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Fundamental issues in quantitative estimation of mineral resources

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Abstract

Several issues considered to be fundamental in quantitative estimation of mineral resources and selection of mineral targets are addressed. Integration of multiple data sets, either by experts or by statistical methods, has become a common practice in estimation of mineral potential. Several major problems in data integration must be solved to significantly improve mineral resource estimation. Issues related to randomness of mineral endowment, basic statistical tools, exceptionalness of ore, and economic truncation and translation are discussed in the first part of the article. A number of important technical problems in data integration are also identified; they include data compilation, information enhancement, information synthesis, and target selection.

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References

  1. Agterberg, F.P., 1981, Application of image analysis and multivariate analysis to mineral resource appraisal: Economic Geology, v. 76, p. 1016–1031.

  2. —— 1988, Application of recent developments of regression analysis in regional mineral resource evaluation,in Chung, C.F., Fabbri, A.G., and Sinding-Larsen, R., eds., Quantitative analysis of mineral and energy resources: Dordrecht, Reidel, p. 1–28.

  3. —— 1989, Computer programs for mineral exploration: Science, v. 245, p. 76–81.

  4. —— 1992, Combining indicator patterns in weights of evidence modeling for resource evaluation: Nonrenewable Resources, v. 1, p. 39–50.

  5. Allais, M., 1957, Method of appraising economic prospects of mining exploration over large territories—Algerian Sahara case study: Management Science, v. 3, p. 285–347.

  6. Barnett, H.J., and Morse, C., 1963, Scarcity and growth,in XXX, eds., The economics of natural resource availability: Baltimore, Johns Hopkins Press (for Resources for the Future), p. XX-XX.

  7. Botbol, J.M., Sinding-Larsen, R., McCammon, R.B., and Gott, G.B., 1978, A regionalized multivariate approach to target selection in geochemical exploration: Economic Geology, v. 73, p. 534–546.

  8. Brinck, J.W., 1972, Prediction of mineral resources and long-term price trends in the nonferrous metal mining industry,in Campbell, F.A., and Wilson, H.D.B., conveners, sec. 4, Mineral deposits: International Geological Congress, 24th, Montreal, 1972 [Proceedings], p. 3–15.

  9. Charles River Associates, Inc., 1978, The economics and geology of mineral supply—An integrated framework for long run policy analysis. Publication prepared for the National Science Foundation, Report No. 327: Boston, Charles River Associates, Inc., 165 p.

  10. Chung, C.F., and Agterberg, F.P., 1980, Regression models for estimating mineral resources from geological map data: Mathematical Geology, v. 12, p. 473–488.

  11. 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, p. 194–197.

  12. Cox, D.P., 1990, Development and use of deposit models in the U.S. Geological Survey,in Eighth IAGOD Symposium Program with Abstracts: Ottawa, Aug. 12–18, 1990, p. A99.

  13. Finch, W.I., and McCammon, R.B., 1987, Uranium resource assessment by the Geological Survey—Methodology and plan to update the national resource base: U.S. Geological Survey Circular 994, 22 p.

  14. Gorelov, D.A., 1982, Quantitative characteristics of geologic anomalies in assessing ore capacity: International Geological Review, v. 24, p. 457–466.

  15. Green, W.R., 1991, Exploration with a computer—Geoscience data analysis and applications: Oxford, Pergamon, 225 p.

  16. Harris, D.P., 1965, An application of multivariate statistical analysis to mineral exploration: The Pennsylvania State University, University Park, Pennsylvania, Ph.D. dissertation, 261 p.

  17. —— 1984, Mineral resources appraisal—Mineral endowment, resources, and potential supply—Concept, methods, and cases: New York, Oxford University Press, 455 p.

  18. —— 1990, Mineral exploration decisions, a guide to economic analysis and modeling: New York, Wiley/InterScience, 436 p.

  19. Harris, D.P., and Carrigan, F.J., 1981, Estimation of uranium endowment by subjective geological analysis—A comparison of methods and estimates for the San Juan Basin, New Mexico: Economic Geology, v. 76, p. 1032–1055.

  20. Harris, D.P., and Chavez, L., 1981, Crustal abundance and a potential supply system,in System and economics for the estimation of uranium potential supply, part II: Grand Junction Office, Colorado, U.S. Department of Energy, p. 385–506.

  21. Harris, D.P., and Pan, G.C., 1990, Subdividing consistent geological areas by relative exceptionalness of additional information—Methods and case study: Economic Geology, v. 85, p. 1072–1083.

