Spartan gibbs random field models for geostatistical applications

Abstract

The inverse problem of determining the spatial dependence of random fields from an experimental sample is a central issue in Geostatistics. We propose a computationally efficient approach based on Spartan Gibbs random fields. Their probability density function is determined by a small set of parameters, which can be estimated by enforcing sample-based constraints on the stochastic moments. The computational complexity of calculating the constraints increases linearly with the sample size. We investigate a specific Gibbs probability density with spatial dependence derived from generalized gradient and Laplacian operators, and we derive permissibility conditions for the model parameters.