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Quantitative Analysis of Mineral Resources for Strategic Planning: Implications for Australian Geological Surveys

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A quantitative valuation study has been made of Australian state surveys with the specific goals of (1) establishing the 'worth' of current programs upgrading state government geoscientific information infrastructure, and (2) considering the results of the valuation in terms of strategic planning. The study has been done from the perspective of the community as a whole and has been undertaken in two phases reflecting the different objectives of Australian state surveys in terms of the exploration industry and government policy-making. This paper reports on the second part of this valuation process, measuring the impact of upgraded survey data on government mineral policy decision processes. The valuation methodology developed is a comparative approach used to determine net benefit foregone by not upgrading information infrastructure. The underlying premise for the geological survey study is that existing and upgraded data sets will have a different probability that a deposit will be detected. The approach used in the valuation of geoscientific data introduces a significant technical component with the requirement to model both favorability of mineral occurrence and probability of deposit occurrence for two different generations of government data. The estimation of mineral potential uses modern quantitative methods, including the U.S. Geological Survey three-part resource-assessment process and computer-based prospectivity modeling. To test the methodology mineral potential was assessed for porphyry copper type deposits in part of the Yarrol Province, central Queensland. Results of the Yarrol case study supports the strategy of the state surveys to facilitate effective exploration by improving accuracy and acquiring new data, as part of resource management. It was determined in the Yarrol Province case study that in going from existing to upgraded data sets the area that would be considered permissible for the occurrence of porphyry type deposits almost doubled. The implication of this result is that large tracts of potentially mineralized land would not be identified using existing data. Results of the prospectivity modeling showed a marked increase in the number of exploration targets and in target rankings using the upgraded data set. A significant reduction in discovery risk also is associated with the upgraded data set, a conclusion supported by the fact that known mines with surface exposure are not identified in prospectivity modeling using the existing data sets. These results highlight the absence in the existing data sets of information critical for the identification of prospective ground.

Quantitative resource assessment and computer-based prospectivity modeling are seen as complementary processes that provide the support for the increasingly sophisticated needs of Australian survey clients. Significant additional gains to the current value of geoscientific data can be achieved through the in-house analysis and characterization of individual data sets, the integration and interpretation of data sets, and the incorporation of information on geological uncertainty.

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  1. Agterberg, F. P., 1989, Systematic approach to dealing with uncertainty of geoscience information in mineral exploration, in Proc. 21st Symp. Application of Computers in the Mineral Industries (Las Vegas, Nevada), p. 165–178.

  2. Agterberg, F. P., 1992, Combining indicator patterns in weights of evidence modeling for resource evaluation: Nonrenewable Resources, v. 1, no. 1, p. 39–50.

  3. 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.

  4. An, P., Moon, W. M., and Bonham-Carter, G. F., 1994, Uncertainty management in integration of exploration data using the belief function: Nonrenewable Resources, v. 3, no. 1, p. 60–71.

  5. Bliss, J.D., ed., 1992, Grade-tonnage and other models for diamond kimberlite pipes: Nonrenewable Resources, v. 1, no. 3, p. 214–230.

  6. 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. Geol. Survey Circ. 995, 6 p.

  7. Bonham-Carter, G. F., 1994, Geographic systems for geoscientists: modelling with GIS: Pergamon Press, Oxford, 398 p.

  8. Bonham-Carter, G. F., and Agterberg, F. P., 1990, Application of a microcomputer-based Geographic Information System to mineral potential mapping, in Hanley, T., and Merriam, D. F., eds., Microcomputer applications in geology II: Pergamon, Oxford, p. 49–74.

  9. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988, Integration of geological data sets for gold exploration in Nova Scotia: Photogrammetric Engineering and Remote Sensing, v. 54, no. 11, p. 1585–1592.

  10. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1990, Weights of evidence modelling: a new approach to mapping mineral potential, in Agterberg, F. P., and Bonham-Carter, G. F., eds., Statistical applications in the earth sciences: Geol. Survey Canada Paper 89–9, p. 171–183.

  11. Chung, C. F., and Agterberg, F. P., 1980, Regression models for estimating mineral resources from geological map data: Math. Geology, v. 12, no. 5, p. 473–488.

  12. Cox, D. P., 1986a, Descriptive model of prophyry copper, in Cox, D. P., and Singer, D. A., eds., Mineral deposit models: U.S. Geol. Survey Bull. 1693, 76 p.

