GSLIB Help: SISIM

GSLIB Help Page: SISIM

Description:

The sisim program is for the simulation of either integer-coded categorical variables or continous variables with indicator data defined from a cdf.
Parameters:

vartype: the variable type (1=continuous, 0=categorical)
ncat: the number of thresholds or categories
cat: the threshold values or category codes (there should be ncat values on this line of input)
pdf: the global cdf or pdf values (there should be ncat values on this line of input)
datafl: the input data in a simplified Geo-EAS file.
icolx, icoly, icolz, and icolvr: the column numbers for the x,y, and z coordinates and the variable to be simulated. One or two of the coordinate column numbers can be set to zero which indicates that the simulation is 2-D or 1-D.
directik: already transformed indicator values are read from this file. Missing values are identified as less than tmin which would correspond to a constraint interval. Otherwise, the cdf data should steadily increase from 0 to 1 and soft categorical probabilities must be between 0 to 1 and sum to 1.0.
icolx, icoly, icolz, and icoli: the columns for the x, y, and z coordinates, and the indicator variables.
imbsim: set to 1 if considering Markov-Bayes option for cokriging with soft indicator data, otherwise, set to 0.
b(z): if imbsim is set to 1, then the B(z) calibration values are needed.
tmin and tmax: all values strictly less than tmin and strictly greater than tmax are ignored.
zmin and zmax: minimum and maximum attribute values when considering a continuous variable
ltail and ltpar specify the extrapolation in the lower tail: ltail=1 implements linear interpolation to the lower limit z_min ltail=2 power model interpolation, with w=ltpar to the lower limit zmin and ltail=3 implements linear interpolation between tabulated quantiles (only for continuous variables).
middle and midpar specify the interpolation within the middle of the distribution: middle=1 implements linear interpolation; middle=2 implements power model interpolation, with w=midpar and middle=3 allows for linear interpolation between tabulated quantile values (only for continuous variables).
utail and utpar specify the extrapolation in the upper tail of the distribution: utail=1 implements linear interpolation to the upper limit zmax, utail=2 implements power model interpolation, with w=utpar, to the upper limit zmax utail=3 implements linear interpolation between tabulated quantiles, and utail=4 implements hyperbolic model extrapolation with w=utpar The hyperbolic tail extrapolation is limited by zmax (only for continuous variables).
tabfl: If linear interpolation between tabulated values is the option selected for any of the three regions then this simplified Geo-EAS format file is opened to read in the values. One legitimate choice is exactly the same file as the conditioning data, i.e., datafl Note that tabfl specifies the tabulated values for all classes.
icolvrt and icolwtt: the column numbers for the values and declustering weights in tabfl Note that declustering weights can be used but are not required - just set the column number less than or equal to zero. If declustering weights are not used, then the class probability is split equally between the sub-classes defined by the tabulated values.
idbg: an integer debugging level between 0 and 3. The larger the debugging level the more information written out.
dbgfl: the file for the debugging output.
outfl: the output grid is written to this file. The output file will contain the results, cycling fastest on x then y then z then simulation by simulation.
nsim: the number of simulations to generate.
nx, xmn, xsiz: definition of the grid system (x axis).
ny, ymn, ysiz: definition of the grid system (y axis).
nz, zmn, zsiz: definition of the grid system (z axis).
seed: random number seed (a large odd integer).
ndmax: the maximum number of original data that will be used to simulate a grid node.
ncnode: the maximum number of previously simulated nodes to use for the simulation of another node.
maxsec: the maximum number of soft data (at node locations) that will be used for the simulation of a node. This is particularly useful to restrict the number of soft data when an exhaustive secondary variable informs all grid nodes.
sstrat: if set to 0, the data and previously simulated grid nodes are searched separately: the data are searched with a super block search and the previously simulated nodes are searched with a spiral search. If set to 1, the data are relocated to grid nodes and a spiral search is used; the parameters ndmin and ndmax are not considered.
multgrid: a multiple grid simulation will be performed if this is set to 1 (otherwise a standard spiral search will be considered).
nmult: the target number of multiple grid refinements to consider (used only if multgrid is set to 1).
noct: the number of original data to use per octant. If this parameter is set less than or equal to 0, then it is not used; otherwise, the closest noct data in each octant are retained for the simulation of a grid node.
radius_hmax, radius_hmin and radius_vert: the search radii in the maximum horizontal direction, minimum horizontal direction, and vertical direction (see angles below).
sang1, sang2 and sang3: the angle parameters that describe the orientation of the search ellipsoid. See the discussion
mik and mikcat: if mik is set to 0, then a full indicator kriging is performed at each grid node location to establish the conditional distribution. If mik is set to 1, then the median approximation is used, i.e., a single variogram is used for all categories; therefore, only one kriging system needs to be solved and the computer time is significantly reduced. The variogram corresponding to category mikcat will be used.
ktype: the kriging type (0 = simple kriging, 1 = ordinary kriging) used throughout the loop over all nodes. SK is required by theory, only in cases where the number of original data found in the neighborhood is large enough can OK be used without the risk of spreading data values beyond their range of influence. The global pdf values (specified with each category) are used for simple kriging.
The following set of parameters are required for each of the ncat categories:

nst, and c0: the number of semivariogram structures and the isotropic nugget constant.
For each of the nst nested structures one must define it the type of structure; cc the c parameter; ang1,ang2,ang3 the angles defining the geometric anisotropy; aa_hmax, the maximum horizontal range; aa_hmin, the minimum horizontal range; and aa_vert, the vertical range. Each semivariogram model refers to the corresponding indicator transform. A Gaussian variogram with a small nugget constant is not a legitimate variogram model for a discontinuous indicator function. There is no need to standardize the parameters to a sill of one since only the relative shape affects the kriging weights.