Abstract
Accurate classification of Mineral Resources is integral to resource evaluation processes, yet conventional rules-based systems often necessitate multiple iterations and manual adjustments, posing enduring challenges within the industry. At ArcelorMittal exploitation minière Canada s.e.n.c. (AMEM), addressing the heterogeneity of the extensive database at Mont-Wright led to the development of an innovative solution where this inherent characteristic of the drill-hole dataset is considered, removing the need for post-processing through additional rules by the Qualified Persons.
AMEM employs a tailored methodology which combines two components: 1) spatial configuration of drill-holes and 2) degree of confidence based on several sources of geologic information. The latter component sets this methodology apart and consists of assigning different confidence factors to the drill-hole dataset prior to classification, notably to aspects that are selected as relevant to resources evaluation. This initial penalization has an impact on the estimate of the spatial configuration component upon which the mineral resources block model is classified while simultaneously eliminating the need for post-processing. The process is complete and is deemed applicable to the complexity of the Mont-Wright deposit while providing inherent flexibility available and determined by the Qualified Person.
This methodology will be illustrated through the existing dataset of the Mont-Wright deposit, spanning seven decades of data collection. Furthermore, its integration into the planning of cost-effective infill drilling campaigns will be discussed, demonstrating its practical applicability and potential for optimization in resource assessment endeavors.