Kriging is a weighted moving average interpolation (extrapolation) method that minimizes the estimated variance of a predicted point (node) with the weighted average of its neighbors. The weighting factors and the variance are calculated using a semivariogram model that describes the differences versus distance for pairs of samples in the input dataset. Determining an optimal semivariogram model is the important first step in producing a defensible kriged estimate. With other software, the burden of fitting the semivariogram is left to the user. In EVS, this task is automated with an expert system.
EVS's kriging is based on sound mathematical and statistical concepts. The U.S. Environmental Protection Agency (EPA) developed a two-dimensional kriging software package that is called Geo-EAS. Geo-EAS version 1.1 was released in September 1988.
However, EVS is not limited to two-dimensional estimation. In fact every object in EVS is truly three-dimensional and all analysis and visualization techniques can be combined in a single display.
Two Dimensional Kriging
EVS gridding options for two-dimensional estimation include: rectilinear grids with uniform spacing in x & y directions; convex hull bounded gridding; and adaptive gridding that automatically refines gridding in the cell (s) surrounding measured samples to ensure that the interpolated results and contours accurately honor measured sample data.
Three Dimensional Kriging
EVS provides a full spectrum of three-dimensional gridding options. These include: rectilinear grids with uniform spacing in x, y, & z directions; rectilinear grids with uniform spacing in x & y directions with z spacing determined by geologic layers; finite difference type grids with variable spacing in x & y directions and z spacing determined by geologic layers; convex hull bounded gridding with z spacing determined by geologic layers; and adaptive gridding that automatically refines gridding in the cell (s) surrounding measured samples to ensure that the interpolated results and isosurfaces accurately honor measured sample data.
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