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Minerals
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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: 30 June 2026 | Viewed by 6952

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 (6 papers)

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Research

25 pages, 21082 KB  
Article
Probabilistic Modeling of Lateritic Nickel Mineral Resources
by Roberto Rolo, Jafar Arief and Selvi Yuminti
Minerals 2026, 16(5), 551; https://doi.org/10.3390/min16050551 (registering DOI) - 20 May 2026
Abstract
Lateritic nickel deposits exhibit complex weathering-driven geometries, strong vertical variability, and complex multivariate geochemical relationships. These characteristics challenge conventional deterministic resource modeling. This paper presents a unified probabilistic workflow for lateritic nickel mineral resource modeling that integrates lithology and grade simulation within a [...] Read more.
Lateritic nickel deposits exhibit complex weathering-driven geometries, strong vertical variability, and complex multivariate geochemical relationships. These characteristics challenge conventional deterministic resource modeling. This paper presents a unified probabilistic workflow for lateritic nickel mineral resource modeling that integrates lithology and grade simulation within a consistent geostatistical framework. The methodology combines unfolding, plurigaussian simulation, multivariate imputation of incomplete datasets, projection pursuit multivariate transformation (PPMT) for decorrelation, and conditional simulation using the Turning Bands algorithm. Application to an Indonesian lateritic nickel deposit demonstrates reproduction of lithological proportions, spatial continuity, marginal distributions, and complex multivariate relationships. The proposed workflow enables explicit quantification of geological and grade uncertainty, providing a basis for uncertainty assessment in recoverable resource estimation and supporting downstream applications such as resource classification and drillhole spacing analysis. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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30 pages, 7636 KB  
Article
Advanced Resource Modelling and Agile Scenario Generation for Mineral Exploration at the Cu-Au (Mo-Ag) San Antonio–Potrerillos District, Chile
by Julian M. Ortiz, Sebastián Avalos, Paula Larrondo, Ximena Prieto, Nicolás Avalos, Bernabé Lopez, Javier Santibañez, Mónica Vukasovic, Nelson Cortés and Jaime Díaz
Minerals 2026, 16(2), 202; https://doi.org/10.3390/min16020202 - 14 Feb 2026
Viewed by 1117
Abstract
Agile and flexible resource modelling is essential for informed decision-making in early-stage mineral project assessment, and in more advanced stages, particularly when compared with conventional deterministic geological modelling and single-estimate resource evaluations. This study presents a case of rapid scenario generation to view, [...] Read more.
Agile and flexible resource modelling is essential for informed decision-making in early-stage mineral project assessment, and in more advanced stages, particularly when compared with conventional deterministic geological modelling and single-estimate resource evaluations. This study presents a case of rapid scenario generation to view, interpret and test the impact of alternative geological and modelling assumptions, including the definition of geological domains, geological interpretation, grade estimation within domains, and the associated uncertainty. The workflows are implemented in Annapurna™ Resource, a cloud-native geostatistical platform designed to support agile, advanced, and multivariate modelling workflows. Focusing on the multi-commodity San Antonio–Potrerillos district, we demonstrate how rapid model construction enables the systematic evaluation of geological and statistical assumptions, contrasting deterministic estimates with probabilistic outcomes and testing their impact on estimated grades and tonnage under multiple scenarios for five elements: copper (Cu), molybdenum (Mo), gold (Au), silver (Ag), and arsenic (As). The approach provides quantitative measures of model reliability, identifies areas of high uncertainty, and supports the prioritization of new drilling to improve geological knowledge, exploration targeting, and resource classification. This case study highlights the value of fast-turnaround, probabilistic modelling not as a replacement for traditional resource reporting, but as a decision-support framework that enhances understanding of the geology, tests the sensitivity of assumptions, and accelerates learning throughout exploration and into operations. The main results suggest that additional drilling can be strategically placed to reduce the geological uncertainty derived from comparing the current interpretation with the probabilistic model built with indicator kriging. Furthermore, this has relevance in reducing the risk in the assessment of the metal content in each area of the deposit. Sensitivity analysis performed over key parameters of the estimation suggests that outliers’ treatment is the most impactful step during estimation. With current technological tools, it is possible to maintain a live resource model, which can be continuously updated to assess the impact of new data and decisions in near real time. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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30 pages, 10362 KB  
