Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping
[Display omitted] •Bayesian optimization is a powerful optimization tool to find the best hyperparameters of machine learning models.•The optimization results provide references for the empirical hyperparameters setting of ensemble learning models.•XGBoost model outperformed RF model with better pre...
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| Veröffentlicht in: | Ore geology reviews Jg. 145; S. 104916 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.06.2022
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| ISSN: | 0169-1368 |
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| Abstract | [Display omitted]
•Bayesian optimization is a powerful optimization tool to find the best hyperparameters of machine learning models.•The optimization results provide references for the empirical hyperparameters setting of ensemble learning models.•XGBoost model outperformed RF model with better prediction ability and stability in the case area.•XGBoost method shows great potential for MPM, offering a significant improvement with BOA method.
Machine learning algorithms have been widely applied in mineral prospectivity mapping (MPM). In this study, we implemented ensemble learning of extreme gradient boosting (XGBoost) and random forest (RF) models to create MPM for magmatic hydrothermal tin polymetallic deposits in Xianghualing District, southern Hunan Province, China. Machine-learning models often require careful adjustment of the learning parameters and model hyperparameters for optimal global performance. However, parameter tuning often entails tedious calculations and sufficient expert experience, which is a time-consuming and labor-intensive process. To obtain the global optimal performance of the XGBoost and RF models, a Bayesian optimization algorithm (BOA) was employed with the aid of 5-fold cross validation to search for the most appropriate hyperparameters of the XGBoost and RF models. After the Bayesian optimization, the AUC values of both models were significantly improved, indicating that the BOA is a powerful optimization tool. The optimization results provide a reference for the empirical hyperparameter setting of ensemble learning models. Through a comparative study, the XGBoost model was shown to be superior to the RF model in terms of accuracy, precision, recall, F1 score, and kappa coefficient. In addition, the receiver operating characteristic curves and prediction–area curves showed that the XGBoost model outperformed the RF model, indicating that the XGBoost model had better prediction ability and stability in the case area. In this study, the XGBoost model shows great potential for MPM, offering a significant improvement over the BOA method. |
|---|---|
| AbstractList | [Display omitted]
•Bayesian optimization is a powerful optimization tool to find the best hyperparameters of machine learning models.•The optimization results provide references for the empirical hyperparameters setting of ensemble learning models.•XGBoost model outperformed RF model with better prediction ability and stability in the case area.•XGBoost method shows great potential for MPM, offering a significant improvement with BOA method.
