Session: 02-03 Modeling, Work Management and Data Analysis Software

Paper Number: 109666

109666 - The Relative Importance of Destructive Analyses for Radiological Characterization With Geostatistics 

Dismantling and decommissioning of nuclear facilities or remediation of contaminated sites are industrial projects with huge challenges. Precise knowledge of the contamination state is required. Radiological evaluations have multiple objectives to be considered: determination of average activity levels, to allow the categorization of surfaces or volumes (sorted into different radioactive waste categories); location of hot spots (small areas with significant activity levels); and estimation of the source term (total activity) contained in soils or building structures. In addition, there are radiation protection and other logistics considerations. Geostatistics quantifications of local and global uncertainties are powerful decision-making tools for better management of remediation projects at contaminated sites, and for decontamination and dismantling projects at nuclear facilities.

The characterization phase should be efficient, and the sampling strategy has to be rational. However, investigations also represent capital expenditure; the cost of radiation protection constraints and laboratory analysis can represent a large amount of money, depending on the radionuclide. Therefore, the entire sampling strategy should be optimized to reduce useless samples and unnecessary measures.

This paper deals with feedback experience over years in the use of geostatistics about the smart use of the variogram to explore spatial data, to break down variance contributions and to model radiological contaminations. Before performing any modelling and estimation calculation, the first and main part of any geostatistical study is to intensively work on the dataset, to explore and validate it. In addition to classical statistics tools such as basemap, histogram and scatter plot, the variogram strengthens this analysis by the identification of spatial inconsistencies, by the decomposition of the different variability contributions (between sample duplicates, measurement replicates and spatial variability) and consequently by the interpretation and modelling of the joint spatial structure with in situ measurements to improve estimates and reduce uncertainties. Then contaminated volume classification proves to be quite robust even with significant bias on lab data, as it results from a non-linear operation (probability of exceeding a threshold). The waste end-state perspective significantly modify classic statistics approaches as radiological data distributions are generally very skewed. Appropriate data processing tools need to be used accordingly.

Presenting Author: Yvon Desnoyers Geovariances

Presenting Author Biography: Yvon Desnoyers now has 15 years of experience in consulting, mentoring, and training as a geostatistician expert at Geovariances. His main proficiency fields are sampling optimization and radiological characterization of nuclear facilities and contaminated sites to drive decommissioning & dismantling, and remediation projects. In addition, Yvon is actively involved in numerous working groups and R&D projects (France, Europe, and international organizations).