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Estimation of semivariogram parameters and evaluation of the effects of data sparsity

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Abstract

Semivariogram parameters are estimated by a weighted least-squares method and a jackknife kriging method. The weighted least-squares method is investigated by differing the lag increment and maximum lag used in the fit. The jackknife kriging method minimizes the variance of the jackknifing error as a function of semivariogram parameters. The effects of data sparsity and the presence of a trend are investigated by using 400-, 200-, and 100-point synthetic data sets. When the two methods yield significantly different results, more data may be needed to determine reliably the semivariogram parameters, or a trend may be present in the data.

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Authors and Affiliations

  1. Water Resources Center, Desert Research Institute, University and Community College System of Nevada, 89512, Reno, Nevada

    Gregg Lamorey & Elizabeth Jacobson

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  1. Gregg Lamorey
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  2. Elizabeth Jacobson
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Lamorey, G., Jacobson, E. Estimation of semivariogram parameters and evaluation of the effects of data sparsity. Math Geol 27, 327–358 (1995). https://doi.org/10.1007/BF02084606

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  • DOI: https://doi.org/10.1007/BF02084606

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