  22. —— 1991, Consistent geological areas for epithermal gold-silver deposits in the Walker Lake quadrangle of Nevada and California, delineated by quantitative methods: Economic Geology, v. 86, p. 142–165.

  23. Hattori, I., 1976, Entropy in Markov chains and discrimination of cyclic patterns in lithologic successions: Mathematical Geology, v. 8, p. 477–497.

  24. Kantsel, A.V., 1967, Function of metal distribution in ores, as genetic characteristics of mineralization process: International Geological Review, v. 9, p. 669–676.

  25. Koch, G.S., Jr., and Papacharalampos, D., 1988, GEOVALUATOR, an expert system for resource appraisal—A demonstration prototype for Kalin in Georgia, U.S.A.,in Chung, C.F., ed., Quantitative analysis of mineral and energy resource: Dordrecht, Reidel, p. 513–527.

  26. Laznicka, P., 1983, Giant ore deposits—A quantitative approach: Global Tectonics and Metallogeny, v. 2, p. 41–63.

  27. McCammon, R.B., 1990, Prospector III,in Agterberg, F.P., and Bonham-Carter, G.F., eds., Statistical applications in the earth sciences: Geological Survey of Canada Paper 89-9, p. 395–404.

  28. McCammon, R.B., Botbol, J.M., Sinding-Larsen, R., and Bowen, R.W., 1983, Characteristic analysis — 1981 — Final program and a possible discovery: Mathematical Geology, v. 15, p. 59–83.

  29. Pan, G.C., 1987, A stochastic approach to optimum decomposition of cyclic patterns in sedimentary processes: Mathematical Geology, v. 19, p. 503–521.

  30. -- 1989, Concepts and methods of multivariate information synthesis for mineral resources estimation: University of Arizona, Tucson, Ph.D. dissertation, 302 p.

  31. Pan, G.C., and Harris, D.P., 1990, Three nonparametric techniques for optimum discretization of quantitative geological measurement: Mathematical Geology, v. 22, p. 699–722.

  32. —— 1991, Geology-exploration endowment models for simultaneous estimation of discoverable mineral resources and endowment: Mathematical Geology, v. 23, p. 507–540.

  33. —— 1992, Estimating a favorability equation for the integration of geodata and selection of mineral exploration target: Mathematical Geology, v. 24, p. 177–202.

  34. -- in press, Delineation of intrinsic geological units: Mathematical Geology, v. 24.

  35. Sabins, F.F., Jr., 1987, Remote sensing—Principles and interpretation, 2d ed.: New York, Freeman, 449 p.

  36. Schwarzacher, W., 1969, The use of Markov chains in the study of sedimentary cycles: Mathematical Geology, v. 1, p. 17–39.

  37. Singer, D.A., and Kouda, R., 1988, Integrating spatial and frequency information in the search for Kuroko deposits of the Hokuroku district, Japan: Economic Geology, v. 83, p. 18–29.

  38. Skinner, B.J., 1976, A second iron age ahead? American Scientist, v. 64, p. 258–269.

  39. Stanley, M., 1992, Statistical trends and discoverability modeling of gold deposits in the Arbitibi greenstone belt, Ontario,in Kim, Y.C., ed., 23rd International Symposium of APCOM: Tucson, Ariz., Society for Mining, Metallurgy, and Exploration, Inc., p. 17–28.

  40. Tomson, I.N., and Polyakova, O.P., 1984, Mineralogical and geochemical indicators of large ore deposits: Global Tectonics and Metallogeny, v. 2, p. 183–186.

  41. Vistelius, A.B., 1960, The skew frequency distributions and the fundamental law of geochemistry: Journal of Geology, v. 68, p. 1–22.

  42. —— 1972, Ideal granite and its properties. I. The stochastic model: Mathematical Geology, v. 4, p. 89–102.

  43. —— 1981, Gravitational stratification,in Graid, R.G., and Labovitz, M.L., eds., Future trends in geomathematics: London, Pion Limited, p. 134–158.

  44. Vistelius, A.B., and Harbaugh, J.W., 1980, Granitic rocks of Yosemite Valley and ideal granite model: Mathematical Geology, v. 12, p. 1–24.

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Pan, G., Harris, D.P. & Heiner, T. Fundamental issues in quantitative estimation of mineral resources. Nat Resour Res 1, 281–292 (1992). https://doi.org/10.1007/BF01782693

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Key words

  • Mineral resource estimation
  • Information synthesis
  • Mineral target