Advancements in Mineral Resource Characterization Using Machine Learning

Dear Colleagues,

The traditional methods of mineral resource characterization have long relied on geological models, statistical techniques, and manual workflows to assess the quantity, quality, and distribution of mineral deposits. However, with the rapid advancement of machine learning (ML), new opportunities are emerging to improve the accuracy, efficiency, and predictive power of these assessments. By leveraging vast and complex datasets, ML algorithms offer innovative solutions to geological and geospatial challenges, optimizing exploration, resource management, and geometallurgical processes.

This Special Issue seeks to explore the latest advancements in applying ML techniques to the characterization of mineral resources. The integration of ML—such as deep learning, neural networks, ensemble methods, and unsupervised learning—into resource modeling workflows holds significant potential for improving the precision of geological models, understanding mineral deposit distribution, and automating time-consuming tasks.

We invite submissions of original scientific research focusing on the following topics:

1. The application of machine learning algorithms to enhance mineral resource characterization, including both supervised and unsupervised learning techniques.
2. The integration of geospatial and geological data with machine learning methods to improve resource models and predict mineral deposit distribution.
3. Innovative data processing and feature selection approaches for enhancing machine learning model performance in geological and geometallurgical contexts.
4. The automation and optimization of workflows through ML, especially in tasks such as remote sensing, geological mapping, and mineral classification.
5. Case studies and practical applications that demonstrate the successful use of machine learning in mineral resource characterization across various geological environments.

This Special Issue will contribute to a deeper understanding of how machine learning can be applied to solve real-world problems in mineral resource characterization. It will serve as a valuable resource for researchers, practitioners, and industries aiming to stay at the forefront of technology-driven advancements in resource modeling.

We look forward to receiving your contributions.

Dr. Nadia Mery
Dr. Mohammad Maleki
Dr. Emmanouil Varouchakis
Dr. Saeed Soltani-Mohammadi
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.

• mineral resource characterization
• machine learning in geology
• geospatial data analysis
• deep learning in mining
• supervised learning
• unsupervised learning
• geological and geometallurgical modeling
• data mining in mining
• neural networks for resource estimation
• predictive modeling in mining
• mineral exploration
• feature selection for geological data
• automated resource estimation
• remote sensing and machine learning
• ensemble methods in mining
• mining data analytics
• optimization in resource estimation
• AI in mineral resource assessment
• geospatial modeling in mining
• mineral deposit modeling
• geometallurgical data analysis