Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook

Abedi, 2012, Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration, J. Appl. Geophys., 83, 35, 10.1016/j.jappgeo.2012.05.003 Abedi, 2012, Support vector machine for multi-classification of mineral prospectivity areas, Comput. Geosci., 46, 272, 10.1016/j.cageo.2011.12.014 Abedi, 2013, Application of fuzzy-AHP method to integrate geophysical data in a prospect scale, a case study: seridune copper deposit, Boll. Geofis. Teor. Appl., 54, 145 Aitken, 2018, A role for data richness mapping in exploration decision making, Ore Geol. Rev., 99, 398, 10.1016/j.oregeorev.2018.07.002 Al-Emran, 2018, The impact of knowledge management processes on information systems: a systematic review, Int. J. Inf. Manag., 43, 173, 10.1016/j.ijinfomgt.2018.08.001 Almasi, 2017, Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran, Ore Geol. Rev., 91, 1066, 10.1016/j.oregeorev.2017.11.001 Bonham-Carter, 1994 Bonham-Carter, 1989, Weights of evidence modelling: a new approach to mapping mineral potential, 171 Buccianti, 2015, The FOREGS repository: modelling variability in stream water on a continental scale revising classical diagrams from CoDA (compositional data analysis) perspective, J. Geochem. Explor., 154, 94, 10.1016/j.gexplo.2014.12.003 Carranza, 2008, Geochemical anomaly and mineral prospectivity mapping in GIS, vol. 11 Carranza, 2009, Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features, Ore Geol. Rev., 35, 383, 10.1016/j.oregeorev.2009.01.001 Carranza, 2009, Improved wildcat modeling of mineral prospectivity, Resour. Geol., 60, 129, 10.1111/j.1751-3928.2010.00121.x Carranza, 2011, From predictive mapping of mineral prospectivity to quantitative estimation of number of undiscovered prospects, Resour. Geol., 61, 30, 10.1111/j.1751-3928.2010.00146.x Carranza, 2017, Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields, Nat. Resour. Res., 26, 379, 10.1007/s11053-017-9348-1 Carranza, 2001, Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines, Explor. Min. Geol., 10, 165, 10.2113/0100165 Carranza, 2001, Geologically-constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines, Nat. Resour. Res., 10, 125, 10.1023/A:1011500826411 Carranza, 2002, Spatial association of mineral occurrences and curvilinear geological features, Math. Geol., 34, 203, 10.1023/A:1014416319335 Carranza, 2016, Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines), Nat. Resour. Res., 25, 35, 10.1007/s11053-015-9268-x Carranza, 2010, Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden), Ore Geol. Rev., 38, 219, 10.1016/j.oregeorev.2010.02.003 Carranza, 2005, Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia, Nat. Resour. Res., 14, 47, 10.1007/s11053-005-4678-9 Carranza, 2009, Mapping of prospectivity and estimation of number of undiscovered prospects for lode-gold, southwestern Ashanti Belt, Ghana, Mineral. Deposita, 44, 915, 10.1007/s00126-009-0250-6 Chen, 2017, Mapping mineral prospectivity using an extreme learning machine regression, Ore Geol. Rev., 80, 200, 10.1016/j.oregeorev.2016.06.033 Chen, 2019, Modelling ore-forming processes through a cosine similarity measure: improved targeting of porphyry copper deposits in the Manzhouli belt, China, Ore Geol. Rev., 107, 108, 10.1016/j.oregeorev.2019.02.006 Cheng, 1999, Fuzzy weights of evidence method and its application in mineral potential mapping, Nat. Resour. Res., 8, 27, 10.1023/A:1021677510649 Chung, 1980, Regression models for estimating mineral resources from geological map data, J. Int. Assoc. Math. Geol., 12, 473, 10.1007/BF01028881 Chung, 1993, The Representation of Geoscience Information for data integration, Nonrenewable Resour., 2, 122, 10.1007/BF02272809 Chung, 1991, Combination rules of spatial geoscience data for mineral exploration, Geoinformatics, 2, 159, 10.6010/geoinformatics1990.2.2_159 Coolbaugh, 2007, Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources, Nat. Resour. Res., 16, 199, 10.1007/s11053-007-9037-6 Cox, 1989 De Caritat, 2016, A continental-scale geochemical atlas for resource exploration and environmental management: the National Geochemical Survey of Australia, Geochem. Explor. Environ. Anal., 16, 3, 10.1144/geochem2014-322 De Quadros, 