Training: Geostatistics for reliable mapping

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The course presents the basic concepts and methods of geostatistics for interpolating between data in environmental applications. The subject matter ranges from variograms to kriging and multivariate geostatistics. The potential pitfalls and hints in the geostatistical analysis of data are highlighted for each of these concepts. Particular attention is paid to the specific problems associated with air pollution.

Event Type:
Workshops/Training Courses
Date:
Nov. 7-9, 2016
Venue:
Geovariances - 49 bis avenue Franklin Roosevelt
Location:
Avon - close to Paris , France

To present the basic concepts and methods of geostatistics for interpolating between data in environmental applications. The subject matter ranges from variograms to kriging and multivariate geostatistics. The potential pitfalls and hints in the geostatistical analysis of data are highlighted for each of these concepts. Particular attention is paid to the specific problems associated with air pollution.

The ability of geostatistics to handle data that are characterised by spatial heterogeneity.

The course is designed for geologists, engineers and other geoscientists requiring a practical introduction to geostatistical methods applied to data analysis, interpolation and simulation. No prior knowledge of geostatistics is required. However a basic understanding of statistics is recommended.

  • Introduction: what is geostatistics? The advantages of using geostatistics over existing methods.
  • The foundation stone of geostatistics: quantifying the spatial behaviour via the variogram. Calculating, modelling and interpreting the directional variograms.
  • Interpolating between the data: the theory and practice of kriging. The estimated values reflect the spatial continuity observed in the data.
  • Multivariate geostatistics and cokriging. Incorporating the correlation between several variables improves the final estimate.
  • Using non-stationary geostatistics to respect the trend present in the data.
  • Integrating multidisciplinary data: incorporating remote sensing data, DTM’s or other co-factors to produce a more realistic image.

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