Gold prospectivity mapping using “SWARA” model on geophysical datasets in parts of Ilesa Schist belt, Southwestern Nigeria

This study employed a knowledge–driven model (stepwise weight assessment ratio analysis (SWARA)) for the prospectivity mapping of gold in parts of Ilesa Schist belt, Southwestern Nigeria. Aeromagnetic, aero-radiometric and remote sensing (Atmospheric Spaceborne Thermal Emission and reflection Radiometer (ASTER) data) datasets were utilized for this study. Data enhancement techniques were performed on the aeromagnetic data to produce the lineament map of the study area. Also, analyses of the aeroradiometric and ASTER datasets were used in delineating hydrothermally altered zones in the study area. Lithology, lineament density, hydrothermal alteration and slope were the factors considered to produce the gold potential map of the study area using the SWARA model for weight assignment. The study showed that NE–SW trending structures aid the transportation of hydrothermal and mineralizing fluids in the study area. Furthermore, gold mineralisation is observed to occur majorly on the granitoids. From the SWARA model, weight assignment results showed that the factors influencing gold mineralisation in descending order of importance are hydrothermal alteration, lithology, lineament density and slope. The produced gold mineralisation map based on the weight assignment classified the study area into five classes: background, low, moderate, high and very high with the southern, western and northwestern axis of the study area having moderate to very high potential of gold mineralisation. Qualitative validation using mining pits showed 73 % success rate while quantitative validation utilizing linear regression model showed a success rate of 74 % substantiating the reliability of the model. • Gold mineralisations are classified as primary and secondary. • Primary and secondary factors considered for this study involved lithology, lineament density, hydrothermal alteration and slope. • The factors were derived from geophysical, geological and ASTER datasets. • The knowledge–driven SWARA model was to weigth factors of gold mineralisation and the C–A fractal model was used to classify prospectivity map in a data driven way. • Qualitative validation and quantitative validations of the prospectivity map were done using mining pits and the quantitative validation was done using linear regression model.