Model for Kriging Dosimetric Data

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.