The gridding methods in Surfer allow you to produce accurate contour, surface, wireframe, vector, image, and shaded relief maps from your XYZ data. The data can be randomly dispersed over the map area, and Surfer's gridding will interpolate your data onto a grid. Use Surfer’s default settings or choose from twelve different gridding methods. Each gridding method provides complete control over the gridding parameters, so you can produce exactly the map you want. If your data are already collected in a regularly spaced rectangular array, you can create a map directly from your data. Computer generated contour maps have never been more accurate.

• Interpolate from up to 1 billion XYZ data points (limited by available memory)
• Produce grids with up to 100 million nodes
• Specify faults and breaklines when gridding
• Choose from one of the powerful gridding methods: Inverse Distance, Kriging, Minimum Curvature, Polynomial Regression, Triangulation, Nearest Neighbor, Shepard's Method, Radial Basis Functions, Natural Neighbor, Moving Average, and Local Polynomial
• Specify isotropic or anisotropic weighting
• You have full control over the grid line geometry including grid limits, grid spacing, and number of grid lines
• Customize search options based on user-defined data sector parameters
• Specify search ellipses at any orientation and scaling
• Use spline smoothing and grid filtering to alter the grid file
• Use grid math to perform mathematic operations between grid files
• Use Nearest Neighbor to create grid files without interpolation
• Use Triangulation to achieve accuracy with large data sets faster
• Detrend a surface using Polynomial Regression, generate regression coefficients in a report, and calculate residuals
• Use data exclusion filters to eliminate unwanted data
• Use duplicate data resolution techniques
• Generate a grid of Kriging standard deviations
• Specify point or block Kriging
• Generate a report of the gridding statistics and parameters including ANOVA regression statistics
• Specify scales and range for each variogram model
• Generate grids from a user-specified function of two variables
• Calculate grids with Data Metrics including: number of points within search ellipse, distance to nearest and farthest neighbor, median, average and offset distance to points within the search ellipse
• Use cross-validation to judge the suitability of the gridding method for the particular data set