Predictive mapping of prospectivity for orogenic gold,Giyani greenstone belt (South Africa) |
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| Institution: | 1. James Cook University, Townsville 4811, Queensland, Australia;2. Geological Survey of Sweden, Uppsala, Sweden;3. Council for Geoscience, Pretoria, South Africa;1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China;2. State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China;3. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China;4. Department of Earth and Space Science and Engineering, Department of Geography, York University, 4700 Keele Street, Toronto, ON M3J1P3, Canada;5. Department of Earth and Oceans, James Cook University, Townsville 4811, Queensland, Australia;1. Institute of Geociences, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil;2. Economic Geology Research Centre (EGRU), James Cook University, Townsville, Queensland, Australia;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands;2. Fomicruz S.E. Alberdi 643, 9400 Río Gallegos, Santa Cruz, Argentina;3. School of Earth and Environmental Sciences, James Cook University, Townsville, Queensland, Australia |
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| Abstract: | 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. |
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