2006, Mineral-potential mapping: a comparison of weights-of-evidence and fuzzy methods, Nat. Resour. Res., 15, 49, 10.1007/s11053-006-9010-9 Elyasi, 2019, Risk-based analysis in mineral potential mapping: application of quantifier-guided ordered weighted averaging method, Nat. Resour. Res., 28, 931, 10.1007/s11053-018-9428-x Fabbri, 2008, On blind tests and spatial prediction models, Nat. Resour. Res., 17, 107, 10.1007/s11053-008-9072-y Ford, 2010, The effect of map scale on geological complexity for computer-aided exploration targeting, Ore Geol. Rev., 38, 156, 10.1016/j.oregeorev.2010.03.008 Ford, 2016, 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, Nat. Resour. Res., 25, 19, 10.1007/s11053-015-9263-2 Gao, 2016, Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China, Ore Geol. Rev., 75, 16, 10.1016/j.oregeorev.2015.12.005 Ghasemzadeh, 2019, Stream sediment geochemical data analysis for district-scale mineral exploration targeting: measuring the performance of the spatial U-statistic and C-A fractal modeling, Ore Geol. Rev., 113, 103115, 10.1016/j.oregeorev.2019.103115 Ghasemzadeh, 2021, Identifying porphyry-Cu geochemical footprints using local neighborhood statistics in Baft area, Iran, Front. Earth Sci., 10.1007/s11707-020-0853-x Gill, 2018, Configuration information system architecture: Insights from applied action design research, Inf. Manag. González-Álvarez, 2010, Hydrothermal Ni prospectivity analysis of Tasmania, Australia, Ore Geol. Rev., 38, 168, 10.1016/j.oregeorev.2010.04.003 Goodchild, 2009, Geographic information systems and science: today and tomorrow, Ann. GIS, 15, 3, 10.1080/19475680903250715 Grabowski, 2014 Groves, 2000, Late-kinematic timing of orogenic gold deposits and significance for computer-based exploration techniques with emphasis on the Yilgarn Block, Western Australia, Ore Geol. Rev., 17, 1, 10.1016/S0169-1368(00)00002-0 Grunsky, 2020, State-of-the-art analysis of geochemical data for mineral exploration, Geochem. Explor. Environ. Anal., 20, 217, 10.1144/geochem2019-031 Grunsky, 2009, Process recognition in multi-element soil and stream-sediment geochemical data, Appl. Geochem., 24, 1602, 10.1016/j.apgeochem.2009.04.024 Hagemann, 2016, Mineral system analysis: Quo vadis, Ore Geol. Rev., 76, 504, 10.1016/j.oregeorev.2015.12.012 Hagemann, 2016, BIF-hosted iron mineral system: a review, Ore Geol. Rev., 76, 317, 10.1016/j.oregeorev.2015.11.004 Harris, 1990 Harris, 2015, Predictive lithological mapping of Canada’s North using Random Forest classification applied to geophysical and geochemical data, Comput. Geosci., 80, 9, 10.1016/j.cageo.2015.03.013 Harris, 1994, Computer-enhancement techniques for the integration of remotely sensed, geophysical, and thematic data for the geosciences, Can. J. Remote. Sens., 20, 210 Harris, 2001, Application of GIS processing techniques for producing mineral prospectivity maps – a case study: mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada, Nat. Resour. Res., 10, 91, 10.1023/A:1011548709573 Harris, 2003, A comparative analysis of favourability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression, Nat. Resour. Res., 12, 241, 10.1023/B:NARR.0000007804.27450.e8 Harris, 2008, Mineral potential modelling for the greater Nahanni ecosystem using GIS based analytical methods, Nat. Resour. Res., 17, 51, 10.1007/s11053-008-9069-6 Harris, 2015, Data-and knowledge-driven mineral prospectivity maps for Canada’s North, Ore Geol. Rev., 71, 788, 10.1016/j.oregeorev.2015.01.004 Hronsky, 2004, The science of exploration targeting, vol. 33, 129 Hronsky, 2011, Self-organized critical systems and ore formation: the key to spatial targeting?, Soc. Econ. Geol. Newsl., 84, 14 Hronsky, 2008, Science of targeting: definition, strategies, targeting and performance measurement, Aust. J. Earth Sci., 55, 3, 10.1080/08120090701581356 Hronsky, 2019, Applying spatial prospectivity mapping to exploration targeting: fundamental practical issues and suggested solutions for the future, Ore Geol. Rev., 107, 647, 10.1016/j.oregeorev.2019.03.016 Huston, 2006, Australian Zn–Pb–Ag ore-forming systems: a review and analysis, Econ. Geol., 101, 1117, 10.2113/gsecongeo.101.6.1117 Joly, 2012, Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: mineral system analysis, targeting model and prospectivity analysis, Ore Geol. Rev., 48, 349, 10.1016/j.oregeorev.2012.05.004 Joly, 2015, Mineral systems approach applied to GIS-based 2D-prospectivity modelling of geological regions: insights from Western Australia, Ore Geol. Rev., 71, 673, 10.1016/j.oregeorev.2015.06.007 Knox-Robinson, 1994 Knox-Robinson, 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., 47, 929, 10.1046/j.1440-0952.2000.00816.x Knox-Robinson, 1997, Towards a holistic exploration strategy: using geographic information systems as a tool to enhance exploration, Aust. J. Earth Sci., 44, 453, 10.1080/08120099708728326 Knox-Robinson, 1996 Kreuzer, 2010, Risk and uncertainty in mineral exploration: implications for valuing mineral exploration properties, vol. 100, 20 Kreuzer, 2008, Linking mineral deposit models to quantitative risk analysis and decision-making in exploration, Econ. Geol., 103, 829, 10.2113/gsecongeo.103.4.829 Kreuzer, 2015, 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, Ore Geol. Rev., 71, 516, 10.1016/j.oregeorev.2014.09.001 Kreuzer, 2020, Introduction to the special issue on spatial modelling and analysis of ore forming processes in mineral exploration targeting, Ore Geol. Rev., 119, 103391, 10.1016/j.oregeorev.2020.103391 Lancianese, 2015, Different spatial methods in regional geochemical mapping at high density sampling: an application on stream sediment of Romagna Apennines, Northern Italy, J. Geochem. Explor., 154, 143, 10.1016/j.gexplo.2014.12.014 Lang, 2018, Related families-based attribute reduction of dynamic covering decision information systems, Knowl.-Based Syst., 162, 161, 10.1016/j.knosys.2018.05.019 Laudon, 2014 Li, 2019, 3D geological modeling for mineral system approach to GIS-based prospectivity analysis: case study of an MVT Pb–Zn deposit, Nat. Resour. Res., 28, 995, 10.1007/s11053-018-9429-9 Li, 2020, Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian–Zhangbaling area, Anhui Province, China, Appl. Geochem., 122, 104747, 10.1016/j.apgeochem.2020.104747 Lisitsin, 2013, Regional prospectivity analysis for hydrothermal-remobilised nickel mineral systems in western Victoria, Australia, Ore Geol. Rev., 52, 100, 10.1016/j.oregeorev.2012.04.001 Lisitsin, 2014, Probabilistic fuzzy logic modeling — quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations, Math. Geosci., 46, 747, 10.1007/s11004-014-9534-1 Luo, 2003, Data-driven fuzzy analysis in quantitative mineral resource assessment, Comput. Geosci., 29, 3, 10.1016/S0098-3004(02)00078-X Lusty, 2012, Reconnaissance-scale prospectivity analysis for gold mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland, Nat. Resour. Res., 21, 359, 10.1007/s11053-012-9183-3 Markwitz, 2010, Magmatic nickel sulfide mineralization in Zimbabwe: review of deposits and development of exploration criteria for prospectivity analysis, Ore Geol. Rev., 38, 139, 10.1016/j.oregeorev.2010.07.004 McCuaig, 2000, The current status and future of the interface between the exploration industry and economic geology research, Rev. Econ. Geol., 13, 553 McCuaig, 2014, The mineral system concept: the key to exploration targeting, Soc. Econ. Geol. Spec. Publ., 18, 153 McCuaig, 2007, Fooling ourselves — dealing with model uncertainty in a mineral systems approach to exploration. Mineral exploration and research — digging deeper, 1435 McCuaig, 2010, Translating the mineral systems approach into an effective exploration targeting system, Ore Geol. Rev., 38, 128, 10.1016/j.oregeorev.2010.05.008 McKay, 2016, 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, Nat. Resour. Res., 25, 125, 10.1007/s11053-015-9274-z Mihalasky, 2001, Mineral potential modelling of gold and silver mineralization in the Nevada Great Basin—a GIS-based analysis using weights of evidence, 10.3133/ofr01291 Mihalasky, 2001, Lithodiversity and its spatial association with metallic mineral sites, Great Basin of Nevada, Nat. Resour. Res., 10, 209, 10.1023/A:1012569225111 Moon, 1990, Integration of geophysical and geological data using evidential belief function, IEEE Trans. Geosci. Remote Sens., 28, 711, 10.1109/TGRS.1990.572988 Nykänen, 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., 17, 29, 