An overview of geostatistics for radiological characterization| Training course
Find out why the geostatistical approach is relevant for radiological characterization, what its added value and application conditions are.
This one-day seminar presents the added value of geostatistics for radiological characterization of nuclear facilities under decommissioning and contaminated sites, taking into account site history, sampling optimization, in-situ radiation mapping, and contaminated volume classification.
Methodological exposition illustrated in the field of site clean-up/dismantling on numerous cases of application. Added-value of the geostatistical methodology as well as its limits of use. Feedback and discussions.
Who should attend?
This course is aimed at project managers and decision-makers involved in decommissioning and dismantling projects who want a practical, synthetic, and pragmatic introduction to geostatistical methods for radiological characterization.
Understanding and estimating the spatial heterogeneity of contamination
- Pros/Cons and underlying assumptions of the usual approaches for radiological characterization
- A practical introduction to the concepts of heterogeneity and spatial variability of pollutants (variogram)
- Operational consequences on the feasibility of given remediation techniques.
Predicting and mapping radiological contamination
- Introduction to kriging and its advantages taking into account spatial variability, best estimation, quantification of associated uncertainty
- Consideration of ancillary information: site history, qualitative observations, and on-site measurements.
Quantify and locate contaminated volumes
- A practical introduction to stochastic simulations
- Presentation of results: global estimation of contaminated volumes and attached uncertainty, local risk of exceeding cleanup levels.
Recommendations for the optimization of site investigation plans
- Implementation of the triptych: Evaluation objective – Sampling strategy – Data processing
- Feedback on the spatial structures of radiological contamination
- Impact on uncertainties with the degradation of sampling or multivariate treatment