Application of fuzzy logic and geometric average: A Cu sulfide deposits potential mapping case study from Kapsan Basin, DPR Korea

Abstract In this paper, two kinds of knowledge-driven methods, one using the fuzzy logic and another using geometric average, were applied to create the mineral potential maps for Cu sulfide deposits in the greenfield Kapsan Basin, DPR Korea. The ore geology studies for the study area have revealed that Cu sulfide deposits of hydrothermal genesis in Kapsan Basin are closely associated with Jurassic intrusions and faulting tectonics. Based on the conceptual model of Cu sulfide deposits and the available spatial datasets in the study area, we used five independent evidential maps for Cu sulfide deposits potential mapping. They include: (1) faults; (2) aeromagnetic anomaly; (3) Cu geochemical data; (4) Pb geochemical data; and (5) Zn geochemical data. The evidential map values were transformed into continuous values of the [0, 1] range using the non-linear fuzzy membership functions; logistic sigmoid and fuzzy Gaussian functions. Because the fuzzy logic and geometric average methods can use the same fuzzification methodology based on suitable membership functions, it is very economic and efficient to simultaneously apply two predictive models for mineral potential mapping of the study area. The preparation of these evidential layers were performed using spatial analyses supported in ArcGIS 10.4 GIS platform based on geological, geophysical and geochemical spatial datasets. The validation and comparative analysis results for the two predictive models demonstrated that most of known mineral occurrences are distributed in areas with high potential values. The target areas classified by the fuzzy logic occupy 15% of the study area and contain 78% of the total number of known mineral occurrences. Compared with the fuzzy logic, the resulting areas by the geometric average occupy 13% of the study area, but contain 93% of the total number of known mineral occurrences. Although the total number of known mineral occurrences is relatively low for the application of ROC (receiver operating characteristics) technique, the areas under the ROC curve (AUC) obtained by two predictive models were greater than 0.5, suggesting that both predictive models and their resulting potential maps are useful for evaluating the prospectivity of Cu sulfide deposits in Kapsan Basin.

[1]  David I. Groves,et al.  Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland , 2008 .

[2]  Chang-Jo Chung,et al.  On Blind Tests and Spatial Prediction Models , 2008 .

[3]  Qiuming Cheng,et al.  Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity , 2009 .

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

[5]  E. Carranza.  Geologically-constrained mineral potential mapping : Examples from the Philippines , 2002 .

[6]  Q. Cheng,et al.  Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping , 1999 .

[7]  Abbas Bahroudi,et al.  Support vector machine for multi-classification of mineral prospectivity areas , 2012, Comput. Geosci..

[8]  E. Carranza,et al.  A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping , 2006 .

[9]  E. Carranza.  Geochemical Anomaly and Mineral Prospectivity Mapping in Gis , 2012 .

[10]  E. Carranza,et al.  Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines , 2001 .

[11]  Emmanuel John M. Carranza,et al.  Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping , 2015, Comput. Geosci..

[12]  A. Porwal,et al.  Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia , 2010 .

[13]  V. Nykänen,et al.  Receiver operating characteristics (ROC) as validation tool for prospectivity models — A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland , 2015 .

[14]  J. Harris,et al.  Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada , 2001 .

[15]  E. Carranza,et al.  Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm , 2015 .

[16]  J. Harris,et al.  Data- and knowledge-driven mineral prospectivity maps for Canada's North , 2015 .

[17]  Mahyar Yousefi,et al.  Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping , 2015, Comput. Geosci..

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

[19]  E. Carranza,et al.  Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration , 2015 .

[20]  L. Ailleres,et al.  Data fusion and porphyry copper prospectivity models, southeastern Arizona , 2014 .

[21]  E. Carranza,et al.  Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines) , 2016, Natural Resources Research.

[22]  R. Zuo,et al.  Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China , 2016 .

[23]  G. Raines,et al.  Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources , 2007 .

[24]  V. Nykänen,et al.  prospectivity AnAlysis of golD using regionAl geophysicAl AnD geochemicAl DAt A from the centrAl lAplAnD greenstone belt , finlAnD , 2007 .

[25]  Emmanuel John M. Carranza,et al.  Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit, NW Iran , 2016, Natural Resources Research.

[26]  Jie Zhao,et al.  Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China , 2011, Comput. Geosci..

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

[28]  Emmanuel John M. Carranza,et al.  Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) , 2015, Comput. Geosci..

[29]  E. Carranza,et al.  Application of Mineral Exploration Models and GIS to Generate Mineral Potential Maps as Input for Optimum Land-Use Planning in the Philippines , 1999 .

[30]  E. Carranza,et al.  Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China , 2015 .

[31]  Mahyar Yousefi,et al.  Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling , 2015, Comput. Geosci..

[32]  Renguang Zuo,et al.  Support vector machine: A tool for mapping mineral prospectivity , 2011, Comput. Geosci..

[33]  Yongjun Lu,et al.  Exploration feature selection applied to hybrid data integration modeling: Targeting copper-gold potential in central Iran , 2015 .

[34]  J. Harris,et al.  Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada , 2016, Natural Resources Research.

[35]  Maysam Abedi,et al.  Data Envelopment Analysis: A knowledge-driven method for mineral prospectivity mapping , 2015, Comput. Geosci..

[36]  E. Carranza,et al.  Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping , 2014 .

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

[38]  Wooil M. Moon,et al.  Integration Of Geophysical And Geological Data Using Evidential Belief Function , 1990 .

[39]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[40]  A. Porwal,et al.  Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis , 2012 .

[41]  O. Kreuzer,et al.  Comparing prospectivity modelling results and past exploration data: a case study of porphyry Cu–Au mineral systems in the Macquarie Arc, Lachlan Fold Belt, New South Wales , 2015 .

[42]  S. Zhang,et al.  Metallogenic information extraction and quantitative prediction process of seafloor massive sulfide resources in the Southwest Indian Ocean , 2016 .

[43]  Guillaume Caumon,et al.  Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials , 2015, Natural Resources Research.

[44]  C. Chung,et al.  Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique , 2008 .

[45]  Ian Briggs.  Machine contouring using minimum curvature , 1974 .

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

[47]  Yongliang Chen.  Mineral potential mapping with a restricted Boltzmann machine , 2015 .

[48]  R. Zuo,et al.  A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China , 2016, Science China Earth Sciences.

[49]  John Miller,et al.  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 , 2016, Natural Resources Research.

[50]  G. Partington,et al.  Chatham Rise nodular phosphate — Modelling the prospectivity of a lag deposit (off-shore New Zealand): A critical tool for use in resource development and deep sea mining , 2015 .

[51]  Yongliang Chen.  MRPM: three visual basic programs for mineral resource potential mapping , 2004, Comput. Geosci..

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