Training

Mineral Resource Estimation by linear geostatistics – Module 2: multivariate context

Model multi-element deposits with consistency. Use PCA, kriging and cokriging to respect relationships between metals, oxides and elements, and improve multivariate resource estimates.
Next session April 7-8, 2027
Duration 2 days
Price EUR 1,100

Objectives

This course provides a solid foundation in geostatistical methods for mineral resource estimation. The skills you will develop will assist you in:

  • Estimating long-term and short-term resources,
  • Producing resource models for mine design,
  • Conducting spatial analysis of drillhole data.

It comprises two modules that can be taken separately:

  • In Module 1, you will learn and practice the standard workflow for estimating resources in a univariate context. This module covers in-depth data analysis, detailed variographic analyses, block modeling, grade distribution interpolation using kriging, estimation validation, and unbiased grade-tonnage curves for short-term resources.
  • Module 2 allows you to progress into the multivariate context by exploring statistical tools such as Principal Component Analysis, applying kriging and cokriging methods for estimating multi-element orebodies and obtaining multivariate models respecting the ratio between main metals, oxides, and elements.

Target audience

Professionals seeking a sound theoretical and practical knowledge of mining geostatistics.

Content

 

  • Use Principal Component Analysis (PCA) to extract the most relevant information from complex multivariate datasets.
  • Estimate non-stationary variables by applying kriging with external drift or universal kriging for more accurate resource modeling.
  • Analyze grade correlations to better understand elements’ relationships and enhance your geostatistical models.
  • Examine joint spatial structure by calculating and interpreting cross-variograms and cross-covariances, even on purely heterotopic datasets.
  • Interpolate correlated grades using advanced cokriging methods: ordinary cokriging, collocated cokriging, and rescaled cokriging.

Outlines

  • Balanced learning approach. Theory paired with practical applications, so you understand and apply the concepts.
  • Hands-on software training. Computer exercises in Isatis.neo, on real datasets.
  • Personalized feedback. Individual guidance from experienced trainers throughout the online sessions.
  • Comprehensive resources. A temporary software license, course documentation, journal files and datasets to keep and reuse after the course.
  • Certificate of completion issued at the end of the session.

Prerequisites

  • A basic understanding of resource concepts such as grade, tonnage, and cut-off is recommended.
  • To expand your knowledge, we recommend attending the complementary advanced short course, Recoverable Resource Estimation.
  • If you want to start with estimation in a univariate context, we recommend Module 1 of this course.

Register, or ask us first

This form handles all three: registration, a quote for yourself or your team, and questions about the content or the prerequisites.

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