Basic concepts in three-part quantitative assessments of undiscovered mineral resources
Tóm tắt
Since 1975, mineral resource assessments have been made for over 27 areas covering 5×106 km2 at various scales using what is now called the three-part form of quantitative assessment. In these assessments, (1) areas are delineated according to the types of deposits permitted by the geology,(2) the amount of metal and some ore characteristics are estimated using grade and tonnage models, and (3) the number of undiscovered deposits of each type is estimated. Permissive boundaries are drawn for one or more deposit types such that the probability of a deposit lying outside the boundary is negligible, that is, less than 1 in 100,000 to 1,000,000. Grade and tonnage models combined with estimates of the number of deposits are the fundamental means of translating geologists' resource assessments into a language that economists can use. Estimates of the number of deposits explicitly represent the probability (or degree of belief) that some fixed but unknown number of undiscovered deposits exist in the delineated tracts. Estimates are by deposit type and must be consistent with the grade and tonnage model. Other guidelines for these estimates include (1) frequency of deposits from well-explored areas, (2) local deposit extrapolations, (3) counting and assigning probabilities to anomalies and occurrences, (4) process constraints, (5) relative frequencies of related deposit types, and (6) area spatial limits. In most cases, estimates are made subjectively, as they are in meteorology, gambling, and geologic interpretations. In three-part assessments, the estimates are internally consistent because delineated tracts are consistent with descriptive models, grade and tonnage models are consistent with descriptive models, as well as with known deposits in the area, and estimates of number of deposits are consistent with grade and tonnage models. All available information is used in the assessment, and uncertainty is explicitly represented.
Tài liệu tham khảo
Allais, M., 1957. Method of appraising economic prospects of mining exploration over large territories-Algerian Sahara case study: Management Science, v. 3, no. 4, p. 285–347.
Arps, J.J., and Roberts, T.G., 1958. Economics of drilling for cretaceous oil on the east flank of Denver-Julesburg basin: American Association of Petroleum Geologists Bulletin, v. 42, no. 11, p. 2549–2566.
Bacchus, Fahiem, 1990, Representing and reasoning with probabilistic knowledge — A logical approach to probabilities: Cambridge, Mass., The MIT Press, 233 p.
Bliss, J.D., ed., 1992a. Developments in mineral deposit modeling: U.S. Geological Survey Bulletin 2004, 168 p.
—, 1992b. Grade-tonnage and other models for diamond kimberlite pipers: Nonrenewable Resources, v. 1, no. 3, p. 214–230.
Bliss, J.D., and Menzie, W.D. in press, Spatial mineral-deposit models and the prediction of undiscovered mineral deposits,in Kirkham, R.V., and others, eds. Mineral deposit modeling: Geological Association of Canada.
Bliss, J.D., Menzie, W.D., Orris, G.J., and Page, N.J., 1987. Mineral deposit density — A useful tool for mineral-resource assessment: U.S. Geological Survey Circular 995, p. 6, abstract.
Chung, C.F., Jefferson, C.W., and Singer, D.A., 1992. A quantitative link among mineral deposit modeling, geoscience mapping, and exploration/resource assessment: Economic Geology, v. 87, no. 1, p. 194–197.
Chung, C.F., Singer, D.A., and Menzie, W.D., 1992. Predicting sizes of undiscovered mineral deposits — An example using mercury deposits in California: Economic Geology, v. 87, no. 4, p. 1174–1179.
Cox, D.P., 1993. Estimation of undiscovered deposits in quantitative mineral resource assessments — Examples from Venezuela and Puerto Rico: Nonrenewable Resources, v. 2, no. 2, p. 82–91.
Cox, D.P. Barton, P.B., and Singer, D.A., 1986, Introduction,in Cox, D.P. and Singer, D.A., eds., Mineral deposit models: U.S. Geological Survey Bulletin 1693, p. 1–10.
Cox, D.P., and Singer, D.A., 1986, Mineral deposit models: U.S. Geological Survey Bulletin 1693, 379 p.
Drew, L.J., and Menzie, W.D., 1993. Is there a metric for mineral deposit occurrence probabilities? Nonrenewable Resources, v. 2, no. 2, p. 92–105.
Harris, D.P., 1984. Mineral resources appraisal: Mineral endowment, resources, and potential supply-Concepts, methods, and cases: Oxford, Oxford Univ. Press, 445 p.
Lasky, S.G., 1948. Trends in technology and outlook for improvement in mineral position-Search for new mineral supplies,in Staffs Bureau of Mines and Geological Survey, Mineral Position of the United States, Washington, D.C., Public Affairs Press, p. 1–10.
MacKevett, E.M., Jr., Singer, D.A., and Holloway, C.D., 1978. Maps and tables describing metalliferous mineral resource potential of southern Alaska: U.S. Geological Survey Open-File Report 78-1-E, 2 sheets, scale 1∶1,000,000, 45 p.
