Truncation map estimation based on bivariate probabilities and validation for the truncated plurigaussian model

TL;DR

This paper introduces a novel method for estimating the truncation map in the truncated plurigaussian model using a colored Voronoi tessellation, optimizing parameters to match bivariate probabilities, and validating the model's predictive performance.

Contribution

It proposes a new approach to estimate the truncation map with unknown Voronoi tessellation parameters, improving the modeling of categorical spatial variables.

Findings

Estimated truncation map closely matches target bivariate probabilities.

Model validation shows improved predictive performance.

Method effectively generalizes to log-data conditioning.

Abstract

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.