Abstract
An algorithm for producing a nonconditional simulation by multiplying the square root of the covariance matrix by a random vector is described. First, the square root of a matrix (or a function of a matrix in general) is defined. The square root of the matrix can be approximated by a minimax matrix polynomial. The block Toeplitz structure of the covariance matrix is used to minimize storage. Finally, multiplication of the block Toeplitz matrix by the random vector can be evaluated as a convolution using the fast Fourier transform. This results in an algorithm which is not only efficient in terms of storage and computation but also easy to implement.
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Davis, M.W. Generating large stochastic simulations—The matrix polynomial approximation method. Math Geol 19, 99–107 (1987). https://doi.org/10.1007/BF00898190
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DOI: https://doi.org/10.1007/BF00898190