Abstract

Sampling error is the degree to which the concentration of an element differs from the true element concentration of the material from which the sample was collected. Gold mineralization commonly exhibits sampling errors as large as 50–100%. As a result, collection and preparation of drill samples from Au mineralization can provide significant challenges for the geoscientist, largely because of the coarse particulate nature of Au. To avoid this, geoscientists have opted to collect and prepare larger drill samples to reduce the magnitude of this ‘nugget effect’.

In the technical note of the same title in EXPLORE (No. 63, July 1988, pp. 12-14), the groundtruth of a computer-simulated Au geochemical anomaly (Figure 2a), and two realizations of that groundtruth were presented.

These two realizations were collected with sample sizes such that an an average of 0.25 and 1 grain per sample (Figures 2b and 2c, respectively) were collected over the anomaly.

A third realization of an unknown groundtruth (with sample sizes corresponding to 0.25 grains per sample) was presented in Figure 2d.

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Explore, 63, 12-14.

Those of us engaged in exploration geochemistry have undoubtedly faced difficulties in the interpretation of geochemical surveys for resistate minerals. Over the past ten years, nugget effects in elements such as tungsten, tin, and now gold have undoubtedly caused many headaches. The large amount of exploration currently under way for Au makes the nugget effects associated with Au particularly important for the exploration geochemist.

Introduction

During recent audits of numerous commercial laboratories, the first author has noticed that many laboratories prepare pulp samples from rock, drill core and drill cuttings of approximately 3 kg mass using large, fixedbowl, shatter box-type, vibratory pulverizers. This preparation method is referred to by the laboratories as "total preparation', because the complete 3 kg sample submitted by the geologists is pulverized before sub-sampling. During these laboratory audits, each laboratory manager was asked if this 3 kg pulverizing equipment produced a pulp equal to or better in quality than the smaller 1 kg shatter box pulverizers also in common use by commercial laboratories. Each of the laboratory managers indicated that the large pulverizer actually produced a pulp product that was inferior in grain size specifications to the 1 kg shatter box pulverizers. The laboratory managers furthermore admitted that the larger pulverizers were used solely because the clients requested the 3 kg pulp in the belief that it results in significantly better sub-sampling (preparation) precision than a 1 kg pulp. This purportedly improved precision was thought to be especially important for samples containing a significant "rare grain' or nugget effect.

Introduction

Auditing resource databases and exploration data sets over the past several decades has exposed the lead author to many instances of data averaging of multiple analyses from a single sample. These data sets were comprised of pulp or reject analyses that showed poor precision upon re-analyses. Some organizations produced a final concentration in their database that was an arithmetic average of all analyses. It was this average concentration that was used in resource grade estimation, or in anomaly definition. In these cases the reason for the re-analyses was a "rare grain" or "nugget" effect in the element being sought, usually gold.

Dr. Francois-Bongarcon takes issue with a number of points in our EXPLORE contribution entitled: "Sample Preparation of 'Nuggety' Samples: Dispelling Some Myths about Sample Size and Sampling Errors". We disagree with much of what he says and discuss his principle points below.

1. Dr. Francois-Bongarcon first suggests that Poisson statistics cannot be used to model sampling error in gold ores because the gold is typically not liberated. This comment ignores the work of Clifton et al. (1969), that has long formed the basis of sampling protocols for rare grains in applied geochemistry. This U.S.G.S. Professional Paper details how the effective nugget size and the effective number of nuggets can be calculated from the relative error of replicate samples of a given size, and how these can be used to estimate the relative error of samples of different sizes. Clifton et al.'s (1969) approach makes no attempt to exactly mimic non-ideal sample characteristics (such as full liberation, or constant grain size and shape), but rather employs an ideal 'equant grain model' that exhibits exactly the same variance structure of the material under examination. Using this Poisson-based model, predictions regarding the magnitude of sampling error can be made for samples of different size.