Introduction to Gridding Methods
Grid method parameters control the interpolation procedures. When you create a grid file, you can usually accept the default gridding method and produce an acceptable map. The differences between gridding methods are in the mathematical algorithms used to compute the weights during grid node interpolation. Each method results in a different representation of your data. It is advantageous to test each method with a typical data set to determine the gridding method that provides you with the most satisfying interpretation of your data.
Because Surfer 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 locations 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 (i.e. increasing grid density) is necessary before you can determine the best method for your data.
The gridding method is selected in the Grid Data dialog. The Grid Data dialog is accessed through the Grids | New Grid | Grid Data command.
Gridding methods include:
- Inverse Distance to a Power
- Minimum Curvature
- Modified Shepard's Method
- Natural Neighbor
- Nearest Neighbor
- Polynomial Regression
- Radial Basis Function
- Triangulation with Linear Interpolation
- Moving Average
- Data Metrics
- Local Polynomial
The General Gridding Recommendations give a quick overview of each gridding method with some advantages and disadvantages of each.
Choosing Methods Based on the Number of XYZ Data Points
Creating a Grid File from an XYZ Data File
Exact and Smoothing Interpolators
General Gridding Recommendation
Producing a Grid File from a Regular Array of Z Values