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Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced...
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Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced Mineral Prospectivity Mapping

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 September 2025 | Viewed by 3573

Special Issue Editors

Dr. Amirabbas Karbalaeiramezanali

E-Mail Website
Guest Editor
New Brunswick Department of Natural Resources and Energy Development, Fredericton, NB E3B 5H1, Canada
Interests: 2D & 3D mineral prospectivity mapping; geophysical and geochemical anomaly mapping; AI-aided mineral exploration; ore deposit modelling; ore-forming magmatic systems
Dr. Jeff Harris

E-Mail Website
Guest Editor
Mineral Exploration Research Centre, Harquail School of Earth Sciences, Laurentian University, 935 Ramsey Lake Road, Sudbury, ON P3E 2C6, Canada
Interests: mineral prospectivity mapping; remote predictive mapping; remote sensing applications to geological mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global demand for critical minerals, driven by technological advances and the shift to renewable energy, necessitates innovative exploration techniques. Traditional methods often fall short in addressing the complexities of locating these essential resources. Smart mineral exploration, utilizing cutting-edge technologies, offers a solution by integrating multi-source data to enhance mineral prospectivity mapping.

This Special Issue explores the transformative potential of smart mineral exploration for critical minerals. In recent years, advances in artificial intelligence (AI) and machine learning (ML) have revolutionized mineral exploration by enabling the integration of diverse datasets from geophysical, geochemical, remote sensing, and geological sources. These innovations have not only enhanced the accuracy and efficiency of mineral prospectivity mapping (MPM), but have also opened new avenues for discovering previously overlooked mineral deposits. This Special Issue explores the transformative impact of these technologies on MPM, emphasizing both knowledge-driven and data-driven approaches.

We welcome specific contributions related to the following:

  • Smart mineral exploration, highlighting innovative tools and techniques that enhance the exploration process.
  • AI- and ML-aided mineral exploration, showcasing case studies and applications that leverage these technologies for improved predictive capabilities.
  • Advanced 2D and 3D knowledge-driven and data-driven MPM, focusing on integrating traditional geological knowledge with modern data analytics.
  • Numerical simulation and big data analytics for mineral exploration, presenting methodologies that handle large datasets to uncover hidden patterns and insights.
  • Geochemical and geophysical anomaly mapping, detailing methods that identify and analyze anomalies indicative of mineral deposits.
  • Remote sensing- and GIS-based MPM, discussing the use of remote sensing data and GIS technologies to map and predict mineral occurrences.

This issue provides an overview of smart mineral exploration, highlighting the synergy between various data sources and technologies. By showcasing the latest research and applications, we aim to inspire innovation and collaboration, contributing to the sustainable exploration of critical minerals.

Dr. Amirabbas Karbalaeiramezanali
Dr. Jeff Harris
Guest Editors

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

  • critical minerals
  • artificial intelligence (AI)
  • machine learning (ML)
  • mineral exploration
  • mineral prospectivity mapping (MPM)
  • geophysical data
  • geochemical data
  • remote sensing
  • knowledge-driven approaches
  • data-driven approaches

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Published Papers (3 papers)

