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On unbiased backtransform of lognormal kriging estimates

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Abstract

Lognormal kriging is an estimation technique that was devised for handling highly skewed data distributions. This technique takes advantage of a logarithmic transformation that reduces the data variance. However, backtransformed lognormal kriging estimates are biased because the nonbias term is totally dependent on a semivariogram model. This paper proposes a new approach for backtransforming lognormal kriging estimates that not only presents none of the problems reported in the literature but also reproduces the sample histogram and, consequently, the sample mean.

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Authors and Affiliations

  1. Department of Environmental and Sedimentary Geology, Institute of Geosciences, University of São Paulo, São Paulo, Brazil

    Jorge Kazuo Yamamoto

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  1. Jorge Kazuo Yamamoto
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Correspondence to Jorge Kazuo Yamamoto.

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Yamamoto, J.K. On unbiased backtransform of lognormal kriging estimates. Comput Geosci 11, 219–234 (2007). https://doi.org/10.1007/s10596-007-9046-x

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  • DOI: https://doi.org/10.1007/s10596-007-9046-x

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