Quality Control of Geochemical and Assay Samples

Content
15 modules

Rating

Course Length
25 hours

Instructor
Edumine Courses

Released
06 Oct 2021

Price
$399.00

Description

In recent years there has been a strong international move toward knowing and improving the quality of information used in the mining industry for mineral project exploration reporting and resource/reserve estimation. In Canada this trend has been accentuated because of recent, highly publicized scams that involved contamination of samples. An important aim of quality control procedures is to minimize the likelihood of such scams so that the public is not misled as to the economic potential of a mineral deposit. Quality control procedures also serve the technical purposes of identifying sources of and quantifying both random errors and unintentional bias in sampling, subsampling and analytical routines and thus provide the basis for improved procedures of data collection that translate into improved resource/reserve estimates.

One of the important reactions in Canada to recent mining scams has been the implementation of what is known as National Instrument 43-101 (NI43-101) in which a wide range of requirements, relating to mineral project reporting and resource/reserve estimation, are laid out. These requirements identify a Qualified Person (QP) who is responsible for all technical matters related to obtaining and publicizing both assay data and resource/reserve figures. This course incorporates a variety of procedures designed to fulfill the requirements of NI43-101 insofar as standard, blank and duplicate samples can be used to define and monitor quality of geochemical and assay values that are the basis of deposit evaluation.

The International Standards Organization (ISO) has developed a variety of widely distributed publications dealing with quality control systems for a wide range of industrial settings. The application of the ISO standards to resource/reserve estimation procedures necessarily involves all steps of the published procedures. Too often quality control is thought of only in terms of quantitative measurements. A broader perspective is essential and must include the categorical and qualitative data that are inherent in geological studies.

This is a premium course which has been peer-reviewed by a committee appointed by the Canadian Institute of Mining, Metallurgy and Petroleum (CIM) and the Society for Mining, Metallurgy and Exploration (SME).

Authors

  • Alastair J. Sinclair

 

Duration: 25 Hours
Access: 90 Days
Category: Exploration
Level: Cross Train
Original Publish Date: June 3, 2015
Revised Date:  October 6, 2021

Need to train a team?

Whether you're looking for a customized training program or developing a team, we have enterprise solutions to fit your needs.
Learn More

Objectives

Course Content

The course comprises 22 viewing sessions at both summary and text level, plus multiple-choice reviews, worked examples and exercises, and a comprehensive glossary. Course duration is equivalent to 24 hours of viewing content. An optional download of sample data files and P-RES software, a statistical tool for interpretation of assay results, is included.

The former title of this course was "Quality Control of Assay Data."

Learning Outcomes

  • Discuss statistical parameters used in error analysis.
  • Discuss and apply statistical tests used in treating duplicate and replicate analyses.
  • Discuss, apply and interpret practical measures of sampling and analytical errors.
  • Discuss sources of error in sampling, subsampling and analytical procedures.
  • Discuss and apply procedures for monitoring data quality.

Recommended Background

  • A degree in geology, metallurgy, mining or related discipline.
  • An understanding of the basic principles and methods of statistics.

Certificate

By completing/passing this course, you will attain the certificate Edumine Certification

Learning Credits

CEU
2.5
PDH
25.0
1.
Table of contents
{{ vm.helper.t('courses.scorm') }}
2.
Module 1: Introduction - Quality Control of Sample Data
{{ vm.helper.t('courses.scorm') }}
3.
Knowledge Check - Module 1: Introduction - Quality Control of Sample Data
{{ vm.helper.t('courses.exam') }}
4.
Module 2: Statistical Parameters Commonly Used in Error Analysis
{{ vm.helper.t('courses.scorm') }}
5.
Knowledge Check - Module 2: Statistical Parameters Commonly Used in Error Analysis
{{ vm.helper.t('courses.exam') }}
6.
Module 3: Statistical Tests Commonly Used in Treating Duplicate and Replicate Analyses
{{ vm.helper.t('courses.scorm') }}
7.
Knowledge Check - Module 3: Statistical Tests Commonly Used in Treating Duplicate and Replicate Analyses
{{ vm.helper.t('courses.exam') }}
8.
Module 4: Practical Measures of Sampling and Analytical Errors
{{ vm.helper.t('courses.scorm') }}
9.
Knowledge Check -Module 4: Practical Measures of Sampling and Analytical Errors
{{ vm.helper.t('courses.exam') }}
10.
Module 5: Sources of Error: Sampling, Subsampling and Analysis
{{ vm.helper.t('courses.scorm') }}
11.
Knowledge Check -Module 5: Sources of Error: Sampling, Subsampling and Analysis
{{ vm.helper.t('courses.exam') }}
12.
Module 6: Monitoring Data Quality
{{ vm.helper.t('courses.scorm') }}
13.
Knowledge Check -Module 6: Monitoring Data Quality
{{ vm.helper.t('courses.exam') }}
14.
Module 7: Exercises in Quality Control
{{ vm.helper.t('courses.scorm') }}
15.
Module 8: Appendices
{{ vm.helper.t('courses.scorm') }}
 
Added about 1 month ago, by Anonymous
it is a great course but there is a little too much statistical calculation
 
Added 5 months ago, by Figueroa
 
Added 5 months ago, by Karla
 
Added 5 months ago, by Rosalia
 
Added 6 months ago, by Anonymous
 
Added 9 months ago, by Francis
 
Added 11 months ago, by Lee
 
Added over 1 year ago, by Anonymous
Fairly confusing presentation (large blocks of text, incomplete calculations shown). Inability to ask clarifying questions makes potential errors very confusing: e.g. Mod2 Les3 - no "m" in equation, but then "m" discussed as variable immediately after; Mod2 Les4, class interval stated for Figure 2 different from that quoted in text; Mod3 Les2 Eq. 6 is not explained at all; Mod3 Les2 eq 3 used without explanation of simplification for 1 dataset; Mod6 Les4 Fig 2 dispersion listed as 105.4 but other value used in calculations, same with dispersion values in table 2 don't match fig 5-8. Many figures of poor quality, clearly screenshots from old PDFs, e.g. Mod5, Less4 too blurry to read at all. When there are multiple correct answers on quizes this is not indicated (easy to mistake for multiple choice). Layout is strange - figures always appearing above explanation in text despite text saying they appear to the right. Some essential context in later modules would be very helpful to know before stats-heavy mod 2 and 3. Could use proofread for grammar. I don't feel like this course provided enough relevant information to design and implement a complete QAQC procedure from sampling through to data analysis - too much descriptive text and not enough practice.
 
Added almost 2 years ago, by Anonymous
Interesting content and great applications to QAQC programs for mining and exploration geochemical programs.
 
Added almost 2 years ago, by Anonymous

Shopping Cart

Your cart is empty