Contamination characterization with multivariate geostatistics and sampling optimization| Training course
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.
- Understand and implement multivariate geostatistics to reduce estimation uncertainty by taking advantage of the correlation between pollutants/contaminants.
- Make the best use of all available data, quantitative and/or semi-quantitative.
- Perform coherent and simultaneous characterizations for several contaminations on the same site.
- 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.
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.