Abstract

The sequential Gaussian algorithm is widespread to simulate Gaussian random fields. In practice, the determination of the successive conditional distributions only accounts for the information available in a moving neighborhood centered on the target location, which provokes a loss of accuracy with respect to a unique neighborhood implementation. In order to reduce this loss of accuracy, iterative methods for solving large kriging systems of equations are used to improve the determination of the conditional distributions, taking the results obtained in a moving neighborhood as a first approximation. Numerical experiments are presented to show the proposed strategies and the improvements in the reproduction of the correlation structure of the simulated random field.