Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea
Tóm tắt
Từ khóa
Tài liệu tham khảo
Agterberg, F. P., 1988, Application of recent developments of regression analysis in regional mineral resource evaluation, in Chung, C. F., Fabbri, A. G., and Sinding-Larsen, R., eds., Quantitative Analysis of Mineral and Energy Resources: D Reidel Publishing, Dordrecht, p. 1–28.
Agterberg, F. P., and Bonham-Carter, G. F., 2005, Measuring performance of mineral-potential maps: Nat. Resour. Res., v. 14, p. 1–17.
Agterberg, F. P., Bonham-Carter, G. F., and Wright, D. F., 1990, Statistical pattern integration for mineral exploration, in Gaal, G., and Merriam, D. F., eds., Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum: Pergamon Press, Oxford, p. 1–21.
An, P., and Moon, W. M., 1993, Evidential reasoning structure for integrating geophysical, geological and remote sensing data, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS): IEEE, Tokyo, p. 2141–2144.
An, P., Moon, W. M., and Rencz, A. N., 1991, Application of fuzzy set theory to integrated mineral exploration: Can. J. Explor. Geophys., v. 27, p. 1–11.
Behnia, P., 2007, Application of radial basis functional link networks to exploration for proterozoic mineral deposits in central Ira: Nat. Resour. Res., v. 16, p. 147–155.
Bonham-Carter, G. F., 1994, Geographic information systems for geoscientists: modeling with GIS, Computer Methods in the Geosciences 13: Pergamon Press, Oxford, pp. 398.
Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988, Integration of geological datasets for gold exploration in Nova Scotia: Photogram. Eng. Remote Sens., v. 54, p. 1585–1592.
Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1989, Weights of evidence modeling: a new approach to mapping mineral potential, in Agterberg, F. P., and Bonham-Carter, G. F., eds., Statistical Applications in the Earth Sciences: Geological Survey of Canada 98, p. 171–183.
Brown, W. M., Gedeon, T. D., Groves, D. I., and Barnes, R. G., 2000, Artificial neural networks: a new method for mineral prospectively mapping: Aust. J. Earth Sci., v. 47, p. 757–770.
Brown, W., Groves, D., and Gedeon, T., 2003, Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping: Nat. Resour. Res., v. 12, p. 183–200.
Carranza, E. J. M., 2004, Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines: Nat. Resour. Res., v. 13, p. 173–187.
Carranza, E. J. M., 2009, Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity: Comput. Geosci., v. 35, p. 2032–2046.
Carranza, E. J. M., and Hale, M., 2000, Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines: Nat. Resour. Res., v. 9, p. 237–253.
Carranza, E. J. M., Hale, M., and Faassen, C., 2008, Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping: Ore Geol. Rev., v. 33, p. 536–558.
Carranza, E. J. M., Woldai, T., and Chikambwe, E. M., 2005, Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia: Nat. Resour. Res., v. 14, p. 47–63.
Chi, K. H., Lee, J. S., Jin, M. S., Chi, S. J., and Park, S. H., 2001, Construction of GIS based geological database of South Korea Area: Korea Institute of Geoscience and Mineral Resources, Ministry of Science & Technology KR-01(T)-08, pp. 210.
Chung, C. F., and Agterberg, F. P., 1980, Regression models for estimating mineral resources from geological map data: Math. Geol., v. 12, p. 473–488.
D’Ercole, C., Groves, D. I., and Knox-Robinson, C. M., 2000, Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectively analysis of Mississippi Valley-type mineralization: Aust. J. Earth Sci., v. 47, p. 913–927.
De Quadros, T. F. P., Koppe, J. C., Strieder, A. J., and Costa, J. F. C. L., 2006, Mineral-potential mapping: a comparison of weights-of-evidence and fuzzy methods: Nat. Resour. Res., v. 15, p. 49–65.
