# Radiological contamination characterization with multivariate geostatistics and sampling optimization| Training course

Learn how to reduce map uncertainty by adding extra points or by using a
a multivariate geostatistical approach to integrating auxiliary data.

## Objectives

• Understand and implement multivariate geostatistics to reduce estimation uncertainty by taking advantage of the correlation between in situ measurements
(count or dose rate) and activity concentration or between easy-to-measure nuclides
and hard-to-detect nuclides.
• Make the best use of all available data, quantitative and/or semi-quantitative.
• Perform coherent and simultaneous characterizations for multiple contaminations on the same site.

## Outlines

• Half of the course is devoted to methodological presentations, the second half to practical exercises on real-life cases to deepen the understanding of concepts. Focus is on illustrations and practical contribution of the covered concepts.
• Computer exercises with Kartotrak.
• Course material provided.

## Who should attend

Engineers, technicians, consultancies, project owners, prime contractors, public bodies, industrial operators who wish to go further with geostatistics.

## Course content

Part 1: Map the contamination taking auxiliary data into account

• Analyze the correlations between the different types of available measurements, quantitative and semi-quantitative: count or dose rate, other nuclides, DEM, soil occupation, lithology, etc.
• Highlight the spatial relationships between variables: cross-variogram calculation and modeling.
• Integrate one or several secondary variables in the interpolation: find out more about the co-kriging and co-simulation principles and implement the methodologies.
• Compare the added value of the multivariate estimates to the univariate estimates.

Part 2: Optimize the density and location of sampling points

• Design the initial sampling plan: random, systematic, circular, judgmental.
• Compute the probability to reach a hot spot according to the sampling mesh and the expected contamination size.
• Optimize the number and location of additional data points to improve the initial characterization.
• Reduce false-negative risks for better waste classification.

To have attended the course 2D mapping of radiological contaminations using geostatistics or have good basic knowledge in geostatistics (variography, kriging).