Abstract
Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration. This is achieved by integrating multiple geoscience datasets using mathematical tools to determine spatial relationships with known mineral occurrences in a GIS environment to produce mineral prospectivity map. The study area lies within Latitudes 9° 00ʹ N to 9° 15ʹ N and 6° 45ʹ to 7° 00ʹ E and is underlain by rocks belonging to the Basement Complex of Nigeria which include migmatitc gneiss, schist, granite and alluvium. The datasets used in this study consist of aeromagnetic, aeroradiometric, structural, satellite remote sensing and geological datasets. Published geologic map of the Sheet 185 Paiko SE was used to extract lithologic and structural information. Landsat images were used to delineate hydroxyl and iron-oxide alterations to identify linear structures and prospective zones at regional scales. ASTER images were used to extract mineral indices of the OH-bearing minerals including alunite, kaolinite, muscovite and montmorillonite to separate mineralized parts of the alteration zones. Aeromagnetic data were interpreted and derivative maps of First Vertical Derivative, Tilt derivative and Analytic signal were used to map magnetic lineaments and other structural attributes while the aeroradiometric dataset was used to map hydrothermally altered zones. These processed datasets were then integrated using Fuzzy Logic modelling to produce a final mineral prospectivity map of the area. The result of the model accurately predicted known deposits and highlighted areas where further detailed exploration may be conducted.
Highlights
Modern mineral exploration efforts in recent times have adopted the integration of different datasets from various sources and surveys
The detection of alteration zones using Landsat 8 OLI images marked by the presence of iron oxides, hydroxylbearing minerals and hydrothermal clays was made possible from false colour composite image band ratios of 6/4, 4/2 and 6/7 in red, green and blue [52,53,54,55,56]
Based on the exploration model considered for the study area, appropriate evidence maps include hydrothermal alteration, host rock and structural maps were developed, weighted and reclassified
Summary
Modern mineral exploration efforts in recent times have adopted the integration of different datasets from various sources and surveys. An important phase in mineral exploration should involve the collection, analysis, interpretation and integration of remotely sensed, geological, geophysical and geochemical datasets. This is done in order to map prospective areas for a more detailed investigation. Mineral Prospectivity Mapping (MPM) is basically classified into empirical (data driven) and conceptual (knowledge driven) methods [1,2]. In the data driven method, known mineral deposits are used as ‘training points’ for examining spatial relationships between the known deposits and geological, geochemical and geophysical features of interest. The identified relationships between input data and training points are quantified and used to establish the importance of each evidence map and subsequently integrated into a single mineral prospectivity map. Examples of the empirical methods used are weights of evidence, logistic regression and neural networks
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