Geostatistical Mineral Resource Estimation & Meeting the New Regulatory Environment: Step by Step from Sampling to Grade Control

DATE: February 12-16, 2018 (dates TBA)
TIME: 9:00 am -5:00 pm
PLACE: Montreal, Canada

INSTRUCTORS: Marcelo Godoy, Newmont Mining Corporation, USA; Roussos Dimitrakopoulos, McGill University, Canada; and Guy Desharnais, SGS Canada Inc., Canada


Download brochure & registration form

 

CONTENT & OBJECTIVES:

This course is designed according to the latest regulations on public reporting of Mineral Resources. It aims at showing how state-of-the-art statistical and geostatistical techniques help answering the requirements of those regulations in an objective and reproducible manner. A particular emphasis is put on understanding sampling and estimation errors and how to assign levels estimation confidence through the application of resource classification fundamentals. In addition to a solid introduction to mining geostatistics this course provides a comprehensive overview of industry’s best practices in the broader field of Mineral Resource estimation.

 

You will learn to:

  • How to use statistical inference to identify problems with the data
  • How to produce models that address the needs of mining companies
  • Compliance with NI43-101, SEC Industry Guide 7 and JORC
  • Effective workflows from data preparation to resource classification
  • How to validate, reconcile and communicate resource estimation results
  • The fundamentals of resource classification and how to apply them in practice
  • How to produce auditable and reproducible resource estimates
  • Estimation of grade control models and best practices in ore selection
  • How to leverage costly geometallurgical testing to reduce risk
  • How to use conditional simulations to quantify uncertainty in resource estimates
  • How to integrate orebody simulations into mine planning 

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

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