October 9 - 13, 2017
Golden, Colorado USA


  • Fundamental statistical concepts used in sampling theory and sampling practices
  • Nine kinds of sampling errors: You must address them one at a time, otherwise sampling is almost always invalid.
  • Heterogeneity of major constituents and trace constituents
  • Examples of common financial losses due to poor sampling practices
  • Definition of Data Quality Objectives
  • Presentation of a new quality strategy based on Data Quality Objectives
  • Synergy between Data Quality Objectives and sampling protocols
  • Definition of basic terms and symbols

Sampling Theory and Practice

  • Errors generated by sample weights
    • Optimization of sampling protocols
    • Description of Heterogeneity Tests, for a normal case, and for a difficult case
  • Errors generated by segregation
  • Practical implementation of sampling protocols
    • Complete review of sources of sampling biases
  • Exploration of the in situ Nugget Effect
  • Selection of realistic, economical cutoff grades
  • Detailed review of existing sampling systems:
    • During exploration (Diamond core, RC, …)
    • At mines (blastholes, …)
    • At plants (cross stream systems, in-stream probes, augers, …)
    • At laboratories (splitters, crushers, pulverizers, shovels, spoons, spatulas, …)
    • For sampling commodities at shipping facilities
    • For sampling the environment
  • Monitoring precision and accuracy at the laboratory
  • Monitoring precision and accuracy of sampling and subsampling protocols
  • Quantifying the awesome cost of sampling precision
  • Suggestions for better sampling standards

Reconciliation problems between the geological model, the mine, and the plant

  • The myth of reconciliation
  • Identification of major sources of reconciliation problems
  • Capitalize on existing data: A gold mine of opportunities
  • Understand the different kinds of heterogeneity and the variability they generate
  • Become more proactive through effective statistical thinking

Management must set priorities

  • Find causes of problems and structural properties you must live with
  • Invest in minimizing causes of problems
  • Find effects of problems and circumstantial properties you cannot control
  • Save money by spending much less on effects of problems
  • Managing visible cost:
    • Historical priority placed on visible cost
    • The accountant's point of view
  • Discovering invisible cost:
    • The staggering cost of constituents grade variability
    • Reconciling statistical and accounting points of view

Introduction to Chronostatistics

  • Critical review of sampling modes: random systematic, stratified random, and random
  • Introduction to variography
  • Advanced variography
  • Introduction to variographic statistical process control

The Moving Average, a pragmatic, simple but delicate tool

  • How much averaging is appropriate
  • The random noise
  • The corrected data

The Relative Difference Plot: The best tool for QC monitoring

  • Detection of a conditional bias as a function of time
  • Detection of a conditional bias as a function of increasing constituent content

An improvement strategy for effective sampling

Workshop practice using sampling software

Progressive workshops included in several lectures as the course progresses is the only way for attendees to take full advantage of what they learn so they can apply important principles as they return to their operations.

Participants will learn about:

  • Installing the software packages
  • Getting comfortable with the Help file of each software package
  • Getting familiar with the many options in each software package
  • How to prepare the necessary data from an Excel worksheet
  • How to import the data to each software package
  • How to customize the data analysis using each software package
  • How to make a thorough interpretation of the results
  • How to initiate or suggest new possibilities for sampling protocols