Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain

Abstract

Amidst the rapid advancement of artificial intelligence and information technology, the emergence of big data and machine learning provides a new research paradigm for mineral exploration. Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the critical geochemical element signals that affect the metallogenic potential of porphyry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish zircon fertility, and these machine learning methods achieve higher accuracy, exceeding 90%, compared with the traditional geochemical methods. Based on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are revealed. In addition, a deposit classification model was constructed, and the t-SNE method was used to visualize the differences in zircon trace element characteristics between porphyry deposits of different mineralization types. The study highlights the promise of machine learning algorithms in metallogenic potential assessment and mineral exploration by comparing them with traditional chemical methods, providing insights into future mineral classification models utilizing sub-mineral geochemical data.