| dc.description |
Forson, E.D., Department of Physics, School of Physical and Mathematical Sciences, College of Basic and Applied Sciences, University of Ghana, Ghana; Wemegah, D.D., College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Hagan, G.B., Department of Physics, School of Physical and Mathematical Sciences, College of Basic and Applied Sciences, University of Ghana, Ghana; Appiah, D., Department of Physics, University of Education, Winneba, Ghana; Addo-Wuver, F., Department of Physics, University of Education, Winneba, Ghana; Adjovu, I., Geodita Resources Limited, Accra, Ghana; Otchere, F.O., College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Mateso, S., Department of Water and Environmental Science and Engineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania, Department of Environmental Engineering and Management, College of Earth Sciences and Engineering, The University of Dodoma, Tanzania; Menyeh, A., College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Amponsah, T., College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana |
en_US |
| dc.description.abstract |
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. � 2022 Elsevier Ltd |
en_US |