This study aims to improve the efficiency of mineral exploration by introducing a novel application of Deep Convolutional Generative Adversarial Networks (DCGANs) to augment geological evidence layers. By training a DCGAN model with existing geological, geochemical, and remote sensing data, we have synthesized new, plausible layers of evidence that reveal unrecognized patterns and correlations. This approach deepens the understanding of the controlling factors in the formation of mineral deposits. The implications of this research are significant and could improve the efficiency and success rate of mineral exploration projects by providing more reliable and comprehensive data for decision-making. The predictive map created using the proposed feature augmentation technique covered all known deposits in only 18% of the study area.
