Titlebook: Basic Linear Geostatistics; Margaret Armstrong Textbook 1998 Springer-Verlag Berlin Heidelberg 1998 3D.Estimator.Fitting.Kriging.Regressio

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Experimental Variograms,t models to them. Several exercises are provided. The practical problems encountered with troublesome experimental variograms are discussed. These include outliers, almost regularly spaced data, and so on.

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Structural Analysis,de by the geostatistician are reviewed. Are the data stationary? Are they isotropic? Should we work with the variables themselves or their accumulations? Should the study be carried out in 2D or 3D?.The first case study is a relatively simple 3D study of an iron ore deposit. As the horizontal and ve

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The Theory of Kriging, averages. Here “best” means minimum variance. Three types of kriging estimators are discussed: ordinary kriging (OK) used when the mean is unknown, kriging the unknown mean value and simple kriging (SK) used when the mean is known..The equations for these three estimators are derived for the statio

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Case Study using Kriging,oint values then block grades. As the model has a high nugget effect, a large kriging neighbourhood is required. The fourth section shows what happens when smaller neighbourhoods are used. The last section illustrates why it is not advisable to krige small blocks from sparse samples, in order to cal

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Estimating the Total Reserves,t already known), and to determine the total ore tonnage and the average grade. As well as knowing the total reserves, it is very important to know how accurate the estimates are. Provided there are not too many samples, kriging can be used to estimate the reserves and the kriging variance will give

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