  13. Cox, D. P., 1986b, Descriptive model of porphyry Au-Cu, in Cox, D. P., and Singer, D. A., eds., Mineral deposit models: U.S. Geol. Survey Bull. 1693, 110 p.

  14. Cox, D. P., 1986c, Descriptive model of porphyry Cu-Mo, in Cox, D. P., and Singer, D. A., eds., Mineral deposit models: U.S. Geol. Survey Bull. 1693, 115 p.

  15. 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.

  16. Cox, D. P., and Singer, D. A., eds., 1986, Mineral deposit models: U.S. Geol. Survey Bull. 1693, 379 p.

  17. Cox, D. P., and Singer, D. A., 1992, Distribution of gold in porphyry copper deposits, in DeYoung, J. H., and Hammerstrom, J. M., eds., Contributions to commodity research: U.S. Geol. Survey Bull. 1877, p. C1–C14.

  18. Cox, D. P., Berger, B. R., Ludington, S., Moring, B. C., Sherlock, M. G., Singer, D. A., and Tingley, J. V., 1996, Delineation of mineral resource assessment tracts and estimation of number of undiscovered deposits in Nevada: Nevada Bur. Mines and Geology Open-File Rep. 96–2, 12/1–12/25 (also available at: www.nbmg.unr.edu/ofr962.).

  19. Dear, J. F., Mckellar, R.G., and Tucker, R. M., 1971, Geology of the Monto 1:250,000 Sheet area: Geol. Survey Queensland Rept. 46.

  20. Drew, L. J., 1997, Undiscovered petroleum and mineral resources, assessment and controversy: Plenum Press, New York, 210 p.

  21. Drew, L. J., and Menzie, W. D., 1993, Is there is metric for mineral deposit occurrence probabilities?: Nonrenewable Resources, v. 2, no. 2, p. 92–105.

  22. Duda, R.O., Heart, P. E., Barrette, P., Gasching, J.G., Konolige, K., Reboh, R., and Slocum, J., 1978, Development of the prospector consultation system for mineral exploration: Stanford Res. Inst. Intern., Final Report, SRI Projects 5821 and 6415, p. 203–221.

  23. Fitzgerald and Associates, 1997, Intrepid geophysical processing, visualisation and interpretation tools: Reference Manual 3, unpaginated.

  24. Goodacre, A., Bonham-Carter, G. F., Agterbert, F. P., and Wright, D. F., 1993, A statistical analysis of spatial associations of seismicity with drainage patterns and magnetic anomalies in western Quebec: Tectonophysics, v. 217, no. 3–4, p. 205–305.

  25. Grasty, R. L., and Minty, B. R. S., 1995, A guide to the technical specifications for airborne gamma-ray surveys: Australian Geol. Survey Organization, Record, 1995/20, 86 p.

  26. Horton, D. J., 1982, Porphyry type copper and molybdenum mineralisation in eastern Queensland: Geol. Survey of Queensland Publ. 378 p.

  27. Kirkegaard, A. G., Shaw, R. D., and Murray, C. G., 1970, Geology of the Rockhampton and Port Clinton 1:250,000 Sheet areas, Queensland: Geol. Survey of Queensland Record 38, 155 p.

  28. Lefebure, D. V., and Ray, G. E., eds., 1995, Selected British Columbia mineral deposit profiles: British Columbia Ministry of Employment and Investment, Open-file Rept. 1995–20, 136 p.

  29. Luo, X., and Dimitrakopoulos, R., 2002, Data driven fuzzy analysis in quantitative mineral resource assessment: Computers & Geosciences, in press.

  30. McCammon, R. B., 1994, Prospector II. Towards a knowledge base for mineral deposits: Math. Geology, v. 26, no. 8, p. 917–936.

  31. Ord, A., 1999, A progress report on geodynamic modelling relevant to the formation and localisation of base metal mineralisation of the Mount Isa type: Workshop Presentation 'Regional and Local Controls on the Formation of Mt Isa Type Base Metal Deposits:Australian Inst. Geoscientists (Brisbane, Queensland), unpaginated.

  32. Pan, G. C., and Harris, D. P., 1992, Estimating a favourability equation for the integration of geodata and selection of mineral exploration targets: Math. Geology, v. 24, no 2, p. 177–202.

