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Amin Beiranvand Pour

3 papers indexed

crossrefMining2026-06-16

Evaluation of Supervised Machine Learning Algorithms for Mapping Hydrothermal Alteration Zones Associated with Porphyry Copper Mineralization Using ASTER Satellite Imagery

Mahin Rostami, Amin Beiranvand Pour

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrotherma…

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crossrefRemote Sensing2023-07-25Cited by 34

Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data

Mahnaz Abedini, Mansour Ziaii, Timofey Timkin, Amin Beiranvand Pour

The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite r…

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crossrefMinerals2023-05-31Cited by 36

Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

Soufiane Hajaj, Abderrazak El Harti, Amine Jellouli, Amin Beiranvand Pour, Saloua Mnissar Himyari, Abderrazak Hamzaoui, et al.

Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) tec…

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