Training

Mineral Resource Estimation by linear geostatistics – Module 1: univariate context

Build a strong foundation in mineral resource estimation. Follow the univariate workflow, from data analysis and variography to block modeling, kriging, validation and grade-tonnage curves.
Next session July 5-6, 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

 

  • Understand the importance of geostatistics in mineral resource estimation: build a solid foundation for informed decision-making.
  • Explore and analyze your data effectively using Exploratory Data Analysis (EDA) and spatial data analysis techniques.
  • Assess data stationarity to ensure consistent, reliable estimates.
  • Prepare your data with confidence using regularization techniques such as compositing and declustering to reduce bias.
  • Master variographic analysis: variogram clouds, directional variograms, and interpretation of spatial structures.
  • Model variograms using automatic, semi-automatic, manual, or interactive tools tailored to your needs.
  • Apply the most relevant kriging methods: ordinary kriging, block kriging, and weight distribution analysis.
  • Build an optimal sample neighborhood with Kriging Neighborhood Analysis (KNA) to enhance estimation accuracy.
  • Validate your models and estimates using cross-validation and other robust validation techniques.
  • Generate grade-tonnage tables and curves to support your technical and economic modeling.

What the course includes

  • 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 expand your skills in estimation within a multivariate context, Module 2 of this course is recommended.

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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