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 650

Special Issue Editors


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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

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Guest Editor
Snowden Optiro, Datamine, Perth, WA 6000, Australia
Interests: geostatistics; stochastic simulations; mineral resource estimation

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Guest Editor
GeoGlobal LLC, Riverton, UT 84065, USA
Interests: mineral resource estimation; NI 43-101; JORC; risk analysis

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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

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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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Published Papers (1 paper)

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Research

23 pages, 8957 KiB  
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 141
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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