Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach
Gold mineral prospectivity mapping is crucial for identifying potential gold-bearing zones and supporting exploration efforts through advanced data analytics. However, many existing models tend to overestimate high-prospectivity areas, introducing biases toward known deposits and limiting their eff...
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| Format: | Article |
| Language: | English |
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FLAYOO PUBLISHING HOUSE LIMITED
2025-04-01
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| Series: | Proceedings of the Nigerian Society of Physical Sciences |
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| Online Access: | https://rans.nsps.org.ng/index.php/pnspsc/article/view/182 |
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| author | Momohjimoh Abdulsalami Joseph Omonile Fahad Abubakar Abdullateef Aliyu kabire Yahaya |
| author_facet | Momohjimoh Abdulsalami Joseph Omonile Fahad Abubakar Abdullateef Aliyu kabire Yahaya |
| author_sort | Momohjimoh Abdulsalami |
| collection | DOAJ |
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Gold mineral prospectivity mapping is crucial for identifying potential gold-bearing zones and supporting exploration efforts through advanced data analytics. However, many existing models tend to overestimate high-prospectivity areas, introducing biases toward known deposits and limiting their effectiveness in discovering new mineralized zones. To enhance exploration accuracy, data-driven approaches that enhance model interpretability and minimize predictive bias are essential. In this study, we applied Support Vector Machines (SVM) and Classification and Regression Trees (CART) to generate gold mineralization maps for Yagba West, utilizing an integrated dataset comprising SRTM DEM, Landsat 8 imagery, geological maps, and aeromagnetic data. The SVM model identified a gold-prospective area of 249.58 km² with an accuracy of 100%, while the CART model delineated a 132.13 km² prospective zone with an accuracy of 93%. This study further revealed that gold occurrences in the study area are predominantly concentrated in quartzite, quartz schist, gabbro, and quartz gabbro formations, primarily along NNE–SSW and NW–SE structural orientations, emphasizing the influence of structural controls on mineralization. These findings underscore the potential of machine learning in enhancing gold prospectivity mapping and optimizing exploration strategies in structurally controlled gold-bearing terrains.
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| format | Article |
| id | doaj-art-5c668265538e40d4b81deae98cec25c8 |
| institution | OA Journals |
| issn | 1115-5876 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | FLAYOO PUBLISHING HOUSE LIMITED |
| record_format | Article |
| series | Proceedings of the Nigerian Society of Physical Sciences |
| spelling | doaj-art-5c668265538e40d4b81deae98cec25c82025-08-20T02:16:55ZengFLAYOO PUBLISHING HOUSE LIMITEDProceedings of the Nigerian Society of Physical Sciences1115-58762025-04-012110.61298/pnspsc.2025.2.182Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approachMomohjimoh Abdulsalami0Joseph Omonile1Fahad Abubakar2Abdullateef Aliyu3kabire Yahaya4Department of Physics, Confluence University of Science and Technology, Osara, NigeriaDepartment of Physics, Confluence University of Science and Technology, Osara, NigeriaDepartment of Geosciences, Confluence University of Science and Technology, Osara, NigeriaDepartment of Physics, Confluence University of Science and Technology, Osara, NigeriaDepartment of Chemistry, Confluence University of Science and Technology, Osara, Nigeria Gold mineral prospectivity mapping is crucial for identifying potential gold-bearing zones and supporting exploration efforts through advanced data analytics. However, many existing models tend to overestimate high-prospectivity areas, introducing biases toward known deposits and limiting their effectiveness in discovering new mineralized zones. To enhance exploration accuracy, data-driven approaches that enhance model interpretability and minimize predictive bias are essential. In this study, we applied Support Vector Machines (SVM) and Classification and Regression Trees (CART) to generate gold mineralization maps for Yagba West, utilizing an integrated dataset comprising SRTM DEM, Landsat 8 imagery, geological maps, and aeromagnetic data. The SVM model identified a gold-prospective area of 249.58 km² with an accuracy of 100%, while the CART model delineated a 132.13 km² prospective zone with an accuracy of 93%. This study further revealed that gold occurrences in the study area are predominantly concentrated in quartzite, quartz schist, gabbro, and quartz gabbro formations, primarily along NNE–SSW and NW–SE structural orientations, emphasizing the influence of structural controls on mineralization. These findings underscore the potential of machine learning in enhancing gold prospectivity mapping and optimizing exploration strategies in structurally controlled gold-bearing terrains. https://rans.nsps.org.ng/index.php/pnspsc/article/view/182Gold mineralSupport vector machineClassification and regression treesYagba west |
| spellingShingle | Momohjimoh Abdulsalami Joseph Omonile Fahad Abubakar Abdullateef Aliyu kabire Yahaya Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach Proceedings of the Nigerian Society of Physical Sciences Gold mineral Support vector machine Classification and regression trees Yagba west |
| title | Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach |
| title_full | Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach |
| title_fullStr | Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach |
| title_full_unstemmed | Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach |
| title_short | Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach |
| title_sort | multisource data fusion for enhanced gold mineral prospectivity mapping in yagba west kogi state a machine learning approach |
| topic | Gold mineral Support vector machine Classification and regression trees Yagba west |
| url | https://rans.nsps.org.ng/index.php/pnspsc/article/view/182 |
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