Efficient generation of conditional simulations by chebyshev matrix polynomial approximations to the symmetric square root of the covariance matrix |
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| Authors: | C R Dietrich and G N Newsam |
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| Institution: | (1) Faculty of Environmental Sciences, School of Environmental Engineering, Griffith University, 4111 Brisbane, Queensland, Australia;(2) Information Technology Division, Defence Science and Technology Organisation, Cooperative Research Centre for Sensor Signal and Information Processing, P.O. Box 1500, 5108 Salisbury, S.A., Australia |
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| Abstract: | Consider the problem of generating a realization y1 of a Gaussian random field on a dense grid of points  1 conditioned on field observations y2 collected on a sparse grid of points 2. An approach to this is to generate first an unconditional realization y over the grid =1  2, and then to produce y1 by conditioning y on the data y2. As standard methods for generating y, such as the turning bands, spectral or Cholesky approaches can have various limitations, it has been proposed by M. W. Davis to generate realizations from a matrix polynomial approximations to the square root of the covariance matrix. In this paper we describe how to generate a direct approximation to the conditional realization y1, on 1 using a variant of Davis' approach based on approximation by Chebyshev polynomials. The resulting algorithm is simple to implement, numerically stable, and bounds on the approximation error are readily available. Furthermore we show that the conditional realization y1 can be generated directly with a lower order polynomial than the unconditional realization y, and that further reductions can be achieved by exploiting a nugget effect if one is present. A pseudocode version of the algorithm is provided that can be implemented using the fast Fourier transform if the field is stationary and the grid 1 is rectangular. Finally, numerical illustrations are given of the algorithm's performance in generating various 2-D realizations of conditional processes on large sampling grids. |
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| Keywords: | Gaussian random fields geostatistics Monte Carlo simulations orthogonal polynomials |
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