Improve chemical pollution or radiological contamination characterization with advanced geostatistics
Understand and implement multivariate geostatistics to reduce estimation uncertainty by taking advantage of the correlation between pollutants/contaminants.
DURATION: 1 DAY | LEVEL: ADVANCED
- 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.
- Size the investigation campaigns according to the remediation objective and the expected level of confidence.
- 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 (licence Premium only).
- 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
- Analyse the correlations between the different types of available measurements, quantitiative and semi-quantitative: other pollutants, DEM, soil occupation, physico-chemical models, indirect indices of pollution, lithology, etc.: calculation of scatter plots, 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.
- Analyse 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.