Optimizing the Solid Mineral Exploration Process Using Machine Learning Techniques in Geological Information Systems

Solid mineral exploration is key in mineral resource prospecting. By integrating machine learning techniques into geological information systems (GIS), new avenues have been paved for improving the efficiency and precision of mineral discovery. This project aims to utilize machine learning techniques within geological information systems to optimize and refine the process of solid mineral exploration. Experiments have shown that the GIS models based on this method have achieved prediction accuracies of 92%, 89%, and 94% on three different datasets, respectively, while the recognition success rates were 87%, 91%, and 88%. The test results indicate that this approach can yield substantial economic benefits. The findings of this project will lay the theoretical foundation for achieving high precision, high success rate, high efficiency, and high economic returns in solid mineral exploration methods, and set a theoretical foundation for efficient prediction and development of mineral resources.