Application of Principal Component Analysis Method Based on Machine Learning to Gold Deposit Type Discrimination: A Case Study of the Geochemical Characteristics of Pyrite

Pyrite is an important auriferous mineral in gold deposits, and its trace element composition can reflect key information such as the property of mineralizing fluids and deposit types. Machine learning methods can efficiently process massive amounts of trace element data in pyrite and conduct related research. Previous studies have trained relevant classifiers using various algorithms such as random forest, decision tree, and neural network. However, there are still problems such as insufficient types of gold deposit for training, limited use of element types, and poor discrimination of deposit types. Therefore, this article adopted the principal component analysis method in machine learning to train and established a discrimination diagram that can intuitively reflect the distinguishing features of most gold deposit types, and then evaluated the robustness of using pyrite chemical elements as discriminators for different deposit types. The dataset comprises 6939 sets of pyrite LA-ICP-MS trace element data from seven deposit types: Carlin, epithermal, orogenic, porphyry, iron oxide-copper-gold (IOCG), sedimentary exhalative (SEDEX) and volcanogenic massive sulfide (VMS), encompassing nearly one hundred deposits. Statistical analysis revealed that pyrite in Carlin and IOCG type deposits exhibits the highest enrichment of trace elements such as Au, As, Cu, and Se, followed by orogenic and epithermal types. Individual elements are significantly enriched in VMS and SEDEX type deposits, while pyrite in porphyry type gold deposits generally has lower trace element contents. Through data preprocessing, ten trace elements were selected, and two-dimensional discrimination diagrams for the composition of pyrite in different genetic types of gold deposits were plotted, and validation discrimination was performed on four deposit examples. The combination of two discriminant diagrams can effectively distinguish the Carlin, porphyry, IOCG, orogenic and epithermal type deposits. However, there is still some overlap between the SEDEX and VMS types, and other geological evidences need to be combined for comprehensive assessment. By comparing the discriminative effects with traditional discriminant diagrams and other machine learning methods, the principal component analysis discriminant diagram constructed in this study has the advantages of being concise and intuitive, covering a broader range of types, emphasizing deposit types, and having the best discriminative effect on Carlin and porphyry type deposits. This demonstrates its effectiveness and accuracy in solving practical deposit issues, providing valuable insights for researchers.