A turning bands program for conditional co-simulation of cross-correlated Gaussian random fields
Computers & Geosciences
A flexible true plurigaussian code for spatial facies simulations
Computers & Geosciences
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An enhanced Gibbs sampler algorithm for non-conditional simulation of Gaussian random vectors
Computers & Geosciences
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This paper deals with the modeling and cosimulation of ordinal coregionalized variables, such as indicators, counts or continuous-valued variables discretized into a limited number of classes. The proposed model relies on truncations of a set of cross-correlated stationary Gaussian random fields. We provide guidelines and algorithms for inferring and validating the structural model (direct and cross variograms of the underlying Gaussian random fields) and constructing realizations conditioned to data. The algorithms are implemented in a set of computer programs and are illustrated with applications to datasets in pest management and mineral resources evaluation.