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