Application of convolution neural networks in gold exploration and prediction in Shandong Province

Rapid progress has been made in the application of big data and artificial intelligence technology in the prediction of mineral resources. However, the application of machine learning technology based on convolutional neural networks remains in the exploration and experimental stages, with few practical examples and accomplishments achieved in the exploration and prediction of mineral resources in China. This study proposed applying convolutional neural networks to the exploration of gold deposits. Specifically, a neural network was trained for 2000 rounds using measured geological, mineral, geophysical, and geochemical data collected from a mineralization region covering an area of 3×10⁴ km² in a gold deposit in Shandong Province. Consequently, a 1D convolutional neural network model with accuracy of 0.95 and a loss rate of 0.11 was obtained. This model was employed to predict the distribution locations of gold deposits (exploration target areas) in other unknown areas in Shandong Province, yielding encouraging outcomes.