Learn the fundamentals of mining geostatistics for resource estimation and build your first block model.
Course Overview
The CFSG (Specialized Training Cycle in Geostatistics) is a high-level training program in mining geostatistics delivered by the Geostatistics Team from Mines Paris and Geovariances. Throughout the training, you will learn the theoretical aspects of the techniques presented and practice them through various exercises and a real-world project. This program will be delivered online in 5 modules over 9 weeks throughout 2025, starting in March.
Who should attend?
- The CFSG training program is meant for mining geologists and engineers willing to achieve a high level of geostatistics and boost their careers.
- Module A is ideal for newcomers to mining geostatistics. Modules B and F are designed for individuals who wish to delve into more advanced geostatistics.
Why Enroll in CFSG Module A?
- Gain hands-on experience with geostatistical tools
- Improve accuracy in resource estimation
- Learn from industry experts
Additional Modules
Each module can be attended independently of each other. However, it is important to note that completion of Module A or having experience in geostatistics and Isatis.neo is a prerequisite for participation in any of these modules.
- Module B: Recoverable resources with nonlinear methods (optional)
May 12-16, 2025 – 5 days
Learn how to compute recoverable resources considering mining selectivity and quantify the uncertainties. - Module C: Non-stationary geostatistics (optional)
June 16-20, 2025 – 5 days
Learn how to constrain the block model with geological trends. - Module D: Facies simulations (optional)
July 7-11, 2025 – 5 days
Learn how to achieve reliable and realistic facies modeling. - Module E: Domaining (optional)
Sept. 8-12, 2025 – 5 days
Get introduced to a powerful machine-learning-based technique for geological domaining. - Module F: Coupling Machine-Learning and geostatistical techniques using Python (optional)
Oct. 6-10, 2025 – 5 days
Learn how to implement Machine Learning techniques in Python code for data classification.
Prerequisites
The course is delivered in English and requires a good level of this language. Sound notions of mathematics are also recommended.
