Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/27148
Title: Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria
Authors: Ejepu, Jude Steven
Abdullahi, Suleiman
Abdulfatai, Ibrahim Asema
Umar, Muhammad Umar
Keywords: Geophysical Methods, Mineral Exploration, Fuzzy Logic Models, Geographic Information Systems, Remote Sensing
Issue Date: 17-Sep-2020
Publisher: Science Publishing Group (Earth Sciences)
Citation: Ejepu Jude Steven, Abdullahi Suleiman, Abdulfatai Asema Ibrahim, Umar Mohammed Umar. Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria. Earth Sciences. Vol. 9, No. 5, 2020, pp. 148-163. doi: 10.11648/j.earth.20200905.12
Series/Report no.: 9 (5);
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.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/27148
ISSN: 2328-5974 (print); 2328-5982 (online)
Appears in Collections:Geology

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