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.).

HEAR FROM OUR CUSTOMERS

"I have used Isatis.neo full on for a big multi-domain multi-element model from compositing to reporting, using gaussian and raw, multiple block sizes, etc. and I’m very impressed. Fast migrations, fast estimation, good reporting, validation, and visualization functionality."


Danny Kentwell, Principal Consultant (Resource Evaluation) - SRK Consulting
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"I found Isatis.neo Resources Workflow extremely useful and helpful. I managed to produce high-level geostatistical models very quickly."


Ashley Brown, Manager, Resource Delineation - Beckman, Brown, & Associates
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"Isatis.neo was amazing; it did everything I could think of and prompted me to do things I hadn't thought of. I haven't come across any other geostatistics software which comes close to its functionality."


Matthew Graham-Ellison, Student -
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Geovariances is pleased to bring you a conversation with Daniel Guibal and Michael Cunningham, both long-time users of Isatis, now happy users of Isatis.neo. They recently used the software to provide a new JORC resource estimate of the Ausgold Katanning Gold Project.


Daniel Guibal | Mike Cunningham, Independent consultants - Condor Geostats Services | Sonny Consulting Services
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"O curso Prática em Geoestatística com uso do software Isatis.neo, promovido pela Universidade Federal do Rio Grande do Sul em parceia com a Geovariances, foi muito intenso e produtivo. Agradeço toda a equipe pelo suporte e pela qualidade do conteúdo."


Saulo da Silva Nunes, Geólogo de Exploração Pleno - Alcoa
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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

October 25, 2022

Once again, Mines Paris' Geostatistics Team and Geovariances bring together their experts to offer online their Specialized Training Cycle...

March 30, 2022

Geovariances se complace en organizar un curso dedicado exclusivamente a las estudiantes universitarias.

February 1, 2022

A Geovariances orgulha-se de ser parceira do Departamento de Engenharia de Minas da Universidade Federal do Rio Grande do Sul...



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Events

13 de dezembro 2022, 10 h BRT
Aprenda mais sobre as técnicas geoestatísticas que permitem incorporar dados imprecisos em seu processo de estimativa de recursos sem perde...
Dec. 14, 2022 - 11:00 am PST
Learn more about the geostatistical techniques that allow you to incorporate inaccurate data in your resource estimation process without losi...
10 de enero de 2023, 10h CLST
Aprenda más sobre las técnicas geoestadísticas que le permiten incorporar datos inexactos en su proceso de estimación de recursos, sin pe...
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Resources

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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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