Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

Mathematical Geology

Volumn 38, Issue 4, 2006, Pages 465-474

  Singer, Donald A ^a  

^a U S GEOLOGICAL SURVEY   (United States)

Author keywords

Classification; Mineral deposit models; Mineral resource assessment

Indexed keywords

MATHEMATICAL MODELS; MINERAL RESOURCES; NEURAL NETWORKS; PROBABILITY; ROCKS;

ARTIFICIAL NEURAL NETWORK; ASSESSMENT METHOD; GIS; MINERAL DEPOSIT; MINERAL RESOURCE; MODELING; PROBABILITY;

EID: 33845597056     PISSN: 08828121     EISSN: 15738868     Source Type: Journal    
DOI: 10.1007/s11004-005-9023-7     Document Type: Article

References (20)

1. Barton, P. B., 1986, Mineralogical index, in Cox, D. P., and Singer, D. A., eds., Mineral deposit models: U.S. Geological Survey Bulletin 1693, p. 318-348.
2. Bliss, J. D., ed., 1992, Developments in mineral deposit modeling: U. S. Geological Survey Bulletin 2004, 168 p.
3. Cox, D. P., and Singer, D. A., eds., 1986, Mineral deposit models: U.S. Geological Survey Bulletin 1693, 379 p.
4. Cox, D. P., Lindsey, D. A., Singer, D. A., and Diggles, M. F., 2003, Sediment-hosted copper deposits of the world - Deposit models and database: U.S. Geological Survey Open-File Report 03-107, http://geopubs.wr.usgs.gov/open-file/of03-107/.
5. McCammon, R. B., 1992, Numerical mineral deposit models, in Bliss, J. D., ed., Developments in deposit modeling: U.S. Geological Survey Bulletin 2004, p. 6-12.
6. Masters, T., 1993, Practical neural network recipes in C++: Academic Press, San Diego, 493 p.
7. Masters, T., 1995, Advanced algorithms for neural networks: A C++ sourcebook: Wiley, New York, 425 p.
8. Mosier, D. L., Singer, D. A., and Salem, B. B., 1983, Geologic and grade-tonnage information on volcanic-hosted copper - zinc - lead massive sulfide deposits: U.S. Geological Survey Open-File Report 83-89, 78 p.
9. Parzen, E., 1962, On estimation of a probability density function and mode: Annal. Math. Statistics, v. 33, p. 1065-1076.
10. Singer, D. A., 1993a, Basic concepts in three-part quantitative assessments of undiscovered mineral resources: Nonrenew. Resour., v. 2, no. 2, p. 69-81.
11. Singer, D. A., 1993b, Development of grade and tonnage models for different deposit types, in Kirkham, R. V., Sinclair, R. V., Thorpe, W. D., and Duke, J. M., eds., Mineral deposit modeling: Geological Association Canada Special Paper 40, p. 21-30.
12. 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. Geol., v. 28, no. 8, p. 1017-1023.
13. Singer, D. A., and Kouda, R., 1997a, Classification of mineral deposits into types using mineralogy with a probabilistic neural network: Nonrenew. Resour., v. 6, no. 1, p. 27-32.
14. Singer, D. A., and Kouda, R., 1997b, Use of a neural network to integrate geoscience information in the classification of mineral deposits and occurrences, in Gubins, A. G., ed., Proceedings of Exploration 97: Fourth Decennial International Conference on Mineral Exploration: Toronto, p. 127-134.
15. Singer, D. A., and Kouda, R., 1999, Examining risk in mineral exploration: Nat. Resour. Res., v. 8, no. 2, p. 111-122.
16. Singer, D. A., and Kouda, R., 2003, Typing mineral deposits using their grades and tonnages in an artificial neural network: Nat. Resour. Res., v. 12, no. 3, p. 201-208.
17. Singer, D. A., Berger, V. I., and Moring, B. C., 2002, Porphyry copper deposits of the world: Database, maps, and preliminary analysis: U.S. Geological Survey Open-File Report 02-268, http://geopubs.wr.usgs.gov/ open-file/of02-268/.
18. Singer, D. A., Mosier, D. L., and Menzie, W. D., 1993, Digital grade and tonnage data for 50 types of mineral deposits - Macintosh version: U.S. Geological Survey Open-File Report 93-280, 1 disk, 52 files.
19. Specht, D., 1990, Probabilistic neural networks: Neural Networks, v. 3, p. 109-118.
20. U.S. Geological Survey National Mineral Resource Assessment Team, 1998, 1998 Assessment of undiscovered deposits of gold, silver, copper, lead, and zinc in the United States: U.S. Geological Survey Circular 1178, online version 1.0.