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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS. Oxford: Pergamon Press.
[2]	Yousefi, M., & Carranza, E. J. (2017). Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values. Journal of African Earth Sciences, 128, 47-60. doi: 10.1016/j.jafrearsci.2016.04.019.
[3]	Nykänen V, Salmirinne H (2007). Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland. Gold in the Central Lapland Greenstone Belt: Geological Survey of Finland, Special Paper 44: 251–269.
[4]	Behnia, P., Kerswill, J., Bonham-Carter, G., & Harris, J. (2009). Prospectivity mapping for gold deposits hosted by iron formation, in a portion of Western Churchill Province that includes Melville Peninsula, Nunavut, Canada. 2009 17th International Conference on Geoinformatics. doi: 10.1109/geoinformatics.2009.5293437.
[5]	Carranza EJM, Hale M (2001). Geologically-constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Nat Resour Res 10: 125–136.
[6]	Masoud Moradi & Sedigheh Basiri & Ali Kananian & Keivan Kabiri (2014). Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo-data, a case study in Central Alborz, Iran. Earth Sci Inform DOI 10.1007/s12145-014-0151-9.
[7]	Zhang, X., Pazner, M., and Duke N. (2007). Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California) ISPRS Journal of Photogrammetry & Remote Sensing 62 (2007) 271–282.
[8]	Quadros TF, Koppe JC, Strieder AJ et al (2006). Mineral-potential mapping: a comparison of weights of evidence and fuzzy methods. Nat Resour Res 15: 49–65.
[9]	Abubakar, A. J., Hashim, M., & Pour, A. B. (2018). Using ASTER Satellite Data for Mapping Hydrothermal Alteration as a Tool in Geothermal Exploration with GPS Field Validation. Advanced Science Letters, 24 (6), 4489-4495. doi: 10.1166/asl.2018.11632.
[10]	Ejepu J. S., Arikawe, E. A. and Abdullahi, S. (2018). Geological, Multispectral and Aeromagnetic Expressions of Pegmatite Hosted Mineralization of Keffi Sheet 208 NE, North-Central Nigeria. American Journal of Modern Physics and Application. Vol. 5, No. 4, pp. 53-69.
[11]	Gabr, S. S., Hassan, S. M. and Sadek, M. F. (2015). Prospecting for new gold-bearing alteration zones at El-Hoteib area, South Eastern Desert, Egypt, using remote sensing data analysis. Ore Geology Reviews, Elsevier.
[12]	Odeyemi, I. B. (1988). Lithostratigraghic and structural relationships of the upper Precambrian metasediments in Igarra area. In: Precambrian Geology of Nigeria. Geological survey of Nigeria, pp. 111-125.
[13]	Rahaman, M. A. (1988). Recent advances in the study of the Basement Complex of Nigeria. Precambrian Geology of Nigeria, Geological Survey of Nigeria Publications, 11-43.
[14]	Oyawoye, M. O. (1962). The Petrology of the District around Bauchi, Northern Nigeria. The Journal of Geology, 70 (5), 604-615. doi: 10.1086/626855.
[15]	Black, R. (1980). Precambrian of West Africa. Episodes, 4, 3–8.
[16]	Ajibade, A. C., Woakes, M. & Rahaman, M. A. (1981). Proterozoic crustal development in the Pan-African regime of Nigeria. In Kroner, A. (Ed.), Precambrian Plate Tectonics, Elsevier, Amsterdam.
[17]	Caby, R. Bertrand, J. M. I. and Black, R. (1981). Pan African ocean closure and continental collision in the Hoggar-Iforas segment, central Sahara. In: A Kroner (Editor) Precambrian plate tectonic. Elsevier, Amsterdam, 407-434.
[18]	Dada, S. S. (2006). Proterozoic evolution of Nigeria. In Oshi O. (Eds.), The Basement Complex of Nigeria and its Mineral Resources (A tribute to Prof. M. A. Rahaman). (pp. 29-44). Akin Jinad and Co. Ibadan.
[19]	Rahaman, M. A. & Ocan, D. (1978). On Relationship in the Precambrian Migmatite-gneiss on Nigeria. Journal of Mining and Geology, 15, 23-32.
