Abstract
Fitting semivariograms with analytical models can be tedious and restrictive. There are many smooth functions that could be used for the semivariogram; however, arbitrary interpolation of the semivariogram will almost certainly create an invalid function. A spectral correction, that is, taking the Fourier transform of the corresponding covariance values, resetting all negative terms to zero, standardizing the spectrum to sum to the sill, and inverse transforming is a valuable method for constructing valid discrete semivariogram models. This paper addresses some important implementation details and provides a methodology to working with spectrally corrected semivariograms.
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References
Deutsch, C. V. and Journel, A. G., 1998, GSLIB: geostatistical software library and user's guide 2nd ed.: Oxford University Press, New York, 369 p.
Isaaks, E. H. and Srivastava, R. M., 1989, An introduction to applied geostatistics: Oxford University Press, New York, 561 p.
Journel, A. G. and Huijbregts, Ch. J., 1978, Mining geostatistics: Academic Press, New York, 600 p.
Press, W. H., Flannery, B. P. and Teukolsky, S. A., 1992, Numerical recipes in FORTRAN 77 the art of scientific computing: Cambridge University Press Programs, 449 p.
Pyrcz, M. J. and Deutsch, C. V., 2006, Semivariogram models based on geometric offsets:Math Geol., v. 38, no. 4, p. 475–488.
Yao, T. and Journel, A. G., 1998, Automatic modeling of (cross) covariance tables using fast Fourier transform: Math Geol., v. 30, no. 6, p. 589–615.
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Pyrcz, M.J., Deutsch, C.V. Spectral Corrected Semivariogram Models. Math Geol 38, 891–899 (2006). https://doi.org/10.1007/s11004-006-9053-9
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DOI: https://doi.org/10.1007/s11004-006-9053-9