Abstract
Current research in deep learning, which is widely used in mineral prospectivity prediction, focuses on obtaining high-performance models to predict mineral resources. However, because the network structure and depth of different algorithms differ, there are some differences in the correlation between the spatial pattern of ore-generating geological big data and the spatial location of discovered ore deposits; this causes instability in the prediction. To solve this problem, this paper proposes the use of ensemble learning to synthesize convolutional neural network algorithms and self-attention mechanism algorithms for mineral prospectivity prediction. In this study, 14 factors related to gold mineralization were selected, 10 types of geochemical exploration data (Au, Ag, As, Cu, Pb, Zn, Hg, Sb, W, and Mo) and 4 geological factors (ductile shear zones, brittle fault zones, mineralization-alteration body zones, and metamorphic quartz sandstone zones). Six classical convolutional neural network models (MobileNet V2, ResNet 50, VGG 16, AlexNet, LeNet, and VIT) were used to extract the features of the metallogenic factors. After training, a network model with an accuracy over 94% was obtained. Then, the mineral prospectivity of an unknown area was predicted. The models were evaluated according to their accuracy. Using these results, ensemble learning was performed, areas with high potential were obtained, and the prospectivity prediction map was drawn. This map provides guidance for gold exploration in the Bawanggou mine area of the northern Hanyin gold orefield, South Qinling, China. This comprehensive method can effectively leverage the advantages of various models, fully extract the internal relationships of deep-level mineralization, and has extremely high extensibility. The calculated results can be made more scientific and stable by adding more mineralization factors and introducing an algorithm with the new structure in the future.