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

Sharapatov A. Saduov A. Assirbek N. Abdyrov M. Zhumabayev B.
June 2025 International Association for Mathematical Geosciences

Applied Computing and Geosciences
2025 #26

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.

Anomaly detection , Geological processes , Isolation forest , KMeans clustering , Mineral composition , One-class SVM

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Department of Geophysics and Seismology, Satbayev University, Almaty, Kazakhstan
“GeoShar LLP, Almaty, Kazakhstan
Institute of Ionosphere, Almaty, Kazakhstan

Department of Geophysics and Seismology
“GeoShar LLP
Institute of Ionosphere

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026