Science of targeting

Mineral exploration comprises three sequential steps: development of a business strategy, creation and application of a targeting model, and follow-up with direct detection in defined high-priority domains. The main geoscientific challenge is the conceptual targeting phase which can lower geological risk and ensure cost-effective direct-detection exploration. A fundamental tenet of conceptual targeting is that ore deposits are part of much more extensive systems, and hence that targeting must be carried out at global through province to district scales. The heterogeneous distribution of ore deposits and their power-law size frequency distribution in individual provinces leads to alternative 'Elephant Country' and 'First Mover' strategies, both of which employ conceptual targeting, but at different scales. The first stage of targeting science involves development of robust, multi-scale targeting models for ore-deposit types, particularly larger examples. The targeting models can then be applied to identify specific targets by interrogating databases compiled as layers of spatially referenced key themes or parameters. At larger scales in immature terrains, a Hierarchical approach is commonly used to progressively reduce terrains and identify targets, whereas a Venn-diagram approach, the basis of most GIS-based prospectivity analyses, is more commonly used in mature terrains where spatial databases are of higher, more homogenous quality. Target ranking is best achieved using a multiplicative probability approach in which it is required that all essential processes in a mineral system must have operated to form a significant ore deposit. In practice, one or more critical spatially referenced parameters are used as proxies for the essential processes to develop a target score, which is a semi-quantitative estimate of probability of the presence of a large ore deposit. Such target ranking can be used in both proactive ground acquisition and reactive submittal-based project acquisition. Once targets have been defined and explored, it is important that there is critical feedback on the robustness of the targeting exercise such that new information is used to build superior databases and/or targeting models for future area-selection programs.