Global Mineral Resource Exploration Using Multi-Sensor Satellite Data and Machine Learning Algorithms

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 359

Special Issue Editors


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Guest Editor
Sibanye-Stillwater Digital Mining Laboratory, University of the Witwatersrand, Johannesburg, South Africa
Interests: geoinformatics; spatial data analysis; data sciences and modelling; cyber-physical systems integration; remote sensing and machine learning applications in mineral resource exploration

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Guest Editor
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa
Interests: GIS and remote sensing; natural resources management; data sciences; spatial modeling; data analytics

Special Issue Information

Dear Colleagues,

In recent years, global mineral resource exploration has seen significant advancements, driven by the fusion of multi-sensor satellite data and the rise in machine learning algorithms. These technological breakthroughs have transformed how we identify and assess mineral deposits, enabling more efficient, cost-effective, and accurate exploration processes. This Special Issue, titled "Global Mineral Resource Exploration Using Multi-Sensor Satellite Data and Machine Learning Algorithms", brings together cutting-edge research that addresses the challenges and opportunities in this rapidly evolving field.

The issue will highlight innovative applications of remote sensing technologies such as hyperspectral, multispectral, and radar imaging, combined with artificial intelligence techniques like neural networks, random forests, and deep learning. These tools are being used to process large datasets and reveal new mineral prospects in previously inaccessible or underexplored regions, from mountainous terrains to deeply buried deposits.

Key topics include the integration of satellite data with geochemical, geological, and geophysical datasets, the development of novel machine learning models for mineral prospectivity mapping, and case studies demonstrating successful applications of these technologies in the field. The contributions in this issue aim to provide a comprehensive overview of the current state of mineral exploration, while also setting the stage for future research and technological advancements.

We invite scholars, researchers, and practitioners to engage with this Special Issue and contribute to the ongoing discourse in this exciting intersection of geoscience and technology.

Dr. Muhammad Mahboob
Dr. Iqra Atif
Guest Editors

Manuscript Submission Information

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Keywords

  • mineral resource exploration
  • multi-sensor satellite data
  • machine learning algorithms
  • remote sensing
  • geospatial data
  • hyperspectral imaging

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Published Papers (1 paper)

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Research

29 pages, 20113 KiB  
Article
Optimized Hydrothermal Alteration Mapping in Porphyry Copper Systems Using a Hybrid DWT-2D/MAD Algorithm on ASTER Satellite Remote Sensing Imagery
by Samane Esmaelzade Kalkhoran, Seyyed Saeed Ghannadpour and Amin Beiranvand Pour
Minerals 2025, 15(6), 626; https://doi.org/10.3390/min15060626 - 9 Jun 2025
Abstract
Copper is typically acknowledged as a critical mineral and one of the vital components of various of today’s fast-growing green technologies. Porphyry copper systems, which are an important source of copper and molybdenum, typically consist of large volumes of hydrothermally altered rocks, mainly [...] Read more.
Copper is typically acknowledged as a critical mineral and one of the vital components of various of today’s fast-growing green technologies. Porphyry copper systems, which are an important source of copper and molybdenum, typically consist of large volumes of hydrothermally altered rocks, mainly around porphyry copper intrusions. Mapping hydrothermal alteration zones associated with porphyry copper systems is one of the most important indicators for copper exploration, especially using advanced satellite remote sensing technology. This paper presents a sophisticated remote sensing-based method that uses ASTER satellite imagery (SWIR bands 4 to 9) to identify hydrothermal alteration zones by combining the discrete wavelet transform (DWT) and the median absolute deviation (MAD) algorithms. All six SWIR bands (bands 4–9) were analyzed independently, and band 9, which showed the most consistent spatial patterns and highest validation accuracy, was selected for final visualization and interpretation. The MAD algorithm is effective in identifying spectral anomalies, and the DWT enables the extraction of features at different scales. The Urmia–Dokhtar magmatic arc in central Iran, which hosts the Zafarghand porphyry copper deposit, was selected as a case study. It is a hydrothermal porphyry copper system with complex alteration patterns that make it a challenging target for copper exploration. After applying atmospheric corrections and normalizing the data, a hybrid algorithm was implemented to classify the alteration zones. The developed classification framework achieved an accuracy of 94.96% for phyllic alteration and 89.65% for propylitic alteration. The combination of MAD and DWT reduced the number of false positives while maintaining high sensitivity. This study demonstrates the high potential of the proposed method as an accurate and generalizable tool for copper exploration, especially in complex and inaccessible geological areas. The proposed framework is also transferable to other porphyry systems worldwide. Full article
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