Enhanced coregionalization analysis for simulating vector Gaussian random fields

This paper deals with the simulation of a stationary vector Gaussian random field whose spatial correlation structure is given by a linear model of coregionalization. Traditionally, simulation is performed by decomposing the vector random field into a set of independent vector random fields with coregionalization models that contain a single nested structure, and a factorization of these fields into principal components. A variation of this approach is proposed, by considering the minimum/maximum autocorrelation factors associated with groups of two nested structures. This variation reduces the total number of independent factors by one-half, thus considerably decreases memory requirements and CPU time for simulation, without any loss of accuracy for reproducing the linear model of coregionalization, regardless of how many nested structures are contained in this model. The proposed approach is implemented in a set of computer programs and illustrated through a synthetic example and a case study in mineral resources evaluation.