MINESTIS, THE ROUTE TO MINERAL RESOURCE ESTIMATES
Minestis is the industry-standard for mineral resource estimation. It is the first software solution that enables reliable and coherent geological domain estimation and resource modeling through a rigorous, yet simplified and secure geostatistics-based approach. Whether you are resource or mine geologist, consultant or auditor, in charge of long- or short-term modeling or resource review, Minestis provides you with the quality, robustness and relevance you need for your mineral resource estimates.
GET MORE ACCURATE MODELS
Adopting Minestis means you access first-class algorithms that originate from Mines ParisTech’s Center for Geostatistics and have been tried and tested for over 20 years through Isatis. Your estimates are top quality, also because Minestis ensures full consistency between geological and block models. And because it makes geostatistics accessible, you remain in the driver’s seat to produce the trusted recoverable resources you need for improving mining selectivity.
BOOST YOUR PRODUCTIVITY
Using Minestis, you enjoy the speed and efficiency with which a resource model is built. Minestis workflow secures and streamlines your estimation process, from domain modeling to resource reporting. A few clicks are enough for the deterministic and risk resource models to be delivered consistently both at panel and SMU scales. Model updating, project review, harmonization, auditing and reporting are more efficient and effective than ever.
TAKE BACK CONTROL OF YOUR RESOURCES
With Minestis, you exactly know how confident you can be with your models. Minestis lets you combine the uncertainties attached to the domain envelopes with the ones attached to your resource estimates to better inform your mining decisions. The software provides you with an auditable trail of parameters and allows repeating the whole process in a click. The reporting facilities are fully compliant with the mining reporting codes requirements.
SAVE TIME AND MONEY
With Minestis, you take advantage of a package fast to learn and to handle, even if you are not highly skilled in geostatistics. This is why the same software can be used at corporate level or on site, facilitating collaborative work. Besides, Minestis provides the techniques that help you optimizing sampling, or, on another level, operational or mineral processing decisions thanks to multivariate analysis (estimation/simulation).
"Minestis sequential and efficient workflows ensures that you are not missing any critical steps in the Resource Evaluation process. It is relativeley simple to execute and does not require any advanced knowledge in the software. I would greatly recommend it."
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MINESTIS KEY FEATURES
• Data handling and quality control
Analyze and validate input data through interactive and dynamically linked statistical representations (histograms, swath-plots, cross-plots). Achieve sample length regularization. Optimize the declustering window size. Perform structural analysis simultaneously on raw and Gaussian transformed grades. Collect, display and precisely locate in 2D and 3D any information about the orebody surroundings: digital elevation model, photogrammetric image, geology, etc.
• Sample clustering and estimation domain modeling
Group borehole samples according to geological, structural and statistical criteria and identifies domains in an automatic way. Interpolate domain boundaries from previously identified domains through implicit modeling. Refine models by setting control points based on your knowledge of the geology. Import domain envelopes from other mining packages. Verify possible overlaps or voids between envelopes and validate domains. Perform uncertainty analysis and compute volume curves on domain volumes. Map the probability that a specific location lies within the domain.
• Resource estimation
Build accurate experimental variograms and identify key continuity directions with variogram maps. Quickly get well-fitted multivariate/multi-directional variogram models. Take into account support and information effects. Achieve univariate or multivariate block estimation through ordinary kriging with moving neighborhood. Define nested neighborhoods. Optimize kriging neighborhood with Kriging Neighborhood Analysis. Estimate resources at global and local scales from Uniform Conditioning (UC), Localized Uniform Conditioning (LUC) and Turning Bands conditional simulations.
• Estimation validation
Control the quality of the estimates through various statistics: histograms, swath plots and cross plots between data and estimates.
• Resource reporting
Report tonnage, metal, grade and/or benefit figures for each defined cutoff and for either panel or SMU support, by domain or over the whole study area. Compute grade-tonnage curves. Build your report and store your modeling parameters on the fly as your project progresses thanks to an integrated word processor.
• Model updating and reviewing
Save estimation workflows and related parameters and easily re-run them as new data become available. Reproduce estimation workflows. Review models in an effective way.
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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.
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.
Drill Hole Spacing Analysis
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.
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