[20]	Grant, N. K. (1978). Structural distinction between a sedimentary cover and an underlying basement in 600 m.y. old Pan-African domain of northwestern Nigeria, West Africa. Geological Society American Bulletin 89: 50-58.
[21]	McCurry, P. (1976). The geology of the Precambrian to Lower Palaeozoic rocks of northern Nig. A review In: C. A. Kogbe (Editor) geology of Nigeria. Elizabethan press Lagos, 15-39.
[22]	Olade, M. A. and Elueze, A. A. (1979). Petrochemistry of Illesha Amphibolites and Precambrian Crustal Evolution in the Pan-African Domain of Southwestern Nigeria. Precambrian Research, 8, Pp. 308-318, 1979.
[23]	Dada, S. S., Lancelot, J. R. and Briqueu, I. (1987). Age and origin of a Pan-African charnockitic complex: U-Pb and Rb-Sr evidence from the charnockitic complex at Toro, Northern Nigeria. Abtr. Vol. 14 Coll. Afri. Geol. Berlin, 72-73.
[24]	Oluyide, P. O. (1988). Structural trends in the Nigerian Basement Complex. In: Precambrian Geology of Nigeria. Geological Survey of Nigeria, pp. 93-98.
[25]	Olasehinde P. I. (1999). An integrated geologic and geophysical exploration technique for groundwater in the Basement Complex of West Central Nigeria. Water Resources Journal, 10, 46-49.
[26]	Olasehinde P. I., Ejepu S. J. & Alabi A. A. (2013). Fracture Detection in a Hard Rock Terrain Using Radial Geoelectric Sounding Techniques. Water Resources Journal 23 (1&2), 1-19.
[27]	Olasehinde, P. I. (2010). The Groundwaters of Nigeria: A Solution to Sustainable National Water Needs. Federal University of Technology, Minna Inaugural Lecture Series 17
[28]	Gupta, R. P. (2003). Remote Sensing Geology, second ed. Springer-Verlag, Berlin.
[29]	USGS/NASA (2015). Landsat 8 (L8) Data User’s Handbook; USGS/NASA: Sioux Falls, SD, USA, p. 106.
[30]	Abrams, M., Hook, S. (2001). ASTER User Handbook (version 2). Jet Propulsion Laboratory, Pasadena, CA-91109, USA. 135 pp.
[31]	Crósta, A. P., Filho, C. R. d. S. (2003). Searching for gold with ASTER. Earth Observation Magazine 12 (5), 38–41.
[32]	Ninomiya, Y., Fu, B., Cudhy, T. J. (2005). Detecting lithology with Advanced Spaceborne Thermal Emission and Refection Radiometer (ASTER) multispectral thermal infrared “radiance-at-senseor” data. Remote Sensing of Environment 99, 127–135.
[33]	Ninomiya, Y., Fu, B., Cudhy, T. J. (2006). Corrigendum to “Detecting lithology with Advanced Spaceborne Thermal Emission and Refection Radiometer (ASTER) multispectral thermal infrared ‘radiance-at-sensor’ data”. Remote Sensing of Environment 101, 567.
[34]	Gad, S., Kusky, T. (2006). Lithological mapping in the Eastern Desert of Egypt, the Barramiya area, using Landsat thematic mapper (TM). Journal of African Earth Sciences 44, 196–202.
[35]	Gad, S., Kusky, T. (2007). ASTER spectral ratioing for lithological mapping in the Arabian Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Research 11, 326–335.
[36]	International Geomagnetic Reference Field-11th Generation (2009). https://www.ngdc.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/GeophysicalModels/iso/xml/IGRF11.xml&view=getDataView&header=none.
[37]	Briggs IC (1974). Machine contouring using minimum curvature. Geophysics 39 (1): 39-48.
[38]	Crippen, R. E., E. J. Hajic, J. E. Estes, and R. G. Blom (1990). Statistical band and band-ratio selection to maximize spectral information in color composite displays, in preparation for submission to international Journal of Remote Sensing.
[39]	Zhang, X.; Pazner, M. Comparison of Lithologic Mapping with ASTER, Hyperion and ETM Data in the Southeastern Chocolate Mountains, USA. Photogramm. Eng. Remote Sens. 2007, 73, 555–561.
