Ore deposits are diverse with much of their diversity attributable to the complex interplay of
ore-forming processes with a variety of geological environments, over a range of scales and …

This two-part paper reports results of data integration using different machine learning
methods for target-scale prospectivity modeling of gold mineralization. The study area is the …

The excellent performance of convolutional neural network (CNN) and its variants in image
classification makes it a potential perfect candidate for dealing with multi-geoinformation …

Selection of non-deposit sites is a challenging issue affecting the application of supervised
algorithms for modeling mineral exploration targets. For this, equal number of deposit and …

Predictive modeling of mineral prospectivity, which integrates diverse evidence of ore-
forming processes based on an ore deposit genetic model constitutes a significant task in …

The Jiaojia-type gold deposits, hosting> 80% of gold resource in the Jiaodong Peninsula,
Eastern China, are characterized by veinlet-and disseminated-style mineralization …

Remote sensing data provide significant information about surface geological features, but
they have not been fully investigated as a tool for delineating mineral prospective targets …

Producing an accurate and valid mineral prospectivity map is one of the most significant
parts of mineral exploration studies. For this purpose, it is needed to obtain valid evidential …

The past two decades have seen a rapid adoption of artificial intelligence methods applied
to mineral exploration. More recently, the easier acquisition of some types of data has …

The demand for critical minerals is rapidly increasing worldwide, yet future global supply
remains uncertain due to the difficulty in discovering new deposits using traditional methods …