Machine learning algorithms have been widely applied in mineral prospectivity mapping (MPM). In this study, we implemented ensemble learning of extreme gradient boosting (XGBoost) and random forest (RF) models to create MPM for magmatic hydrothermal tin polymetallic deposits in Xianghualing District, southern Hunan Province, China. Machine-learning models often require careful adjustment of the learning parameters and model hyperparameters for optimal global performance. However, parameter tuning often entails tedious calculations and sufficient expert experience, which is a time-consuming and labor-intensive process. To obtain the global optimal performance of the XGBoost and RF models, a Bayesian optimization algorithm (BOA) was employed with the aid of 5-fold cross validation to search for the most appropriate hyperparameters of the XGBoost and RF models. After the Bayesian optimization, the AUC values of both models were significantly improved, indicating that the BOA is a powerful optimization tool. The optimization results provide a reference for the empirical hyperparameter setting of ensemble learning models. Through a comparative study, the XGBoost model was shown to be superior to the RF model in terms of accuracy, precision, recall, F1 score, and kappa coefficient. In addition, the receiver operating characteristic curves and prediction–area curves showed that the XGBoost model outperformed the RF model, indicating that the XGBoost model had better prediction ability and stability in the case area. In this study, the XGBoost model shows great potential for MPM, offering a significant improvement over the BOA method. |
| ArticleNumber | 104916 |
| Author | Li, Nan Yin, Jiangning |
| Author_xml | – sequence: 1 givenname: Jiangning surname: Yin fullname: Yin, Jiangning – sequence: 2 givenname: Nan surname: Li fullname: Li, Nan email: ln20211225@163.com |
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| Cites_doi | 10.1016/j.sedgeo.2012.03.007 10.1016/j.gexplo.2021.106839 10.1016/j.oregeorev.2015.02.016 10.1007/s11430-015-5178-3 10.1016/j.gexplo.2008.03.004 10.1023/B:NARR.0000007804.27450.e8 10.1007/s11053-008-9062-0 10.1016/j.patcog.2015.03.009 10.1109/ACCESS.2017.2766203 10.1016/j.apgeochem.2020.104710 10.5194/bg-7-3019-2010 10.1016/j.sedgeo.2016.01.007 10.1016/S0037-0738(98)00118-3 10.1155/2019/8503252 10.1016/j.cageo.2020.104667 10.1007/978-3-030-05318-5_6 10.1016/j.jhydrol.2015.06.008 10.1007/s00126-006-0084-4 10.1016/j.oregeorev.2017.08.016 10.1023/A:1023818214614 10.1038/s41598-020-65232-5 10.1007/s00126-017-0725-9 10.1016/j.earscirev.2019.02.023 10.1016/j.oregeorev.2019.103028 10.1016/j.gexplo.2015.10.008 10.1016/j.oregeorev.2006.10.002 10.1016/j.oregeorev.2009.01.001 10.1016/j.oregeorev.2016.04.023 10.1016/j.eswa.2008.01.018 10.1016/S0009-2541(03)00187-6 10.1002/widm.1249 10.1007/s12518-018-0229-z 10.1016/j.oregeorev.2015.12.005 10.1109/JPROC.2015.2494218 10.1016/j.oregeorev.2014.10.006 10.1016/j.sedgeo.2007.11.002 10.1007/s11053-020-09668-6 10.1016/j.oregeorev.2018.11.019 10.1016/j.oregeorev.2018.09.005 10.1007/s11053-017-9345-4 10.1016/j.oregeorev.2006.12.001 10.3390/min10020102 10.1016/j.palaeo.2013.04.020 10.3906/yer-1401-9 10.1016/j.gexplo.2014.02.013 10.1007/s11053-019-09564-8 10.1016/j.oregeorev.2018.01.029 10.1007/978-1-4419-9326-7_1 10.1016/j.cageo.2011.12.014 10.1016/j.cageo.2017.10.005 10.1007/s11053-022-10038-7 10.1093/oxfordjournals.pan.a004868 10.1093/biomet/75.1.11 10.1016/j.oregeorev.2014.08.012 10.1016/j.oregeorev.2018.02.019 10.1023/B:MATG.0000041180.34176.65 10.1016/j.oregeorev.2010.02.003 