Predictive modeling of geophysical anomalies in the metasediments of Bugaji area, part of Malumfashi Schist Belt, North-Western Nigeria

The Bugaji area, situated within the Malumfashi Schist Belt of northwestern Nigeria, primarily consists of metasediments that include quartzo-feldspathic and pelitic schists, and gneiss. However, this area poses a challenge in mineral exploration due to limited outcrop exposures and complex subsurface structures. Hence, there is the need for exhaustive geophysical studies and supplementary approaches to accurately delineate lithologies and structures. Therefore, this study combines field mapping and geophysical techniques with artificial intelligence (AI) modeling, comprising supervised learning algorithms, to overcome this exploration problem. Utilizing sophisticated AI techniques, specifically the Random Forest Classifier and K-Nearest Neighbor algorithms, geophysical data (gravity, magnetic, and radiometric measurements) were processed and analyzed. The AI model effectively filled data gaps, and identified potential lithological variations and prospective mineralization zones based on geophysical signatures derived from the integrated dataset. The AI modeling's commendable average accuracy of 85 % in predicting values underscores its efficacy in interpreting geophysical data. The success of random forest in the geological mapping process can be attributed to its ability to handle high-dimensional data, capture non-linear relationships between input variables, and mitigate overfitting. The integrated approach enhanced our understanding of subsurface geology in the Bugaji area.