Contamination characterization with multivariate geostatistics and sampling optimization

Learn how to reduce map uncertainty by adding extra points or by using 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).


Registered professional members of AusIMM are eligible to claim 8 PD Hours when attending this short course IN-PERSON.
Registered professional members of AusIMM are eligible to claim 8 PD Hours when attending this short course IN-PERSON.

 

Registered professional members of AusIMM are eligible to claim 6 PD Hours when attending this short course ONLINE.
Registered professional members of AusIMM are eligible to claim 6 PD Hours when attending this short course ONLINE.

Prerequisites

CONTACT US

training@geovariances.com
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