Abstract
ABSTRACT This study applies machine learning to detect and classify anomalous minerals within a large mineralogical dataset, enhancing geological exploration and resource identification. Using Isolation Forest and One-Class SVM, we identified rare minerals with distinct physical and chemical properties that deviate from common mineral compositions. These anomalies were further grouped using KMeans clustering into three categories, each linked to different geological formation environments: evaporitic, metamorphic, and magmatic processes. The study also evaluates the reliability of these machine learning models using a statistical benchmark and explores the role of deep learning in improving anomaly detection. The findings demonstrate the potential of unsupervised learning to enhance mineral classification, reduce exploration costs, and improve predictive modeling for rare mineral deposits. Future research will refine these methods by integrating Deep Isolation Forest, Autoencoders, and Graph Neural Networks, further strengthening machine learning applications in geosciences.