[40]	Ninomiya, Y. (2003). A stabilized vegetation index and several mineralogic indices defined for ASTER VNIR and SWIR data. Proceedings of IEEE 2003 International Geoscience and Remote Sensing Symposium: IGARSS'03, 3, pp. 1552–1554.
[41]	Nabighian, N. N. (1984). Towards a Three-dimensional Automatic Interpretation of Potential Field Data via Generalized Hilbert Transforms: Fundamental Relations, Geophysics49, 780–786.
[42]	Ansari, A.H. and Alamdar, K. (2009) Reduction to the Pole of Magnetic Anomalies Using Analytic Signal. World Applied Sciences Journal, 7, 405-409.
[43]	Roest, W. R., Verhoef, J., Pilkington, M. (1992). Magnetic interpretation using 3-D analytic signal. Geophysics 57, 116–125.
[44]	Ostrovskiy, E. (1975). Antagonism of radioactive elements in wallrock alterations fields and its use in aerogamma spectrometric prospecting. International Geology Review, 17 (4), 461-468. doi: 10.1080/00206817509471687.
[45]	Lillesand, T. M., Kiefer, R. W. (2004). Remote Sensing and Image Interpretation, fifth ed. John Wiley and Sons, Inc., New York.
[46]	Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing, 3rd ed., Academic Press, London.
[47]	Gupta, R. (2016). Enhanced Edge Detection Technique for Satellite Images. Cloud Computing and Security Lecture Notes in Computer Science, 273-283. doi: 10.1007/978-3-319-48671-0_25.
[48]	Batista, C. T., Veríssimo, C. U. V., Amaral, W. S. (2014). Levantamento de feições estruturais lineares a partir de sensoriamento remoto – uma contribuição para o mapeamento geotécnico na Serra de Baturité, Ceará. Geologia USP. Série Científica, 14 (2), 67-82. http://dx.doi.org/10.5327/Z1519-874X201400020004.
[49]	Kovesi, P. (1997). Symmetry and Asymmetry from Local Phase, in: Tenth Australian Joint 593 Converence on Artificial Intelligence. pp. 2–4. 594.
[50]	Kovesi, P. (1999). Image Features from Phase Congruency. The MIT Press, Videre: Journal of 595 Computer Vision Research Volume 1, 1–26.
[51]	Boleneus DE, Raines G, Causey J et al., (2001) Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling. US Department of the Interior, US Geological Survey, Menlo Park.
[52]	Sabins, F. (1997). Remote Sensing: Principles and interpretation (2nd ed.). NY: Freeman.
[53]	Knepper, D. H., Jr., (1989). Mapping hydrothermal alteration with Landsat Thematic Mapper data, Lee, Keenan, ed., Remote sensing in exploration geology — A combined short course and field trip: 28th International Geological Congress Guidebook T182, p. 13–21.
[54]	Khalid A., Elsayed Z. and AbdelHalim H. (2014). The Use of Landsat 8 OLI Image for the Delineation of Gossanic Ridges in the Red Sea Hills of NE Sudan. American Journal of Earth Sciences. Vol. 1, No. 3. pp. 62-67.
[55]	Pour, A. B., Hashim, M. & Marghany, M. (2014). Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data: a case study from Bau gold field, Sarawak, Malaysia. Arab J Geosci 7: 2393. https://doi.org/10.1007/s12517-013-0969-3.
[56]	Ducart, D. F., Crosta, A. P., Filho, C. R., & Coniglio, J. (2006). Alteration Mineralogy at the Cerro La Mina Epithermal Prospect, Patagonia, Argentina: Field Mapping, Short-Wave Infrared Spectroscopy, and ASTER Images. Economic Geology, 101 (5), 981-996. doi: 10.2113/gsecongeo.101.5.981.
[57]	Telford W. M., Geldart, L. P. & Sheriff, R. E. (1990). Applied geophysics, Cambridge University Press.
[58]	Lyatsky, H.V. (2004): The meaning of anomaly; Recorder, Canadian Society of Exploration Geophysicists, v. 29, no. 6, p. 50-51.
[59]	Ostrovskiy, E. A. (1975). Antagonism of radioactive elements in wallrock alteration fields and its use in aerogamma spectrometric prospecting. Int. Geol. Rev. 17 (4), 461–468.

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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