Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria

Ejepu Jude Steven; Abdullahi Suleiman; Abdulfatai Asema Ibrahim; Umar Mohammed Umar

doi:doi:10.11648/j.earth.20200905.12

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Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria

Ejepu Jude Steven, Abdullahi Suleiman, Abdulfatai Asema Ibrahim, Umar Mohammed Umar

Published in Earth Sciences (Volume 9, Issue 5)

Received: 23 July 2020 Accepted: 22 August 2020 Published: 17 September 2020

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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.

Published in	Earth Sciences (Volume 9, Issue 5)
DOI	10.11648/j.earth.20200905.12
Page(s)	148-163
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Geophysical Methods, Mineral Exploration, Fuzzy Logic Models, Geographic Information Systems, Remote Sensing

References

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Ejepu Jude Steven, Abdullahi Suleiman, Abdulfatai Asema Ibrahim, Umar Mohammed Umar. (2020). Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria. Earth Sciences, 9(5), 148-163. https://doi.org/10.11648/j.earth.20200905.12

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ACS Style

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 Sci. 2020, 9(5), 148-163. doi: 10.11648/j.earth.20200905.12

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AMA Style

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 Sci. 2020;9(5):148-163. doi: 10.11648/j.earth.20200905.12

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@article{10.11648/j.earth.20200905.12,
  author = {Ejepu Jude Steven and Abdullahi Suleiman and Abdulfatai Asema Ibrahim and Umar Mohammed Umar},
  title = {Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria},
  journal = {Earth Sciences},
  volume = {9},
  number = {5},
  pages = {148-163},
  doi = {10.11648/j.earth.20200905.12},
  url = {https://doi.org/10.11648/j.earth.20200905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20200905.12},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria
AU  - Ejepu Jude Steven
AU  - Abdullahi Suleiman
AU  - Abdulfatai Asema Ibrahim
AU  - Umar Mohammed Umar
Y1  - 2020/09/17
PY  - 2020
N1  - https://doi.org/10.11648/j.earth.20200905.12
DO  - 10.11648/j.earth.20200905.12
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 148
EP  - 163
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20200905.12
AB  - 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.
VL  - 9
IS  - 5
ER  -

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Author Information

Ejepu Jude Steven

Department of Geology, School of Physical Sciences, Federal University of Technology, Minna, Nigeria
Abdullahi Suleiman

Department of Geology, School of Physical Sciences, Federal University of Technology, Minna, Nigeria
Abdulfatai Asema Ibrahim

Department of Geology, School of Physical Sciences, Federal University of Technology, Minna, Nigeria
Umar Mohammed Umar

Department of Geology and Mining, Faculty of Applied Sciences and Technology, Ibrahim Badamasi Babangida University, Lapai, Nigeria

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Ejepu Jude Steven, Abdullahi Suleiman, Abdulfatai Asema Ibrahim, Umar Mohammed Umar. (2020). Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria. Earth Sciences, 9(5), 148-163. https://doi.org/10.11648/j.earth.20200905.12

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ACS Style

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 Sci. 2020, 9(5), 148-163. doi: 10.11648/j.earth.20200905.12

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AMA Style

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 Sci. 2020;9(5):148-163. doi: 10.11648/j.earth.20200905.12

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@article{10.11648/j.earth.20200905.12,
  author = {Ejepu Jude Steven and Abdullahi Suleiman and Abdulfatai Asema Ibrahim and Umar Mohammed Umar},
  title = {Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria},
  journal = {Earth Sciences},
  volume = {9},
  number = {5},
  pages = {148-163},
  doi = {10.11648/j.earth.20200905.12},
  url = {https://doi.org/10.11648/j.earth.20200905.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20200905.12},
  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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Predictive Mapping of the Mineral Potential Using Geophysical and Remote Sensing Datasets in Parts of Federal Capital Territory, Abuja, North-Central Nigeria
AU  - Ejepu Jude Steven
AU  - Abdullahi Suleiman
AU  - Abdulfatai Asema Ibrahim
AU  - Umar Mohammed Umar
Y1  - 2020/09/17
PY  - 2020
N1  - https://doi.org/10.11648/j.earth.20200905.12
DO  - 10.11648/j.earth.20200905.12
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 148
EP  - 163
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20200905.12
AB  - 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.
VL  - 9
IS  - 5
ER  -

Copy | Download