Article · Applied Computing and Geosciences · May 10, 2025

Prediction of rare and anomalous minerals using anomaly detection and machine learning techniques

Abish Sharapatov, А. Садуов, Nazerke Assirbek, Madiyar Abdyrov, Beibit Tenelovich Zhumabayev

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.

Geochemistry and Geologic MappingAnomaly Detection Techniques and ApplicationsMachine Learning in Materials Sciencerare mineralsmineral classificationanomaly detection

Prediction of rare and anomalous minerals using anomaly detection and machine learning techniques

Abstract

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