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

AbstractLarge 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.

[1]  V. Nykänen.  Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield , 2008 .

[2]  F. Agterberg,et al.  Weights of evidence modelling: a new approach to mapping mineral potential , 1990 .

[3]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[4]  Raimundo NetunoVillas,et al.  Gold deposits of the Carajás mineral province: deposit types and metallogenesis , 2001, Mineralium Deposita.

[5]  John C. Davis,et al.  Computers in geology---25 years of progress , 1993 .

[6]  V. Ojala,et al.  Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper – gold deposits in the northern Fennoscandian Shield, Finland , 2008 .

[7]  D. Groves,et al.  Science of targeting: definition, strategies, targeting and performance measurement , 2008 .

[8]  W. M. Moon,et al.  An object-oriented knowledge representation structure for exploration data integration , 1994 .

[9]  Saro Lee,et al.  Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea , 2010 .

[10]  Q. Cheng,et al.  Weights of evidence modeling and weighted logistic regression for mineral potential mapping , 1993 .

[11]  陈志军,et al.  Omnibus Weights of Evidence Method Implemented in GeoDAS GIS for Information Extraction and Integration , 2008 .

[12]  D. Singer,et al.  A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks , 1999 .

[13]  L. Feltrin.  Predictive modelling of prospectivity for Pb-Zn deposits in the Lawn Hill Region, Queensland, Australia , 2008 .

[14]  Emmanuel John M. Carranza,et al.  Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India , 2003 .

[15]  E. Carranza.  Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines , 2004 .

[16]  Frank P. Bierlein,et al.  Distribution of orogenic gold deposits in relation to fault zones and gravity gradients: targeting tools applied to the Eastern Goldfields, Yilgarn Craton, Western Australia , 2006 .

[17]  D. Groves,et al.  Orogenic gold and geologic time: a global synthesis , 2001 .

[18]  C. Knox-Robinson.  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 , 2000 .

[19]  A. J. Strieder,et al.  Mineral-Potential Mapping: A Comparison of Weights-of-Evidence and Fuzzy Methods , 2006 .

[20]  E. Carranza,et al.  Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential, Baguio District, Philippines , 2001 .

[21]  Kok Wai Wong,et al.  Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[22]  Tom Gedeon,et al.  Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples , 2003 .

[23]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[24]  E. Carranza,et al.  Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites, Lundazi District, Zambia , 2005 .

[25]  Guocheng Pan,et al.  A Comparative Analysis of Favorability Mappings by Weights of Evidence, Probabilistic Neural Networks, Discriminant Analysis, and Logistic Regression , 2003 .

[26]  Q. Cheng,et al.  Conditional Independence Test for Weights-of-Evidence Modeling , 2002 .

[27]  G. Raines,et al.  Assessment method for epithermal gold deposits in Northeast Washington State using weights-of-evidence GIS modeling , 2001 .

[28]  G. Bonham-Carter,et al.  Uncertainty management in integration of exploration data using the belief function , 1994 .

[29]  S. Cox,et al.  The St Ives mesothermal gold system, Western Australia: a case of golden aftershocks? , 2004 .

[30]  G. Bonham-Carter,et al.  On Knowledge-based Approach Of Integrating Remote Sensing, Geophysical And Geological Information , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[31]  G. Bonham-Carter.  Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .

[32]  F. Agterberg,et al.  A Modified Weights-of-Evidence Method for Regional Mineral Resource Estimation , 2011 .

[33]  E. Carranza,et al.  Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines , 2003 .

[34]  Steven Micklethwaite,et al.  Fault-segment rupture, aftershock-zone fluid flow, and mineralization , 2004 .

[35]  Frédéric Alexandre,et al.  Knowledge Recovery for Continental-Scale Mineral Exploration by Neural Networks , 2003 .

[36]  L. Feltrin,et al.  Modelling the giant, Zn-Pb-Ag Century deposit, Queensland, Australia , 2009, Comput. Geosci..

[37]  Zhang Shengyuan,et al.  Omnibus Weights of Evidence Method Implemented in GeoDAS GIS for Information Extraction and Integration , 2008 .

[38]  Frank van Ruitenbeek,et al.  Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[39]  D. Groves,et al.  Late-kinematic timing of orogenic gold deposits and significance for computer-based exploration techniques with emphasis on the Yilgarn Block, Western Australia , 2000 .

[40]  Tom Gedeon,et al.  Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping , 2003 .

[41]  Graeme F. Bonham-Carter,et al.  Measuring the Performance of Mineral-Potential Maps , 2005 .

[42]  Tom Gedeon,et al.  Artificial neural networks: A new method for mineral prospectivity mapping , 2000 .

[43]  D. Groves,et al.  Metallogenesis of the Carajás Mineral Province, Southern Amazon Craton, Brazil: Varying styles of Archean through Paleoproterozoic to Neoproterozoic base- and precious-metal mineralisation , 2008 .

[44]  E. Carranza.  Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features , 2009 .

[45]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[46]  E. Carranza,et al.  Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India , 2001 .

[47]  P. Lusty,et al.  Reconnaissance-Scale Prospectivity Analysis for Gold Mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland , 2012, Natural Resources Research.

[48]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[49]  Wooil M. Moon,et al.  Integration and fusion of geological exploration data: a theoretical review of fuzzy logic approach , 1998 .

[50]  Alok Porwal,et al.  Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping , 2003 .