Sequential Simulation with Iterative Methods

  • Daisy Arroyo
  • Xavier Emery
  • María Peláez
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 17)


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.


Iterative Method Conjugate Gradient Method Sequential Simulation Gaussian Random Field Gaussian Random Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was funded by the Chilean program MECESUP UCN0711 and the FONDECYT project 11100029.


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Northern Catholic UniversityAntofagastaChile
  2. 2.University of ChileSantiagoChile

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