Introduction to the Special Issue: Mineral prospectivity analysis and quantitative resource estimation

Ore Geology Reviews

Volumn 38, Issue 3, 2010, Pages 121-127

  Porwal, Alok K ^a,b   Kreuzer, Oliver P ^c,d  

^a CURTIN UNIVERSITY   (Australia)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^c Mitchell River Group   (Australia)

^d MACQUARIE UNIVERSITY   (Australia)

Author keywords

Mineral prospectivity analysis; Quantitative resource estimation

Indexed keywords

EID: 77957757031     PISSN: 01691368     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.oregeorev.2010.06.002     Document Type: Editorial

References (76)

1. Agterberg F.P. A probability index for detecting favourable geological environments. Canadian Institute of Mining and Metallurgy 1971, 10:82-91.
2. Agterberg F.P. Probabilistic models to evaluate regional mineral potential. Mathematical methods in Geoscience, Symposium held at Pribram, Czechoslovakia, October 1973, 1973:3-38.
3. Agterberg F.P. Automatic contouring of geological maps to detect target areas for mineral exploration. Mathematical Geology 1974, 6:373-395.
4. Agterberg F.P., Bonham-Carter G.F. Deriving weights-of-evidence from geoscience contour maps for prediction of discrete events. Proceedings of the 2nd APCOM Symposium, Berlin, Germany 1990, 2:381-395.
5. An P., Moon W.M., Rencz A. Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics 1991, 27:1-11.
6. Apel M. Predict: A 3-D Weights of Evidence Mineral Potential Modelling Plug-in to Gocad Available at, (last accessed 21 June 2010). http://www.geo.tu-freiberg.de/~apelm/predict.htm.
7. Ballard J.R., Palin J.M., Williams I.S., Campbell I.H., Faunes A. Two ages of porphyry intrusion resolved for the super-giant Chuquicamata copper deposit of northern Chile by ELA-ICP-MS and SHRIMP. Geology 2001, 29:383-386.
8. Bárdossy G., Fodor J. Evaluation of Uncertainties and Risks in Geology: New Mathematical Approaches to their Handling 2004, Springer-Verlag, Berlin Heidelberg, 221 pp.
9. Barker R. Recent exploration in the Hauraki Goldfield, Coromandel Peninsula. New Zealand Mining 1994, 13:9-15.
10. Bawiec W.J., Spanski G.T. EMINERS version 2.0.0.10-quick-start guide. U.S. Geological Survey Open File Report 04-1344 Available at:, (last accessed on 9 June 2010). http://pubs.usgs.gov/of/2004/1344.
11. Bonham-Carter G.F., Agterberg F.P., Wright D.F. Weights of evidence modelling: a new approach to mapping mineral potential. Geological Survey Canada Paper 89-9 1989, 171-183. 171-183. F.P. Agterberg, G.F. Bonham-Carter (Eds.).
12. Brown W.M., Gedeon T.D., Groves D.I., Barnes R.G. Artificial neural networks: a new method for mineral prospectivity mapping. Australian Journal of Earth Sciences 2000, 47:757-770.
13. Campbell A.N., Hollister V.F., Duda R.O. Recognition of a hidden mineral deposit by an artificial intelligence program. Science 1982, 217:927-929.
14. Carranza E.J.M. Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines. Natural Resources Research 2004, 13:173-187.
15. Carranza E.J.M., Hale M. Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research 2001, 10:125-136.
16. Carranza E.J.M., Sadeghi M. Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geology Reviews 2010, 38:219-241. (this issue).
17. Cheng Q., Agterberg F.P. Fuzzy weights of evidence and its application in mineral potential mapping. Natural Resources Research 1999, 8:27-35.
18. Duda R.O., Hart P.E., Nilsson N.J., Sutherland G.L. Semantic network representations in rule-based interference systems. Pattern-Directed Inference Systems 1978, 203-221. Academic Press. D.A. Waterman, F. Hayes-Roth (Eds.).
