Abstract

Pb-Zn resources are critical strategic assets for many nations. The Dian-Dongbei (northeastern Yunnan) region in Yunnan Province is a significant production area for these resources in China, boasting considerable prospecting potential. However, conventional exploration methods are increasingly inadequate, as they often fail to rapidly and effectively identify concealed mineralization information. To tackle this challenge, we propose a hybrid GAN-ResNet convolutional neural network methodology. This approach constructs a data-driven prospecting model for Pb-Zn deposits in the Dian-Dongbei region, utilizing multi-source geoscientific data encompassing geology, geophysics, geochemistry, and remote sensing (Geo-Phys-Chem-RS) to conduct quantitative mineral prospectivity mapping. A GAN model was introduced to augment the multi-source geoscientific data based on the concepts of random down-sampling and pseudo-window size. The quality of the generated synthetic samples was evaluated using the Peak Signal-to-Noise Ratio (PSNR) metric. The results show that the synthetic samples achieved an average PSNR value of 33.67 dB, effectively preserving the original features of the geoscientific data. This confirms the feasibility and quality of the data generated by this augmentation method. Furthermore, when applied to train the ResNet model, this augmented data effectively increased the prediction accuracy from 0.765 to 0.842. The results demonstrate that the integrated GAN-ResNet method produces prediction maps with higher accuracy. Moreover, it significantly refines and narrows down the target areas with high mineralization potential. This precision can substantially reduce exploration costs, representing a marked improvement in prediction efficacy.

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