  The truncated plurigaussian model is often used to simulate the spatial
distribution of random categorical variables such as geological facies. The
problems addressed in this paper are the estimation of parameters of the
truncation map for the truncated plurigaussian model. Unlike standard
truncation maps, in this paper a colored Voronoi tessellation with number of
nodes, locations of nodes, and category associated with each node all treated
as unknowns in the optimization. Parameters were adjusted to match categorical
bivariate unit-lag probabilities, which were obtained from a larger pattern
joint distribution estimates from the Bayesian maximum-entropy approach
conditioned to the unit-lag probabilities. The distribution of categorical
variables generated from the estimated truncation map was close to the target
unit-lag bivariate probabilities. The validation of the predictive performance
of the model is evaluated using scoring rules, and conditioning of the latent
Gaussian fields to log-data is generalized for the case when the truncated
bigaussian model is governed by a colored Voronoi tessellation of the
truncation map.