Menzie, W.D., and Singer, D.A., 1990, A. course on mineral resource assessment — Proceedings of International Symposium on Mineral Exploration: The Use of Artificial Intelligence, October 29–November 2, 1990, Tokyo and Tsukuba, Japan, p. 177–188.
Mosier, D.L., Singer, D.A., and Berger, B.R., 1986, Grade and tonnage model of Comstock epithermal veins,in Cox, D.P., and Singer, D.A., eds., Mineral deposit models: U.S. Geological Survey Bulletin 1693, p. 151–153.
Mosier, D.L., Singer, D. A., and Salem, B.B., 1983, Geologic and grade-tonnage information on volcanic-hosted copper-zinclead massive sulfide deposits: U.S. Geological Survey Openfile Report 83–89, 78 p.
Murphy, A.H., and Winkler, R.L., 1984. Probability forecasting in meteroology: Journal American Statistical Association, v. 79, no. 387, p. 489–500.
Orris, G.J., and Bliss, J.D., eds. 1991, Industrial mineral deposit models-Descriptive deposit models: U.S. Geological Survey Open-File Report 91-11a, 78 p.
Reed, B.L., Menzie, W.D., McDermott, M., Root, D.H., Scott, W., and Drew, L.J., 1989, Undiscovered lode tin resources of the Seward Peninsula, Alaska: Economic Geology, v. 84, no. 7, p. 1936–1947.
Root, D.H., Menzie, W.D., and Scott, W.A., 1992, Computer Monte Carlo simulation in quantitative resource estimation: Nonrenewable Resources, v. 1, no. 2, p. 125–138.
Sangster, D. F., 1986, Application of mineral deposit models to regional assessments — Discussion,in Cargill, S.M., and Green, S.B., eds. Prospects for mineral resource assessments on public lands — Proceedings of the Leesburg Workshop: U.S. Geological Survey Circular 980, p. 287–292.
Shulman, M., Burger, H., Kliem, C., and Skala, W., 1992. Review of resource analysis and related studies-Working Group, Mathematical Geology, Freie Universität Berlin: Nonrenewable Resources v. 1, no. 1, p. 97–106.
Singer, D.A., 1975. Mineral resource models and the Alaskan Mineral Resource Assessment Program,in Vogely, W.A., ed., Mineral materials modeling — A state-of-the-art review: Baltimore, Johns Hopkins University Press, p. 370–382.
Singer, D.A., in press a, Development of grade and tonnage models for different deposit types,in Kirkham, R.V., and others, eds., Mineral deposit modeling: Geological Association of Canada.
Singer, D.A., in press b, Conditional estimates of the number of podiform chromite deposits,in Bliss, J.D., and Peterson, J., Spatial mineral deposit modeling: U.S. Geological Survey Bulletin.
Singer, D.A., and Cox, D.P., 1988, Applications of mineral deposit models to resource assessment,in U.S. Geological Survey Yearbook Fiscal Year 1987, p. 55–57.
Singer, D.A., and DeYoung, J.H., Jr., 1980, What can gradetonnage relations really tell us?in Guillemin, Claude, and Lagny, Phillipe, eds., Ressources minérale [Mineral resources]: Bureau de Recherches Géologiques et Minières Mémoire no. 106, p. 91–101.
Singer, D.A., and MacKevett, E.M., 1977, Mineral resources map of the McCarthy quadrangle, Alaska: U.S. Geological Survey Miscellaneous Field Studies Map MF 773-C, 1 sheet, scale 1∶250,000.
Singer, D.A., and Mosier, D.L., 1981a, The relation between exploration economics and the characteristics of mineral deposits,in Ramsey, J.B., ed., The economics of exploration for energy resources: Greenwich, Conn., JAI Press, p. 313–326.
—, 1981b, Review of regional mineral resource assessment methods: Economic Geology, v. 76, no. 5, p. 1006–1015.
Singer, D.A., Page, N.J., Bagby, W.C., Cox, D.P., and Ludington, Steve, 1987, Assessment of metallic mineral resources of Costa Rica,in U.S. Geological Survey, Direccion General de Geologia, Minas e Hidrocarburos, and Universidad de Costa Rica, 1987 [A mineral resource assessment of the Republic of Costa Rica]: U.S. Geological Survey Miscellaneous Invest. Series Map I-1865, scale 1∶500,000, p. 58–64.
Stanley, M.C., 1992, Statistical trends and discoverability modeling of gold deposits in the Abitibi Greenstone Belt, Ontario: Proceedings 23rd Applications of Computers to Mining, Tucson, Ariz., p. 17–28.
Stern, Hal, 1991. On the probability of winning a football game: American Statistician, v. 45, no. 3, p. 179–183.
Taylor, H.K., 1985. Cutoff grades-Some further reflections: Transactions Institution of Mining and Metallurgy (Section A: Mining Industry), v. 94, p. A204-A216.