Utilizing remote sensing and aerogeophysical data for spatial decision support system for optimal gold mineralization site identification in North-Central Nigeria

Abstract

Remote sensing is employed to detect potential gold reserves in the study. • The research facilitates comprehension of structural geology and its mapping. • FOPCA and aeromagnetic techniques are utilized for advanced interpretation. • GIS modeling enhances the understanding of zones with mineral prospects.This study presents a comprehensive investigation utilizing remote sensing data and aerogeophysical datasets to identify potential economic gold mineralization sites in Zungeru, North-Central Nigeria. The research objectives include understanding the structural setting, mapping lithologic contacts, and detecting hydrothermally altered zones. Spatial suitability analysis was conducted on processed datasets. Feature-Oriented Principal Component Analysis (Crosta approach) was applied to selected Landsat 8 OLI imagery bands for mapping hydroxyl and iron oxide alterations. Additionally, regional topographic lineaments were mapped using SRTM DEM, and subsurface lineaments were identified using aeromagnetic data. Interpretation of the aeromagnetic data revealed complex anomalies associated with various structural characteristics, such as faults, folds, and fractures. The investigation of hydrothermal alteration zones through gamma-ray spectrometry data and K/eTh ratio analysis demonstrated a significant connection with igneous intrusions, further supported by ternary maps of the radioelements. Five thematic maps representing factors that control gold mineralization were integrated using GIS modeling to identify prospective zones. Five distinct mineral-prospective zones are delineated, showing varying levels of mineralization potential. The "Very Good" to "Good" zones are predominantly aligned in the NNE direction, with some occurrence in the NW quadrant. The spatial correlation with lithological characteristics and lineament features suggests their strong influence on mineral prospectivity. Around 76% of gold mineralization is within the most favorably projected zones.Both models show considerable reliability, with predictive accuracies surpassing 70%, as validated using the ROC/AUC metric. The Fuzzy Logic model showcases the highest predictive efficacy, with a prediction level of 78.3%. The validation results of gold mining sites from the models indicate a percentage agreement of 75%. The study guides future exploration efforts, directing them toward areas with the highest promise of finding gold. This work is instrumental in optimizing resource allocation and maximizing the efficiency of exploration endeavors, paving the way for more targeted and successful gold prospecting in the region.