Application of deep machine learning methods for structural analysis of ore bodies and prediction of optimal mining zones

The article studies application of deep machine learning methods to analyze the structure of ore bodies and prediction of optimal zones of mining mineral deposits. The research is aimed at developing a methodology of using deep learning to improve efficiency of the exploration and mining processes. Contemporary neural network architectures, including the convolutional and recurrent models, were used to solve the problems identified. An array of geological data on various types of deposits was used as an empirical base. The results demonstrate a significant increase in the accuracy of structural analysis of ore bodies (up to 95%) and in the efficiency of forecasting high-yield zones (up to 15% gain in production). Implementation of the developed approaches has the potential to reduce exploration costs by up to 30% and reduce environmental risks by optimizing mining operations. The obtained conclusions are of high value for the theory and practical application of artificial intelligence in the mining industry, opening prospects for further research in this direction.