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

"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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Training very good in all aspects: organization, material, applicability of the methodology and technical knowledge of the consultant. We will certainly have more challenges and we know that we may count on you.


Daniel Bernardes Raposo, Senior Geologist - CMOC International
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Geovariances technical support is personalized, which I really enjoy. Many software companies provide a totally impersonal "call center" type of support, which certainly discourages us to use the support and the software itself. Not Geovariances.


Ana Chiquini, Resource Geologist - Glencore Zinc Technical Services
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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

July 15, 2020

With this new version of Isatis.neo, Geovariances continue to implement advanced geostatistical techniques. In particular, we have added t...

July 15, 2020

Avec cette nouvelle version d’Isatis.neo, Geovariances poursuit l’implémentation de techniques géostatistiques avancées. En particu...

June 8, 2020

Students benefit from academic licenses of Isatis.neo to experience geostatistics techniques applied to resource evaluation.



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Events

February 4-5, 2021
Stop by our booth to learn more about Isatis.neo, our new software solution for geostatistics and resource estimation.
July 12-16, 2021
Attend our paper presentations and discover a new methodology that provides unsurpassed performances in kriging and simulations. Learn how ge...
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Resources

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video

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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