Contamination characterization with multivariate geostatistics and sampling optimization

Learn how to integrate secondary pollutants or contaminants and semi-quantitative measures in the mapping of the pollutant of interest to reduce map uncertainty using a multivariate geostatistical approach.

Objectives

  • Understand and implement multivariate geostatistics to reduce estimation uncertainty by taking advantage of the correlation between pollutants/contaminants.
  • Make best use of all available data, quantitative and/or semi-quantitative.
  • Perform coherent and simultaneous characterizations for several 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 pollution/contamination of interest taking into account other pollutants/secondary variables

  • Analyze the correlations between the different types of available measurements, quantitative and semi-quantitative: other pollutants, DEM, soil occupation, physicochemical models, indirect indices of pollution, lithology, etc.: calculation of scatter plots and coefficients of correlation.
  • Highlight the spatial relationships between pollutants: multivariate variogram calculation and modeling.
  • Integrate one or several secondary variables in the interpolation: find out more about the co-kriging principles and implement the methodology.
  • Analyze the inputs of cokriging compared to kriging.

Part 2: Optimize the density and location of sampling points

  • Design the initial sampling plan.
  • Compute the probability to reach a hot spot according to the sample size and the studied contamination size.
  • Optimize the number and location of new sampling spots to improve the initial characterization.
  • Reduce false-negative risks.

Prerequisites

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

CONTACT US

training@geovariances.com
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