SRK has a great deal of experience in assisting companies to implement minerals exploration programs from greenfields to advanced projects. The initial stage of any exploration program is targeting. At this stage, exploration activities are focused onto specific areas with the highest potential for discovery, and on developing strategies to systematically explore them. Unfortunately, dedicated exploration targeting is often overlooked.
Ideally, targeting shouldn’t create too many false leads, but equally shouldn’t be so simplistic that it misses the next big one. When assessing the exploration potential of large areas, decisions should be based on a combination of available data and expert knowledge. Predictive tools use the characteristics of known mineral deposits to identify similar features in exploration datasets such as geochemistry, geology, structure and geophysics. Expert-driven and data-driven are the two different approaches used in predictive targeting.
The expert-driven approach to exploration targeting relies on understanding the mineralising system or potential for a minerals system to occur. It is useful in greenfield areas where information regarding mineralisation is limited. The areas can be compared with similar geological settings with known mineralisation, where an exploration model or rationale for future exploration can be developed on sound geological reasoning. This approach uses the concept of geological processes, in which mineralisation, moving from a source along a pathway, is focused and becomes localised in a trap. Each element is ranked, based on the geologist’s judgment, and the features are combined in a 2D or 3D GIS package. The cumulative effect of intersecting features of interest highlights target areas.
Data-driven targeting methods provide a robust and repeatable technique for investigating regions with large amounts of data, particularly in brownfield exploration. The data-driven approaches require little understanding of the actual mechanism of mineralisation, but use empirical evidence to rank each element. The most common approach is the Weights of Evidence technique, where a spatial feature (e.g. fault, geochemical anomaly, specific rock type) is assigned a statistical weighting based on its statistical relationship with identified mineralisation. This approach allows even very large and complex datasets to be integrated in an unbiased manner. In many cases, features related to mineralisation can be identified, allowing exploration to focus on prospective regions, independent of past interpretations.
The targeting processes SRK has developed provide a robust geological framework and mineralisation models on which companies can base exploration programs.