19. Fagan R.K. Modelling how much gold is left to be found in the Eastern Goldfields of W.A. Outcrop to orebody: innovative geoscience in exploration and mining, Abstract Volume. Australian Institute of Geoscientists Bulletin 2006, 44:23-25.
20. Feltrin L., Baker T., Scott M., Fitzell M., Wilkinson K. A three-dimensional weights of evidence model for the Drummond basin in NE Queensland: quantitative assessment of controlling variables for epithermal Au. Australian Earth Sciences Convention (AESC) 2008, Abstracts 2008, 96.
21. Folinsbee R.E. World's view-from Alph to Zipf. Geological Society of America Bulletin 1977, 88:897-907.
22. González-Álvarez I., Porwal A., Beresford S.W., McCuaig T.C., Maier W.D. Hydrothermal Ni prospectivity analysis of Tasmania, Australia. Ore Geology Reviews 2010, 38:168-183. (this issue).
23. Guj P., McCuaig T.C., Garzon R. Can the petroleum industry's probabilistic approach to targeting and resources definition be applied in predicting mineral discoveries?. Conference Proceedings 2, Mineral Exploration and Research: Digging Deeper, 9th Biennial SGA Meeting 2007, 1467-1470. Dublin, Ireland. C.J. Andrews (Ed.).
24. Hagemann S.G., Cassidy K.F. Archean orogenic lode-gold deposits. Reviews in Economic Geology 2000, 13:9-68. S.G. Hagemann, P.E. Brown (Eds.).
25. Harris, D.P., 1965. An application of multivariate statistical analysis to mineral exploration. Unpublished Ph.D. Dissertation, Pennsylvania State University, 261 pp.
26. Harris D.P. Alaska's base and precious metals resources: a probabilistic regional appraisal. Quarterly of the Colorado School of Mines 1969, 64:295-327.
27. Harris D.P., Pan G.C. Mineral favorability mapping: a comparison of artificial neural networks, logistic regression and discriminate analysis. Natural Resources Research 1999, 8:93-109.
28. Gold deposits of the Archaean Yilgarn Block, Western Australia: nature, genesis and exploration guides. Geology Department and University Extension, University of Western Australia Publication 1990, 20. 407 pp. S.E. Ho, D.I. Groves, J.M. Bennett (Eds.).
29. Hronsky J.M.A. The science of exploration targeting. University of Western Australia and Centre for Global Metallogeny Publication 2004, 33:129-133. J. Muhling (Ed.).
30. Hronsky J.M.A., Groves D.I. Science of targeting: definition, strategies, targeting and performance measurement. Australian Journal of Earth Sciences 2008, 55:3-12.
31. Huston D., Jaireth S. Metal endowment of cratons, terranes and districts: Insights from a quantitative analysis of regions with giant and super-giant deposits. Ore Geology Reviews 2010, 38:288-303. (this issue).
32. Jaques A.L., Jaireth S., Walshe J.L. Mineral systems of Australia: an overview of resources, settings and processes. Australian Journal of Earth Sciences 2002, 49:623-660.
33. Joly A., Porwal A., McCuaig T.C. 3D geophysical and geological modeling for understanding the gold mineral systems in the Tanami Orogen, Western Australia. Geophysical Research Abstracts 2010, 12. EGU2010-15171.
34. Katz S.S. Emulating the Prospector expert system with a raster GIS. Computers & Geosciences 1991, 17:1033-1050.
35. Knox-Robinson C.M. Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping with reference to the orogenic gold mineralization potential of the Kalgoorlie Terrane, Western Australia. Australian Journal of Earth Sciences 2000, 47:929-942.
36. Knox-Robinson C.M., Wyborn L.A.I. Towards a holistic exploration strategy: using geographic information systems as tool to enhance exploration. Australian Journal of Earth Sciences 1997, 44:453-463.
37. Kreuzer O.P., Etheridge M.A. Risk and uncertainty in mineral exploration: implications for valuing mineral exploration properties. Australian Institute of Geoscientists News 2010, 100:20-28.
