Harness geostatistical methods for insightful data analysis, accurate surface mapping, robust subsurface property modeling, and effective uncertainty quantification.
2-day course – September 14-15, 2026 – Level: Fundamentals
Learning outcomes
- Enhanced data insight: Develop a deep understanding of your data through advanced geostatistical analysis, leading to more informed decision-making.
- Improved mapping quality: Learn to create high-quality maps by integrating various data types, ensuring comprehensive spatial representations.
- Uncertainty quantification: Master techniques to quantify uncertainties in your models, providing a clear assessment of confidence levels in your results.
- Technique selection: Understand the assumptions underlying different geostatistical methods to select the most suitable approach for your data
Course content
DAY 1: ANALYSING DATA AND THEIR VARIABILITY IN SPACE AND MAPPING
- Introduction:
– Explore the added value of geostatistical methods over traditional deterministic interpolation techniques.
– Gain insights into usual deterministic interpolation methods (nearest neighbor, moving average, inverse distance, etc.) and their application limits.
- Exploratory data analysis (EDA) and validation:
– Utilize statistical tools for data analysis and quality control, identifying outliers and understanding data distributions: mean, variance, histogram, correlation coefficients, linear regression, etc.
– Visualize data in 2D and 3D to comprehend spatial patterns.
- Spatial variability assessment:
– Understand and quantify spatial variability through experimental variogram calculation, interpretation, and modeling.
– Learn to fit theoretical variogram models.
- Interpolation by kriging:
– Grasp the principles and properties of kriging and address the smoothing effect.
– Define appropriate neighborhoods (single or moving, size, number of samples, etc.).
– Analyze kriging weights based on sample positions and variogram models.
DAY 2: REFINING THE MAPS
- Cross-validation:
– Implement cross-validation to validate variogram models and ensure model reliability.
- The different variants of kriging:
– Apply simple, ordinary, with variance in measurement error, etc.
- Multivariate geostatistics:
– Analyze correlations between multiple data types, quantitative and semi-quantitative (e.g., remote sensing data, DEMs, soil occupation, physicochemical models, lithology, etc.), using scatter plots and correlation coefficients.
– Analyse the spatial relationships between variables by calculating and modeling multivariate variograms.
– Integrate secondary variables into interpolation through cokriging techniques, including collocated cokriging, to reduce interpolation uncertainty.
– Analyze the inputs of cokriging in comparison to kriging.
- Non-stationary geostatistics:
– Address data showing trends or drifts using non-stationary geostatistical methods.
- Simulations and risk analysis:
– Introduction to simulations for risk analysis, highlighting their added value with practical examples.
- Practical exercises:
– Engage in hands-on exercises to apply learned concepts to real-life cases, reinforcing understanding and skill development.
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
This course is ideal for geoscientists looking for a practical, synthetic, and pragmatic introduction to geostatistical methods for surface and subsurface modeling.
Prerequisites
No prior knowledge of geostatistics is required; however, a basic understanding of elementary statistics is recommended to facilitate comprehension of the course material.
Explore Geovariances’ training offerings
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.