10.1007/s11053-008-9062-0 Nykänen, 2007, Spatial analysis techniques as successful mineral-potential mapping tools for orogenic Gold deposits in the Northern Fennoscandian Shield, Finland, Nat. Resour. Res., 16, 85, 10.1007/s11053-007-9046-5 Nykänen, 2008, Reconnaissance scale conceptual fuzzy-logic prospectivity modeling for iron oxide copper—gold deposits in the northern Fennoscandian Shield, Finland, Aust. J. Earth Sci., 55, 25, 10.1080/08120090701581372 Nykänen, 2008, Combined conceptual/empirical prospectivity mapping for orogenic gold in the Northern Fennoscandian Shield, Finland, Aust. J. Earth Sci., 55, 39, 10.1080/08120090701581380 Pan, 2000 Parsa, 2017, A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits, Nat. Resour. Res., 27, 15, 10.1007/s11053-017-9351-6 Parsa, 2017, An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets, Int. J. Appl. Earth Obs. Geoinf., 58, 157 Partington, 2010, Developing models using GIS to assess geological and economic risk: an example from VMS copper gold mineral exploration in Oman, Ore Geol. Rev., 38, 197, 10.1016/j.oregeorev.2010.02.002 Partington, 2004, 239 Payne, 2014, From 2D to 3D: prospectivity modelling in the Taupo Volcanic Zone, New Zealand, Ore Geol. Rev. Pirajno, 2009 Pirajno, 2010, Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: examples from NW China and Altay-Sayan (Siberia), J. Geodyn., 50, 325, 10.1016/j.jog.2010.01.018 Pirajno, 2016, A classification of mineral systems, overviews of plate tectonic margins and examples of ore deposits associated with convergent margins, Gondwana Res., 33, 44, 10.1016/j.gr.2015.08.013 Pirajno, 2011, A review of mineral systems and associated tectonic settings of northern Xinjiang, NW China, Geosci. Front., 2, 157, 10.1016/j.gsf.2011.03.006 Pirajno, 2015, Besshi-type mineral systems in the Palaeoproterozoic Bryah Basin, Capricorn Orogen, Western Australia: implications for tectonic setting and geodynamic evolution, Geosci. Front. Porwal, 2010, Introduction to the special issue: mineral prospectivity analysis and quantitative resource estimation, Ore Geol. Rev., 38, 121, 10.1016/j.oregeorev.2010.06.002 Porwal, 2003, Extended weights-of-evidence modelling for predictive mapping of base metal deposit potential in Aravalli province, western India, Explor. Min. Geol., 10, 155 Porwal, 2003, Artificial neural networks for mineral potential mapping: a case study from Aravalli Province, Western India, Nat. Resour. Res., 12, 155, 10.1023/A:1025171803637 Porwal, 2003, Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping, Nat. Resour. Res., 12, 1, 10.1023/A:1022693220894 Porwal, 2004, A hybrid neuro-fuzzy model for mineral potential mapping, Math. Geol., 36, 803, 10.1023/B:MATG.0000041180.34176.65 Porwal, 2006, Bayesian network classifiers for mineral potential mapping, Comput. Geosci., 32, 1, 10.1016/j.cageo.2005.03.018 Porwal, 2006, A hybrid fuzzy weights-of-evidence model for mineral potential mapping, Nat. Resour. Res., 15, 1, 10.1007/s11053-006-9012-7 Porwal, 2010, Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia, Ore Geol. Rev., 38, 184, 10.1016/j.oregeorev.2010.04.002 Porwal, 2015, Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area, Western Australia, Ore Geol. Rev., 71, 839, 10.1016/j.oregeorev.2014.10.016 Prado, 2020, Modeling of Cu-Au prospectivity in the Carajas mineral province (Brazil) through machine learning: dealing with imbalanced training data, Ore Geol. Rev., 124, 103611, 10.1016/j.oregeorev.2020.103611 Rahimi, 2021, Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof, Appl. Geochem., 104940, 10.1016/j.apgeochem.2021.104940 Roy, 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., 29, 260, 10.1016/j.oregeorev.2005.10.002 Sadeghi, 2013, Rare earth element distribution and mineralization in Sweden: an application of principal component analysis to FOREGS soil geochemistry, J. Geochem. Explor., 133, 160, 10.1016/j.gexplo.2012.10.015 Sadeghi, 2015, Analysis and mapping of soil geochemical anomalies: implications for bedrock mapping and gold exploration in Giyani area, South Africa, J. Geochem. Explor., 154, 180, 10.1016/j.gexplo.2014.11.018 Singer, 2006, Typing mineral deposits using their associated rocks and grades and tonnages in a probabilistic