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Geochemistry: Exploration, Environment, Analysis; May 2008; v. 8; no. 2; p. 115-127; DOI: 10.1144/1467-7873/07-156
© 2008 Geological Society of London
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Original Article

Quantitative assessment of the success of geochemical exploration techniques using minimum probability methods

Clifford R. Stanley1 and Ryan R.P. Noble2

1 Department of Earth and Environmental Science, Acadia University, Wolfville, Nova Scotia, B4P 2R6, Canada(cliff.stanley{at}acadiau.ca)
2 CRC LEME, CSIRO Exploration and Mining, Kensington, Western Australia, 6102, Australia(r.noble{at}curtin.edu.au)

Hypergeometric statistics have been used to establish a quantitative measure of performance for geochemical exploration techniques over known mineral showings. An alternative and complementary measure of exploration performance is geochemical contrast, which determines how convincing or compelling a geochemical result is. Using the identical philosophy employed to assess exploration ‘accuracy’, the Student's t distribution is used to create a quantitative measure of ‘geochemical contrast’. First, thresholds are selected to separate anomalous and background populations. Then, Student's t test statistics for each of these sets of anomalous and background samples are calculated, and the Student's t probability is determined for the highest test statistic. This probability describes the chance that the anomalous and background populations were derived from the same underlying distribution. If this probability is low, then the concentrations of the anomalous and background samples are very different, and high geochemical contrast exists. If this probability is high, the alternative is true. As a result, this approach can be used to quantitatively compare the geochemical contrast of competing exploration techniques. Furthermore, because both of these ‘accuracy’ and ‘geochemical contrast’ measures are probabilities that vary inversely with exploration performance, their joint probability (their product) can be used to collectively rate the performance of exploration techniques.

KEYWORDS: Student's t distribution, hypergeometric distribution, threshold selection, anomaly recognition, geochemical contrast, accuracy, precision, selective extraction






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