Abstract
The indicator conditional simulation technique provides stochastic simulations of a variable that (i) honor the initial data and (ii) can feature a richer family of spatial structures not limited by Gaussianity. The data are encoded into a series of indicators which then are used to estimate the conditional probability distribution (cpdf) of the variable under study at any unsampled location. Once the cpdf has been estimated, any particular simulated value is obtained by straightforward Monte-Carlo drawing. Each new simulated value is included in the conditioning data set so that the next simulated values at other locations be conditioned to it. This technique has the advantage over other more traditional techniques such as the turning bands method in that it is not multiGaussian related. The user has full control of the bivariate (2-point) statistics imposed on the simulated field instead of controlling a mere covariance model. The source code is provided in C according to the ANSI standard.
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