CNN and Transformer-Based Mineral Prospectivity Mapping for Gold Exploration in the Sandstone Greenstone Belt, Yilgarn Craton, Western Australia

The Yilgarn Craton hosts some of the world’s largest orogenic gold deposits, yet discovery rates have declined sharply as near-surface resources approach exhaustion. Exploring deeper, covered terrains demands new predictive tools that transcend the limitations of conventional mineral prospectivity mapping (MPM). Here we integrate convolutional neural networks (CNNs) and Vision Transformers to construct a data-driven MPM framework trained on 6028 gold occurrences across 16 map sheets in the Yilgarn Craton. The CNN achieves 79.3% classification accuracy by capturing local structural features; the Vision Transformer attains 74.0% but identifies prospective zones in data-sparse regions that the CNN misses. An empirical test was conducted in the untrained Sandstone Greenstone Belt to verify the model’s generalization ability. The results reveal that most known gold deposits lie within the high metallogenic potential zones defined by the model. Meanwhile, three prospective targets are newly delineated in this area based on model prediction, including northwest-trending ultramafic units, a basalt-sediment transition zone and NW-SE trending amphibolite units along the Edale Shear Zone. These targets are hardly identifiable by conventional exploration techniques and merit further field investigation. These results demonstrate that CNN–Transformer integration provides a robust, complementary framework for orogenic gold exploration in covered terrains.