A Reconnaissance Method for Delineation of Tracts for Regional-Scale Mineral-Resource Assessment Based on Geologic-Map Data

Springer Science and Business Media LLC - Tập 11 - Trang 241-248 - 2002
Gary L. Raines1, Mark J. Mihalasky1
1U.S. Geological Survey, Mackay School of Mines, MS 176, University of Nevada, Reno

Tóm tắt

The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible.

Tài liệu tham khảo

Agterberg, F. P., 1989, LOGDIA-FORTRAN 77 program for logistic regression with diagnostics: Computers & Geosciences, v. 15, no. 4, p. 599-614. Agterberg, F. P., Bonham-Carter, G. F., Cheng, Q., and Wright, D. F., 1993, Weights of evidence modeling and weighted logistic regression for mineral potential mapping, in Davis, J. C., and Herzfeld, U. C., eds., Computers in Geology, 25 Years of Progress: Oxford Univ. Press, Oxford, England, p. 13-32. Agterberg, F. P., Bonham-Carter, G. F., and Wright, D. F., 1990, Statistical pattern integration for mineral exploration, in Gaál, G., and Merriam, D. F., eds., Computer Applications in Resource Estimation Prediction and Assessment of Metals and Petroleum: Pergamon Press, Oxford, p. 1-12. Bateman, P. C., and Wahrhaftig, C., 1966, Geology of the Sierra Nevada, in Bailey, E. H., ed., Geology of Northern California: California Divison Mines and Geology, Bull. 190, p. 125-128. Bonham-Carter, G. F., 1994, Geographic Information Systems for geoscientists-modeling in GIS: Elsevier Science, New York, 398 p. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988, Integration of geological datasets for gold exploration in Nova Scotia: Photogramm. Eng., v. 54, no. 11, p. 1585-1592. Briskey, J. A., Schulz, K. J., Mossesso, J. P., Leif, R. H., and Cunningham, C. G., 2001, It's time to know the planet's mineral resources: Geotimes, v. 46, no. 3, p. 14-19, http://www.geotimes.org/mar01/feature1.html. Cohen, J., 1960, A coefficient of agreement for nominal scales: Educ. Psychol. Meas. v. 20, no. 1, p. 37-46. Guilbert, J. M., and Parks, C. F., 1986, The geology of ore deposits: W.H. Freeman and Company, New York, 405p. Griffiths, J. C, and Smith, C. M., Jr., 1992, Mineral resources versus geologic diversity in small areas: Computers & Geosciences v. 18, no. 5, p. 477-486. Griffiths, J. C., Watson, A. T., and Menzie, W. D., 1980, Relationship between mineral resources and geologic diversity, in Miall, A. D., ed., Facts and Principles ofWorld Petroleum Occurrence: Can. Soc. Petroleum Geology Mem. 6, p. 329-341. Hollister, V. F., 1978, Geology of porphyry copper deposits of the Western Hemisphere: Soc. Mining Engineers, New York, p. 123-148. Kemp, L. D., Bonham-Carter, G. F., Raines, G. L., and Looney, C. G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis. http://ntserv.gis.nrcan.gc.ca/sdm/. King, P. B., and Beikman, H. M., 1974, Geologic map of the United States: U.S. Geol. Survey, 3 plates, scale 1: 2,500,000. Long, K. R., De Young, J. H., Jr., and Ludington, S. D., 1998, Database of significant deposits of gold, silver, copper, lead, and zinc in the United States; Part A, Database Description and Analysis; Part B, Digital Database: U.S. Geol. Survey Open-File Rept. 98-206, 35 p., one 3.5 inch diskette. Lord, D., Etherridge, M. Willson, M. Hall, G., and Uttley, P., 2001, Measuring exploration success: an alternative to discovery cost-per-ounce method of quantifying exploration effectiveness: SEG Newsletter, No. 45, p. 1 and 10-16. Longley, P. A., Goodchild, M. F., Maguire, D. J., and Rhind, D. W., 2001, Geographic information systems and science: JohnWiley & Sons, New York, p. 150-151. Mihalasky, M. J., 1999, Mineral potential modelling of gold and silver mineralization in the Nevada Great Basin-a GIS-based analysis using weights of evidence: unpubl. doctoral dessertation Univ. Ottawa, 360 p., 144 figs. Mihalasky, M. J., and Bonham-Carter, G. F., 1999, The spatial relationship between mineral deposits and lithologic diversity in the Nevada Great Basin, in Lippard, S. J., Naess, A., and Sinding-Larsen, R., eds., Proc. IAMG'99, Trondheim, Norway, p. 369-374. Mihalasky, M. J., and Bonham-Carter, G. F., 2001, Lithodiversity and its spatial association with metallic mineral sites, Nevada great basin: Natural, Resources Research, v. 10, no. 3, pp. 209-226. Raines, G. L., 1999, Evaluation of weights of evidence to predict epithermal gold deposits in the Great Basin of the western United States: Natural Resourses Research v. 8, no. 4, p. 257-276. Raines, G. L., 2002, Comparison and description of geologic maps using FRAGSTATS-Aspatial statistics program: Computers & Geosciences, v. 28, no. 2, p. 169-177. Raines, G. L., Bonham-Carter, G. F., and Kemp, L. D., 2000, Predictive probabilistic modeling using Arcview GIS: ArcUser, v. 3, no. 2, p. 45-48. Schruben, P. G., Arndt, R. E., and Bawiec, W. J., 1997, Geology of the conterminous United States at 1:2,500,000 scale-a digital representation of the 1994 P. B. King and H. M. Beikman map: U. S. Geol. Survey Digital Data Series 11, Release 2,CD-ROM. http://minerals.usgs.gov/kb/ U.S. Geological Survey, National MineralsTeam (Ludington, Steve, and Cox, Dennis, editors), 1996, Data base for a national mineral-resource assessment of undiscovered deposits of gold, silver, copper, lead, and zinc in the conterminous United States: U.S. Geol. Survey Open File Rept. 96-96, CD-ROM. U.S. Geological Survey, Minerals Team, 1998, 1998 Assessment of undiscovered deposits of gold, silver, copper, lead, and zinc in the United States: U.S. Geol. Survey Circ. 1178, 21 p. and CD-ROM. Wright, D. F., 1996, Evaluating volcanic hosted massive sulphide favourability using GIS-based spatial data integration models, Snow Lake Area, Manitoba: unpubl. doctoral dissertation, Univ. Ottawa, 344 p. Wright, D. F., and Bonham-Carter, G. F., 1996. VHMS favorability 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: Can. Geol. Survey of Canada Bull. 426, p. 339-376, 387-401.