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Gridding Methods

Different gridding methods provide different interpretations of your data because each method calculates grid node values using a different algorithm. If you are not satisfied with the map of your data, you might consider producing grid files using several different gridding methods and comparing the results.

Where maps are created from gridded data, the original data are not necessarily honored in the grid file. When you post the original data points on a contour map, some of the contour lines might be positioned "wrong" relative to the original data. This happens because the location of the contour lines are determined solely by the interpolated grid node values and not directly by the original data. Some methods are better than others in preserving your data, and sometimes some experimentation is necessary before you can determine the best method for your data.

There are a number of different gridding algorithms, for example Inverse Distance to a Power, Kriging, Minimum Curvature, Modified Shepard's Method, Natural Neighbor, Nearest Neighbor, Polynomial Regression, Radial Basis Function, Triangulation with Linear Interpolation, and so on.

I have found the range of methods available in Surfer to be very impressive, allowing far more flexibility than many other (more expensive) gridding & contouring packages.