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Research

28 pages, 6120 KiB  
Article
Machine Learning Classification of Fertile and Barren Adakites for Refining Mineral Prospectivity Mapping: Geochemical Insights from the Northern Appalachians, New Brunswick, Canada
by Amirabbas Karbalaeiramezanali, Fazilat Yousefi, David R. Lentz and Kathleen G. Thorne
Minerals 2025, 15(4), 372; https://doi.org/10.3390/min15040372 - 2 Apr 2025
Cited by 1 | Viewed by 455
Abstract
This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be [...] Read more.
This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe2O3T vs. Gd and Al2O3 vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power. Full article
(This article belongs to the Special Issue Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced Mineral Prospectivity Mapping)
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29 pages, 20187 KiB  
Article
Applying Mineral System Criteria to Develop a Predictive Modelling for Epithermal Gold Mineralization in Northern New Brunswick: Using Knowledge-Driven and Data-Driven Methods
by Farzaneh Mami Khalifani, David R. Lentz, James A. Walker and Fereshteh Khammar
Minerals 2025, 15(4), 345; https://doi.org/10.3390/min15040345 - 27 Mar 2025
Viewed by 609
Abstract
Using mineral prospectivity mapping (MPM), the mineral systems approach enables the identification of geological indicators linked to ore formation. This approach streamlines exploration by minimizing the time and cost required to identify areas with the highest mineral potential. With its extensive till cover [...] Read more.
Using mineral prospectivity mapping (MPM), the mineral systems approach enables the identification of geological indicators linked to ore formation. This approach streamlines exploration by minimizing the time and cost required to identify areas with the highest mineral potential. With its extensive till cover and dense forests limiting bedrock exposure, New Brunswick provides an ideal environment to test this approach. The New Brunswick portion of the Canadian Appalachians hosts a diverse range of gold deposits and occurrences that formed during various stages of the Appalachian orogeny. In northern New Brunswick and the adjacent Gaspé Peninsula, the Tobique–Chaleur Zone contains several orogenic and epithermal gold systems that are closely associated with a large-scale crustal fault and its offshoots, i.e., the long-lived trans-crustal Rocky Brook–Millstream Fault system. To identify favorable zones for epithermal gold mineralization in northwestern New Brunswick, this study employed MPM by translating key mineral system components—such as ore metal sources, fluid pathways, traps, and geological controls—into mappable criteria for regional-scale analysis. The data were modeled through the integration of knowledge-based and data-driven methods, including fuzzy logic, geometric average, and logistic regression approaches. The concentration–area (C–A) fractal model was applied to reclassify the final maps based on prospectivity values obtained from these three approaches, dividing the mineral prospectivity maps into six classes, with threshold values emphasizing high-favorability zones. The fuzzy overlay model had the highest predictive accuracy (AUC 0.97), followed by the geometric average model (AUC 0.93), whereas the logistic regression identified more tightly constrained high-potential zones. In the prospectivity models, known epithermal gold mineralization consistently overlaps with regions of high favorability. This suggests a positive result from the use of MPM, indicating that this approach could be applicable to other regions and types of ore deposits. Full article
(This article belongs to the Special Issue Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced Mineral Prospectivity Mapping)
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24 pages, 13220 KiB  
Article
Evaluation of Deep Isolation Forest (DIF) Algorithm for Mineral Prospectivity Mapping of Polymetallic Deposits
by Mobin Saremi, Milad Bagheri, Seyyed Ataollah Agha Seyyed Mirzabozorg, Najmaldin Ezaldin Hassan, Zohre Hoseinzade, Abbas Maghsoudi, Shahabaldin Rezania, Hojjatollah Ranjbar, Basem Zoheir and Amin Beiranvand Pour
Minerals 2024, 14(10), 1015; https://doi.org/10.3390/min14101015 - 8 Oct 2024
Cited by 7 | Viewed by 1565
Abstract
Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in the Feizabad area, in northeastern Iran, using unsupervised anomaly detection methods, i.e., [...] Read more.
Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in the Feizabad area, in northeastern Iran, using unsupervised anomaly detection methods, i.e., isolation forest (IForest) and deep isolation forest (DIF) algorithms. As mineralization events are rare and complex, traditional approaches continue to encounter difficulties, despite advances in MPM. In this respect, unsupervised anomaly detection algorithms, which do not rely on ground truth samples, offer a suitable solution. Here, we compile geospatial datasets on the Feizabad area, which is known for its polymetallic mineralization showings. Fourteen evidence layers were created, based on the geology and mineralization characteristics of the area. Both the IForest and DIF algorithms were employed to identify areas with high mineralization potential. The DIF, which uses neural networks to handle non-linear relationships in high-dimensional data, outperformed the traditional decision tree-based IForest algorithm. The results, evaluated through a success rate curve, demonstrated that the DIF provided more accurate prospectivity maps, effectively capturing complex, non-linear relationships. This highlights the DIF algorithm’s suitability for MPM, offering significant advantages over the IForest algorithm. The present study concludes that the DIF algorithm, and similar unsupervised anomaly detection algorithms, are highly effective for MPM, making them valuable tools for both brownfield and greenfield exploration. Full article
(This article belongs to the Special Issue Smart Exploration of Critical Minerals: Integrating Multi-Source Data for Enhanced Mineral Prospectivity Mapping)
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