Quantitative prediction of gold lodes in gold mineralization series based on large-scale mineralization information measurement
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The ability to quantitatively predict Au lodes in Au mineralization series with data from large-scale mineralization information is greatly needed. This paper discusses how to (1) classify the oreforming information, (2) set up the mineralization information model, (3) divide the statistical units within the minimum area of the mineralization anomalies, (4) select the comprehensive ore-forming (controlling) information variables, and (5) carry out the quantitative prediction using some newly proposed statistical models. Finally, the quantitative prediction results for Au lodes in a Au deposit, the Aohan Banner in Mongolia, are provided. Among the three first-grade prediction targets, two were tested and have been found to have industrial significance.
Key wordsblind mineralization superimposed information gold lodes multiple dimension quantitative prediction weighted matrix analysis
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- Boyle, R. W., 1979, The Geochemistry of Gold and Its Deposits: Geological Survey of Canada, Bulletin 280, 584 p.Google Scholar
- Harris, D. P., and Pan, G., 1991, Consistent Geologic Areas for Epithermal Gold-Silver Deposits in the Walker Lake Quadrangle of Nevada and California: Delineated by Quantitative Methods: Econ. Geol., v. 86, n. 1, p. 142–165.Google Scholar
- Liu Anzhou, 1986, Quantitative Appraisal of Mineral Resources from Comprehensive Mineralization Information: Journal of Changchun College of Geology, People's Republic of China (in Chinese), Special Issue, p. 25–34.Google Scholar
- Liu Anzhou, 1991, Mineralization Information Theory and the Large Scale Quantitative Prediction of Ore Deposits: Jilin University Press, Jilin, People's Republic of China, (in Chinese). p. 13–31.Google Scholar
- Pan, G., 1985, Mineral resource appraisal on pegmatic Nb-Ta deposits in Fujian Province of China: Unpublished M.Sc. thesis, Changchun College of Geology (in Chinese), 141 p.Google Scholar
- Zhao Pengda, Hu Wanglian, and Li Ziquan, 1983, Statistical Prediction of Ore Deposits: Geological Publishing House, Bejing, People's Republic of China (in Chinese), p. 111–113.Google Scholar