Critical mineral prospectivity mapping on the Gawler craton using a new machine learning framework

In recent years, the pace of technological development has accelerated along with the demand for minerals critical to sectors like defence, aerospace, automotive, renewable energy, and telecommunications. Countries increasingly seek access to reliable, secure, and resilient supplies of critical minerals, while global supply is uncertain due to market, technical, and commercial risks of exploration projects. This has made exploration geologists apply new technologies like artificial intelligence (AI) to increase the success rate of exploration projects. Recently, machine learning as a subset of AI has been successfully applied in different fields, such as spatial data analysis, to address different problems. This study proposes a machine learning-based framework for generating prospectivity maps of critical minerals focusing on the Gawler Craton in South Australia. This framework benefits from different novel machine learning methods for various purposes, including an improved generative adversarial network to overcome the class imbalance problem of the training dataset and the combination of positive and unlabelled learning and random forest as the main classifier for predicting mineralisation in the target area. We evaluated the efficiency of our proposed framework by creating prospectivity maps of mafic-ultramafic intrusion-hosted cobalt, chromium, and nickel mineralisation in the Gawler Craton. Various exploration datasets are used to generate input features, including publicly available geological, geophysical, and remote sensing datasets. We use known mineral occurrences as positive samples and randomly created a number of samples throughout the study area as unlabelled samples. Based on our results and different evaluation metrics, the model's performance is stable, and its accuracy is significantly higher than the model generated by a conventional approach using a standard random forest classifier. Our prospectivity maps show a strong spatial correlation between high probability values and known mineral occurrences and predicts several potential greenfield regions for future exploration.