Subsections:

The SIM2D procedure uses an LU decomposition technique to produce a spatial simulation for a Gaussian random field with a specified mean and covariance structure in two dimensions.

The simulation can be conditional or unconditional. If it is conditional, a set of coordinates and associated field values are read from a SAS data set. The resulting simulation honors these data values.

You can specify the mean structure as a quadratic function in the coordinates. Specify the semivariance by naming the form and supplying the associated parameters, or by using the contents of an item store file that was previously created by PROC VARIOGRAM.

PROC SIM2D can handle anisotropic and nested semivariogram models. Seven covariance models are supported: Gaussian, exponential, spherical, cubic, pentaspherical, sine hole effect, and MatÃ©rn. A single nugget effect is also supported.

You can specify the locations of simulation points in a GRID statement, or they can be read from a SAS data set. The grid specification is most suitable for a regular grid; the data set specification can handle any irregular pattern of points.

The SIM2D procedure writes the simulated values for each grid point to an output data set. The SIM2D procedure uses ODS Graphics to create graphs as part of its output. For general information about ODS Graphics, see ChapterÂ 21: Statistical Graphics Using ODS. For more information about the graphics available in PROC SIM2D, see the section ODS Graphics.