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Minerals
Special Issues
Geostatistical Methods and Practices for Specific Ore Deposits
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Geostatistical Methods and Practices for Specific Ore Deposits

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 December 2025 | Viewed by 2343

Special Issue Editors

Dr. Omid Asghari

E-Mail Website
Guest Editor
1. Department of Mining Engineering, University of Tehran, 1439957131 Tehran, Iran
2. Faculty of Science—Earth & Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
Interests: geostatistics; resource estimation; geometallurgy; machine learning in mining
Dr. Oscar Rondon

E-Mail Website
Guest Editor
Snowden Optiro, Datamine, Perth, WA 6000, Australia
Interests: geostatistics; stochastic simulations; mineral resource estimation
Dr. Abani R. Samal

E-Mail Website
Guest Editor
GeoGlobal LLC, Riverton, UT 84065, USA
Interests: mineral resource estimation; NI 43-101; JORC; risk analysis
Prof. Dr. Jeff Boisvert

E-Mail Website
Guest Editor
Civil and Environmental Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: geostatistical modeling of nonstationary domains

Special Issue Information

Dear Colleagues,

This Special Issue welcomes contributions on topics of high relevance and practical significance in the mining industry. We are particularly interested in solutions derived from extensive hands-on experience with specific ore deposit types, which have proven effective in real-world mining operations. Submissions should offer approaches that are not only tailored to a particular deposit type—such as porphyry copper, epithermal gold, orogenic gold, iron ore, lateritic nickel, magmatic nickel sulfide, PGE deposits, chromite, REEs, bauxite, coal, uranium, lithium pegmatites, and kimberlites—but can also be adapted and applied to similar cases elsewhere. The goal is to share practical insights that have emerged from repeated engagement with these deposit types that can benefit broader applications across the mining sector.

Dr. Omid Asghari
Dr. Oscar Rondon
Dr. Abani R. Samal
Prof. Dr. Jeff Boisvert
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

  • geostatistics
  • mineral resource estimation
  • grade control
  • ore deposit types
  • geological modeling
  • drilling strategy
  • resource classification
  • ore characterization
  • sampling
  • database management

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

Published Papers (2 papers)

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Research

22 pages, 13954 KB  
Article
Multivariate Simulation in Non-Stationary Domains: A Framework for Accurate Data Reproduction
by Rita M Teal, João Felipe Costa and Navid Mojtabai
Minerals 2025, 15(11), 1145; https://doi.org/10.3390/min15111145 - 31 Oct 2025
Viewed by 371
Abstract
Accurate multivariate Gaussian simulation is critical for resource assessment and mine planning, especially in polymetallic deposits where strong trends, data bias, and multivariate outliers introduce complexity. In this scenario, standard workflows applied to non-stationary domains may result in undesirable data statistics reproduction, especially [...] Read more.
Accurate multivariate Gaussian simulation is critical for resource assessment and mine planning, especially in polymetallic deposits where strong trends, data bias, and multivariate outliers introduce complexity. In this scenario, standard workflows applied to non-stationary domains may result in undesirable data statistics reproduction, especially the multivariate relationships between variables. This study proposes an enhanced simulation framework that integrates data standardization, multivariate outlier detection, trend modeling and removal, and a dual application of the Projection Pursuit Multivariate Transform (PPMT). The approach is demonstrated within a high-grade mineralized breccia domain of the Peñasquito deposit, utilizing data from diamond core and reverse circulation (RC) drill holes, including Au, Ag, Pb, and Zn. Bias in RC data was corrected using data standardization, and multivariate outliers were identified through the application of a robust Mahalanobis distance. Trend modeling was performed using a moving window average and was removed using the Gaussian Mixture Model and Stepwise Conditional Transform. PPMT was applied both before and after trend modeling in order to improve decorrelation and simulation performance. Results show improved data reproduction through histograms, variograms, and complex relationships, as well as correlation coefficients. Cross-validation confirms reduced bias and improved accuracy. This research highlights the importance of treating multivariate outliers and applying PPMT both before and after trend modeling. The study demonstrates that applying PPMT twice is more effective for managing persistent non-stationary features, especially in high-grade domains. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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23 pages, 8957 KB  
Article
Geometallurgical Cluster Creation in a Niobium Deposit Using Dual-Space Clustering and Hierarchical Indicator Kriging with Trends
by João Felipe C. L. Costa, Fernanda G. F. Niquini, Claudio L. Schneider, Rodrigo M. Alcântara, Luciano N. Capponi and Rafael S. Rodrigues
Minerals 2025, 15(7), 755; https://doi.org/10.3390/min15070755 - 19 Jul 2025
Viewed by 790
Abstract
Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was [...] Read more.
Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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