About this document

Machine Learning for Magnetite Geochemistry by mohamed is a document available to read on EtoBox.

This study explores the use of eXtreme Gradient Boosting (XGBoost) machine learning to classify different types of mineral deposits based on magnetite trace element geochemistry. Analyzing a dataset of 3,865 magnetite samples, the model achieved a high accuracy of 96% and identified key elements such as Ni, Ga, Sc, and V as significant for classification. The research also highlights the role of deep volatile fluids in the formation of the Jinchuan Ni-Cu-PGE deposit, demonstrating the potential of machine l

Author

mohamed

Language

EN