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Geochemistry: Exploration, Environment, Analysis
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Geochemistry: Exploration, Environment, Analysis; 2008; v. 8; issue.2; p. 129-138;
DOI: 10.1144/1467-7873/07-157
© 2008 Geological Society of London

Original Article

Missed hits or near misses: determining how many samples are necessary to confidently detect nugget-borne mineralization

Clifford R. Stanley

Department of Earth and Environmental Science, Acadia University, Wolfville, Nova Scotia, B4P 2R6, Canada(cliff.stanley{at}acadiau.ca)

The probability of collecting a sample containing at least one large nugget from an exploration prospect (and thus detecting the nugget-borne mineralization) can be calculated using Poisson statistics and an equant grain model that describes the sampling characteristics of mineralization containing a range of nugget sizes. This procedure requires an estimate of the mass-weighted, average (effective) nugget grain size in the mineralized material, and an estimate of the (expected) grade of mineralization. Using these parameters, the number of effective nuggets in an equivalent equant grain model that describes the sampling characteristics of mineralization can be determined and used to estimate the Poisson probability of collecting at least one large nugget in a real sample. With this information, the probability of collecting m large nugget-bearing samples from a set of n samples can be determined using binomial statistics, providing the explorationist with an estimate of how well a prospect containing nugget-borne mineralization will be assessed using those n samples. Software can be used to perform the associated calculations.

Key Words: nugget effect • Poisson distribution • binomial distribution • equant grain model • probability • number of samples • prospect evaluation