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

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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“I used Isatis for optimizing drillhole spacing. This saved considerably on drilling costs and on time.”


Ashley Brown, Manager, Resource Delineation - KAZ Minerals
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"Minestis is revolutionary in that it completely democratizes advanced geostatistics at the mine site with minimal input from a consultancy or head office. Combined with Isatis,these two software packages can add immense value to any company with concomitant minimization of risk"


Ashley Brown, Manager, Resource Delineation - KAZ Minerals
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"J'ai beaucoup apprécié l'expertise en formation de Geovariances lors d'une première expérience d'un cours organisé à Institut des Sciences de la Terre à Dakar sur l'estimation des ressources minérales."


Abdoul Aziz Ndiaye, Directeur - Institut des Sciences de la Terre (UCAD), Sénégal
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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

July 23, 2019

Geovariances is pleased to provide the Indonesian Economic Geology Society MGEI with Isatis licenses for their workshop on advanced geosta...

January 24, 2019

Geovariances anuncia su cooperación con la Universidad Nacional de Colombia y la Asociación de Profesionales del Sector Minero de Colomb...

September 17, 2018

With its brand-new module for Ore Control, Minestis 2018 is more than ever the quintessential software for Mineral Resource Estimation, of...



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Events

July 22-24, 2019
Stop by our booth #34 to know more about our brand new software solution Isatis.neo Mining Edition
29 julho, 10 am (horário do Brasil).
Webinar | Como avaliar a quantidade relevante de furos de sondagem necessários para uma classificação de recursos com a qual você esteja ...
30 julio, 10 am, hora de Santiago
¿Cómo elegir el espaciado óptimo de la campaña de sondaje con el objetivo de la clasificación de recursos minerales? Acompáñenos en el...
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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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