38. Kreuzer O.P., Etheridge M.A., Guj P. Australian junior exploration floats, 2001-06, and their implications for IPOs. Resources Policy 2007, 32:159-182.
39. Kreuzer O.P., Etheridge M.A., Guj P., McMahon M.E., Holden D.J. Linking mineral deposit models to quantitative risk analysis and decision-making in exploration. Economic Geology 2008, 103:829-850.
40. Kreuzer, O.P., Markwitz, V., Porwal., A.K., Mccuaig, T.C., submitted for publication. A continent-wide study of Australia's uranium potential. Part I: GIS-assisted manual prospectivity analysis. Ore Geology Reviews.
41. Lewkowski C., Porwal A., González-Álvarez I. Genetic programming applied to base-metal prospectivity mapping in the Aravalli Province, India. Geophysical Research Abstracts 2010, 12. EGU2010-15171.
42. Lord D., Etheridge M.A., Willson M., Hall G., Uttley P.J. Measuring exploration success: an alternative to the discovery-cost-per-ounce method of quantifying exploration success. Society of Economic Geologists Newsletter 2001, 45(1):10-16.
43. Mamuse A., Beresford S., Porwal A., Kreuzer O.P. Assessment of undiscovered nickel sulphide resources, Kalgoorlie Terrane, Western Australia: Part 1 Deposit and endowment density models. Ore Geology Reviews 2010, 37:141-157.
44. McCammon R.B., Kork J.O. One-level prediction-a numerical method for estimating undiscovered metal endowment. Nonrenewable Resources 1992, 1:139-147.
45. McCammon R.B., Finch W.I., Kork J.O., Bridges N.J. An integrated data-directed numerical method for estimating the undiscovered mineral endowment in a region. Nonrenewable Resources 1994, 3:109-122.
46. McCuaig T.C., Guj P. Balancing risk and reward in the face of increasing global exploration maturity. Outcrop to orebody: innovative geoscience in exploration and mining, Abstract Volume. Australian Institute of Geoscientists Bulletin 2006, 44:3-8.
47. McCuaig T.C., Hronsky J.M.A. The current status and future of the interface between the exploration industry and economic geology research. Reviews in Economic Geology 2000, 13:553-559. S.G. Hagemann, P.E. Brown (Eds.).
48. McCuaig T.C., Kreuzer O.P., Brown W.M. Fooling ourselves - dealing with model uncertainty in a mineral systems approach to exploration. Mineral Exploration and Research-Digging Deeper. 9th Biennial SGA Meeting, Dublin, Proceedings 2007, 1435-1438.
49. Mccuaig, T.C., Porwal, A., Gessner, K., 2009. Fooling ourselves: recognizing uncertainty and bias in exploration targeting. Centre for Exploration Targeting Quarterly News, The University of Western Australia, 2(7), 1 and 5-8.
50. McCuaig T.C., Beresford S., Hronsky J. Translating the mineral systems approach into an effective exploration targeting system. Ore Geology Reviews 2010, 38:128-138. (this issue).
51. McGaughey J., Perron G., Parsons S., Chalke T. Gocad workflows for expert-system decision making. Proceedings 26th Gocad Research Conference, Nancy, France 2009, Available at: http://www.mirageoscience.com/technology/software_workflows.php (last accessed on 21 June 2010).
52. Merriam D.F., Drew J., Schuenemeyer J.H. Zipf's Law: a viable geological paradigm?. Natural Resources Research 2004, 13:265-271.
53. Nykänen V., Ojala V.J. Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the northern Fennoscandian Shield, Finland. Natural Resources Research 2007, 16:85-92.
54. Paliwal H.V., Bhatnagar S.N., Haldar S.K. Lead-zinc resource prediction in India: an application of Zipf's law. Mathematical Geology 1986, 18:539-549.
