GMS includes an interpolation option associated with the 2D scatter
point module called **Gaussian Sequential Simulation (GSS)**.
This option is used to generate a set of scalar data sets (Gaussian fields)
using a Gaussian sequential simulation. This is somewhat similar to indicator
kriging or T-PROGS in that it generates a set of equally probable results
which exhibit heterogeneity and are conditioned to values at scatter points.
However, the resulting arrays are floating point scalar data sets, rather
than the integer arrays produced by T-PROGS and indicator kriging.

The results of a GSS can be used in combination with the new Multiplier Array option for parameters. It is now possible to associate one or more scalar data sets with an array-based parameter. When MODFLOW is executed, the parameter starting value is multiplied by the data set to produce the input array. This makes it possible to use the results of the Gaussian sequential simulation as input for parameter fields for a stochastic (Monte Carlo) simulation.

The new GSS tool is based on the FIELDGEN code developed by John Doherty. John Doherty describes GSS as follows:

*The process of stochastic field generation
by sequential simulation is very easy to understand. At each field point
an expected field value and a field standard deviation pertaining to that
point are first determined. These are calculated through kriging from
points to which field values have already been assigned, as well as from
points at which conditioning data exists (if available). Using the expected
value and standard deviation calculated in this way, a random field value
is generated based on the assumption of a Gaussian probability distribution.
The field value thus obtained can then be used in generating expected
values and standard deviations at other field points at which field generation
then takes place in the same way.*

GSS is a form of Kriging but it is listed in the GMS interface as a new interpolation scheme. This new option will differ from Kriging in the following ways:

- GSS uses the FIELDGEN utility developed by John Doherty
to perform the interpolation rather than the GSLIB code used by kriging.
FIELDGEN is a modified version of the
**sgsim**utility in GSLIB so many of the options are quite similar to those used for normal kriging. - As is the case with T-PROGS, the user enters the number of desired simulations and FIELDGEN produces N arrays, rather than one array.
- It can only be used for 2D interpolation and it will only work when interpolating to 3D cell-centered grids.
- It can work with or without a scatter point set. If a scatter point set is provided, the resulting fields are conditioned to the values at the scatter points. Otherwise the user defines a mean and a variogram and the values are randomly generated.

The first step in setting up a GSS is to import a set of scatter points
with the values to which you intend to condition your simulation. This
step can be skipped if you have no conditioning data. The next step is
to select the **Gaussian Simulation Options** command in
the **Interpolation** menu in the **2D Scatter Point**
module. This brings up the following dialog:

The **Solution name** at the top is the name that will
be applied to the set of Gaussian fields. The **Number of realizations**
item is the desired number of Gaussian fields. The original GSLIB code
was designed to work with uniform grids (constant cell sizes). The **Non-uniform
grid** option controls how the data are converted to a non-uniform
grid (if necessary). The **Edit Variogram** button should
be selected to set up a model variogram using the GMS variogram
editor.
A model variogram must be defined whether or not you have scatter points
for conditioning.

Once the GSS options are selected, the next step is to run the simulation. This is accomplished by selecting the Run Gaussian Simulation command in the Interpolation menu. During the simulation, you should see a window displaying the progress of the simulation:

Once the simulation is finished, you should see a new folder appear in the Project Explorer window which has the name of the simulation and contains a set of data set arrays:

Clicking on each data set icon makes it the active data set for contouring. The data set properties can be viewed by double-clicking on the icon. The following image represents a sample Gaussian realization: