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Advancements in Mineral Resource Characterization Using Machine Learning

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 16255

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Special Issue Editors

Dr. Nadia Mery

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Guest Editor

Department of Mining Engineering, University of Chile, Santiago 8370450, Chile
Interests: geostatistics; machine learning; ore body evaluation; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals

Dr. Mohammad Maleki

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Department of Metallurgical and Mining Engineering, Universidad Católica del Norte, Antofagasta 1270709, Chile
Interests: geostatistical modeling; resource estimation; data analysis
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Dr. Emmanouil Varouchakis

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School of Mineral Resources Engineering, Technical University of Crete, University Campus, 73100 Chania, Greece
Interests: space-time geostatistics; gaussian processes; non-Eucledian spatial metrics; risk analysis; groundwater; sustainable development in mining
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Dr. Saeed Soltani-Mohammadi

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Guest Editor

Department of Mining Engineering, University of Kashan, Kashan 87137, Iran
Interests: mineral resource evaluation; geostatistics; machine learning; metaheuristic optimization methods

Special Issue Information

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:

The application of machine learning algorithms to enhance mineral resource characterization, including both supervised and unsupervised learning techniques.
The integration of geospatial and geological data with machine learning methods to improve resource models and predict mineral deposit distribution.
Innovative data processing and feature selection approaches for enhancing machine learning model performance in geological and geometallurgical contexts.
The automation and optimization of workflows through ML, especially in tasks such as remote sensing, geological mapping, and mineral classification.
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 250 words) can be sent to the Editorial Office for assessment.

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

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

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Published Papers (7 papers)

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Research

21 pages, 2951 KB

Open AccessArticle

Evaluating SWIR Spectral Data and Random Forest Models for Copper Mineralization Discrimination in the Zhunuo Porphyry Deposit

by Jiale Cao, Lifang Wang, Xiaofeng Liu and Song Wu

Minerals 2026, 16(2), 213; https://doi.org/10.3390/min16020213 - 19 Feb 2026

Viewed by 458

Abstract

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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26 pages, 5845 KB

Open AccessArticle

Automated 3D Multivariate Domaining of a Mine Tailings Deposit Using a Continuity-Aware Geostatistical–AI Workflow

by Keyumars Anvari and Jörg Benndorf

Minerals 2025, 15(12), 1249; https://doi.org/10.3390/min15121249 - 26 Nov 2025

Cited by 2 | Viewed by 956

Abstract

Geochemical data from mine tailings are layered, compositional, and noisy, complicating automated domaining. This study introduces a continuity-aware workflow the Geostatistical k-means Recurrent Neural Network (GkRNN) that links compositional preprocessing and geostatistical continuity to sequence learning, allowing depth order and lateral context to influence final domain labels. The workflow begins with a centered log-ratio (CLR) transform, followed by construction of a spectral embedding derived from kernelized direct and cross variograms. Clustering is carried out in this embedded space, and depth sequences are regularized with a hidden Markov model (HMM) model and a long short-term memory (LSTM) network. When applied to a multivariate set of tailing drillholes, stratigraphically coherent zones were obtained, depthwise proportions were stabilized, and vertical as well as lateral semivariograms remained consistent with laminated material. Compared with k-means and Gaussian Mixture baselines, over-segmentation was reduced and the intended layered architecture was recovered in most drillholes. The result is a reproducible domaining workflow that enables clearer grade estimation and more transparent risk evaluation. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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20 pages, 1483 KB

Open AccessEditor’s ChoiceArticle

Integrating Geological Domains into Machine Learning for Ore Grade Prediction: A Case Study from a Porphyry Copper Deposit

by Mohammad Maleki, Nadia Mery, Saed Soltani-Mohammadi, Jordan Plaza-Carvajal and Emmanouil A. Varouchakis

