Geostatistics for Mineral Resource Estimation
Geostatistics is the most efficient and powerful framework to characterise, estimate and manage your mineral resource.
Geologists or mining engineers can apply geostatistics at all stages of the mine life cycle: from exploration to development, production and even for site remediation. Geostatistics offers a wide range of methodologies adapted to all commodities and styles of deposits.
Geovariances’ scientific rigour, continuous innovation and geostatistical expertise guarantee the quality of your evaluations at different stages of the development of your projects (feasibility studies, bankable studies, desktop reviews, etc.).
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GEOSTATISTICS MADE ACCESSIBLE Discover Isatis.neo
A top-ten iron producing company sought a solution to produce weekly resource models instead of monthly models to improve their decision process regarding the destination of the mined materials. Geovariances developed automated routines based on Isatis.neo batch and Python coding
Geovariances recently achieved a study for a top-ten Aluminum producing company which demonstrated that they could perform reliable resource classification with only 75% of the holes they are used to drill, resulting in a money saving of 25% for drilling campaigns.
The study demonstrated the possibility of using the kriging variances in drill-hole spacing analysis (DHSA) for SMU uncertainty. That paves the way for an easier integration of the characterization of uncertainty in the decision making process.
"The new ore reserve represents a significant increase when compared to the last audit report."
"Geovariances is able to apply the scientific theory to the resolution of practical industrial questions while supporting the Eramet’s internal skills to master the necessary knowledge and tools to achieve studies."
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Which block size for mineral resource estimation
A key aspect of mineral resource estimation (MRE) is the definition of the block dimensions used to estimate the deposit attributes.
A satisfactory compromise is to be found to get an estimate that allows making decisions upon volumes that are representative of the physical reality of the operation while being aware that the density of information available at the time of estimation probably does not warrant the direct estimation of such volumes.
Through this white paper, learn how to choose a relevant support size for mineral resource estimation.
Localized Multivariate Uniform Conditioning
Estimating tonnage and grade, from sparse data, at a mining scale resolution is a challenge. Uniform Conditioning (UC), provides a powerful approach to estimating recoverable resources at a local scale, i.e. predicting the local distributions of SMUs (selective mining units) within larger panels conditional to neighbouring information.
Through Geovariances long-lasting experience in applying UC (and now LMUC), learn how LMUC helps you optimise the accuracy of your predicted recoverable resource estimates and access the information you have available regarding recoveries predicted at the mining (SMU) scale.
Use of Simulations for Mining Applications
Linear Interpolation techniques like kriging are inappropriate to deal with issues that require a full characterisation of the spatial distribution. The simulations provide a huge flexibility to deal with the complexity of the mining process and an access to the uncertainty assessment.
Through Geovariances multiple experiences in developing successfully various simulation strategies in different environments (kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, alteration and hydrothermal type deposits), learn how geostatistical simulations can help in resource estimation and classification.
Geological Facies Simulations
Whatever the resource involved – oil & gas, coal or metallic resources – capturing the variability of the geological parameters is essential at the modelling stage as the characteristics of the distributions of key parameters conditioning the resource recovery (e.g. rock properties, grades, etc.) are informed by the geological context. A large variety of simulation techniques is available to model geological facies.
Through Geovariances strong experience in developing successfully simulation strategies for different geological environments (e.g. kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, hydrothermal type deposits, etc.), learn how to choose the best facies modelling technique according to the specific geological depositional environment. Analyse each method advantages and drawbacks.
Uncertainty of Mineral Resource Estimates From Confidence Intervals to Resource Classification
Resource classification methodologies are still under research and debate. Most of the time, ad hoc techniques, based on simple and easy to get criteria, are applied.
Hints and pitfalls of these methodologies are worth deeper thinking about. The probabilistic framework of geostatistics seems adapted to provide quantitative inputs to that process as it is particularly appropriate to assess uncertainty in resource models and thus appraise the risk.
Through this white paper, find out more about the geostatistics-based classification methodologies, their pros and cons.
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Amélioration de l’estimation des teneurs en alumine d’un gisement de kaolin à l’aide des simulations par bandes tournantes | XVe Journées de géostatistique 2021 - V. Bouchet (Imerys), M.C. Febvey (Geovariances), Hélène Binet (Geovariances), Armand Dubus (Geovariances)
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Co-kriging of log ratios: a worked alternative method | Clint Ward, Cliffs, Ute Mueller ECU
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Local Uncertainty Benchmarking – A coal case study | Written by C. Mawdesley, D. Barry, O. Bertoli and R. Saha
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Sensitivity study of the estimation variance approximation of a quotient | Comparison with Conditional Simulations in the Mn Deposit of Bangombé (Gabon)
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Data classification using geostatistical hierarchical clustering for robust and dynamic domaining
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Implication of geological domains data for modeling and estimating resources from NKout iron deposit (South-Cameroun) | Implication of geological domains data for modeling and estimating resources from NKout iron deposit (South-Cameroun)
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Full paper: On measuring the spatial sampling density of a deposit for mineral resource classification | Full paper - On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard
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Poster: On measuring the spatial sampling density of a deposit for mineral resource classification | On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard
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Application of Pluri-Gaussian simulations and conditional simulation for geological modelling and estimation of a nickel deposit in New Caledonia | Febvey, M C, Desassis, N, Le Guen, M and Isatelle, F, 2019 - Proceedings Mining Geology 2019, pp 135–149 (The Australasian Institute of Mining and Metallurgy: Melbourne)
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How the use of stratigraphic coordinates improves grade estimation | Rubio, Ricardo Hundelshaussen, Koppe, Vanessa Cerqueira, Costa, João Felipe Coimbra Leite, & Cherchenevski, Pablo Koury. (2015) - Rem: Revista Escola de Minas, 68(4), 471-477. https://doi.org/10.1590/0370-44672015680057