Eddy, B. G., Bonham-Carter, G. F., and Jefferson, C. W., 1995, Mineral resource assessment of the Parry Islands, high Arctic, Canada: a GIS-base fuzzy logic model, Proceedings Canadian Conference on GIS, CD ROM session C3, Paper 4.
Garrett, J., 1994, Where and why artificial neural networks are applicable in civil engineering: J. Comput. Civil Eng., v. 8, p. 129–130.
Harris, D., and Pan, G., 1999, Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis: Nat. Resour. Res., v. 8, p. 93–109.
Harris, D., Zurcher, L., Stanley, M., Marlow, J., and Pan, G., 2003, A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression: Nat. Resour. Res., v. 12, p. 241–255.
Hines, J. W., 1997, Fuzzy and artificial neural approaches in engineering: Wiley, New York, pp. 201.
Jianping, C., Gongwen, W., and Changbo, H., 2005, Quantitative prediction and evaluation of mineral resources based on GIS: a case study in Sanjiang region, southwestern China: Nat. Resour. Res., v. 14, p. 285–294.
Kim, J. H., Kee, W. S., and Seo, S. K., 1996, Geological structures of the Yeoryang-Imgye area, northern part of Mt. Taebaek Region, Korea: J. Geol. Soc. Korea, v. 32, p. 1–15.
Kim, J. C., Koh, H. J., Lee, S. R., Lee, C. B., Choi, S. J., and Park, K. H., 2001, Explanatory note the Gangreung-Sokcho Sheet: Korean Institute of Geoscience and Mineral Resources KR-M 25-08 2001, pp. 76.
Knox-Robinson, C. M., 2000, Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia: Aust. J. Earth Sci., v. 47, p. 929–941.
Koh, S. M., Kim, S. Y., Lee, D. J., Kim, D. O., Lee, H. Y., Kim, Y. U., Yoo, J. H., Kim, Y. I., Ryoo, C. R., and Song, M. S., 2003, Construction of the data-base and assessment of domestic mineral resources III (area of 1:250,000 Seoul and Gangreung Geological Sheets): Ministry of Commerce, Industry and Energy KR-2002-C-14-2003-R, pp. 84.
Koo, S. B., Cho, J. D., Lee, T. S., Park, Y. S., Lim, M. T., Choi, J. H., Sung, N. H., Hwang, H. S., and Koh, I. S., 2001, Regional geophysical exploration: Korean Institute of Geoscience and Mineral Resources, Ministry of Commerce, industry and Energy KR-2000-R-11-2001-R, pp. 70.
Lee, S., Choi, J. W., and Woo, I., 2004, The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea: Geosci. J., v. 8, p. 51–60.
Lee, C. H., and Park, H. I., 1996, Epithermal gold-silver mineralization and depositional environment carbonate-hosted replacement type Baegjeon Deposits, Korea: Econ. Environ. Geol., v. 29, p. 105–117.
Lee, S., Ryu, J. H., and Kim, I. S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslide, v. 4, p. 327–338.
Lee, J. S., Seo, H. J., and Hwnag, I. H., 1998, Regional geochemical mapping of the Kangneung Sheet (1:250,000): Korean Institute of Geoscience and Mineral Resources, Korea Institute of Geology, Mining & Materials KR-98(C)-02, pp. 147.
Leite, E. P., and Souza Filho, C. R., 2009, Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajas Mineral Province, Brazil: Geophys. Prospect., v. 57, p. 1049–1065.
Luo, X., and Dimitrakopoulos, R., 2003, Data-driven fuzzy analysis in quantitative mineral resource assessment: Comput. Geosci., v. 29, p. 3–13.
Moon, W. M., 1990, Integration of geophysical and geological data using evidence theory function: IEEE Trans. Geosci. Remote Sens., v. 28, p. 711–720.
Moon, W. M., 1993, On mathematical representation and integration of multiple spatial geoscience data sets: Can. J. Remote Sens., v. 19, p. 63–67.
