Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets

• Published in: Journal of African earth sciences (1994) Vol. 190; p. 104504
• Main Authors: Forson, Eric Dominic, Wemegah, David Dotse, Hagan, Godfred Bright, Appiah, Desmond, Addo-Wuver, Fortune, Adjovu, Isaac, Otchere, Frank Osae, Mateso, Said, Menyeh, Aboagye, Amponsah, Theophilus
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2022
• Subjects: Data-driven approach; Evidential layer; Logistic function; Multi-index overlay; Normalized density; Prediction-area plots; Logistic function; Prediction-area plots; Normalized density; Data-driven approach; Evidential layer; Multi-index overlay
• ISSN: 1464-343X; 1879-1956
• DOI: 10.1016/j.jafrearsci.2022.104504

The comparative relevance of each geospatial component of mineralization differs from one geological terrane to the other because various sought-after mineral deposit-types synonymously differ in different geological terranes. Hence, the possibility of employing a conceptual model to obtain a relationship or a quantitative function between various geospatial features (evidential layers) with respect to the mineral being sought is laudable, though these features may not necessarily have a generically related effect with the mineral being sought. As a consequence, there is the need to employ a technique that has the capacity to recognize the efficient and inefficient geospatial indicators of the mineral deposit-type being sought. In view of this, this study employed the logistic function, concentration-area fractal model and the prediction-area (P-A) plot to transform and discretize the continuous value of each evidential layer as well as generating intersection points of prediction rate indicators that are essential in obtaining the normalized densities, which were subsequently employed in generating the objective weight for each evidential layer in a data-driven way. The P-A and the normalized density techniques employed were vital in recognizing the indicator and non-indicator criteria. The results obtained acknowledged the potassium concentration layer as a non-indicator of gold mineralization within the study area and subsequently recognized the hydroxyl bearing mineral concentration layer as the most plausible indicator criteria among the six evidential layers (lineament density, iron concentration, hydroxyl concentration, gravity anomaly, magnetic anomaly and potassium concentration) employed in this study. These five indicator criteria were integrated to generate a mineral prospectivity map (MPM) over the study area based on the data-driven multi-index overlay approach adopted. The prediction rate for each of the 6 evidential layers (5 of which were the indicator criteria) as well as the MPM produced indicates that, the generation of objective weights in a data-driven manner via normalized density enhances the predicting ability of the MPM produced in comparison with the individual evidential layers. •Geospatial anomaly normalization using the logistic function. .•Evidential layer discretization using the concentration-area (C-A) fractal model.•Prediction rate assessment of each evidential layer using the prediction-area (P-A) plot.•Generation of objective weights for each evidential layer based on the normalized density technique.•Generation of mineral prospectivity map based on the data-driven multi-index overlay technique.