Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana
Date
2023
Authors
Journal Title
Journal ISSN
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Publisher
Heliyon
Abstract
In this study, predictive models that characterize gold potential zones within the Josephine
Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods
comprising frequency ratio and information value. These predictive models were evaluated using
known locations of gold (Au) occurrence datasets and compared to each other. The mineral
prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were con structed by determining the spatial correlation between known locations of Au occurrences and
eight mineralization related factors. The locations of these known Au occurrences, which char acterize regions of anomalously high Au geochemical concentration and regions of previous or
ongoing artisanal mining operations were identified by using geographic positioning systems
(GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study
area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The
predictive capacity of each of the MPMs generated was determined by employing the area under
the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive
models produced based on the information value and the frequency ratio approaches were
respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are
good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing
down the highly prospective zones of mineral occurrences within the study area. However, the
overall predictive potential of the frequency ratio approach was better than the model produced
by the information value approach.
Description
Research Article
Keywords
Mineral prospectivity modeling, Information value, Frequency ratio