Natural Resources Research

, Volume 19, Issue 2, pp 103–124 | Cite as

Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea

  • Hyun-Joo Oh
  • Saro Lee


The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPML and MPMW gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPIL and MPIW.


Gold–silver mineral potential mapping GIS artificial neural network Korea 



This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and Technology of Korea.


  1. 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.Google Scholar
  2. Agterberg, F. P., and Bonham-Carter, G. F., 2005, Measuring performance of mineral-potential maps: Nat. Resour. Res., v. 14, p. 1–17.CrossRefGoogle Scholar
  3. 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.Google Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. 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.CrossRefGoogle Scholar
  7. Bonham-Carter, G. F., 1994, Geographic information systems for geoscientists: modeling with GIS, Computer Methods in the Geosciences 13: Pergamon Press, Oxford, pp. 398.Google Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.Google Scholar
  22. Garrett, J., 1994, Where and why artificial neural networks are applicable in civil engineering: J. Comput. Civil Eng., v. 8, p. 129–130.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Hengl, T., 2006, Finding the right pixel size: Comput. Geosci., v. 32, p. 1283–1298.CrossRefGoogle Scholar
  26. Hines, J. W., 1997, Fuzzy and artificial neural approaches in engineering: Wiley, New York, pp. 201.Google Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.Google Scholar
  37. 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.CrossRefGoogle Scholar
  38. Luo, X., and Dimitrakopoulos, R., 2003, Data-driven fuzzy analysis in quantitative mineral resource assessment: Comput. Geosci., v. 29, p. 3–13.CrossRefGoogle Scholar
  39. Moon, W. M., 1990, Integration of geophysical and geological data using evidence theory function: IEEE Trans. Geosci. Remote Sens., v. 28, p. 711–720.CrossRefGoogle Scholar
  40. Moon, W. M., 1993, On mathematical representation and integration of multiple spatial geoscience data sets: Can. J. Remote Sens., v. 19, p. 63–67.Google Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. Pan, G. C., 1996, Extended weights of evidence modeling for the pseudo-estimation of metalgrades: Nonrenew. Resour., v. 5, p. 53–76.CrossRefGoogle Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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.Google Scholar
  48. 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.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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.Google Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.CrossRefGoogle Scholar
  55. 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.CrossRefGoogle Scholar
  56. Skabar, A. A., 2005, Mapping mineralization probabilities using multilayer perceptrons: Nat. Resour. Res., v. 14, p. 109–123.CrossRefGoogle Scholar
  57. Skabar, A., 2007, Modeling the spatial distribution of mineral deposits using neural networks: Nat. Resource Model., v. 20, p. 435–450.CrossRefGoogle Scholar
  58. 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.Google Scholar
  59. 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.CrossRefGoogle Scholar
  60. Xu, S., Cui, Z. K., Yang, X. L., and Wang, G. J., 1992, A preliminary application of weights of evidence in gold exploration in Xionger mountain region, Henan province: Math. Geol., v. 24, p. 663–674.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geology 2010

Authors and Affiliations

  1. 1.Geoscience Information CenterKorea Institute of Geoscience & Mineral Resources (KIGAM)DaejeonKorea

Personalised recommendations