10.1016/j.oregeorev.2020.103611 10.1046/j.1440-0952.2000.00807.x 10.1016/j.gsf.2020.04.014 10.1111/j.1365-2478.2008.00779.x 10.1016/j.lithos.2018.05.001 10.1016/j.ejor.2015.05.030 10.1016/j.oregeorev.2017.11.013 10.1007/s11053-020-09742-z 10.1145/2939672.2939785 10.1016/j.cageo.2014.10.004 10.1016/j.cageo.2005.03.018 10.1007/s11053-010-9112-2 10.1016/j.oregeorev.2010.05.008 10.1016/j.cageo.2010.11.001 10.1016/j.cageo.2016.01.012 10.1016/j.oregeorev.2019.103005 10.1016/j.oregeorev.2014.10.030 10.1016/j.oregeorev.2005.10.002 10.1016/j.apgeochem.2021.104940 10.1007/s11053-015-9268-x 10.1007/s11053-017-9335-6 10.1007/s11053-006-9012-7 10.1093/biomet/80.2.339 10.1016/j.jog.2010.01.018 10.1016/j.apgeochem.2012.10.031 10.1360/03yd0384 10.1016/j.cageo.2011.06.023 10.1109/ACCESS.2018.2818678 10.1016/j.oregeorev.2012.04.001 10.1190/tle40020099.1 10.1016/j.lithos.2006.12.009 10.1016/j.oregeorev.2019.04.003 10.1016/j.apgeochem.2021.105043 10.1080/08120099.2017.1328705 10.1016/j.cageo.2010.09.014 10.1007/s11053-017-9355-2 10.1007/s11053-019-09598-y 10.1016/j.oregeorev.2021.104264 10.1007/s11053-007-9036-7 10.1007/s11053-019-09483-8 10.1016/j.oregeorev.2007.07.001 10.1016/j.cageo.2009.02.008 10.1016/j.gexplo.2013.08.013 10.1111/j.1751-3928.2010.00121.x 10.1080/13658816.2014.885527 10.1007/s00710-014-0355-1 10.1130/G48615.1 10.1023/A:1010933404324 10.1016/j.oregeorev.2015.05.019 10.1016/j.cageo.2015.03.007 10.1130/0091-7613(1992)020<0391:TPUOHH>2.3.CO;2 10.1016/j.lithos.2016.10.010 10.1016/j.lithos.2020.105952 10.1023/A:1025171803637 10.1016/j.oregeorev.2011.09.003 10.1016/j.cageo.2014.10.014 10.1007/s11053-020-09700-9 10.1007/s11053-020-09789-y 10.1016/j.oregeorev.2012.05.004 10.1016/j.gexplo.2013.07.009 10.1007/s11053-018-9375-6 10.1016/j.oregeorev.2021.104213 10.1007/BF02272809 10.1007/s11053-021-09984-5 10.1016/j.geomorph.2006.12.036 10.1080/08120090701581364 10.1126/science.aar5169 10.1016/j.oregeorev.2017.04.029 10.1007/s11053-018-9428-x 10.1111/j.1755-6724.2007.tb00951.x 10.1016/j.cageo.2008.05.003 10.1016/j.apgeochem.2013.02.009 10.1016/j.cageo.2014.09.007 10.1016/j.apgeochem.2020.104747 10.1016/j.oregeorev.2016.06.033 10.1016/j.margeo.2015.01.015 10.1016/j.oregeorev.2017.11.001 10.1016/j.oregeorev.2016.02.010 10.1007/s11004-007-9106-8 10.2113/0100165 10.1007/s11053-019-09510-8 10.1109/IJCNN.2003.1223683 10.1007/s11053-021-09871-z 10.1016/j.cageo.2015.10.006 10.1007/s12583-020-1365-z 10.1080/19475705.2017.1294113 10.1080/0143116021000031791 10.1016/j.oregeorev.2014.08.010 |
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| Keywords | Mineral prospectivity mapping Random forest Ensemble learning Bayesian optimization XGBoost K-fold cross validation |
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| References | Egozcue, Pawlowsky-Glahn, Mateu-Figueras, Barcelo-Vidal (b0240) 2003; 35 Wang, Zuo, Xiong (b9045) 2020; 29 Liu, W., Zhang, M., Luo, Z., Cai, Y., 2017. An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access, 5,24417-24425. http://dx.doi.org/10.1109/ACCESS.2017.2766203. Li, Li, Yuan, Jowitt, Zhang, Zhou, Wu (b0365) 2020; 122 Almasi, Yousefi, Carranza (b0025) 2017; 91 Rigol-Sanchez, Chica-Olmo, Abarca-Hernandez (b0560) 2003; 24 Leite, Desouza (b0335) 2009; 35 Chen, Lu, Li (b0150) 2014; 140 Chung, Fabbri (b0205) 1993; 2 Brochu, E., Cora, V., de Freitas, N. 