Comparing Gold Values - Acme...

Comparing Gold Values Determined by Fire Assay and Aqua Regia

Bureau Veritas Minerals

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The complexity of gold bearing systems means that geochemistry must often do more than determine how much gold is in a rock. Fire assay remains the benchmark technique for quantifying gold, but minor and trace element data are often used to vector within a mineralised system with Aqua Regia as the leach of choice. Aqua Regia is very good at dissolving gold, and as a consequence geochemists and geologist are often faced with reconciling two gold numbers for each sample, and sometimes they do not match. This article examines how the distribution of gold in geological samples can affect the representivity of a sub sample and the challenges this creates for data integration.

Comparing Gold ValuesDetermined by Fire Assay and Aqua Regia

It can sometimes be difficult to compare data from different analytical techniques, especially when they utilize very different sample weights. Fire assay is always done on a large subsample to ensure representivity and to mitigate against the common situation where gold occurs as large (at least relatively) particles. Trace element data is usually determined on much smaller subsamples to reduce costs, and because most elements have a relatively homogeneous distribution within a sample. So the question is, what happens when you report a heterogeneously distributed element like gold from techniques with very different test weights?

Figure 1 at right shows data for a set of samples that were analyzed for Au using both a 50g fire assay charge, and a 0.5g subsample leached with Aqua Regia. The results actually show relatively good agreement between the two methods. This is encouraging as it suggests that a substantial amount of the Au is present as fine particles, and that a sufficient number of particles are captured by the small test portion used for the Aqua Regia leach to produce a representative result (compared to the 50 g Fire assay).

From Figure 1 we observe a small positive bias towards the 50 g fire assay. This could be attributed to the fire assay method being a total digestion, whereas, Aqua Regia is a partial digest. There may be a few very fine particles of Au being entirely encapsulated by refractory minerals (i.e. quartz) that are not available to leaching.

The data in figure 2 is from a suite of samples that was subsequently determined to contain coarse gold by follow up metallics fire assay, and shows a bias in Aqua Regia results that becomes increasingly pronounced at higher grades. Compared to figure 1, however, figure 2 demonstrates that for some samples, the Aqua Regia results are inconsistently higher than the fire assay. The difference is not systematic. When clients receive data like these, they always ask the predictable question: how can a partial extraction produce a higher result than a total decomposition like fire assay?

Figure 2 illustrates that at low Au grade, there is good agreement between FA and Aqua Regia suggesting that there is a relatively consistent component of homogeneously distributed fine gold particles. As the grade increases, there is

Au

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Figure 1

Geochem instrumentation

Au ppm: 50g Fire Assay�Au_PPM Linear (Au_PPM)

an ever higher probability that larger particles of Au are sampled, and this is the underlying cause for the observed bias. While the coarse grains appear to be much less abundant (because most of the data agree well), when one is captured in a test portion they effect the results of the two tests in very different ways. Imagine a single nugget of Au is captured in each of the analytical methods. One nugget in a 50g sample will have a much smaller effect on the reported concentration than it would if present in a 0.5g sample. In fact, that nugget would have an effect 100 times larger on the smaller sample. Since Aqua Regia is very effective at dissolving free gold, we can now see why a positive bias for a partial extraction makes sense when coarse free gold is present.

To illustrate further, error envelopes have been added to figure 2 indicating the expected error for a 0.5 g analysis versus a 50 g analysis for Au. The dashed blue line marks the bottom limit on the negative error to be expected (let’s call it the floor) due to all the fine gold present that is consistently sampled in both test portion weights. The black line is the positive corollary of the blue line and is the expected positive error if error is random and has an even distribution about the mean. From figure 2, however, we observe that there are many more points above the positive error envelope than below the negative error envelope. The positive bias is much larger due to the huge impact that a single large nugget of Au has on a 0.5 g analysis (there is really no upper limit or “ceiling” on this). The concept has been drawn stylistically (not to scale) using the black error bars in figure 2. They show not only that the expected error is larger for the low sample weight test, but that the expected positive error is larger than the expected negative error.

The comparison of data from different analytical techniques needs to be performed carefully, particularly for gold. Depending on the mineral species in which an element resides, each test will be more or less effective at extraction. The important thing to remember is that in many cases the results may not be exactly the same. We can use the differences, along with our understanding of the sample mineralogy, to gain additional insight into the geological systems we study.

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Figure 2

Au ppm: 50g Fire Assay�Au_PPM Linear (Au_PPM)

Fire Assay department

Loading samples for instrumentation

y = 1.2053xR² = 0.8737

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Au ppm: 50g Fire Assay

Au Assay Comparison -‐ All Assays greater than 0.2ppm

Au_PPM Linear (Au_PPM)

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