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Statistical approach to inverse distance interpolation

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Abstract

Inverse distance interpolation is a robust and widely used estimation technique. Variants of kriging are often proposed as statistical techniques with superior mathematical properties such as minimum error variance; however, the robustness and simplicity of inverse distance interpolation motivate its continued use. This paper presents an approach to integrate statistical controls such as minimum error variance into inverse distance interpolation. The optimal exponent and number of data may be calculated globally or locally. Measures of uncertainty and local smoothness may be derived from inverse distance estimates.

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Acknowledgments

This research was partially supported by Alberta Ingenuity Foundation, University of Alberta and industry sponsors of the Centre for Computational Geostatistics.

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Authors and Affiliations

  1. Centre for Computational Geostatistics, Department of Civil and Environmental Engineering, University of Alberta, 3-133 NREF Building, Edmonton, AB, T6G 2W2, Canada

    Olena Babak & Clayton V. Deutsch

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  1. Olena Babak
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  2. Clayton V. Deutsch
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Correspondence to Olena Babak.

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Babak, O., Deutsch, C.V. Statistical approach to inverse distance interpolation. Stoch Environ Res Risk Assess 23, 543–553 (2009). https://doi.org/10.1007/s00477-008-0226-6

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