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599v1 [cs.LG]. Ahneman, Estrada, Lin, Dreher, Doyle (b0005) 2018; 360 Sahin (b0580) 2020; 2 Mao, J.W., Pirajno, F., Cook, N., 2011. Mesozoic metallogeny in East China and corresponding geodynamic settings-an introduction to the special issue. Ore Geol. Rev. 43, 1-7. Coimbra, Rodriguez-Galiano, Olóriz, Chica-Olmo (b0215) 2014; 73 McCuaig, Beresford, Hronsky (b9035) 2010; 38 Cascalho, Costa, Dawson, Milne, Rocha (b0130) 2016; 334 Carranza, Sadeghi, Billay (b0120) 2015; 71 Pirajno (b0520) 2010; 50 Ford, Blenkinsop (b0255) 2008; 55 Carranza (b0095) 2009; 35 Chen, Zhao (b0170) 2021; 135 Brown, Gedeon, Groves, Barnes (b0065) 2000; 47 Lisitsin, Gonz’alez-Alvarez, Porwal (b0380) 2013; 52 Sevastjanova, Hall, Alderton (b0600) 2012; 280 Peng, Hu, Burnard (b0510) 2003; 200 Li, Huang, Wang, Wang (b0395) 2016; 79 Zuo (b0795) 2017; 26 Hallsworth, Chisholm (b0285) 2008; 203 Kiangala, Wang (b0430) 2021; 4 Li, Wang, Carranza (b0370) 2016; 89 Saljoughi, Hezarkhani (b0590) 2016; 10 Meng (b0455) 1994; 16 Zuo (b0790) 2014; 139 Ziaii, Pouyan, Ziaei (b0745) 2009; 100 Cheng, Bonham-Carter, Wang, Zhang, Li, Xia (b0200) 2011; 37 Amano, Taira (b0030) 1992; 20 Yousefi, Kreuzer, Nykänen, Hronsky (b0720) 2019; 111 Zhang, Zuo, Xiong (b0750) 2016; 59 Xiong, Y., Zuo, R., 2018.GIS-based rare events logistic regression for mineral prospectivity mapping. Comput. Geosci., 111, 18-25. Chen (b0155) 2015; 71 Budholiya, Shrivastava, Sharma (b0070) 2020 Yousefi, Carranza (b0705) 2015; 74 Abedi, Norouzi, Fathianpour (b0020) 2013; 21 Zuo, Xiong, Wang, Carranza (b0800) 2019; 192 Carranza (b0105) 2010; 60 Zhang, Qian, Mao, Huang, Huang, Si (b0755) 2018; 6 Chen, Li, Sun, Ireland, Tian, Hu, Yang, Chen, Xu (b0140) 2016 Chen, Wu (b9005) 2019; 28 Ding, Ma, Lu, Zhang (b0225) 2018; 95 Chen,T., Guestrin, C., 2016.XGBoost:A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference. 2016: 785-794. 10.1145/2939672.2939785. Yousefi, M., Carranza, E.J.M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., Mihalasky, M.J.,2021. Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook. J. Geochem. Explor.,1-12. Sun, Li, Wu, Chen, Zhu, Hu (b0630) 2020; 10 Lisitsin (b0400) 2015; 71 Yin, Zuo, Xiong (b0695) 2021 Shu (b0610) 2006; 12 Xiong, Zuo (b0680) 2021; 147 Hu, X., Gong, Y., Pi, D., Zhang, Z., Zeng, G., Xiong, S., Yao, S., 2017. Jurassic magmatism related Pb-Zn-W-Mo polymetallic mineralization in the central Nanling range, South China: geochronologic, geochemical, and isotopic evidence from the Huangshaping deposit. Ore Geol. Rev. 10.1016/j.oregeorev.2017.08.016. Mao, Zheng, Xie, Lehmann, Goldfarb (b0450) 2021; 49 Feurer, M., Klein, A., Eggensperger K, Springenberg JT, Blum M, Hutter F., 2019. Auto-sklearn: efficient and robust automated machine learning, part of the springer series on challenges in machine learning book series (SSCML). 10.1007/978-3-030-05318-5_6. Xu, Li, Xie, Cai, Niu, Liu (b0685) 2021; 104316 Bonham-Carter (b0045) 1994 Carranza, Laborte (b0115) 2015; 74 Chen, Wu (b0160) 2017; 64 Carranza, E.J.M., Laborte, A.G., 2015. