Formation

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

Take your estimation skills into the multivariate world. This module shows you how to model multi-element orebodies with PCA, kriging, and co-kriging producing consistent models that respect the real ratios between metals, oxides, and elements. Join the training to handle complex deposits with precision.
Prochaine session July. 8-9, 2026
Durée 2days
Prix EUR 1150

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.
  • This Module 2 allows you to progress into the multivariate context by exploring statistical tools such as Principal Component Analysis, applying kriging and co-kriging methods for estimating multi-element orebodies and obtaining multivariate models respecting the ratio between main metals, oxides, and elements.

Course 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.w²

Outlines 

  • Balanced learning approach: The course combines theory with practical applications, ensuring concepts are understood and applied effectively.
  • Hands-on software training: Engage in computer-based exercises using Isatis.neo software, reinforcing learning through real-world data scenarios.
  • Personalized feedback: Receive individualized guidance and feedback from experienced trainers during online sessions to support your learning journey.
  • Comprehensive resources: Access detailed course materials, including documentation, journal files, and datasets, to reinforce learning and facilitate application post-training.

Who should attend 

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

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, Module 1 of this course is recommended.

This course can also be followed by an “à la carte” workshop based on your data.