Predictive modelling of mineral prospectivity using GIS is a valid and progressively more
accepted tool for delineating reproducible mineral exploration targets. In this study, machine …

Abstract Machine learning algorithms have been widely applied in mineral prospectivity
mapping (MPM). In this study, we implemented ensemble learning of extreme gradient …

Mineral prospectivity mapping (MPM) aims to reduce the areas for searching of mineral
deposits. Various statistical models that have been successfully adopted to delineate …

Convolutional neural network (CNN) has demonstrated promising performance in
classification and prediction in various fields. In this study, a CNN is used for mineral …

Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …

This study intends to showcase the application of Extreme Gradient boosting (XGboost), a
state-of-the-art ensemble-learning technique, for district-scale mineral potential mapping …

Abstract Machine learning methods that have been used in data-driven predictive modeling
of mineral prospectivity (eg, artificial neural networks) invariably require large number of …

The gradient boosting decision tree is a well-known machine learning algorithm. Despite
numerous advancements in its application, its efficiency still needs to be improved for large …

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for
delineation of undiscovered prospective targets in mineral exploration, has been spurred by …

Identification of anomalies related to mineralization and integration of multi-source
geoscience data are essential for mapping mineral prospectivity. In this study, we applied …