neural network, Math. Geol., 38, 465, 10.1007/s11004-005-9023-7 Singer, 2008, Mineral deposit densities for estimating mineral resources, Math. Geosci., 40, 33, 10.1007/s11004-007-9127-3 Singer, 1999, Examining risk in mineral exploration, Nat. Resour. Res., 8, 111, 10.1023/A:1021838618750 Singer, 2003, Typing mineral deposits using their grades and tonnages in an artificial neural network, Nat. Resour. Res., 12, 201, 10.1023/A:1025128021384 Singer, 2010 Sun, 2019, GIS-based mineral prospectivity mapping using machine learning methods: a case study from Tongling ore district, eastern China, Ore Geol. Rev., 109, 26, 10.1016/j.oregeorev.2019.04.003 Tsoukalas, 1997 Wang, 2020, Manganese potential mapping in western Guangxi-southeastern Yunnan (China) via spatial analysis and modal-adaptive prospectivity modeling, Trans. Nonferrous Metals Soc. China, 30, 1058, 10.1016/S1003-6326(20)65277-3 Wyborn, 1994, Australian Proterozoic mineral systems: essential ingredients and mappable criteria Xiong, 2017, Effects of misclassification costs on mapping mineral prospectivity, Ore Geol. Rev., 82, 1, 10.1016/j.oregeorev.2016.11.014 Xiong, 2021, A positive and unlabeled learning algorithm for mineral prospectivity mapping, Comput. Geosci., 147, 104667, 10.1016/j.cageo.2020.104667 Xiong, 2018, Mapping mineral prospectivity through big data analytics and a deep learning algorithm, Ore Geol. Rev., 102, 811, 10.1016/j.oregeorev.2018.10.006 Yousefi, 2017, Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area, SE Iran, Ore Geol. Rev., 83, 200, 10.1016/j.oregeorev.2016.12.024 Yousefi, 2017, Analysis of zoning pattern of geochemical indicators for targeting of porphyry-Cu mineralization: a pixel-based mapping approach, Nat. Resour. Res., 26, 429, 10.1007/s11053-017-9334-7 Yousefi, 2015, Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping, Comput. Geosci., 74, 97, 10.1016/j.cageo.2014.10.014 Yousefi, 2015, Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling, Comput. Geosci., 79, 69, 10.1016/j.cageo.2015.03.007 Yousefi, 2015, Geometric average of spatial evidence data layers: a GIS-based multi-criteria decision-making approach to mineral prospectivity mapping, Comput. Geosci., 83, 72, 10.1016/j.cageo.2015.07.006 Yousefi, 2016, Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration, Nat. Resour. Res., 25, 3, 10.1007/s11053-014-9261-9 Yousefi, 2017, Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values, J. Afr. Earth Sci., 128, 47, 10.1016/j.jafrearsci.2016.04.019 Yousefi, 2016, Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping, J. Geochem. Explor., 164, 94, 10.1016/j.gexplo.2015.10.008 Yousefi, 2017, Introduction to the special issue: GIS-based mineral potential targeting, J. Afr. Earth Sci., 12, 1, 10.1016/j.jafrearsci.2017.02.023 Yousefi, 2012, Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping, J. Geochem. Explor., 115, 24, 10.1016/j.gexplo.2012.02.002 Yousefi, 2013, Weighted drainage catchment basin mapping of stream sediment geochemical anomalies for mineral potential mapping, J. Geochem. Explor., 128, 88, 10.1016/j.gexplo.2013.01.013 Yousefi, 2014, Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping, Geochem. Explor. Environ. Anal., 14, 45, 10.1144/geochem2012-144 Yousefi, 2019, Exploration information systems—a proposal for the future use of GIS in mineral exploration targeting, Geol. Rev., 111, 103005, 10.1016/j.oregeorev.2019.103005 Zhang, 2016, 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, Sci. China Earth Sci., 59, 556, 10.1007/s11430-015-5178-3 Zuo, 2020, Geodata Science-based mineral prospectivity mapping: a review, Nat. Resour. Res., 29, 3415, 10.1007/s11053-020-09700-9 Zuo, 2011, Support vector machine: a tool for mapping mineral prospectivity, Comput. Geosci., 37, 1967, 10.1016/j.cageo.2010.09.014 Zuo, 2020, Effects of random negative training samples on mineral prospectivity mapping, Nat. Resour. Res., 29, 3443, 10.1007/s11053-020-09668-6 Zuo, 2019, Deep learning and its application in geochemical mapping, Earth Sci. Rev., 192, 1, 10.1016/j.earscirev.2019.02.023 Zuo, 2021, Uncertainties in GIS-based mineral prospectivity mapping: key types, potential impacts and possible solutions, Nat. Resour. Res., 10.1007/s11053-021-09871-z