Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex
The geological terrain of Northern Nigeria presents a complex mineral resource landscape that requires systematic exploration. This study applies a machine learning framework to geomagnetic data to enhance the identification of subsurface mineralized structures. Through the integration of analytic s...
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Elsevier
2025-06-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000611 |
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| author | Ema Abraham Ayatu Usman Ifunanya Amano |
| author_facet | Ema Abraham Ayatu Usman Ifunanya Amano |
| author_sort | Ema Abraham |
| collection | DOAJ |
| description | The geological terrain of Northern Nigeria presents a complex mineral resource landscape that requires systematic exploration. This study applies a machine learning framework to geomagnetic data to enhance the identification of subsurface mineralized structures. Through the integration of analytic signal processing with machine learning classifiers (Random Forest (RF) and Gradient Boosting (GB)), we analyze magnetic anomalies to predict subsurface geological features with a classification accuracy of 95.5%. The results identify mineral-rich zones across various depths, ranging from near-surface (280 m) to deep crustal levels (> 2000 m), with key prospective areas including Het, Kagoro, and Durbi. These regions contain mineral deposits such as monazite, tantalite, columbite, tourmaline, beryl, and kaolin. The study achieves a Pearson correlation coefficient of 0.956 between predicted and observed subsurface structures, demonstrating the effectiveness of this approach in mineral exploration. The methodology not only validates known geological features but also reveals previously unrecognized mineral-rich structures, contributing to a more data-driven strategy for resource assessment in geologically complex regions. |
| format | Article |
| id | doaj-art-3fde09ee1f584a3ebd4c3e05a761ec1c |
| institution | Kabale University |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-3fde09ee1f584a3ebd4c3e05a761ec1c2025-08-20T03:45:11ZengElsevierMachine Learning with Applications2666-82702025-06-012010067810.1016/j.mlwa.2025.100678Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complexEma Abraham0Ayatu Usman1Ifunanya Amano2Corresponding authors.; Department of Geology/Geophysics, Alex Ekwueme Federal University, Ndufu-Alike Ikwo, P.M.B. 1010 Abakaliki, Ebonyi, NigeriaCorresponding authors.; Department of Geology/Geophysics, Alex Ekwueme Federal University, Ndufu-Alike Ikwo, P.M.B. 1010 Abakaliki, Ebonyi, NigeriaDepartment of Geology/Geophysics, Alex Ekwueme Federal University, Ndufu-Alike Ikwo, P.M.B. 1010 Abakaliki, Ebonyi, NigeriaThe geological terrain of Northern Nigeria presents a complex mineral resource landscape that requires systematic exploration. This study applies a machine learning framework to geomagnetic data to enhance the identification of subsurface mineralized structures. Through the integration of analytic signal processing with machine learning classifiers (Random Forest (RF) and Gradient Boosting (GB)), we analyze magnetic anomalies to predict subsurface geological features with a classification accuracy of 95.5%. The results identify mineral-rich zones across various depths, ranging from near-surface (280 m) to deep crustal levels (> 2000 m), with key prospective areas including Het, Kagoro, and Durbi. These regions contain mineral deposits such as monazite, tantalite, columbite, tourmaline, beryl, and kaolin. The study achieves a Pearson correlation coefficient of 0.956 between predicted and observed subsurface structures, demonstrating the effectiveness of this approach in mineral exploration. The methodology not only validates known geological features but also reveals previously unrecognized mineral-rich structures, contributing to a more data-driven strategy for resource assessment in geologically complex regions.http://www.sciencedirect.com/science/article/pii/S2666827025000611Earth scienceGeomagnetic anomalyMachine learningMineral explorationSubsurface structural prediction |
| spellingShingle | Ema Abraham Ayatu Usman Ifunanya Amano Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex Machine Learning with Applications Earth science Geomagnetic anomaly Machine learning Mineral exploration Subsurface structural prediction |
| title | Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex |
| title_full | Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex |
| title_fullStr | Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex |
| title_full_unstemmed | Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex |
| title_short | Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex |
| title_sort | machine learning based classification of geological structures from magnetic anomaly data case study of northern nigeria basement complex |
| topic | Earth science Geomagnetic anomaly Machine learning Mineral exploration Subsurface structural prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000611 |
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