Applications of deep learning for mineral exploration and geological data analysis in mining

The speed at which deep learning (DL) is developing has brought a new dawn in the field of mineral exploration and analysis of geological data, and it has provided extremely useful tools to meet the increasing complexity and size of the geoscience data. In this review the importance of DL applications in mineral exploration is discussed in general and within the fields of remote sensing image classification, geophysical anomaly detection, geochemical pattern recognition and drilling data interpretation in particular. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and transformer models form DL architectures that have been found to be most effective in the automation and improvement of geological tasks previously limited by manual interpretation and a lack of scalability. Other challenges evidenced in the study are data heterogeneity, deficiency of labeled datasets, and interpretability of models. In addition, this paper addresses future research topics, with the integration of multimodal data, better explainability, transfer learning, and real-time decision-making systems. This review highlights how deep learning can change the face of geoscience today and how it can create a more novel, productive and controllable mineral exploration.