Estimating mineral resources is performed at all stages of the mine life cycle. Geostatistics will ensure you get the maximum value out of the available information: investigate data quality, focus on the reliable data, discount unreliable, erroneous data and produce accurate resource estimates. Maximise the return on your data investment and then use the results to optimise future data collection.

Statistical and Geostatistical skills can be used in all aspects of resource estimation:

 

DATA QC

The relevance of any resource estimate is directly dependent upon the quality of your input data. Statistical and Geostatistical analyses provide quantitative inputs for:

  • Assessing the validity of your different data sources
  • Rejecting imprecise or biased information
  • Combining different data sources

 

GEOLOGICAL DOMAIN VALIDATION

The definition of robust geological envelopes is paramount for ensuring the quality of your mineral resource estimation. The envelopes must be relevant (i.e. as detailed as the data allows them to be) and robust (to changes in the local geological rationales). Geostatistics can help assuring they are fit for the purpose of producing a MRE you can trust by focusing on:

  • Statistical Homogeneity
  • Boundary Analysis and Boundary Treatment
  • Domain Combination or Sub-division

Geological modeling with Minestis

CHARACTERIZATION OF SPATIAL VARIABILITY

This is the only way you can ensure the estimation methodology you use will be completely adapted to the deposit you are modeling:

  • Exploratory Data Analysis
  • Experimental Variography
  • Data transforms
  • Variogram Modeling
  • Cross-Validation

 

IN SITU ESTIMATION AT ALL STAGES

  • Exploration
  • Feasibility
  • Grade Control

 

RECOVERABLE RESOURCE ESTIMATION

A sound evaluation of Recoverable Resources is a pre-requisite for Reserve Calculation. They help determine the portion of the deposit that is technically recoverable when one applies a cut-off to selective mining units (SMU’s) to be mined at production stage. They are usually much smaller than the Panels that can safely be estimated from exploration data. Linear Interpolation is thus no longer applicable and one needs to resort to non-linear geostatistics to estimate these recoverable resources.

  • Testing grade architecture to inform the choice of an adapted Change of Support Model
  • Global or Local recoverable resource estimation
  • Characterization of uncertainty of the recovery functions (link with simulation)
  • Localization of models to facilitate communication to mine planning engineers
  • Validation and reporting.

 

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