2010Oxford University Press eBooksRequires access

Grade-and-Tonnage Models

Donald A. Singer, W. David Menzie

Open publisher page 2 citations

Abstract

Mineral deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different (Singer, Cox, and Drew, 1975; Singer and Kouda, 2003), and (2) deposit types occur in different geologic settings that can be identified from geologic maps. If assessments were only conducted to estimate amounts of undiscovered metals, we would need contained metal models, but determining whether the metals might be economic to recover is an important quality of most assessments, and grades and tonnages are necessary to estimate economic viability of mineral deposits (see chapter 5). In this chapter, we focus on the first part of three-part assessments: grade-and-tonnage models. Too few thoroughly explored mineral deposits are available in most areas being assessed for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits, so we need mineral-deposit models that are generalized. Well-designed and well-constructed grade-and-tonnage models allow mineral economists to determine the possible economic viability of the resources in the region and provide the foundation for planning. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy-makers. Grade-and-tonnage models are fundamental in the development of other kinds of models such as deposit-density and economic filters. Frequency distributions of tonnages and average grades of well-explored deposits of each type are employed as models for grades and tonnages of undiscovered deposits of the same type in geologically similar settings. Grade-and-tonnage models (Cox and Singer, 1986; Mosier and Page, 1988; Bliss, 1992a, 1992b; Cox et al., 2003; Singer, Berger, and Moring, 2008) combined with estimates of number of undiscovered deposits are the fundamental means of translating geologists’ resource assessments into a language that decision-makers can use. For example, creation of a grade-and-tonnage model for rhyolite-hosted Sn deposits in 1986 demonstrated for the first time that 90 percent of such deposits contain less than 4,200 tons of ore. This made it clear that an ongoing research project by the U.S. Geological Survey on this deposit type could have no effect on domestic supplies of tin, and the project was cancelled.

About this research paper

What this paper is about

Mineral deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different (Singer, Cox, and Drew, 1975; Singer and Kouda, 2003), and (2) deposit types occur in different geologic settings that can be identified from geologic maps. If assessments were only conducted to estimate amounts of undiscovered metals, we would need contained metal models, but determining whether the metals might be economic to recover is an important quality of most assessments, and grades and tonnages are necessary to estimate economic viability of mineral deposits (see chapter 5). In this chapter, we focus on the first part of three-part assessments: grade-and-tonnage models. Too few thoroughly explored mineral deposits are available in most areas being assessed for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits, so we need mineral-deposit models that are generalized. Well-designed and well-constructed grade-and-tonnage models allow mineral economists to determine the possible economic viability of the resources in the region and provide the foundation for planning. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy-makers. Grade-and-tonnage models are fundamental in the development of other kinds of models such as deposit-density and economic filters. Frequency distributions of tonnages and average grades of well-explored deposits of each type are employed as models for grades and tonnages of undiscovered deposits of the same type in geologically similar settings. Grade-and-tonnage models (Cox and Singer, 1986; Mosier and Page, 1988; Bliss, 1992a, 1992b; Cox et al., 2003; Singer, Berger, and Moring, 2008) combined with estimates of number of undiscovered deposits are the fundamental means of translating geologists’ resource assessments into a language that decision-makers can use. For example, creation of a grade-and-tonnage model for rhyolite-hosted Sn deposits in 1986 demonstrated for the first time that 90 percent of such deposits contain less than 4,200 tons of ore. This made it clear that an ongoing research project by the U.S. Geological Survey on this deposit type could have no effect on domestic supplies of tin, and the project was cancelled.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Mineral deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different (Singer, Cox, and Drew, 1975; Singer and Kouda, 2003), and (2) deposit types occur in different geologic settings that can be identified from geologic maps. If assessments were only conducted to estimate amounts of undiscovered metals, we would need contained metal models, but determining whether the metals might be economic to recover is an important quality of most assessments, and grades and tonnages are necessary to estimate economic viability of mineral deposits (see chapter 5). In this chapter, we focus on the first part of three-part assessments: grade-and-tonnage models. Too few thoroughly explored mineral deposits are available in most areas being assessed for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits, so we need mineral-deposit models that are generalized. Well-designed and well-constructed grade-and-tonnage models allow mineral economists to determine the possible economic viability of the resources in the region and provide the foundation for planning. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy-makers. Grade-and-tonnage models are fundamental in the development of other kinds of models such as deposit-density and economic filters. Frequency distributions of tonnages and average grades of well-explored deposits of each type are employed as models for grades and tonnages of undiscovered deposits of the same type in geologically similar settings. Grade-and-tonnage models (Cox and Singer, 1986; Mosier and Page, 1988; Bliss, 1992a, 1992b; Cox et al., 2003; Singer, Berger, and Moring, 2008) combined with estimates of number of undiscovered deposits are the fundamental means of translating geologists’ resource assessments into a language that decision-makers can use. For example, creation of a grade-and-tonnage model for rhyolite-hosted Sn deposits in 1986 demonstrated for the first time that 90 percent of such deposits contain less than 4,200 tons of ore. This made it clear that an ongoing research project by the U.S. Geological Survey on this deposit type could have no effect on domestic supplies of tin, and the project was cancelled.

Key concepts: Tonnage, Mineral deposit, Mineral resource classification, Resource (disambiguation), Geology, Mining engineering, Estimation, Quality (philosophy)

Related papers

Back to paper searchBrowse research topicsOriginal source
Grade-and-Tonnage Models — Research Paper | ScholarLens