  33. 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: Econ. Geology, v. 84, no. 7, p. 1936–1947.

  34. Root, D. H., Menzie, W.D., and Scott, W. A., 1992, ComputerMonte Carlo simulation in quantitative resource estimation: Nonrenewable Resources, v. 1, no. 2, p. 125–138.

  35. Scott, M., 2000, Valuing Australian state geological surveys: quantitative analysis for strategic planning: unpubl. doctoral disseration, W. H. Bryan Mining Geology Research Centre, Univ. Queensland, Australia, 453 p.

  36. Scott, M., Dimitrakopoulos, R., and Brown, R. 2001, Valuing regional geoscientific data acquisition programs: addressing issues of quantification, uncertainty and risk: Natural Resources FORUM, submitted.

  37. Sinclair, A. J., and Woodsworth, G. J., 1970, Multiple regression as a method of estimating exploration potential in an area near terrace, B.C.: Econ. Geology, v. 65, no. 8, p. 998–1003.

  38. Singer, D. A., 1993a, Basic concepts in three-part quantitative assessments of undiscovered mineral resources: Nonrenewable Resources, v. 2, no. 2, p. 69–81.

  39. Singer, D. A., 1993b, Development of grade and tonnage models for different deposit types, in Kirkham, R. V., Sinclair, W. D., Thorpe, R. I., and Duke, J. M., eds., Mineral deposit modelling: Geol. Assoc. Canada Spec. Paper 40, p. 21–30.

  40. Singer, D. A., and DeYoung, J. H., 1980, What can grade-tonnage relationships really tell us?, in Guillemin, C., and Lagny, P., eds., Resources minerale: Bureau de Recherches Geologiques et Minieres Memoire 106, p. 91–101.

  41. Singer, D. A., and Kouda, R., 1996, Application of a feedforward neural network in the search for kuroko deposits in the Hokuroku District, Japan: Math. Geology, v. 28, no. 8, p. 1017–1023.

  42. Singer, D. A., and Kouda, R., 1997, Classification of mineral deposits into types using mineralogy with a probabilistic neural network: Nonrenewable Resources, v. 6, no. 1, p. 27–32.

  43. Singer, D. A., and Kouda, R., 1999, A comparison of the weights of evidence method and probabilistic neural networks: Nonrenewable Resources, v. 8, no. 4, p. 287–298.

  44. Singer, D. A., and MacKevett, E. M., 1977, Mineral resources map of the McCarthy quadrangle, Alaska: U.S. Geol. Survey Misc. Field Studies Map, MF 773–C, 1 sheet, scale 1:250,000.

  45. Singer, D. A., Mosier, D. L., and Cox, D.P., 1986, Grade and tonnage model of porphyry Cu, in Cox, D. P., and Singer, D. A., eds., Mineral deposit models: U.S. Geol. Survey Bull. 1693, p. 117–119.

  46. Singer, D. A., Menzie, W. D., Sutphin D., Mosier, D. L., and Bliss, J. D., 2001, Mineral deposit density—an update, in Schulz, K., ed., Methods in global mineral resource assessment:U.S. Geol. Survey Prof. Paper 1640, 39 p.

  47. Taylor, R. B., and Steven, T. A., 1983, Definitions of mineral resource potential: Econ. Geology, v. 78, no. 6, p. 1268–1270.

  48. Titley, S. R., and Anthony, E.Y., 1989, Laramide mineral deposits in Arizona, in Jenney, J. P., and Reynolds, S. J., eds., Geologic evolution of Arizona: Arizona Geol. Society Digest, v. 17, p. 485–514.

  49. Wright, D. F., and Bonham-Carter, G. F., 1996. VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area, in Bonham-Carter, G. F., Galley, A. G., and Hall, G. E. M., eds., Extechi: a multidisciplinary approach to massive sulphide research in the rusty lake-snow lake greenstone belts, Manitoba: Geol. Survey Canada Bull. 426, p. 339–376, 387–401.

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Scott, M., Dimitrakopoulos, R. Quantitative Analysis of Mineral Resources for Strategic Planning: Implications for Australian Geological Surveys. Natural Resources Research 10, 159–177 (2001). https://doi.org/10.1023/A:1012536823294

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  • strategic planning
  • quantitative resource assessment
  • mineral potential modeling