Minerals 2025, 15(11), 1175; https://doi.org/10.3390/min15111175 - 8 Nov 2025

Viewed by 1666

Abstract

Accurate grade prediction in porphyry copper deposits requires not only capturing spatial continuity but also accounting for geological controls. This study evaluates the added value of incorporating alteration and mineralization domains into machine learning (ML) models for copper grade estimation at the Iju porphyry Cu deposit, Iran. We compare four scenarios: spatial coordinates only, coordinates + alteration, coordinates + mineralization, and coordinates + both domains. A three-stage workflow was developed, in which Random Forest classifiers—optimized with Particle Swarm Optimization (PSO-RF)—classify alteration and mineralization zones, which are later integrated into regression models for ore grade prediction. Model performance was assessed using nested spatial cross-validation and benchmarked against Support Vector Machines (SVM). In comparative analysis, the PSO-RF framework consistently outperformed SVM, achieving more balanced accuracy between training and testing data and demonstrating greater robustness to class imbalance in domain classification. Moreover, results show that combining alteration and mineralization domains improves predictive performance (R² = 0.78; RMSE was reduced by 5.6% relative to coordinates-only). Although numerically moderate, this reduction in error translates into more reliable tonnage and grade estimations near cut-off grades, thereby enhancing the economic confidence of resource evaluations. These findings demonstrate that integrating multiple geological domains can improve both the accuracy and interpretability of ML-based grade models, providing a practical and reproducible workflow for porphyry copper resource evaluation. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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30 pages, 3046 KB

Open AccessEditor’s ChoiceArticle

Geostatistically Enhanced Learning for Supervised Classification of Wall-Rock Alteration Using Assay Grades of Trace Elements and Sulfides

by Abhishek Borah, Parag Jyoti Dutta and Xavier Emery

Minerals 2025, 15(11), 1128; https://doi.org/10.3390/min15111128 - 29 Oct 2025

Cited by 1 | Viewed by 2449

Abstract

The spatial zoning of wall-rock alteration is a useful guide for exploration of porphyry deposits. The current techniques to typify and quantify alteration types have a component of subjectivity and may not reconcile with mineralogical observations. An alternative is to apply machine learning (ML) to classify alteration based on geochemical and mineralogical feature variables. However, classification loses accuracy because of natural and artificial short-scale variability and missing information, or because it ignores the spatial correlations of the feature variables. Here we show that these inconveniences can be overcome by replacing these variables with proxies obtained through geostatistical simulation. The use of such proxies improves the accuracy scores by eight percentual points by removing the noise affecting the feature variables and infilling their missing values. Furthermore, the uncertainty in the classification predictions can be quantified accurately. Our results demonstrate how geostatistics enriches ML to achieve higher predictive performance and handle incomplete and noisy data sets in a spatial setting. This synergy has far-reaching consequences for decision making in mining exploration, geological modeling, and geometallurgical planning. Beyond the presented pioneering application, we expect our approach to be used in supervised classification problems that arise in varied disciplines of natural sciences and engineering and involve regionalized data. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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39 pages, 13361 KB

Open AccessEditor’s ChoiceArticle

Mineralogical, Petrological, 3D Modeling Study and Geostatistical Mineral Resources Estimation of the Zone C Gold Prospect, Kofi (Mali)

