For example, you can create a grid where the Z values for the grid nodes is the average Z value for all points in the search area, the number of points in the search, or the slope of the samples within the search. Data Metrics is used to create grids of information about the data.This gridding method is a reasonable alternative to Nearest Neighbor for generating grids from large, regularly spaced data sets. Moving Average extracts intermediate-scale trends and variations from large, noisy data sets, and it is fast even for very large data sets. Moving Average is most applicable to large and very large data sets (e.g.Triangulation with Linear Interpolation does not extrapolate Z values beyond the range of data. When you use small data sets, Triangulation with Linear Interpolation generates distinct triangular faces between data points. Triangulation with Linear Interpolation is fast.Modified Shepard's Method can extrapolate values beyond your data's Z range. Modified Shepard's Method is similar to Inverse Distance to a Power but does not tend to generate "bull's eye" patterns, especially when a smoothing factor is used.This method produces a result quite similar to Kriging. It compares to Kriging since it generates the best overall interpretations of most data sets. Radial Basis Function is quite flexible.Then they subtract the Polynomial Regression grid from the Kriging grid and create a map of the resulting grid. They’ll grid their data with Kriging, and then again with Polynomial Regression to get the trend. EXAMPLE: Many users use this gridding method in conjunction with Kriging to either produce 1st, 2nd or 3rd order residual maps, or remove a trend from their data.This method can extrapolate grid values beyond your data's Z range. Polynomial Regression is very fast for any amount of data, but local details in the data are lost in the generated grid. Polynomial Regression processes the data so that underlying large-scale trends and patterns are shown.Nearest Neighbor does not extrapolate Z grid values beyond the range of data. When your observations lie on a nearly complete grid with few missing holes, this method is useful for filling in the holes, or creating a grid file with the NoData value assigned to those locations where no data is present. Nearest Neighbor is useful for converting regularly spaced (or almost regularly spaced) XYZ data files to grid files.Natural Neighbor does not extrapolate Z grid values beyond the range of data. It does not generate data in areas without data. Natural Neighbor generates good contours from data sets containing dense data in some areas and sparse data in other areas.Minimum Curvature can extrapolate values beyond your data's Z range. The internal tension and boundary tension allow you control over the amount of smoothing. Minimum Curvature generates smooth surfaces and is fast for most data sets but it can create high magnitude artifacts in areas of no data.Co-kriging can extrapolate grid values beyond your data's Z range. This method requires a good understanding of your data to produce effective results. For larger data sets, Co-kriging can be rather slow. This method required three variograms to be modelled: for the primary dataset, the secondary dataset, and the cross-variogram. Co-Kriging If you have two correlated datasets, and you wish to use the relationship between those two datasets to increase the accuracy of interpolation for one of the datsets, then grid your data using Cokriging.Kriging can extrapolate grid values beyond your data's Z range. For larger data sets, Kriging can be rather slow. Kriging is the default gridding method because it generates a good map for most data sets. In general, we would most often recommend this method. With most data sets, Kriging with the default linear variogram is quite effective. Kriging is one of the more flexible methods and is useful for gridding almost any type of data set.Inverse Distance to a Power does not extrapolate Z values beyond the range of data. Inverse Distance to a Power is fast but has the tendency to generate "bull's-eye" patterns of concentric contours around the data points.This page also provides a general description of the type of data distribution that each gridding method was designed to work best with. In addition to the information below, the General Gridding Recommendations page in the Help provides descriptions for each of the gridding methods. Smoothing Factor, Quadratic Neighbors, Weighting Neighbors Maximum Residual, Maximum Iteration, Relaxation Factor, Internal Tension, Boundary Tension Variogram, Standard Deviations Grid, Estimation Method, Cokriging Type Variogram, Standard Deviations Grid, Kriging Type, Drift Type Surfer offers 13 different gridding methods to choose from.
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