Enhanced coregionalization analysis for simulating vector Gaussian random fields

• Published in: Computers & geosciences Vol. 42; pp. 126 - 135
• Main Authors: Emery, Xavier, Ortiz, Julián M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2012
• Subjects: autocorrelation; case studies; Computer programs; Computer simulation; computer software; computers; Factorization; Gaussian; Linear model of coregionalization; linear models; Mathematical analysis; Mathematical models; Mineral resources; Minimum/maximum autocorrelation factors; Principal component analysis; Sequential Gaussian simulation; Vectors (mathematics); Minimum/maximum autocorrelation factors; Linear model of coregionalization; Sequential Gaussian simulation; Principal component analysis
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2011.09.007

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. ► Vector Gaussian random fields can be decomposed into independent factors. ► Typically, factors are principal components associated with each nested structure. ► Considering minimum/maximum autocorrelation factors yields half as many factors. ► Computer programs for cosimulation are provided and illustrated with case studies.