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

2010 ◽  
Author(s):  
Donald Singer ◽  
W. David Menzie
Keyword(s):  
Mineral Resource ◽  
Decision Maker ◽  
Mineral Resources ◽  
Resource Assessment ◽  
Economic Effects ◽  
Decision Makers ◽  
Frequency Distributions ◽  
Undiscovered Deposits ◽  
Quantitative Mineral Resource Assessment

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.

Introduction

2010 ◽  
Author(s):  
Donald Singer ◽  
W. David Menzie
Keyword(s):  
Mineral Resource ◽  
Regional Development ◽  
Decision Maker ◽  
National Policy ◽  
Mineral Resources ◽  
Integrated Approach ◽  
Decision Makers ◽  
The People ◽  
Or Economics ◽  
Undiscovered Deposits

Every day, somewhere in the world, decisions are made about how public lands that might contain undiscovered resources should be used or whether to invest in exploration for minerals. Less frequently, decisions are made concerning mineral resource adequacy, national policy, and regional development. Naturally, the people making the decisions would like to know the exact consequences of the decisions before the decisions are made. Unfortunately, it is not possible to inform these decision-makers, with any certainty, about amounts, discoverability, or economics of undiscovered mineral resources. The kind of assessment recommended in this book is founded in decision analysis in order to provide a normative framework for making decisions concerning mineral resources under conditions of uncertainty. Our goal is to make explicit the factors that can affect a mineral-related decision so that the decision-maker can clearly see the possible consequences of the decision. This means that we start with the question of what kinds of issues decision-makers are trying to resolve and what types and forms of information would aid in resolving these issues. This book has a different purpose than academic reports common to many assessments, and it is not designed to help select sites for exploration. The audience for products of assessments discussed here comprises governmental and industrial policy-makers, managers of exploration, planners of regional development, and similar decision-makers. Some of the tools and models presented here are useful for selection of exploration sites, but that is a side benefit. The focus of this book is on the practical integration of the fundamental kinds of information needed by the decision-maker. The integrated approach to assessment presented in this book focuses on three assessment parts and the models that support them. The first part uses models of tonnages and grades to estimate possible tonnages and grades of undiscovered deposits. The second part develops mineral resource maps that explore whether an area’s geology permits the existence of one or more types of mineral deposits. The product of this part of the assessment is identification of so-called permissive tracts of land.

Quantitative mineral resource assessment of undiscovered PGE resources in Finland

2010 ◽  
Vol 38 (3) ◽  
pp. 270-287 ◽  
Author(s):  
K. Rasilainen ◽  
P. Eilu ◽  
T. Halkoaho ◽  
M. Iljina ◽  
T. Karinen
Keyword(s):  
Mineral Resource ◽  
Resource Assessment ◽  
Mineral Resource Assessment ◽  
Quantitative Mineral Resource Assessment

Estimating the Number of Undiscovered Deposits

2010 ◽  
Author(s):  
Donald Singer ◽  
W. David Menzie
Keyword(s):  
Percent Level ◽  
Fixed Number ◽  
Mineral Deposit ◽  
Decision Makers ◽  
Frequency Distributions ◽  
The Third ◽  
Undiscovered Deposits ◽  
Unbiased Estimates ◽  
Biased Estimates ◽  
Descriptive Models

The third part of three-part assessments is the estimate of some fixed but unknown number of deposits of each type that exist in the delineated tracts. Until the area being considered is thoroughly and extensively drilled, this fixed number of undiscovered deposits, which could be any number including 0, will not be known with certainty. This number of deposits has meaning only in terms of a grade-and-tonnage model. If this requirement did not exist, any wisp of minerals could be considered worthy of estimation, and even in small regions, we would need to estimate millions of “deposits.” For example, it is not difficult to imagine tens of thousands of fist-sized skarn copper “deposits” in parts of western United States—even in this example, we have used “deposit” size to provide important information. In another example, Wilson et al. (1996) estimated five or more epithermal gold vein deposits at the 90 percent level but provided no grade-and-tonnage model, so these estimated deposits could be any size. To provide critical information to decision-makers, the grade-and-tonnage model is key, and the estimated number of deposits that might exist must be from the grade-and-tonnage frequency distributions. In three-part assessments, the parts and estimates are internally consistent in that delineated tracts are consistent with descriptive models, grade-and-tonnage models are consistent with descriptive models and with known deposits in the area, and estimates of number of deposits are consistent with grade-and-tonnage models. Considerable care must be exercised in quantitative resource assessments to prevent the introduction of biased estimates of undiscovered resources. Biases can be introduced into these estimates either by a flawed grade-and-tonnage model or by the lack of consistency of the grade-and-tonnage model with the number-of-deposit estimates. For this reason, consistency of estimates of number of deposits with the grade-and-tonnage models is the most important guideline. Issues about consistency of mineral deposit models are discussed in chapters 3 through 6. Grade-and-tonnage models (chapter 6), which are the first part of three-part assessments, are of particular concern. In this chapter, the focus is on making unbiased estimates of the number of undiscovered deposits.

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