Abstract
The efficacy of geostatistics depends, to a large extent, on the quality of the estimate obtained for the variogram. Several robust estimators of the variogram have been proposed recently. A close examination of many examples of non robust variograms suggests that this work has been going in the wrong direction. The variogram cloud approach used by Chauvet (5) seems a more fruitful starting point. Several ways of fitting variogram models using the variogram cloud will be discussed. The sensitivity of the ultimate Kriging weights and the kriging variance to small changes in the variogram model or in the location of sample points will also be studied.
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Armstrong, M. (1984). Improving the Estimation and Modelling of the Variogram. In: Verly, G., David, M., Journel, A.G., Marechal, A. (eds) Geostatistics for Natural Resources Characterization. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3699-7_1
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DOI: https://doi.org/10.1007/978-94-009-3699-7_1
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