by Jean-Jacques Royer and Niakalé Camara

Minerals 2025, 15(8), 843; https://doi.org/10.3390/min15080843 - 8 Aug 2025

Cited by 2 | Viewed by 2879

Abstract

A 3D model integrating mineralogical, petrological, and geostatistical resource estimation was developed for Zone C of the Kofi Birimian gold deposit in Western Mali. Petrographic analysis identified two forms of gold mineralization: (i) native gold or electrum inclusions within pyrite, and (ii) disseminated native gold along pyrite fractures. Four types of hydrothermal alteration–epidotization, chloritization, carbonatization, and albitization were observed microscopically. Statistical analysis of geochemical data classified five lithologies: mafic dyke, felsic dyke, diabase, faulted breccia, and intermediate quartz diorite. Minerals identified petrographically were corroborated by multivariate correlations among elements (Cr, Fe, Ni, Al, Ti, Na, and Ca), as revealed by Principal Component Analysis (PCA). A 3D borehole-based model revealed spatial correlations between hydrothermal alteration zones and associated geochemical anomalies, notably tourmalinization (B) and albitization (Na), with the latter serving as a key indicator for new exploration targets. The spatial associations of anomalous Ag, B, Hg, As, and Na commonly linked to tourmalinization suggest favorable zones for gold and silver mineralization. Geostatistical analysis identified isotropic continuous mineralized structures for most elements, including gold. Spherical isotropic variograms with ranges from 35 to 75 m were fitted for in situ resource estimation (e.g., silver ≈ 40 m; gold ≈ 60 m). The resulting estimated resources (indicated + inferred), based on a 1.0 g/t Au cut-off, are 2.476 Mt at 3.5 g/t Au indicated (0.278 Moz or 8.67 t), and 1.254 Mt at 2.78 g/t Au inferred (0.112 Moz or 3.49 t). This study provides a framework for identifying new mineralized zones, and the multidisciplinary approach demonstrates the connections between mineralogy and the information embedded in geochemical datasets, which are revealed through appropriate tools and an understanding of the underlying processes. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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27 pages, 15736 KB

Open AccessEditor’s ChoiceArticle

Predicting Manganese Mineralization Using Multi-Source Remote Sensing and Machine Learning: A Case Study from the Malkansu Manganese Belt, Western Kunlun

by Jiahua Zhao, Li He, Jiansheng Gong, Zhengwei He, Ziwen Feng, Jintai Pang, Wanting Zeng, Yujun Yan and Yan Yuan

Minerals 2025, 15(2), 113; https://doi.org/10.3390/min15020113 - 24 Jan 2025

Cited by 5 | Viewed by 3650

Abstract

This study employs multi-source remote sensing information and machine learning methods to comprehensively assess the geological background, structural features, alteration anomalies, and spectral characteristics of the Malkansu Manganese Ore Belt in Xinjiang. Manganese mineralization is predicted, and areas with high mineralization potential are delineated. The results of the feature factor weight analysis indicate that structural density and lithological characteristics contribute most significantly to manganese mineralization. Notably, linear structures are aligned with the direction of the manganese belt, and areas exhibiting high controlling structural density are closely associated with the locations of mineral deposits, suggesting that structure plays a crucial role in manganese production in this region. The Area Under the Curve (AUC) values for the Random Forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost) models were 0.975, 0.983, and 0.916, respectively, indicating that all three models achieved a high level of performance and interpretability. Among these, the NB model demonstrated the highest performance. By algebraically overlaying the predictions from these three machine learning models, a comprehensive mineralization favorability map was generated, identifying 11 prospective mineralization zones. The performance metrics of the machine learning models validate their robustness, while regional tectonics and stratigraphic lithology provide valuable characteristic factors for this approach. This study integrates multi-source remote sensing information with machine learning methods to enhance the effectiveness of manganese prediction, thereby offering new research perspectives for manganese forecasting in the Malkansu Manganese Ore Belt. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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17 pages, 7198 KB

Open AccessArticle

DCGAN-Based Feature Augmentation: A Novel Approach for Efficient Mineralization Prediction Through Data Generation

by Soran Qaderi, Abbas Maghsoudi, Amin Beiranvand Pour, Abdorrahman Rajabi and Mahyar Yousefi

Minerals 2025, 15(1), 71; https://doi.org/10.3390/min15010071 - 13 Jan 2025

Cited by 18 | Viewed by 2480

Abstract

This study aims to improve the efficiency of mineral exploration by introducing a novel application of Deep Convolutional Generative Adversarial Networks (DCGANs) to augment geological evidence layers. By training a DCGAN model with existing geological, geochemical, and remote sensing data, we have synthesized new, plausible layers of evidence that reveal unrecognized patterns and correlations. This approach deepens the understanding of the controlling factors in the formation of mineral deposits. The implications of this research are significant and could improve the efficiency and success rate of mineral exploration projects by providing more reliable and comprehensive data for decision-making. The predictive map created using the proposed feature augmentation technique covered all known deposits in only 18% of the study area. Full article

(This article belongs to the Special Issue Advancements in Mineral Resource Characterization Using Machine Learning)

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