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 use Isatis.neo to validate block models generated with other software. I also like its specific tools, such as Flattening, which I used to rotate a vein and allowed me to increase kriging efficiency from 40% to 70%."


Antonio Umpire, Unit Manager Group Resource Estimation & Reporting - SIBANYE-STILLWATER
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"He vuelto a encantarme con Isatis.neo, conociendo sus herramientas cada vez más poderosas en simulaciones y con una grafica mejorada /// I am happy with Isatis.neo's continued performance. I have found its simulation tools to be increasingly powerful, with improved graphics."


Ricardo Líbano Granada, Geólogo Senior de Recursos - Antofagasta Minerals
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“O recurso mineral de 2023, estimado pelo Isatis.neo, aumentou Life of Mine e o valor dos ativos minerais da EuroChem /// The 2023 mineral resource, estimated through Isatis.neo, increased Life of Mine and Eurochem's mineral asset value.”


Rodrigo De Andrade Miotto, Specialist Geologist - Mining resources - EuroChem
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"Con Isatis.neo, usted puede implementar rápidamente flujos de trabajo en proyectos con múltiples dominios y variables a modelar /// With Isatis.neo, you can quickly implement workflows in projects involving multiple domains and variables."


Sergio Igancio Salinas Rozas, Geologist – Geostatistician - GeoEstima
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"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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WHAT IS HAPPENING IN YOUR INDUSTRY?

News

May 7, 2024

Join Roberto Rolo's talks and workshops to learn about Isatis.neo's features like facies and grade simulation and Python-based workflow sc...

February 20, 2024

Explorez le potentiel de la géostatistique pour l'estimation et la classification des ressources minérales. Découvrez les meilleures pr...

January 26, 2024

Enroll in CFSG, the online specialized training cycle in mining geostatistics, learn from the mining geostatistics experts and acquire the...



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Events

Sept. 2-6, 2024
Geovariances is sponsoring the 2024 Geostats Congress! Join us for engaging presentations and an interactive workshop. See you there!
2-3 octobre 2024
Vous souhaitez comprendre comment la géostatistique peut vous aider à améliorer votre compréhension du sous-sol ? Venez en discuter sur n...
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Resources

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Use of Simulations for Mining Applications

Linear interpolation techniques – such as kriging – are inappropriate for dealing with issues that require a full characterization of spatial distribution (for example, probability of exceeding a threshold, variability of a product per mining period, recoverable resources at various cut-offs, etc.).

Only conditional simulations reproduce the true variability of your orebody. They are flexible in their application to complex mining processes and uncertainty assessment.

Through Geovariances’ multiple experiences in developing a variety of simulation strategies in different environments: kimberlite pipes, turbiditic and carbonate reservoirs, porphyry copper, alteration and hyd,rothermal type deposits, learn how geostatistical simulations can help in resource estimation and classification.

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.

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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