Gridded Magnetic Data Talwani 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 Miller Projection Depth to Moho Data Ontology about the process of creating crustal models of the Earth with the use of Gravity, Magnetic, and Receiver Function data. Gravity Data Contouring Parameter 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 UTM Projection Nearest Neighbor Topography Depth to Moho Contour Map Calc Bouger Anomaly Label Interval Zone Magnetic Contouring Density Contrast 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 Determine Profile Line Projected Magnetic Data Cell Size Gravity Contouring Gridded 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 Albers Projection Projected Data Profile Line Nafe-Drake Gridded Data Projected Gravity Data Interval Bouguer Anomaly Map 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 Contouring Magnetic Data Receiver Function Data Processed Magnetic Data 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 Velocity to Density Km/s -> Kg/m^3. A well known algorithm is that presented by Nafe, J.E. and Drake, C.L.: Variation with depth in shallow and deep water marine sediments of porosity, density and the velocities of compressional and shear waves, Geophysics 22, 523-552 (1957). XYZ Data Min Max Map Projection Determine Depth To Moho 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 Gridding Parameter Contour Map Crustal Model Also referred to as Processed Gravity Data, or Complete Bouguer Anomaly Data Processed Gravity Data 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 Forward Modeling Projection Parameter Inspect Map Magnetic Anomaly Map