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Testing the correctness of the sequential algorithm for simulating Gaussian random fields
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DOI：10.1007/s00477-004-0211-7.png)
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Abstract
En 中文
The sequential algorithm is widely used to simulate Gaussian random fields. However, a rigorous application of this algorithm is impractical and some simplifications are required, in particular a moving neighborhood has to be defined. To examine the effect of such restriction on the quality of the realizations, a reference case is presented and several parameters are reviewed, mainly the histogram, variogram, indicator variograms, as well as the ergodic fluctuations in the first and second-order statistics. The study concludes that, even in a favorable case where the simulated domain is large with respect to the range of the model, the realizations may poorly reproduce the second-order statistics and be inconsistent with the stationarity and ergodicity assumptions. Practical tips such as the 'multiple-grid strategy' do not overcome these impediments. Finally, extending the original algorithm by using an ordinary kriging should be avoided, unless an intrinsic random function model is sought after.
Keywords:
sequential Gaussian simulation
multigaussian distribution
kriging neighborhood
screening effect
ergodic fluctuations
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