Integration of Grades, Tonnages, Number of Deposits, and Economic Effects

Now that all of the fundamental parts of a quantitative mineral resource assessment have been discussed, it is useful to reflect on why all of the work has been done. As mentioned in chapter 1, it is quite easy to generate an assessment of the “potential” for undiscovered mineral resources. Aside from the question of what, if anything, “potential” means, there is the more serious question of whether a decision-maker has any use for it. The three-part form of assessment is part of a system designed to respond to the needs of decision-makers. Although many challenging ideas are presented in this book, it has a different purpose than most academic reports. This book has the same goal as Allais (1957)—to provide information useful to decision makers. Unfortunately, handing a decision-maker a map with some tracts outlined and frequency distributions of some tonnages and grades along with estimates of the number of deposits that might exist along with their associated probabilities is not really being helpful—these need to be converted to a language understandable to others. This chapter summarizes how these various estimates can be combined and put in more useful forms. If assessments were conducted only to estimate amounts of undiscovered metals, we would need contained metal models and estimates of the number of undiscovered deposits. Grades are simply the ratio of contained metal to tons of ore (chapter 6), so contained metal estimates are available for each deposit. In the simplest of all cases, one could estimate the expected number of deposits with equation 8.1 (see chapter 8) and multiply it by the expected amount of metal per deposit, such as the 27,770 tons of copper in table 9.1, to make an estimate of the expected amount of undiscovered metal. As pointed out in chapter 1, expected amounts of resources or their values can be very misleading because they provide no information about how uncommon the expected value can be with skewed frequency distributions that are common in mineral resources; that is, uncertainty is ignored.

The Future of Quantitative Resource Assessments

The difference between the ideas presented by Allais (1957) fifty years ago and those presented in this book reflect a significant growth in knowledge since his work, and the recognition of the value of, and ways to capture, geologic information. We now can use geologic maps to divide large regions into parts that could contain different kinds of mineral deposits, and we know that these different kinds of mineral deposits are significantly different in the amounts and qualities of minerals of interest to society, which affect chances that the deposits will be sought, found, and exploited by society. It is important to remember that our goal is to provide unbiased estimates of undiscovered mineral resources and then to minimize the uncertainty associated with the estimates. Here we point out where there are opportunities to improve the three-part form of quantitative mineral resource assessment. Many of these opportunities come from identified sources on uncertainties in present assessments of all kinds, such as assessing resources under cover. Some of the improvements can be made in parts of the present assessments that are not completed such as economic filters. Additional opportunities come from the possibilities of harnessing the power of new technologies such as probabilistic neural networks to well-designed applications in these kinds of assessments. Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Uncertainties about undiscovered resources can be addressed and reduced through improved mineral deposit models, better economic filters and simulators, and application of new technologies to integrate information and by better dealing with geographic uncertainty due to covered terrains (Singer, 2001). Finally, all of these possible ways to improve assessments rely on careful applications of the tools. Research opportunities in quantitative resource assessment could be identified in at least three ways: (1) by listing unfinished or flawed parts of assessment tools, (2) by pointing to new technologies that could improve assessments, and (3) by focusing on tasks that could most significantly reduce uncertainties in assessments, and here we consider each.