Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.
KeywordsMineral Prospectivity MappingCase-Based ReasoningMetallogenic Case RepresentationMetallogenic Case RetrievalEastern Kunlun Mountains
A. K. Porwal and O. P. Kreuzer, “Introduction to the Special Issue: Mineral Prospectivity Analysis and Quan- titative Re-source Estimation,” Ore Geology Reviews, Vol. 38, No. 3, 2010, pp. 121-127. doi:10.1016/j.oregeorev.2010.06.002
G. F. Bonham-Carter, F. P. Agterberg and D. F. Wright, “Weights of Evidence Modeling: A New Approach to Mapping Mineral Potential,” In: F. P. Agterberg, et al., Eds., Statistical Applications in the Earth Sciences, Ca- nadian Government Publishing Centre, Ottawa, 1989, pp. 171-183.
F. P. Agterberg, “Combining Indicator Patterns in Weights of Evidence Modeling for Resource Evaluation,” Natural Re-sources Research, Vol. 1, No. 1, 1992, pp. 39-50. doi:10.1007/BF01782111
J. R. Harris, L. Wilkinson and E. C. Grunsky, “Effective Use and Interpretation of Lithogeochemical Data in Re- gional Min-eral Exploration Programs: Application of Geo- graphic Infor-mation Systems (GIS) Technology,” Ore Geology Reviews, Vol. 16, No. 3-4, 2000, pp. 107-143. doi:10.1016/S0169-1368(99)00027-X
E. J. M. Carranza, “Weights of Evidence Model of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines,” Natural Resources Research, Vol. 13, No. 3, 2004, pp. 173-187. doi:10.1023/B:NARR.0000046919.87758.f5
B. Daneshfar, A. Desrochers and P. Budkewitsch, “Miner-al-Potential Mapping for MVT Deposits with Limited Data Sets Using Landsat Data and Geological Evidence in the Borden Basin, Northern Baffin Island, Nunavut, Can- ada,” Natural Resources Research, Vol. 15, No. 3, 2006, pp. 129-149. doi:10.1007/s11053-006-9020-7
A. Porwal, I. González-álvarez, V. Markwitz, T. C. Mc- Cuaig and A. Mamuse, “Weights-of-Evidence and Logis- tic Regres-sion Modeling of Magmatic Nickel Sulfide Pros- pectivity in the Yilgarn Craton, Western Australia,” Ore Geology Reviews, Vol. 38, No. 3, 2010, pp. 184-196. doi:10.1016/j.oregeorev.2010.04.002
B. B. He, C. H. Chen and Y. Liu, “Gold Resources Potential Assessment in Eastern Kunlun Mountains of China Combining Weights-of-Evidence Model with GIS Spatial Analysis Tech-nique,” Chinese Geographical Science, Vol. 20, No. 5, 2010, pp. 461-470. doi:10.1007/s11769-010-0420-6
F. P. Agterberg, G. F. Bonham-Carter, Q. M. Cheng and D. F. Wright, “Weights of Evidence Model and Weighted Logistic Regression in Mineral Potential Mapping,” In: J. C. Davis, et al., Eds., Computers in Geology, Oxford Uni- versity Press, New York, 1993, pp. 13-32.