Article
Real-Time Updating of Geochemical and Geometallurgical Spatial Models with Multivariate Ensemble Kalman Filtering: Application to Golgohar Iron Deposit
by Sajjad Talesh Hosseini, Omid Asghari, Xavier Emery, Jörg Benndorf, Andisheh Alimoradi and Sara Mehrali
Minerals 2026, 16(2), 141; https://doi.org/10.3390/min16020141 - 28 Jan 2026
Viewed by 858
Abstract
This paper presents an updatable stochastic geometallurgical framework that integrates geochemical compositions and processing-related variables within a unified spatial modeling and data assimilation workflow. The framework combines multivariate geostatistical simulation with real-time updating based on the Ensemble Kalman Filter, allowing stochastic realizations to [...] Read more.
This paper presents an updatable stochastic geometallurgical framework that integrates geochemical compositions and processing-related variables within a unified spatial modeling and data assimilation workflow. The framework combines multivariate geostatistical simulation with real-time updating based on the Ensemble Kalman Filter, allowing stochastic realizations to be sequentially adjusted as new production data become available. The methodology accounts for geological uncertainty, compositional constraints, and multivariate dependencies. This is achieved by combining the isometric log-ratio transformation with flow anamorphosis within a multivariate Gaussian framework. As a result, compositional geochemical variables and metallurgical responses can be updated consistently while preserving their physical and statistical relationships. The framework is demonstrated using the Gol Gohar iron ore deposit as a case study. Exploration drill hole data and production-scale blast hole measurements are assimilated within an ore control context. The results indicate that the update-enabled simulation approach reduces prediction errors and spatial uncertainty, while capturing complex, non-linear relationships among geometallurgical variables. The framework is generic and can be applied to other deposits where real-time integration of geological, geochemical, and processing information is needed to support operational decision-making. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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20 pages, 9883 KB  
Article
Optimizing Drilling in Brownfield Ni-Cu Depositional Systems Based on the Integration of Geochemical, Geophysical and Drill-Hole Data
by Céline Scheidt, Francisco Tomazoni Neto, David Zhen Yin and Jef Karel Caers
Minerals 2026, 16(1), 82; https://doi.org/10.3390/min16010082 - 15 Jan 2026
Viewed by 951
Abstract
Effective drillhole placement is critical to the success of mineral exploration, particularly in brownfield settings where subsurface information remains sparse despite the availability of data from adjacent, previously explored areas. To address the challenges of uncertainty in resource estimation and the high cost [...] Read more.
Effective drillhole placement is critical to the success of mineral exploration, particularly in brownfield settings where subsurface information remains sparse despite the availability of data from adjacent, previously explored areas. To address the challenges of uncertainty in resource estimation and the high cost of drilling, we present a drilling sequence optimization framework guided by geophysical and surface geochemical data. The framework integrates statistical learning and geostatistical simulation to construct a set of prior models of intrusion and nickel grade distribution. These models are used to quantify the expected reduction in uncertainty for each potential drillhole by evaluating their corresponding Efficacy of Information (EOI). This approach allows the sequential selection of drillhole locations that maximize information gain while managing exploration risk. We apply the methodology to a case study in the Curaçá Valley, Brazil, where prior data from a well-characterized nearby zone inform predictions in the adjacent target area. The results demonstrate that incorporating prior geological knowledge from nearby areas into the drilling strategy can significantly improve targeting efficiency and reduce uncertainty in early-stage brownfield exploration. Full article
(This article belongs to the Special Issue Geostatistical Methods and Practices for Specific Ore Deposits)
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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 785
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
Cited by 2 | Viewed by 1319
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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