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Show UNDERSTANDING THE PROCESS OF ORE SAMPLING THE UNIQUE CHARACTERISTIC OF A SAMPLE AS A PRODUCT The importance of an a priori control of sample reliability stems from a unique characteristic a sample possesses as the product of a sampling procedure. Most of the procedures we apply in mining activities provide us with results, or products, which can be judged, in terms of their quality, on the basis of direct examination: for instance simple visual inspection tells us if a batch of sieved material has been sieved properly to the expected sizes, or if a model we fit to experimental data fits the data very well or poorly. A sample presents a very different case. Its only quality is what we call "representativeness" or more simply "reliability". If the sample is "representative", then the decisions based on its measurement (e.g. grade, moisture content, proportion of a given fraction size, etc.) will be well informed, and therefore taken at a low risk level; if it is not, then wrong or sub-optimal decisions will be made, with a number of unfavorable economic consequences which mayor may not be detectable and predictable . The main point is that the quality of the sample cannot be checked once the sample has been taken (say, by direct observation) . The sample itself does not contain any indicators of its quality or absence thereof. In other words, once a sample has been taken, it will be used for measurements and subsequent decisions, but, unless its gathering and processing have been properly controlled before it was taken, it will always be too late, after the fact, to know whether the sample was representative. Once the sample is taken, its reliability cannot be tested. Unfortunately, our most major decisions are based on measurements made on samples. It is therefore of the utmost importance that the reliability of the samples, and the processes to which they are subjected to be turned into assay values, be controlled before they are taken, in terms of both accuracy and precision. This is the field of application of Sampling Theory. THE UNSEEN BENEFITS OF GOOD SAMPLING PRACTICES Good sampling is obviously very desirable and often of great economic importance, but the negative impact of poor sampling is often invisible. Although specific geostatistical tools can now be applied to quantify some of the economic benefits of better sampling practices, the issue has not yet been widely documented, and this economic importance is not always clearly perceived. A few typical examples will better illustrate this point. Improved sampling practices and sample preparation usually result in much more reliable grade predictions, which at the grade control level immediately translate into better 'produced' ACTUAL grades, and less ore being sent to the waste dump. Grade control could be seen as similar to the challenge of outlining the shape of an object on a very blurred photograph, and good sampling as one of the ways to bring the photograph in better focus. As a result, in most mines, a relative increase of 2 percent of the 'produced' grade, cumulated with a comparable decrease in the tonnage of economic ore unduly sent to the waste dump usually mean significant economic benefits. For instance, based on its past experience, a major, progressive American gold mining company has calculated that the cost of not using the proper approach to sampling and grade control would amount to an average $6.70 per ounce of gold production , or in their case, a total of $ 10 million per year. Very few mining operations, however, get into these kinds of calculations, and in general, the amount of ore sent to the waste is not evaluated, and certainly never equated to the effectiveness of sampling. At the project evaluation stage, the unseen benefits are even more striking when put in the spotlight. A survey by a major investment counseling firm (Lassonde, 1990) showed that out of 49 mining projects identified as disappointments or failures, 31 were to be blamed on improper predictions of |
