Predictive mapping of prospectivity for orogenic gold, Giyani greenstone belt (South Africa)

We present a mineral systems approach to predictive mapping of orogenic gold prospectivity in the Giyani greenstone belt (GGB) by using layers of spatial evidence representing district-scale processes that are critical to orogenic gold mineralization, namely (a) source of metals/fluids, (b) active pathways, (c) drivers of fluid flow and (d) metal deposition. To demonstrate that the quality of a predictive map of mineral prospectivity is a function of the quality of the maps used as sources of spatial evidence, we created two sets of prospectivity maps — one using an old lithologic map and another using an updated lithological map as two separate sources of spatial evidence for source of metals/fluids, drivers of fluid flow and metal deposition. We also demonstrate the importance of using spatially-coherent (or geologically-consistent) deposit occurrences in data-driven predictive mapping of mineral prospectivity. The best predictive orogenic gold prospectivity map obtained in this study is the one that made use of spatial evidence from the updated lithological map and spatially-coherent orogenic gold occurrences. This map predicts 20% of the GGB to be prospective for orogenic gold, with 89% goodness-of-fit between spatially-coherent inactive orogenic gold mines and individual layers of spatial evidence and 89% prediction-rate against spatially-coherent orogenic gold prospects. In comparison, the predictive gold prospectivity map obtained by using spatial evidence from the old lithological map and all gold occurrences has 80% goodness-of-fit but only 63% prediction-rate. These results mean that the prospectivity map based on spatially-coherent gold occurrences and spatial evidence from the updated lithological map predicts exploration targets better (i.e., 28% smaller prospective areas with 9% stronger fit to training gold mines and 26% higher prediction-rate with respect to validation gold prospects) than the prospectivity map based on all known gold occurrences and spatial evidence from the old lithological map

Data integration for interpretive bedrock mapping in the Giyani area (South Africa)

The Giyani greenstone belt is situated in the Limpopo province of South Africa. All previous maps, field observations and available geo-data sets were integrated using image fusion to recognise lithological variations: Mafic and granitic rocks showed strong contrast in DEM. Single images of airborne radiometric data as well as composite maps of U showed very good correlations with lithology. Soil geochemistry data were interpolated, and principal components analysis was applied for major and trace elements, as well as ratio maps of geochemical data were used for interpretive bedrock mapping. All useful fused images were interpreted to compile a new bedrock map for follow-up fieldwork

Analysis and mapping of soil geochemical anomalies: implications for bedrock mapping and gold exploration in Giyani area, South Africa

Previous exploration activities in the Giyani greenstone belt (GGB) were guided by the availability of outcrops, particularly iron formation, as this rock was considered to be the main host rock for gold mineralisation in the belt, although the majority of the known prospects/deposits are hosted by mafic rocks. However, there is no reliable lithological map available for the GGB, because most of it is covered by regolith, and thus in the early 1990s most mining and exploration companies in the GGB have abandoned their work as they were discouraged by the scarcity of outcrops, the small sizes of existing deposits and the low gold prices at that time. In the present study, major and trace element geochemical data from a high-density soil geochemical survey (1 sample/km2) have been subjected to statistical and spatial analyses to support bedrock mapping and gold exploration. Maps are presented for major oxides, trace elements and selected respective ratio maps, and principal components (PC). The PC analysis was performed on clr-transformed data of selected trace elements known to be associated with gold mineralisation. The first six PCs explain about 78% of the total variance. PC4 representing Sb–As–Te–Cr–Au association best reflects the known gold mineralisation and was, therefore, used as a thematic layer. The information provided by various composite maps of different major/trace element data, as well as PC maps, has been used to produce an interpretive bedrock map outlining major lithological units in the study area. As gold mineralisation in the Giyani greenstone belt is hosted by certain known lithologies, the map is useful in indicating potential gold bearing areas