Geostatistical Evaluation of Mineral Resources

Geostatistical Evaluation of Mineral Resources and Their Uncertainty under the New Regulatory Environment: 

This course aims to show how state-of-the-art statistical and geostatistical techniques help answer the requirements of the latest regulations on Mineral Resources public reporting in an objective and reproducible manner. A particular emphasis is placed on understanding the various sources of uncertainty and error through the mineral resources estimation process and how to characterize the geological confidence through the application of quantitative and qualitative criteria for mineral resources classification. Within the broader context of the engineering modeling discipline, this course provides a comprehensive overview of the industry best practices for mineral resources modeling.

 Attendees will learn:

• How to conceive of and produce mineral resource models that address the short- and long-term needs of mining companies
• How to extract geological insights from the statistical analysis of data
• The sources of error and uncertainty at different stages in the mineral resources modeling process
• The advantages and limitations of different estimation and simulation methods
• How to incorporate the knowledge of geological controls of mineralization in the mineral resources workflow
• How to validate and reconcile mineral resources estimates
• How to communicate mineral resources estimates and their confidence in compliance with the standards of NI43-101, SEC Mining Disclosure Rules, and JORC
• The various uses of conditional simulations, such as quantification of the uncertainty in mineral resources and value of information studies, strategic mine planning, and project evaluation
• How to integrate orebody simulations into mining planning

View the full course outline here.

Please note: It is strongly recommended that participants bring a laptop. No previous exposure to statistics and geostatistics is required.

Instructors: David F. Machuca (SRK Consulting, Canada) and Roussos Dimitrakopoulos (McGill University, Canada)