JOINT SIMULATION OF MULTIPLE-VARIABLES WITH A MARKOV-TYPE COREGIONALIZATION MODEL

Many applications are multivariate in character and call for stochasti c images of the joint spatial variability of multiple variables condit ioned by a prior model of covariances and cross-covariances. This pape r presents an algorithm to perform cosimulation of such spatially inte rcorrelated variables. This new algorithm builds on a Markov-type hypo thesis whereby collocated information screens further away data of the same type, allowing cosimulation without the burden of a full cokrigi ng. The proposed algorithm is checked against a synthetic mu[ti-Gaussi an reference dataset, then against a multi-Gaussian cosimulation appro ach using full cokriging. The results indicate that the proposed algor ithm perform as well as the full cokriging approach in reproducing the univariate and bivariate statistics of the reference set, yet at less cpu cost.