55. Partington G. Prospectivity analysis in action: the Auzex Resources Ltd (AZX) story as applied to granite related mineral systems in Eastern Australia and New Zealand. Australian Earth Sciences Convention, Perth, July 20-24th 2008 2008, 195.
56. Partington G. Developing models using GIS to assess geological and economic risk: An example from VMS copper gold mineral exploration in Oman. Ore Geology Reviews 2010, 38:197-207. (this issue).
57. Penney S.R., Allen R.M., Harrisson S., Lees T.C., Murphy F.C., Norman A.R., Roberts P.A. A global-scale exploration risk analysis technique to determine the best mineral belts for exploration. Transactions of the Institute of Mining and Metallurgy, Section B: Applied Earth Science 2004, 113:183-196.
58. Porwal, A., 2006. Mineral potential mapping with mathematical geological models. ITC Ph.D. Dissertation, University of Utrecht, ISBN 90-6164-240-X, 289 pp.
59. Porwal A., Carranza E.J.M., Hale M. Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research 2003, 12:1-25.
60. Porwal A., Carranza E.J.M., Hale M. Artificial neural networks for mineral potential mapping: a case study from Aravalli province, western India. Natural Resources Research 2003, 12:155-177.
61. Porwal A., Carranza E.J.M., Hale M. A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology 2004, 36:803-826.
62. Porwal A., Carranza E.J.M., Hale M. A Hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research 2006, 15:1-14.
63. Porwal A., Yu L., Gessner K. SVM-based base-metal prospectivity modeling of the Aravalli Orogen, northwestern India. Geophysical Research Abstracts 2010, 12. EGU2010-15171.
64. Porwal A., González-Álvarez I., Markwitz V., McCuaig T.C., Mamuse A. Weights of evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geology Reviews 2010, 38:184-196. (this issue).
65. Reddy R.K.T., Bonham-Carter G.F., Galley A.G. Developing a geographic expert system for regional mapping of volcanogenic massive sulphide (VMS) deposit potential. Nonrenewable Resources 1992, 2:112-124.
66. Root D.H., Menzie W.D., Scott W.A. Computer Monte Carlo simulation in quantitative resource estimation. Nonrenewable Resources 1992, 1:125-138.
67. Rowlands N.J., Sampey D. Zipf's law-an aid to resource inventory prediction in partially explored areas. Mathematical Geology 1977, 9:383-391.
68. Schuenemeyer J.H., Drew L.J. A procedure to estimate the parent population of the size of oil and gas fields as revealed by a study of economic truncation. Mathematical Geology 1983, 15:145-161.
69. Sinclair A.J., Woodsworth G.L. Multiple regression as a method of estimating exploration potential in an area near Terrace, B.C. Economic Geology 1970, 65:998-1003.
70. Singer, D.A., 1972. Multivariate statistical analysis of the unit regional value of mineral resources. Unpublished Ph.D. Dissertation, Pennsylvania State University, 211 pp.
71. Singer D.A. Progress in integrated quantitative mineral resource assessments. Ore Geology Reviews 2010, 38:242-250. (this issue).
72. Singer D.A., Kouda R. A comparison of the weights of evidence method and probabilistic neural networks. Natural Resources Research 1999, 8:287-298.
73. Singer D.A., Menzie W.D. Quantitative mineral resource assessments: an integrated approach 2010, Oxford University Press, New York, 219 pp.
74. Wyborn L.A.I., Heinrich C.A., Jaques A.L. Australian Proterozoic mineral systems: essential ingredients and mappable criteria. Australian Institute of Mining and Metallurgy Publication Series 1994, 5:109-115. P.C. Hallenstein (Ed.).
75. Zipf G.K. The Psycho-biology of Language: An Introduction to Dynamic Philology 1935, Houghton Mifflin Company, Boston, 336 pp.
76. Zipf G.K. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology 1949, Hafner Publishing Company, 573 pp.