A geological resource—more precisely termed a "mineral resource" in the context of internationally recognized reporting codes—refers to a concentration or occurrence of a solid material of intrinsic economic interest in or on the Earth's crust in such form, grade or quality, and quantity that there are reasonable prospects for eventual economic extraction. In the mining industry for bauxite, gold, iron ore, and diamonds, the estimation, classification, and reporting of mineral resources is a scientifically rigorous, commercially sensitive, and legally regulated process that forms the foundation of mining project valuation and investment decision-making.
Mineral resources are classified into three categories—Inferred, Indicated, and Measured—reflecting increasing levels of geological confidence in the estimate, as defined by internationally recognized reporting codes including the JORC Code (Australasia), NI 43-101 (Canada), SAMREC (South Africa), and the PERC Reporting Standard (Europe). These codes require that resource estimates be prepared by or under the supervision of a Competent Person (Qualified Person under NI 43-101) with relevant experience in the commodity, deposit type, and location being reported.
The geological resource estimation process for gold mines involves the construction of a three-dimensional geological model defining ore zone domains, geostatistical analysis of gold grade distributions within each domain (variography, grade capping, declustering), interpolation of grades into a block model using methods such as ordinary kriging (OK), multiple indicator kriging (MIK), or simulation, and application of a resource classification scheme based on data density and geological confidence criteria.
In bauxite, resources are estimated by interpolating key quality parameters (Al2O3, SiO2, Fe2O3, TiO2, moisture) from auger and drillhole composites into a block model or polygonal estimation framework, typically stratified by laterite horizon. The resource tonnage is calculated using measured or estimated bulk density values applied to the volume defined by the ore zone model.
In iron ore, resource estimation must capture the complex stratigraphic and structural architecture of BIF-hosted deposits, requiring sophisticated domaining and multivariate interpolation approaches to simultaneously estimate Fe, SiO2, Al2O3, P, and other quality parameters in the block model.
In diamond mining, resource estimation is uniquely challenging due to the extremely sparse and high-value nature of diamonds, requiring the application of probabilistic grade estimation methods based on large bulk sample datasets, stone size distribution modeling, and diamond price modeling to convert estimated carats per hundred tonnes (cpht) into an economic value estimate.