A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

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• 作者：Arianne Ford ; John M. Miller ; Augusto G. Mol
• 关键词：Carajás mineral province ; Evidential belief functions ; Fuzzy logic ; Mineral potential mapping ; Orogenic gold ; Weights of evidence
• 刊名：Natural Resources Research
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：25
• 期：1
• 页码：19-33
• 全文大小：5,542 KB
• 参考文献：Agterberg, F. P. (2011). A modified weights-of-evidence method for regional mineral resource estimation. Natural Resources Research, 20, 95–101.CrossRef
  Agterberg, F. P., & Bonham-Carter, G. F. (2005). Measuring the performance of mineral-potential maps. Natural Resources Research, 14, 1–17.CrossRef
  Agterberg, F. P., Bonham-Carter, G. F., Cheng, Q., & Wright, D. F. (1993). Weights of evidence modeling and weighted logistic regression for mineral potential mapping. In J. Davis & U. C. Herzfeld (Eds.), Computers in geology—25 years of progress (pp. 13–32). New York: Oxford University Press.
  Agterberg, F. P., & Cheng, Q. (2002). Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11, 249–255.CrossRef
  An, P., Moon, W. M., & Bonham-Carter, G. F. (1992). On knowledge-based approach to integrating remote sensing, geophysical and geological information. In Proceedings of international geoscience and remote sensing sympositum (IGARSS) (pp. 34–38), Houston.
  An, P., Moon, W. M., & Bonham-Carter, G. F. (1994a). An object-oriented knowledge representation structure for exploration data integration. Nonrenewable Resources, 3, 132–145.CrossRef
  An, P., Moon, W. M., & Bonham-Carter, G. F. (1994b). Uncertainty management in integration of exploration data using the belief function. Nonrenewable Resources, 3, 60–71.CrossRef
  Bierlein, F. P., Murphy, F. C., Weinberg, R. F., & Lees, T. (2006). Distribution of orogenic gold deposits in relation to fault zones and gravity gradients: targeting tools applied to the Eastern Goldfields, Yilgarn Craton, Western Australia. Mineralium Deposita, 41, 107–126.CrossRef
  Boleneus, D. E., Raines, G. L., Causey, J. D., Bookstrom, A. A., Frost, T. P., & Hyndman, P. C. (2002). Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling. U.S. geological survey open file report OF01-501.
  Bonham-Carter, G. (1994). Geographic information systems for geoscientists: Modelling with GIS. Oxford: Pergamon Press.
  Bonham-Carter, G. F., Agterberg, F. P., & Cheng, Q. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. In Agterberg, F. P., & Bonham-Carter, G. F., (Eds.), Statistical applications in the earth sciences (pp. 171–183). Geological Survey of Canada.
  Bougrain, L., Gonzalez, M., Bouchot, V., Cassard, D., Lips, A. L. W., Alexandre, F., & Stein, G. (2003). Knowledge recovery for continental-scale mineral exploration by neural networks. Natural Resources Research, 12, 173–181.CrossRef
  Brown, W., Gedeon, T. D., & Groves, D. I. (2003a). Use of noise to augment training data: A neural network method of mineral-potential mapping in regions of limited known deposit examples. Natural Resources Research, 12, 141–152.CrossRef
  Brown, W. M., Gedeon, T. D., Groves, D., & Barnes, R. G. (2000). Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences, 47, 757–770.CrossRef
  Brown, W., Groves, D., & Gedeon, T. (2003b). Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping. Natural Resources Research, 12, 183–200.CrossRef
  Burrough, P. A., & McDonnell, R. (1998). Principles of geographical information systems. Oxford: Oxford University Press.
  Carranza, E. J. M. (2004). Weights-of-evidence modelling of mineral potential: A case study using small number of prospects, Abra, Philippines. Natural Resources Research, 13, 173–187.CrossRef
  Carranza, E. J. M. (2009). Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geology Reviews, 35, 383–400.CrossRef
  Carranza, E. J. M., & Hale, M. (2001). Geologically constrained fuzzy mapping of gold mineralization potential, Baguio District, Philippines. Natural Resources Research, 10, 125–136.CrossRef
  Carranza, E. J. M., & Hale, M. (2003). Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geology Reviews, 22, 117–132.CrossRef
  Carranza, E. J. M., Van Ruitenbeek, F. J. A., Hecker, C., Van der Meijde, M., & Van der Meer, F. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation, 10, 374–387.CrossRef
  Carranza, E. J. M., Woldai, T., & Chikambwe, E. M. (2005). Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Natural Resources Research, 14, 47–63.CrossRef
  Cox, S. F., & Ruming, K. (2004). The St Ives mesothermal gold system, Western Australia-a case of golden aftershocks? Journal of Structural Geology, 26, 1109–1125.CrossRef
  De Quadros, T. F. P., Koppe, J. C., Strieder, A. J., & Costa, J. F. C. L. (2006). Mineral-potential mapping: A comparison of weights-of-evidence and fuzzy methods. Natural Resources Research, 15, 49–65.CrossRef
