- SPAIN (ZARAGOZA): 24-28/11/25
Course Brief
This course is targeted at delegates who are already competent in basic Resource Estimation skills and need to take their modelling to a more advanced level. The methods presented in this course are intended to help practitioners get more useful information out of their existing kriged models and presents some alternatives to make the models more useful. Delegates who attend this next level course into the use of geostatistics can bring their own data apply the methods under the guidance of our Subject Matter Expert. This course uses Python and other open-source software for demonstrations and exercises.
This course is part of a series of technical training and professional development courses for Mineral Resource specialists in a mining environment. Although much of the content is general to the mineral resource industry, it provides delegates who are already competent in Resource Estimation with the opportunity to enhance their estimation techniques.
Several advanced techniques and case studies are presented during the course. This course is designed for global exposure and presents the estimation methods most widely used in the mining industry. On the last day we look at some machine learning techniques that can enhance the quality of the estimates.
Key Learning Objectives
- Review of estimation methods for Mineral Resources
- Statistical Sampling, Variance and “Support”
- Indicator (Categorical and MIK) Estimation
- Conditional Simulation
- Introduction to machine learning techniques for geological domaining
Experience Required of Trainees
- This course assumes that the delegates have a good understanding of basic estimation methods such as variogram modelling, nearest neighbour, inverse distance, Kriging and other similar estimation methods based on the method of least squares.
- The concepts and examples used in this course assume that the delegates have several years’ of experience in Mineral Resource Estimation at grade control, mine evaluation or exploration stages.
- Exploration geologists should have some exposure to grade control/mine planning requirements for mineral resource models.
- It is assumed that Delegates are competent in the use of and have access to :
- General use software, Microsoft Word, Excel and PowerPoint for analysis and presentations and some exercises.
- This Course uses Python with pygeostat, stand-alone GSLIB executable files and/or gstlearn Python packages for some modules as they are open-source and do not require extensive training in navigating the software. It does not assume competence in Python coding and scripts can be run with guided input from the instructor. Experience with writing macros or coding is advantageous.
- Anaconda distribution for Python and Jupyter notebooks (free and easily installed).
- Suitable commercial geological modelling/mineral resource estimation software (Datamine, Surpac, Vulcan, Isatis etc). They are not required for the course but delegates in the past have found them helpful when using their own data