Geochemical discrimination of pyrite in diverse ore deposit types through statistical analysis and machine learning techniques

• Published in: The American mineralogist Vol. 109; no. 5; pp. 846 - 857
• Main Authors: Li, Hengxu, Zhang, Zhaochong, Zhang, Ruixuan, Xie, Qiuhong, Zhang, Lei, Santosh, M.
• Format: Journal Article
• Language: English
• Published: Washington Mineralogical Society of America 27.05.2024; Walter de Gruyter GmbH
• Subjects: Accuracy; Apatite; Classifiers (sedimentation); Composition; Copper; Discriminant analysis; discrimination diagrams; Fluids; Fugacity; Gold; Iron oxides; Machine learning; Mineral deposits; Molybdenum; Neural networks; Orogeny; Porphyry copper; Pyrite; Sediment deposits; Sediments; Statistical analysis; Statistical methods; Sulphides; Support vector machines; Trace elements

Pyrite is a ubiquitous mineral in many ore deposits and sediments, and its trace element composition is mainly controlled by temperature, oxygen fugacity, pH, compositions of fluids, and host rock composition. A discriminant analysis (DA), based on multi-element compositions of pyrite, distinguishes...