Geostatistical interpolation of chemical concentration |
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Authors: | Peter K. Kitanidis Kuo-Fen Shen |
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Affiliation: | Civil Engineering, Stanford University, Stanford, California 94305-4020, USA |
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Abstract: | ![]() Measurements of contaminant concentration at a hazardous waste site typically vary over many orders of magnitude and have highly skewed distributions. This work presents a practical methodology for the estimation of solute concentration contour maps and volume averages (needed for mass calculations) from data obtained from the analysis of water and soil samples. The methodology, which is an extension of linear geostatistics, produces a point estimate, i.e., a representative value, as well as a confidence interval, which contains the true value with a given probability. The approach uses a parsimonious model that accounts for the skewness by adding only one parameter to those used in linear geostatistics (variograms or generalized covariances). The resulting nonlinear kriging method is not substantially more difficult to use than linear geostatistics. The methodology is most appropriate when concentration measurements are available on a reasonably dense grid and no additional information (based on modeling flow and transport) can be used. We present and illustrate through an application, a practical approach to estimate all the parameters needed and to select and test the model. |
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Keywords: | geostatistics best linear unbiased estimation kriging parameter estimation maximum likelihood restricted maximum likelihood transformations non-Gaussian solute concentration |
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