Probability Graphs and their Application in Mineral Exploration

Probability Graphs and their Application in Mineral Exploration

Areas of Study: Exploration and Geology

Qualifies for CMS

Qualifies for Certification

This course covers the application of probability graphs in mineral exploration, illustrated with numerous practical examples.

Author:

Online Course Online Courses

Enroll for Access to All Online Courses

Enrollees have access to all self-paced online courses.

Certification available for Not Available
  • Audience Level:
  • Professional
  • Enrollment:
  • Required
  • Duration:
  • 16 hours

Course Summary

Introduction

Probability graphs display a data set as a cumulative distribution. The most significant use of probability graphs applied to mineral exploration data is in the recognition of the number of populations in a data set, and the partial or complete partitioning of individual values into their respective groups or populations. The significance of the resulting groupings or populations of data must be interpreted.

Interpreting probability graphs is largely a matter of understanding the implications of the patterns that result when data sets are plotted. These implications are not always fully appreciated, and in some cases, the conclusions drawn from the probability graphs are incorrect.

In this course, you will learn how probability graphs can supplement analyses done using histograms, and how this can be beneficial when interpreting mineral exploration data. The course explains data distributions and populations. You will learn that probability graphs are an easy way to estimate the forms of distributions and their parameters. They are a useful tool to present and analyze many types of numeric data that are the product of mineral exploration programs. The course also highlights general advantages, but also limitations of using probability graphs, and provides useful procedure tips to draw up the graphs.

Note that this course assumes a working knowledge of simple statistical concepts (e.g. arithmetic mean, variance, standard deviation, normal density distribution, etc.). The course content uses a clear-cut, idealized approach illustrated by real life practical examples used throughout the mining industry. The Appendix includes a variety of interpretations of published probability graphs with alternate interpretations and discussion on the analytical approaches used by the original publications.

Course Content

The course consists of 17 viewing sessions of 45–60 minutes each with supporting figures, tables, and multiple choice course reviews. Course duration is equivalent to approximately 16 hours of viewing content.

Learning Outcomes

  • Describe how probability graphs are an easy way to estimate the forms of data distributions and their parameters, and can supplement the use of histograms when analyzing data.
  • Apply probability graphs as a tool to present and analyze many types of numeric data.
  • Recognize the value of using probability graphs in a mineral exploration context to identify populations in a data set.

Recommended Background

  • A working knowledge of simple statistical concepts such as arithmetic mean, variance, standard deviation, and normal density distribution.
  • Familiarity with the basic objectives of mineral exploration.

Alastair J. Sinclair

Alastair J. Sinclair obtained his B.A.Sc. and M.A.Sc. degrees in geological engineering from the University of Toronto (1957 and 1958) and a Ph.D. in Economic Geology from the University of British Columbia (1964). From 1962 to 1964 he taught in the Dept. of Geology, University of Washington, Seattle; and from 1964 to 1998 taught at the University of British Columbia.

In addition to teaching at UBC he was Head of the Department of Geological Sciences (1985-1990) and Director of Geological Engineering (1991-1998). He is presently Professor Emeritus in geological engineering at the University of British Columbia. For many years he taught courses in Economic Geology, Mineral Inventory Estimation and Mineralography and Ore Microscopy. His research activities have focused on Mineral Exploration Data Analysis, Resource Estimation of Mineral Deposits and Quality Control Aspects of Resource Evaluation.

He has presented a wide range of short courses for mining companies and professional organizations and has consulted widely for the international mining industry; he continues to be active in these fields.