Introduction to Geostatistics

Explains spatial correlation in geostatistics via semivariogram, differences between sample pairs at varying distances, and shows constructing an experimental variogram and fitting a theoretical model such as exponential or gaussian.

Explore how to compute the experimental semivariogram (H-scattergram) by pairing samples at varying lag distances, using head-tail analysis to reveal spatial correlation.

Calculate and interpret the semi-variogram for geostatistics by computing semi-variance at increasing lag distances (30, 60, 90, 120 m), log results, and plot the variogram curve.

Form a practical variogram by building the experimental variogram, identifying the nugget effect from downhole data, choosing few structural components, and defining the range and sill.

Explore component types of variograms, including spherical, exponential, gaussian, linear, and general linear models, and learn their shapes and applications for grade and quality estimates, surface features, and mining contexts.

Explore anisotropy, the property that varies with direction; learn to measure it with three perpendicular variograms, compute the anisotropy ratio from model ranges, and distinguish geometric versus zonal anisotropy.

Learn the typical variography workflow for resource estimation, from estimating the nugget effect with downhole semivariograms to omni-directional and 3D directional variograms for a 3D continuity-based model.