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

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 co-kriging methods for estimating multi-element orebodies and obtaining multivariate models respecting the ratio between main metals, oxides, and elements.

– 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 consistency and reliability in your 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 Neighbourhood Analysis (KNA) to enhance estimation accuracy.
– Validate your models and estimations through cross-validation and robust validation techniques.
– Generate grade-tonnage tables and curves to support your technical and economic modeling.

• 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.

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

• A basic understanding of resource concepts such as grade, tonnage, and cut-off is recommended.
• To extend your knowledge, we recommend attending the complementary advanced short course Recoverable Resource Estimation.
• If you want to extend your skills in estimation within a multivariate context, Module 2 of this course is recommended.