Advanced Mineral Resource Estimation

  • INDONESIA (Jakarta): 19-23/8/22

Course Brief

The online Advanced Mineral Resource Estimation course is part of a series of technical training and professional development courses for Mineral Resource specialists in a mining environment.  It provides delegates who are already competent in Resource Estimation with the opportunity to enhance their estimation techniques, ability to interpret results and communicate options to management and other stakeholders.

Basic mineral resource estimation courses focus on getting accurate or at least useful estimates of the global, local and block means.  Estimation variance is included but generally, sources and consequences of the variability and the estimates are not dealt with in detail.

In addition, there are types of deposits where the mean grade is not a useful measure and where the distribution of grades and proportion above a cut-off are needed for mine planning or mineral processing.

The material in this course follows basic Mineral Resource estimation methodology.  The concepts and methodologies are tools to help answer questions when an average block grade is not sufficient for mine planning or decisions to progress or stop a project. 

Course Focus

This course looks at the problems of variability from the earliest stages of evaluation and the risk or uncertainty analysis after the kriging (linear regression) estimate of the mean.  It also looks at techniques to extract distribution information within the kriged blocks.

The course is designed for on demand access and has no live lectures.  This allows for participants who may work in remote areas, have work schedules that do not allow for regular live lectures or have irregular success to a good internet connection to complete the course when they have time and facilities to do so.  Online support is available by email. 

Basic Skills

Advance Mineral resource estimation requires skills beyond the button pushing of basic workflows or commercial software or pre-set mine routines.  One of the skills is coding, either for macros, and scripts within commercial software or data processing when a spreadsheet is not sufficient.  This course uses Python and GSLIB for coding because,

  • Both are free open- source software and are quick and easy to learn for beginners
  • Python has a large number of packages for all sorts of calculations and good online support
  • Python is good for data analysis and can replace Excel with practice
  • GSLIB is still the source code for some parts of commercial geostatistical software
  • GSLIB has some coding available in Python or can be access via Python scripting
  • The same Python skills can be used with other commercial software for scripting. 

Course content. 

The Course has seven parts. Each succeeding part builds on the earlier skills.

The first part introduces Python and some exercises to import, manipulate and plot data.

The second part of the course looks at the problem of variability in the input. The inherent distribution of a characteristic in a material and as well as sampling design have an effect on the estimates of the mean and variance and other statistics.  A discussion on the implications for statistical domaining will also be presented.

The third part looks at sampling distributions and sampling structure.  What does data look like with small and large data sets and the implications on domaining and estimations.

The fourth part goes into detail on Volume and Variance. Understanding this is often critical for mine planning, knowing why your grade control estimate is different to the resource estimate, choosing the right parameters from your kriging neighbourhood analysis (KNA) and the implications at the exploration stage for interpreting sampling results.

The fifth part deals with categorical variables.  The Indicator estimation method is introduced for two or more variables and Multiple Indicator Kriging of continuous variable.

The sixth part deals with simulations.  Sequential Gaussian and Sequential Indicator Simulations are introduced.

The last part contains a project that participants must complete to demonstrate how the methods are used . A data set will be given but participants can use a small set of their own data.

 

This course does not deal with exploratory data analysis, variograms, KNA or kriging in any depth. It is assumed that participants are fully competent in these geostatistical tools and methods.  Where a variogram model or a kriged estimate is needed the required models will be supplied.