Exploring the application of deep mineral exploration prediction based on multi-source geological data

In the process of seeking to improve the efficiency of underground mineral exploration, this paper significantly improves the accuracy of mineral exploration prediction by integrating geophysical, geochemical, and remote sensing data, and using deep learning techniques such as convolutional neural networks（CNN） and generative adversarial networks（GAN）. This method not only breaks through the limitations of traditional prospecting techniques, optimizes data processing processes, but also improves the model’s ability to interpret complex geological information. The research results indicate that the model integrating multi-source data and deep learning algorithms has shown significant advantages in copper exploration, laying the foundation for the progress and innovation of geological exploration technology, and opening up new research and application directions.