Gridded Data Radial Basis Functions It is quite flexible, and like Kriging, generates among the best overall interpretations of most data sets. This method produces results that are quite similar to Kriging. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Min Max Projection Parameter Determine Profile Line Also referred to as Processed Gravity Data, or Complete Bouguer Anomaly Data Processed Gravity Data Gridding Gridding produces a regularly spaced array of Z values from irregularly spaced XYZ data. Contour maps and Surface plots require the regular distribution of data points in grid [.GRD] files. The term "irregularly spaced" implies that the points are randomly distributed over the extent of the map area meaning that the distance between data points is not consistent over the map. When the XYZ data is randomly spaced over the map area, there are many "holes" in the distribution of data points. Gridding fills in the holes by extrapolating or interpolating Z values in those locations where no data exists. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Contouring Parameter Miller Projection Map Projection Label Interval Depth to Moho Contour Map Polynomial Regression It processes the data so that underlying large scale trends and patterns are shown. This is used for trend surface analysis. Polynomial Regression is very fast for ny amount of data, but local details in the data are lost in the generated grid. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Interval Processed Magnetic Data Receiver Function Data Talwani Ontology about the process of creating crustal models of the Earth with the use of Gravity, Magnetic, and Receiver Function data. Kriging It is one of the more flexible methods and is useful for gridding almost any type of data set. With most data sets, Kriging with a linear variogram is quite effective. In general this is the method that we would most often recommend. Kriging is the default gridding method because it generates the best overall interpretation of most data sets. For larger data sets, however, Kriging can be rather slow. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Gridded Gravity Data Projected Gravity Data UTM Projection Magnetic Data Profile Line Determine Depth To Moho Cell Size Inverse Distance It is fast but has the tendency to generate "bull's-eye" patterns of concentric contours around the data points. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Minimum Curvature It generates smooth surfaces and is fast for most data sets. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Crustal Model Projected Data Projected Magnetic Data Albers Projection Shepards Method It is similar to Inverse Distance but does not tend to generate "bull's eye" patterns, especially when a Smoothing factor is used. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Forward Modeling XYZ Data Bouguer Anomaly Map Zone Depth to Moho Data Nearest Neighbor Contour Map Inspect Map Topography Gravity Contouring Contouring Gridding Parameter Magnetic Anomaly Map Magnetic Contouring Gravity Data Triangulation with Linear Interpolation It is fast with all data sets. When you use small data sets Triangulation generates distinct triangular facets between data points. One advantage of triangulation is that, with enough data, triangulation can preserve break lines defined in a data file. For example, if a fault is delimited by enough data points on both sides of the fault line, the grid generated by triangulation will show the discontinuity. SRC: www.seismo.unr.edu/ftp/pub/louie/class/333/contour/surfer.html Gridded Magnetic Data