5. Principles of 3D Quantitative Prediction of Mineral Resources

This chapter delves into the principles and methodologies of 3D quantitative prediction of mineral resources, focusing on advanced technologies like 3D geological modeling and machine learning. It begins with the foundational theories and methodologies, including mathematical geology, multivariate statistical analysis, and metallogenic prediction theory. The text then explores the construction of 3D geological models using various data sources such as remote sensing images, geological maps, and geophysical data. A significant portion of the chapter is dedicated to the application of machine learning in prospecting model prediction, highlighting the use of big data and text mining to formulate initial mineralization models. The chapter also discusses the integration of 3D solid and block models for positional, quantitative, and probability prediction and evaluation of deep mineral resources. Additionally, it covers the mathematical methods commonly used in mineral prediction and the quantitative evaluation of deep mineral resources potential. The chapter concludes with an analysis of result integration and verification engineering deployment, emphasizing the importance of advanced technologies in managing and analyzing complex geological data. Readers will gain insights into the latest methodologies and technologies used in mineral resource prediction, making this chapter a valuable resource for professionals in the field.