Abstract:
Geospatial modeling of mineral prospective regions is essential, owing to its significant contribution towards the
development and economic gains of many mineral-endowed countries including, Ghana. Thus, the primary
objective of this study is to delineate mineral potential zones in the Gomoa Area of Ghana’s southern Kibi Winneba belt in order to supplement mineral resources in Ghana’s existing mineral prospective zones. To ach ieve the aforementioned objective, researchers generated predictive models characterising gold mineralisation
prospects within the study area by employing machine learning techniques comprising support vector machines
(SVM) and naive bayes (NB) classifiers on mineral-related conditioning factors. These mineral-related factors
(geoscientific thematic layers) were sourced from geophysical, remote sensing, and geological datasets. The
resulting mineral prospective models (MPM) produced based on SVM and NB classifiers were exhibited in binary
classes (prospective and non-prospective zones). Regions delineated as prospective zones within the study area
were, respectively estimated to cover an area extent of 181.62 km2 and 296.02 km2 for the SVM-derived MPM
and NB-derived MPM and analogously characterise 22.07% and 35.97% of the study area. The ability of these
two models to predict was determined using the area under the receiver operating characteristic curve (AUC).
The AUC scores obtained for the SVM-derived MPM and the NB-derived MPM were, respectively, 0.90 and 0.83.
Outputs of the AUC scores generally indicate that the two models produced have good accuracy, although the
SVM-derived MPM performed better than that of the NB-derived MPM. Thus, the machine learning-based
mineral prospectivity models produced in this study are worthy outputs to guide the planning of detailed
mineral exploration surveys within the study area.
