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An improved spectral turning-bands algorithm for simulating stationary vector Gaussian random fields
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DOI：10.1007/s00477-015-1151-0.png)
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Abstract
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We propose a spectral turning-bands approach for the simulation of second-order stationary vector Gaussian random fields. The approach improves existing spectral methods through coupling with importance sampling techniques. A notable insight is that one can simulate any vector random field whose direct and cross-covariance functions are continuous and absolutely integrable, provided that one knows the analytical expression of their spectral densities, without the need for these spectral densities to have a bounded support. The simulation algorithm is computationally faster than circulant-embedding techniques, lends itself to parallel computing and has a low memory storage requirement. Numerical examples with varied spatial correlation structures are presented to demonstrate the accuracy and versatility of the proposal.
Keywords:
Matrix-valued covariance functions
Spectral density
Importance sampling
Matern covariance
Compactly supported covariance
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