Geostatistics is a branch of applied statistics that deals with the analysis, estimation, and simulation of spatially correlated data, developed specifically to address the challenges of characterizing geological phenomena across three-dimensional space. In mining — whether for bauxite, gold, iron ore, or diamonds — geostatistics provides the mathematical framework for converting sparse drill hole data into continuous resource models that quantify the tonnage and grade of economically recoverable mineralization. The discipline was pioneered by Georges Matheron and Danie Krige in the 1960s, and the term "kriging" — a core geostatistical estimation technique — derives from Krige's name. Variogram analysis is the foundational step in geostatistics, where the spatial continuity and directional anisotropy of a geological variable (such as alumina content, gold grade, iron percentage, or diamond carats per hundred tonnes) are modeled using semi-variograms. Kriging methods — including ordinary kriging, simple kriging, and indicator kriging — are then applied to estimate grade values at unsampled locations while quantifying estimation uncertainty. Conditional simulation techniques such as Sequential Gaussian Simulation (SGS) generate multiple equally probable realizations of the orebody, enabling probabilistic risk assessment of resource and reserve estimates. Geostatistics is embedded in internationally recognized reporting standards such as JORC, NI 43-101, and SAMREC, making it integral to public reporting of mineral resources and ore reserves across all commodity sectors.
