Geostatistical sampling optimization of contaminated facilities

Abstract

Geostatistics applied to radiological evaluation of nuclear premises provides methods to estimate radiological activities, together with their associated uncertainty. It enables a sophisticated sampling methodology combining radiation map and destructive samples. The radiological assessment is divided in two steps: first, a regular control of the surface activity is performed. Then, to assess the true contamination, concrete samples are collected and analyses are performed at several locations within the premises. These two types of measurement are first dealt separately, then cokriging techniques are applied to estimate the contamination over the premise, taking both information into account. This paper presents a methodological study of geostatistical and computational approaches to target suitable areas for additional radiological measures. In order to compare the proposed augmented sampling designs, several optimization criteria are taken into account. Their diversity ensures the coverage of a wide range of real problematics. Two algorithms (greedy algorithm and simulated annealing) are developed to optimize the chosen criterion value as a function of the location of the additional points. The sampling scenarios obtained with the different algorithms are compared in terms of optimization performance and computational efficiency.