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

Learn the fundamental concepts of geostatistics to confidently estimate your mineral resources.

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.

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.