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A comparative study of simulation techniques for two dimensional data honouring specified exponential semivariograms

Published online by Cambridge University Press:  17 February 2009

P. I. Brooker  and
M. A. Stewart
P. I. Brooker
Affiliation:
Department of Geology and Geophysics, University of Adelaide, South Australia 5005.
M. A. Stewart
Affiliation:
Teletraffic Research Centre, University of Adelaide, South Australia 5005.
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Abstract

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The effectiveness of four techniques for producing wide sense stationary data with exponential semivariograms is examined. Comparison is made primarily on the basis of the observed semivariograms. The LU decomposition of the covariance matrix appears to most accurately model specified semivariograms, whilst the more computationally efficient Matrix Polynomial approximation and Turning Bands methods may be more useful in practice.

Type
Research Article
Information
The ANZIAM Journal , Volume 36 , Issue 3 , January 1995 , pp. 249 - 260
Copyright
Copyright © Australian Mathematical Society 1995

References

[1]Brooker, P. I., “Two-dimensional simulation by turning bands”, Mathematical Geology 17 (1985) 81–90.Google Scholar
[2]Brooker, P. I., Cock, G. C. and Stewart, M. A., “Comparison of methods for simulation of two dimensional data honouring specified spherical semivariograms”, Mathematics and Computers in Simulation 33 (1992) 489–494.Google Scholar
[3]Davis, M. W., “Generating large stochastic simulations - the matrix polynomial approximation method”, Mathematical Geology 19 (1987) 99–108.Google Scholar
[4]Davis, M. W., “Production of conditional simulations via the lu triangular decomposition of the covariance matrix”. Mathematical Geology 19 (1987) 91–98.Google Scholar
[5]Journel, A. G. and Huijbregts, C. J., Mining geostatistics (Academic Press, London, 1978).Google Scholar
[6]Mantoglou, A. and Wilson, J. L., “The turning bands method for simulation of random fields using line generation by spectral method”, Water Resources Research 18 (1982) 1379–1394.CrossRefGoogle Scholar
[7]Tompson, A. F. B., Ababou, R. and Gelhar, L. W., “Implementation of the three-dimensional turning bands random field generator”, Water Resources Research 25 (1989) 2227–2243.CrossRefGoogle Scholar
[8]Wilkinson, J. H., “A priori error analyses of algebraic processes”, Proceedings of the International Congress of Mathematicians (1968) 629–631.Google Scholar