E. J. M. Carranza and M. Hale, “Logistic Regression for Geo-logically Constrained Mapping of Gold Potential, Ba- guio District, Philippines,” Exploration and Mining Geology, Vol. 10, No. 3, 2001, pp. 165-175. doi:10.2113/0100165
F. Aminzadeh, “Applications of Fuzzy Expert Systems in Inte-grated Oil Exploration,” Computers & Electrical En- gineering, Vol. 20, No. 2, 1994, pp. 89-97. doi:10.1016/0045-7906(94)90023-X
X. Luo and R. Dimitrakopoulos, “Data-Driven Fuzzy Ana- lysis in Quantitative Mineral Resource Assessment,” Com- puters & Geosciences, Vol. 29, No. 1, 2003, pp. 3-13. doi:10.1016/S0098-3004(02)00078-X
K. Koike, S. Matsuda, T. Suzuki and M. Ohmi, “Neural Net-work-Based Estimation of Principal Metal Contents in the Ho-kuroku District, Northern Japan, for Exploring Ku- roko-Type Deposits,” Natural Resources Research, Vol. 11, No. 2, 2002, pp. 135-156. doi:10.1023/A:1015520204066
J. P. Rigol-Sanchez, M. Chica-Olmo and F. Abarca-Her- nandez, “Artificial Neural Networks as a Tool for Mineral Potential Mapping with GIS,” International Journal of Remote Sensing, Vol. 24, No. 5, 2003, pp. 1151-1156. doi:10.1080/0143116021000031791
P. Gumiel, D. J. Sanderson, M. Arias, S. Roberts and A. Martín-Izard, “Analysis of the Fractal Clustering of Ore Depo-sits in the Spanish Iberian Pyrite Belt,” Ore Geology Reviews, Vol. 38, No. 4, 2010, pp. 307-318. doi:10.1016/j.oregeorev.2010.08.001
G. P. Lekkas, N. M. Avouris and L. G. Viras, “Case- Based Reasoning in Environmental Monitoring Applications,” Applied Artificial Intelligence, Vol. 8, No. 3, 1994, pp. 359-376. doi:10.1080/08839519408945448
A. Holt and G. L. Benwell, “Applying Case-Based Reasoning Techniques in GIS,” International Journal of Geographical Information Science, Vol. 13, No. 1, 1999, pp. 9-25. doi:10.1080/136588199241436
J. A. Ye and X. Shi, “Integrating Case-Based Reasoning and GIS for Handling Planning Applications,” Journal of Urban Planning Department, No. 3, 2001, pp. 34-40.
Y. Y. Du, W. Wen, F. Cao and J. Min, “A Case-Based Reason-ing Approach for Land Use Change Prediction,” Expert Systems with Applications, Vol. 37, 2010, pp. 5745-5750. doi:10.1016/j.eswa.2010.02.035
R. C. Schank and P. A. Robert, “Scripts, Plans, Goals, and Un-derstanding: An Inquiry into Human Knowledge Structures,” Lawrence Erlbaum, Hillsdale, 1977.
L. D. Kemp, G. F. Bonham-Carter and G. L. Raines, “Arc- WofE: ArcView Extension for Weights of Evidence Map- ping [EB/OL],” 1999. http://gis.nrcan.gc.ca/software/arcview/wofe
G. C. Wang, Q. H. Wang, P. Jian and Y. H. Zhu, “Zircon SHRIMP Ages of Precambrian Metamorphic Basement Rocks and Their Tectonic Significance in the Eastern Kunlun Moun-tains, Qinghai province, China,” Earth Science Frontiers , Vol. 11, No. 4, 2004, pp. 481-490 .
Y. S. Pan, W. M. Zhou and R. H. Xu, “The Early Palaozoic Geological Features and Evolutions of the Kunlun Mountain,” Science in China (Series D), Vol. 26, No. 4, 1996, pp. 302-307.
X. D. Guo, Y. J. Zhang, G. G. Liu, A. J. Pan and F. Zhang, “Metallogenic Regularities and Prospecting Di- rection of Gold and Copper in Eastern Kunlun,” Gold Geology, Vol. 10, No. 4, 2004, pp. 16-22.
Z. Z. Qian, Z. G. Hu, J. Q. Liu and H. M. Li, “Active Con- ti-nental Margin and Regional Metallogenesis of the Pa- laeo-Tethys in the East Kunlun Mountains,” Geotectonica et Metallogenia, Vol. 24, No. 2, 2000, pp. 134-139.
T. L. Saaty and L. G. Vargas, “Uncertainty and Rank Or- der in the Analytic Hierarchy Process,” European Journal of Opera-tional Research, Vol. 32, No. 1, 1987, pp. 107- 117. doi:10.1016/0377-2217(87)90275-X