Geology,geodynamics and orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin,Ghana, West Africa |
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| Institution: | 1. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, 400076, India;2. Centre for Exploration Targeting, University of Western Australia, Crawley, 6009 WA, Australia;3. Corporate Geoscience Group, PO Box 5128, Rockingham Beach, WA 6969, Australia;4. Economic Geology Research Centre, James Cook University, Townsville, QLD 4811, Australia;1. UQAC, Université du Québec à Chicoutimi, 555 Boulevard de l''Université, Chicoutimi, Québec G7H 2B1, Canada;2. LAMEQ, Laboratoire de Métallogénie Expérimentale et Quantitative, Université du Québec à Chicoutimi (UQAC), 555 Boulevard de l''Université, Chicoutimi, Québec G7H 2B1, Canada;3. SEMAFO Inc. Société d''Exploitation Minérale en Afrique de l''Ouest, 100 Boulevard Alexis-Nihon, 7e Étage, St-Laurent, Québec H4M 2P3, Canada;1. Université de Ouagadougou, Burkina Faso;2. Université de Toulouse, CNRS, Géosciences Environnement Toulouse, Institut de Recherche pour le Développement, Observatoire Midi-Pyrénées, 14 Av. Edouard Belin, F-31400 Toulouse, France;3. ONG-D Le Soleil dans la Main asbl, 48, Duerfstrooss, L-9696 Winseler, Luxembourg;4. IFAN Cheikh Anta Diop, Dakar, Senegal;5. B2Gold Corp., 595 Burrard Street, Vancouver, BC V7X 1J1, Canada;6. GF Consult bvba, Antwerpsesteenweg 644, 9040 Gent, Belgium |
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| Abstract: | This paper describes the geology and tectonics of the Paleoproterozoic Kumasi Basin, Ghana, West Africa, as applied to predictive mapping of prospectivity for orogenic gold mineral systems within the basin. The main objective of the study was to identify the most prospective ground for orogenic gold deposits within the Paleoproterozoic Kumasi Basin. A knowledge-driven, two-stage fuzzy inference system (FIS) was used for prospectivity modelling. The spatial proxies that served as input to the FIS were derived based on a conceptual model of gold mineral systems in the Kumasi Basin. As a first step, key components of the mineral system were predictively modelled using a Mamdani-type FIS. The second step involved combining the individual FIS outputs using a conjunction (product) operator to produce a continuous-scale prospectivity map. Using a cumulative area fuzzy favourability (CAFF) curve approach, this map was reclassified into a ternary prospectivity map divided into high-prospectivity, moderate-prospectivity and low-prospectivity areas, respectively. The spatial distribution of the known gold deposits within the study area relative to that of the prospective and non-prospective areas served as a means for evaluating the capture efficiency of our model. Approximately 99% of the known gold deposits and occurrences fall within high- and moderate-prospectivity areas that occupy 31% of the total study area. The high- and moderate-prospectivity areas illustrated by the prospectivity map are elongate features that are spatially coincident with areas of structural complexity along and reactivation during D4 of NE–SW-striking D2 thrust faults and subsidiary structures, implying a strong structural control on gold mineralization in the Kumasi Basin. In conclusion, our FIS approach to mapping gold prospectivity, which was based entirely on the conceptual reasoning of expert geologists and ignored the spatial distribution of known gold deposits for prospectivity estimation, effectively captured the main mineralized trends. As such, this study also demonstrates the effectiveness of FIS in capturing the linguistic reasoning of expert knowledge by exploration geologists. In spite of using a large number of variables, the curse of dimensionality was precluded because no training data are required for parameter estimation. |
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