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

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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"Great piece of software helping the Resource Geologist to investigate its dataset to take the best decision while estimating."


Olivier Masset, P. Geo, Head of Mineral Resources and Reconciliation Department / Orano Mining - Orano
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"I have had a chance to test some parts of Isatis.neo and am amazed at how the menus and many functionalities are nicely streamlined. Everything seems to be carefully and logically arranged, especially for new users. For the old Isatis users, I think it is just a matter of..."


Toivo N Mufeti, Senior Project Geologist - Debmarine Namibia
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"We've got excellent results with the clustering tool available in Isatis.neo. The domains created with Isatis.neo have been validated with the reconciliation of mined areas and the results of the technological studies."


Italo Rodrigo Lima Barreto, Senior Geologistation - Mineração Rio do Norte MRN
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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

June 1, 2021

A Geovariances orgulha-se de organizar um curso dedicado, exclusivamente, ao público universitário feminino.

January 22, 2021

You're a user of Isatis.neo? Isatis? Minestis? Your experience and opinion are most valuable! We invite you to be part of our user panel a...

January 21, 2021

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

12 de agosto 2021 - 10h00 CLT
Únase a nuestro experto geoestadístico y descubra una herramienta en Isatis.neo basada en Machine Learning que le permite identificar autom...
11 de agosto 2021 - 10h00 BRT
Junte-se ao nosso especialista em geoestatística e descubra uma ferramenta no Isatis.neo baseada em Machine Learning que permite identificar...
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Resources

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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