Nykanen, V., 2008, Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield: Nat. Resour. Res., v. 17, p. 29–47.
Nykanen, V., and Raines, G. L., 2006, Quantitative analysis of scale of aeromagnetic data raises questions about geologic-map scale: Nat. Resour. Res., v. 15, p. 213–222.
Nykanen, V., and Salmirinne, H., 2007, Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland, in Ojala, V. J., ed., Gold in the Central Lapland Greenstone Belt: Geological Survey of Finland, Special Paper 44, p. 251–269.
Oh, H. J., and Lee, S., 2008, Regional probabilistic and statistical mineral potential, mapping of gold–silver deposits using GIS in the Gangreung Area, Korea: Resource Geol., v. 58, p. 171–187.
Pan, G. C., 1996, Extended weights of evidence modeling for the pseudo-estimation of metalgrades: Nonrenew. Resour., v. 5, p. 53–76.
Paola, J. D., and Schowengerdt, R. A., 1995, A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery: Int. J. Remote Sens., v. 16, p. 3033–3058.
Park, H. I., Chang, H. W., and Jin, M. S., 1988, K-Ar ages of mineral deposits in the Taebaek Mountain district: J. Korean Inst. Mining Geol., v. 21, p. 57–67.
Porwal, A., Carranza, E. J. M., and Hale, M., 2003, Artificial neural networks for mineral potential mapping: a case study from Aravalli Province, western India: Nat. Resour. Res., v. 12, p. 155–177.
Porwal, A., Carranza, E. J. M., and Hale, M., 2006, A hybrid fuzzy weights-of-evidence model for mineral potential mapping: Nat. Resour. Res., v. 15, p. 1–14.
Raines, G. L., 1999, Evaluation of weights of evidence to predict epithermal-gold deposits in the Great Basin of the Western United States: Nat. Resour. Res., v. 8, p. 257–276.
Raines, G. L., Connors, K. A., and Chorlton, L. B., 2007, Porphyry copper deposit tract definition—a global analysis comparing geologic map scales: Nat. Resour. Res., v. 16, p. 191–198.
Rencz, A. N., Harris, J. R., Watson, G. P., and Murphy, B., 1994, Data integration for mineral exploration in the Antigonish Highlands, Nova Scotia: application of GIS and remote sensing: Can. J. Remote Sens., v. 20, p. 257–267.
Rigol-Sanchez, J. P., Chica-Olmo, M., and Abarca-Hernandez, F., 2003, Artificial neural networks as a tool for mineral potential mapping with GIS: Int. J. Remote Sens., v. 24, p. 1151–1156.
Roy, R., Cassard, D., Cobbold, P. R., Rossello, E. A., Bailly, L., and Lips, A. L. W., 2006, Predictive mapping for copper-gold magmatic-hydrothermal systems in NW Argentina: use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS: Ore Geol. Rev., v. 29, p. 260–286.
Singer, D. A., and Kouda, R., 1996, Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku District, Japan: Math. Geol., v. 28, p. 1017–1023.
Skabar, A. A., 2005, Mapping mineralization probabilities using multilayer perceptrons: Nat. Resour. Res., v. 14, p. 109–123.
Skabar, A., 2007, Modeling the spatial distribution of mineral deposits using neural networks: Nat. Resource Model., v. 20, p. 435–450.
Tangestani, M. H., and Moore, F., 2001, Porphyry copper potential mapping using the weights-of-evidence modeling a GIS northern Shahr-e-Babak Iran: Aust. J. Earth Sci., v. 48, p. 913–927.
Tangestani, M. H., and Moore, F., 2002, The use of Dempster-Shafer model and GIS in integration of geoscientific data for porphyry copper potential mapping, north of Shahr-e-Babak, Iran: Int. J. Appl. Earth Observation Geoinformation, v. 4, p. 65–74.