  Dempster, A. P. (1967). Upper and lower probabilities induced by multivalued mapping. Annals of Mathematical Statistics, 38, 325–339.CrossRef
  Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society: Series B, 30, 205–247.
  Feltrin, L. (2009). Predictive modelling of prospectivity for Pb–Zn deposits in the Lawn Hill Region, Queensland, Australia. Computers and Geosciences, 35, 108–133.CrossRef
  Fung, C. C., Iyer, V., Brown, W., & Wong, K. W. (2005). Comparing the performance of different neural networks architectures for the prediction of mineral prospectivity. In Proceedings of the fourth international conference on machine learning and cybernetics (pp. 394–398), Guangzhou.
  Goldfarb, R. J., Groves, D. I., & Gardoll, S. J. (2001). Orogenic gold and geologic time: A global synthesis. Ore Geology Reviews, 18, 1–75.CrossRef
  Grainger, C. J., Groves, D. I., Tallarico, F. H. B., & Fletcher, I. R. (2008). Metallogenesis of the Carajás Mineral Province, Southern Amazon Craton, Brazil: Varying styles of Archean through Paleoproterozoic to neoproterozoic base- and precious-metal mineralization. Ore Geology Reviews, 33, 451–489.CrossRef
  Groves, D. I., Goldfarb, R. J., Knox-Robinson, C. M., Ojala, J., Gardoll, S., Yun, G. Y., & Holyland, P. (2000). Late-kinematic timing of orogenic gold deposits and significance for computer-based exploration techniques with emphasis on the Yilgarn Block, Western Australia. Ore Geology Reviews, 17, 1–38.CrossRef
  Harris, D., Zurcher, L., Stanley, M., Marlow, J., & Pan, G. (2003). A comparitive analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Natural Resources Research, 12, 241–255.CrossRef
  Hronsky, J. M. A., & Groves, D. I. (2008). Science of targeting: Definition, strategies and targeting performance measurement. Australian Journal of Earth Sciences, 55, 3–12.CrossRef
  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. Australian Journal of Earth Sciences, 47, 929–941.CrossRef
  Lusty, P. A. J., Scheib, C., Gunn, A. G., & Walker, A. S. D. (2012). Reconnaissance-scale prospectivity analysis for gold mineralization in the southern Uplands-Down-Longford Terrane, Northern Ireland. Natural Resources Research, 21, 359–382.CrossRef
  McCuaig, T. C., & Beresford, S. (2009). Scale dependence of targeting criteria in light of recent advances in understanding mineral systems. In 10th Biennial SGA Meeting (pp. 123–125), Townsville.
  Micklethwaite, S., & Cox, S. F. (2004). Fault-segment rupture, aftershock-zone fluid flow, and mineralization. Geology, 32, 813–816.CrossRef
  Moon, W. M. (1998). Integration and fusion of geological exploration data: A theoretical review of fuzzy logic approach. Geoscience Journal, 2, 175–183.CrossRef
  NetunoVillas, R., & Santos, M. D. (2001). Gold deposits of the Carajás mineral province: deposit types and metallogenesis. Mineralium Deposita, 36, 300–331.CrossRef
  Nykänen, 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. Natural Resources Research, 17, 29–48.CrossRef
  Nykänen, V., Groves, D. I., Ojala, V. J., Eilu, P., & Gardoll, S. J. (2008). Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper-gold deposits in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences, 55, 25–38.CrossRef
  Oh, H. J., & Lee, S. (2010). Application of artificial neural network for gold-silver deposits potential mapping: A case study of Korea. Natural Resources Research, 19, 103–124.CrossRef
  Porwal, A., Carranza, E. J. M., & Hale, M. (2001). Extended weights-of-evidence modeling for predictive mapping of base metal deposit potential, Aravalli province, India. Exploration and Mining Geology Journal, 10, 273–287.CrossRef
  Porwal, A., Carranza, E. J. M., & Hale, M. (2003a). Artificial neural networks for mineral potential mapping. Natural Resources Research, 12, 1–25.CrossRef
  Porwal, A., Carranza, E. J. M., & Hale, M. (2003b). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12, 155–171.CrossRef
  Singer, D. A., & Kouda, R. (1999). A Comparison of the weights of evidence method and probabilistic neural networks. Natural Resources Research, 8, 287–298.CrossRef
  Tsoukalas, L. H., & Uhrig, R. E. (1997). Fuzzy and neural approaches in engineering. New York: Wiley.
  Zhang, S., Cheng, Q., & Chen, Z. (2008). Omnibus weights of evidence method implemented in GeoDAS GIS for information extraction and integration. Journal of China University of Geosciences, 19, 404–409.CrossRef
  
• 作者单位：Arianne Ford (1)
  John M. Miller (1)
  Augusto G. Mol (2)
  
  1. Centre for Exploration Targeting, University of Western Australia, Crawley, WA, 6009, Australia
  2. Troy Resources Limited, Unit 12, First Floor, 11 Ventnor Ave, West Perth, WA, 6005, Australia
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Mathematical Applications in Geosciences
  Economic Geology
  Sedimentology
  Hydrogeology
  
• 出版者：Springer Netherlands
• ISSN：1573-8981

文摘

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.