Conditional co-simulation of continuous and categorical variables for geostatistical applications

The modeling of uncertainty in continuous and categorical regionalized variables is a common issue in the geosciences. We present a hybrid continuous/categorical model, in which the continuous variable is represented by the transform of a Gaussian random field, while the categorical variable is obtained by truncating one or more Gaussian random fields. The dependencies between the continuous and categorical variables are reproduced by assuming that all the Gaussian random fields are spatially cross-correlated. Algorithms and computer programs are proposed to infer the model parameters and to co-simulate the variables, and illustrated through a case study on a mining data set.