Mineral Resource Estimation by Linear Geostatistics – Module 1: Univariate Context 

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

2-day course – July 6-7, 2026 – Level: Fundamentals

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, kriging-based interpolation of grade distributions, 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.

Course 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 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.

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

Explore Geovariances’ training offerings

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Geovariances – Datamine France provides training for mining professionals seeking to strengthen their geostatistics expertise. Our courses blend theory with hands-on practice.

Flexible delivery formats, including online, hybrid, and face-to-face, are complemented by on-demand training, available for both in-company and public sessions, and tailored to your specific needs.

Develop skills in key areas such as:
– Local and recoverable resource estimation
– Uncertainty and risk analysis
– Resource classification
– Geological domain modeling
– Drill Hole Spacing Analysis
– Machine Learning

On-demand hands-on sessions on Isatis.neo and Isatis.py allow you to directly apply geostatistical methods in industry-standard software.