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Here, we present holistic targeting models for lithium–cesium–tantalum (LCT) pegmatites in Western Australia, the world’s largest supplier of hardrock lithium ores, and Ontario, an emerging hardrock lithium mining jurisdiction. In this study, the LCT pegmatite targeting models, informed by a review of this deposit type and framed in the context of a mineral system approach, served to identify a set of targeting criteria that are mappable in the publicly available exploration data for Western Australia and Ontario. This approach, which formed the basis for artificial intelligence (AI)-powered mineral potential modeling (MPM), using multiple, complimentary modeling techniques, not only delivered the first published regional-scale views of lithium potential across the Archean to Proterozoic terrains of Western Australia and Ontario, but it also delivered an effective framework for exploration and revealed hidden trends. For example, we identified a statistically verifiable proximity relationship between lithium, gold, and nickel occurrences and confirmed a significant size differential between LCT pegmatites in Western Australia and Ontario, with the former typically containing much larger resources than the latter. Overall, this regional-scale targeting study served to demonstrate the power of precompetitive, high-quality geoscience data, not only for regional-scale targeting but also for the development of camp-scale targets that have the resolution to be investigated using conventional prospecting techniques. Importantly, MPM does not generate ‘treasure maps’. Rather, MPM provides another tool in the ‘exploration toolbox’, and its output should be taken as the starting point for further investigations.

The data-driven index overlay technique is an amended version of the conventional index overlay method, which has been utilized for mineral potential modeling (MPM) in brown-fields exploration, whereby a data-driven way is utilized to determine the relative significance of both individual evidence maps and evidential values. Although this method evades exploration bias in the conventional index overlay MPM resulting from categorization of evidential maps into some optional classes, it still possesses two major issues: (i) since this technique utilizes the plots of prediction-area (P-A) for defining the importance degree of every evidential map, it carries exploration bias resulting from the use of known mineral occurrences (KMOs) and (ii) it is not applicable in green-fields areas where there may be no KMOs. In this study, we propound an improved index overlay MPM to overcome the deficiencies described above, whereby every individual evidential map is rendered a weight without expert opinion and without the use of KMOs through a modified Shannon’s entropy method. To illustrate this procedure, we applied it to the brown-fields Dolatabad district of southern Iran as a MPM case study for podiform chromite deposits. The results indicated that the improved index overlay approach outperformed the data-driven index overlay method for modeling the potential of the podiform chromite deposits. After successful testing of the improved index overlay technique, we applied it to the green-fields Birjand district of eastern Iran in order to ascertain promising zones for further detailed exploration of the targeted deposit.

This paper presents the results of an integrated targeting study covering the central-eastern Archean Yilgarn Craton of Western Australia, a region renowned for its substantial gold endowment (>40 Moz Au). The cornerstones of this study included custom-built geophysical and remote sensing targeting tools, a new lithostructural interpretation of the area, a targeting model based on the mineral systems approach, and a best-practice mineral potential modeling (MPM) workflow employing five complementary modeling techniques. The geophysical targeting tools were used to identify proximity, association, and abundance relationships between gold mineralization and gravity ridges or edges, as well as 95th-percentile K/Th radiometric and remotely sensed goethite–clay–iron feature depth index ratio anomalies. The lithostructural interpretation revealed structural trends oblique or orthogonal to the NNW-SSE-striking greenstone belts, likely representing important structural controls on gold mineralization. Fry analysis, used to assess the spatial distribution of geological point patterns, showed similar directions of maximum gold occurrence alignment. Together, these observations proved to be strong predictors of gold prospectivity in the MPM component of this targeting study. The MPM not only identified most known gold occurrences but also highlighted several underexplored areas with significant potential. The highest-priority MPM targets represent roughly an order-of-magnitude reduction in search space, the hallmark of a well-performing and practically useful targeting methodology.

In this paper an improved prediction-area plot has been developed. This type of plot includes performance measures similar to other existing methods (receiver operating characteristics, success-rate curves and ordinary prediction-area plots) and, therefore, offers a reliable method for evaluating the performance of spatial evidence maps and prospectivity models. To demonstrate the reliability of the improved prediction-area plot proposed, we investigated the benefits of augmented targeting criteria through remotely sensed exploration features, compared to only geological map-derived criteria, for mineral prospectivity analysis using as an example the podiform chromite deposits of the Sabzevar Ophiolite Belt, Iran. The application of the newly developed improved prediction-area plot to the prospectivity models generated in this study indicated that the augmented targeting criteria by using remote sensing data perform better than non-updated geological map-derived criteria, and that model effectiveness can be improved by using an integrated approach that entails geologic remote sensing.

Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.