Ninth International Geostatistics Congress, Oslo, Norway
June 11 – 15, 2012

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Theory 1

Abstract No.:



Sequential simulation with iterative methods


D. Arroyo, Northern Catholic University (CL)
X. Emery, University of Chile (CL)
M. PelŠez , Northern Catholic University (CL)


The sequential Gaussian algorithm is widely used to simulate Gaussian random fields. In practice, the determination of the successive conditional distributions only uses 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 (namely, the Gauss-Seidel and Generalized Minimal Residual methods) 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 field.




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