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Show 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 grades and reserves, which is directly linked with the control of sample data reliability and proper use . It is clear from these statistics that neither the potential benefits of better sampling and sample grade processing, nor the actual quality of the grades derived from the samples, are usually obvious to the project evaluator. The problem of controlling the reliability of mining samples therefore is both economically more important, and technically more d ifficult, than would appear at first glance, and only the proper use of Sampling Theory offers some protection against the negative impact of this unfavorable, albeit fairly general, situation . BENEFITS TO BE EXPECTED It would most likely be impossible to inventory and describe all the benefits that good sampling control may be able to generate. However, here are a few of the benefits that can be expected from the study of the sampling parameters particular to a given project: Field sampling procedures: proper understanding of the theory allows the early detection of sampling biases, the design of adequate sample collection procedures, the control of the representativeness of the primary sample, the economic optimization of the amount of sample to be taken . Segregation phenomena are a major negative factor when sampling broken ore . Sampling theory addresses segregation , and offers methods of reducing its impact on the reliability of the samples. When the primary samples are later prepared in the secondary sampling (mass reduction) operations, each one of which can potentially jeopardize the reliability of the initial sample. Sample preparation protocols can be optimally designed using Sampling Theory so that the overall effect of primary and secondary sampling operations are kept below an acceptable, preset, threshold of uncertainty. With the resulting improved drill hole data reliability , the following hidden variograms , improved data analysis and grade data interpretation resulting in improved ore grade predictions, economic ore delineation, hence improved mine planning and design; improved drill hole grade correlations , better understanding of the nature of the mineralization; proper assessment of the real 'nugget effect'; improved understanding and handling of grade outliers. Improved control of mining and ore process testing : better design of sampling procedures for the testing of bulk mining as well as metallurgical processes, resulting in lower decision risk levels; better informed conclusions on the |
