A Mineral Resource Estimate is the calculated tonnage, grade, and quality of mineralization contained within a deposit, derived from the integration of geological mapping, drilling, sampling, geophysical data, and statistical or geostatistical modelling techniques such as kriging, inverse distance weighting, or polygonal estimation. The estimate forms the technical foundation from which classified resources and subsequently mineral reserves are derived, and is typically generated using three-dimensional block modelling software such as Datamine, Surpac, Vulcan, or Leapfrog. The estimation methodology and associated challenges differ by commodity: gold resource estimates must account for nugget effect and grade outlier management, often requiring top-cutting or indicator kriging; iron ore estimates incorporate density, iron content, and deleterious elements such as alumina, silica, and phosphorus; bauxite estimates focus on available alumina, reactive silica, and the gibbsite-to-boehmite ratio that governs processing behaviour; and diamond resource estimation is uniquely challenging because individual stones cannot be assayed like a continuous grade variable, requiring bulk sampling programs and probabilistic modelling of stones per hundred tonnes together with revenue-per-carat modelling based on size frequency distribution and diamond valuation.
