Martins Uchenna Obidiegwu, MSc
M.U. Obidiegwu1,2, N.P. Szabó1, M.A. Abbakar Mohammed1, A.A. Mohieldain1
1Department of Geophysics, University of Miskolc, Miskolc, Hungary, Miskolc, Hungary
2Department of Geology, University of Maiduguri, Borno State, Nigeria,Maiduguri, Borno State, Nigeria
The Abakaliki area of Ebonyi State, situated within the Lower Benue Trough of southeastern Nigeria, is renowned for its intricate structural framework and substantial mineral potential, particularly for base metals and associated mineralization. This study offers an innovative integrated analysis of aeromagnetic and radiometric data to identify structurally controlled and alteration-related mineral zones in the Abakaliki region.
Aeromagnetic data were processed to obtain reduced-to-pole and residual magnetic anomaly maps, from which analytic signal amplitude (ASA), tilt derivative, and total horizontal derivative (THDR) were derived to enhance fault structures and lithologic boundaries. Spectral radiometric data (K, U, Th) were processed to generate ternary composites and alteration-sensitive ratio maps (K/Th, U/Th) for identifying hydrothermal alteration signatures.
To enhance the detection of subtle anomalies, the Analytical Hierarchy Process (AHP), a decision-making framework that considers multiple criteria to structure complex problems, and a Deep Learning algorithm (Convolutional Neural Network (CNN)) were developed to classify and predict high-prospectively zones using combined aeromagnetic-radiometric attributes. Euler deconvolution and spectral depth analyses were employed to validate the depth and geometry of the anomalous sources.
The study reveals strong spatial correlations among magnetic lineaments, radiometric enrichments, and predicted mineralization zones, confirming the effectiveness of the AI-driven data-processing workflow. This approach offers a reproducible, data-driven framework for mineral exploration in the Abakaliki region of Nigeria and similar underexplored terrains worldwide.