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Minerals
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Feature Papers in Mineral Exploration Methods and Applications 2025
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Feature Papers in Mineral Exploration Methods and Applications 2025

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 287

Special Issue Editor

Prof. Dr. Michael S. Zhdanov

E-Mail Website
Guest Editor
Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, USA
Interests: theoretical and applied geophysics; inverse theory; joint inversion; mineral exploration; electromagnetic, gravity, magnetic, and seismic methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Feature Papers in Mineral Exploration Methods and Applications 2025", will focus on recent developments in mineral exploration methods and their applications in studying mineral deposits.

The Special Issue will present by-invitation-only original research and review papers from prominent researchers in the field of mineral resource exploration, including geological, geophysical, geochemical methods, and remote sensing. In addition, contributions on historical, technical, and practical aspects of exploration for mineral deposits will be highlighted. We invite contributions from around the world, especially those emphasizing emerging mineral exploration techniques and novel interpretation schemes, including machine learning and AI-added data analysis. Finally, papers on novel methods of mineral resource prospecting and their application, including mathematical aspects of multiple data processing as well as joint interpretation and examples of successful case studies, will also be featured in this Special Issue.

Considering the complex challenges of modern-day exploration, the main focus of this Special Issue will be on presenting the key technical and scientific advances that will improve exploration success and lead to the discovery and successful development of mineral deposits. 

Prof. Dr. Michael S. Zhdanov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geochemical surveys
  • geological surveys
  • geophysical surveys
  • mineral exploration
  • remote sensing
  • rock physics
  • mineral deposits

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

29 pages, 16357 KiB  
Article
Evaluation of Heterogeneous Ensemble Learning Algorithms for Lithological Mapping Using EnMAP Hyperspectral Data: Implications for Mineral Exploration in Mountainous Region
by Soufiane Hajaj, Abderrazak El Harti, Amin Beiranvand Pour, Younes Khandouch, Abdelhafid El Alaoui El Fels, Ahmed Babeker Elhag, Nejib Ghazouani, Mustafa Ustuner and Ahmed Laamrani
Minerals 2025, 15(8), 833; https://doi.org/10.3390/min15080833 - 5 Aug 2025
Abstract
Hyperspectral remote sensing plays a crucial role in guiding and supporting various mineral prospecting activities. Combined with artificial intelligence, hyperspectral remote sensing technology becomes a powerful and versatile tool for a wide range of mineral exploration activities. This study investigates the effectiveness of [...] Read more.
Hyperspectral remote sensing plays a crucial role in guiding and supporting various mineral prospecting activities. Combined with artificial intelligence, hyperspectral remote sensing technology becomes a powerful and versatile tool for a wide range of mineral exploration activities. This study investigates the effectiveness of ensemble learning (EL) algorithms for lithological classification and mineral exploration using EnMAP hyperspectral imagery (HSI) in a semi-arid region. The Moroccan Anti-Atlas mountainous region is known for its complex geology, high mineral potential and rugged terrain, making it a challenging for mineral exploration. This research applies core and heterogeneous ensemble learning methods, i.e., boosting, stacking, voting, bagging, blending, and weighting to improve the accuracy and robustness of lithological classification and mapping in the Moroccan Anti-Atlas mountainous region. Several state-of-the-art models, including support vector machines (SVMs), random forests (RFs), k-nearest neighbors (k-NNs), multi-layer perceptrons (MLPs), extra trees (ETs) and extreme gradient boosting (XGBoost), were evaluated and used as individual and ensemble classifiers. The results show that the EL methods clearly outperform (single) base classifiers. The potential of EL methods to improve the accuracy of HSI-based classification is emphasized by an optimal blending model that achieves the highest overall accuracy (96.69%). The heterogeneous EL models exhibit better generalization ability than the baseline (single) ML models in lithological classification. The current study contributes to a more reliable assessment of resources in mountainous and semi-arid regions by providing accurate delineation of lithological units for mineral exploration objectives. Full article
(This article belongs to the Special Issue Feature Papers in Mineral Exploration Methods and Applications 2025)
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