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: application of Random Forests algorithm. Ore Geol. Rev., 71, 777-787. Chen, Chen, Wang (b0185) 2008 Oh, Lee (b0495) 2010; 19 Yuan, S.D., Mao, J., Cook, N.J., Wang, X., Liu, X., Yuan, Y., 2015. A Late Cretaceous tin metallogenic event in Nanling W-Sn metallogenic province: constraints from U-Pb, Ar-Ar geochronology at the Jiepailing Sn-Be-F deposit, Hunan, China. Ore Geol. Rev. 65, 283-293. Joly, Porwal, McCuiag (b9025) 2012; 48 Porwal, Carranza, Hale (b0545) 2006; 32 Li, Yan, Zhong, Xia, Wang (b0350) 2015; 363 Liu, Gilbert, Cepeda, Lysdahl, Piciullo, Hefre, Lacasse (b0420) 2020; 12 Brandmeier, Zamora, Nykänen, Middleton (b9000) 2020; 29 Li, Huang, Wang, Wang (b0355) 2016; 79 Hu, X.L., Gong, Y.J., Pi, D.H., Zhang, Z.J., Zeng, G.P., Xiong, S.F., Yao, S.Z., 2017. Jurassic magmatism related Pb-Zn-W-Mo polymetallic mineralization in the central Nanling Range, South China: geochronologic, geochemical, and isotopic evidence from the Huangshaping deposit. Ore Geol. Rev. 91, 877-895. Polikar, R., 2012.Ensemble learning. In: Ensemble Machine Learning, Springer, pp. 1-34. Zuo, Kreuzer, Wang, Xiong, Zhang, Wang (b0815) 2021; 30 Chen, Zhou, Zhang, Li, Fan, Sun, Chen, Zhang (b0135) 2005; 48 Chen, Sui (b0175) 2022; 41 Nykäne, Niiranen, Molnar, Lahti, Korhonen, Cook (b0490) 2017; 26 Parsa, Carranza, Ahmadi (b0505) 2022; 31 Zuo (b0805) 2020; 29 Porwal, Carranza, Hale (b0535) 2004; 36 Skabar, A.A., 2003.Mineral potential mapping using feed-forward neural networks. In Proceedings of the international joint conference on neural networks, 3, 1814-1819, Portland, OR, the United States, IEEE Press. Carranza, Hale, Faassen (b0085) 2008; 33 Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. Int. Joint Conf. Artif. Intel. 14 (2),1137-1145. Nie, Peng, Pfaff, Möller, Garzanti, Andò, Stevens, Bird, Chang, Song, Liu, Ji (b0480) 2013; 381–382 Harris, Zurcher, Stanley, Marlow, Pan (b0280) 2003; 12 Wang, Lai, Chen, Yang, Zhao, Bai (b0650) 2015; 527 Parsa, Maghsoudi, Yousefi (b0500) 2018; 92 Zuo, R., Xia, Q., Zhang, D., 2013. A comparison study of the C-A and S-A models with singularity analysis to identify geochemical anomalies in covered areas. Appl. Geochem. 33, 165-172. Behnia (b0040) 2007; 16 Morton, Hallsworth (b0470) 1999; 124 Rahimi, Abedi, Yousefi, Bahroudi, Elyasi (b0555) 2021; 128 Prado, de Souza Filho, Carranza, Motta (b0550) 2020; 124 Carranza, Hale (b0075) 2001; 10 Chung, Fabbri (b0210) 2008; 94 Zhang, Zuo, Xiong (b0770) 2016; 59 Cheng (b9015) 2007; 32 Liu, Zhou, Xia (b0410) 2018; 27 Peng, Zhou, Hu, Shen, Uan, Bi, Du, Qu (b0515) 2006; 41 Zhang, Zuo (b9070) 2021; 136 Nykänen (b0485) 2008; 17 McMillan, Haber, Peters, Fohring (b0440) 2021; 40 Yang, Zhang, Yang, Hong (b9065) 2022; 31 Chen, Yu, Bi (b0190) 2021; 382-383 Schill, Jockel, Drescher, Timm (b0595) 1993; 80 King, Zeng (b0310) 2001; 9 Lessmann, Baesens, Seow, Thomas (b0340) 2015; 247 Li, Zuo, Xiong, Peng (b0385) 2021; 30 Sagi, Rokach (b0585) 2018; 8 Yin, Lindsay, Teng (b0700) 2020; 120 Yang, Zhang, Yang, Hong, Shi (b0690) 2021 Yousefi, Carranza (b0710) 2015; 79 Abedi, Norouzi, Bahroudi (b0010) 2012; 46 Porwal, Carranza, Hale (b0530) 2003; 12 Breslow, Cain (b0055) 1988; 75 Cheng, Xia, Li, Zhang, Chen, Zuo, Wang (b0195) 2010; 7 Snoek, J., Larochelle, H., Adams, R.P., 2012. Practical Bayesian optimization of machine learning algorithms. In: Shawe-Taylor, J, Zemel, R.S., Bartlett, P.L. (Eds.), Proceedings of the 24th International Conference on Neural Information Processing Systems. Curran Associates Inc., New York, United States, pp. 2951–2959. Abedi, Torabi, Norouzi (b0015) 2013; 54 Dong, Li, Shen, Dong, Li, Yin, Tang (b0235) 2020; 27 Ji, S.,Wang, X., Zhao, W., Guo, D., 2019. An application of a three-stage XGBoost-based model to sales forecasting of a cross-border e-commerce enterprise. Math. Problems Eng. Luo, Xiong, Zuo (b0425) 2020; 122 Porwal, Carranza, Hale (b0540) 2006; 15 Guo, Wang, Yuan, Wu, Yin (b0270) 2015; 109 Wu, Mao, Yuan, Dai, Wang (b0665) 2017; 53 Rodriguez-Galiano, Chica-Olmo, Chica-Rivas (b0565) 2014; 28 Skabar (b0620) 2007; 39 Carranza, Sadeghi (b0100) 2010; 38 Wang, Zhao, Cheng (b0640) 2011; 37 Wyborn, L.A.I., Heinrich, C.A., Jaques, A.L., 1994. Australian Proterozoic mineral systems: essential ingredients and mappable criteria. In: The AusIMM Annual Conference, vol. 1994. AusIMM Darwin, pp. 109-115. Chen, Y.Q., Chen, J.G., Wang, X.Q., et al., 2008.GIS-Based Integrated Quantitative Assessments of Mineral Resources. Geological Publishing House, Beijing. Yuan, Peng, Shen, Hu, Dai (b0735) 2007; 81 Li, Chen, Liu, Wang (b0375) 2021; 32 Breiman (b0050) 2001; 45 Carranza, Laborte (b0125) 2016; 25 Nanni, Lumini (b0475) 2009; 36 Yu, C.W., N., 2009. Regional metallogenic zonation in Nanling area: time-space synchronization in complex metallogenic system. Beijing: Geological Publishing House. Lee, Jeong, Lee, Jeong (b0325) 2019; 9 Li, Hu, Yang, Peng, Li, Bi (b0345) 2007; 97 Wang, Zhao, Cheng (b0645) 2013; 134 Wu, Li, Algeo, Jiang, Zhou (b0660) 2018; 102 Xiong, Zuo (b9050) 2016; 86 Rokach (b0570) 2019; 85 Zuo, Wang (b0810) 2020; 29 Shahriari, Swersky, Wang, Adams, De Freitas (b0605) 2015; 104 Yousefi, Nykänen (b0715) 2016; 164 Li, Xia, Zhao, Gui, Leng (b0390) 2020; 29 Li, Wu, Evans, Jiang, Zhou (b0360) 2018; 312–313 Zhang, Carranza, Wei, Xiao, Yang, Xiang, Xu (b0765) 2021; 30 Sun, T., Chen, F., Zhong, L.X., Liu, W.M., Wang, Y., 2019. GIS-based mineral prospectivity mapping using machine learning methods: a case study from Tongling ore district, eastern China. Ore Geol. Rev. 109, 26-49. https://10.1016/j.oregeorev.201 9.04.003. Zuo, Carranza (b0775) 2011; 37 Hosseiny, Nazari, Smith, Nataraj (b0290) 2020; 10 Ding, Ma, Lu, Zhang (b0230) 2018; 94 Zuo, Xia, Wang (b0780) 2013; 28 Ding, Ma, Lu, Zhang, Zhang (b0220) 2016; 77 Elyasi, Bahroudi, Abedi (b0245) 2019; 28 Tayebi, Tangestani, Vincent (b0635) 2014; 23 Hariharan, Tirodkar, Porwal, Bhattacharya, Joly (b0275) 2017; 26 Luo, Zuo, Xiong, Wang (b9030) 2021; 131 Liu, Zhou, Zhang, Wang (b0405) 2017; 100 Chen, Wu (b0165) 2017; 80 Barak, Bahroudi, Jozanikohan (b0035) 2018; 9 Carranza, E.J.M., 2008.Geochemical Anomaly and Mineral Prosp Gao (10.1016/j.oregeorev.2022.104916_b0260) 2016; 75 Wang (10.1016/j.oregeorev.2022.104916_b9045) 2020; 29 Yousefi (10.1016/j.oregeorev.2022.104916_b0715) 2016; 164 Zhang (10.1016/j.oregeorev.2022.104916_b0755) 2018; 6 Coimbra (10.1016/j.oregeorev.2022.104916_b0215) 2014; 73 Yuan (10.1016/j.oregeorev.2022.104916_b0735) 2007; 81 Chen (10.1016/j.oregeorev.2022.104916_b0185) 2008 Chung (10.1016/j.oregeorev.2022.104916_b0210) 2008; 94 Chen (10.1016/j.oregeorev.2022.104916_b0170) 2021; 135 10.1016/j.oregeorev.2022.104916_b0110 Chen (10.1016/j.oregeorev.2022.104916_b0165) 2017; 80 Cheng (10.1016/j.oregeorev.2022.104916_b0195) 2010; 7 Bonham-Carter (10.1016/j.oregeorev.2022.104916_b0045) 1994 Porwal (10.1016/j.oregeorev.2022.104916_b0535) 2004; 36 10.1016/j.oregeorev.2022.104916_b0080 Yin (10.1016/j.oregeorev.2022.104916_b0700) 2020; 120 Lessmann (10.1016/j.oregeorev.2022.104916_b0340) 2015; 247 Liu (10.1016/j.oregeorev.2022.104916_b0405) 2017; 100 Elyasi (10.1016/j.oregeorev.2022.104916_b0245) 2019; 28 Nykäne (10.1016/j.oregeorev.2022.104916_b0490) 2017; 26 Zuo (10.1016/j.oregeorev.2022.104916_b0805) 2020; 29 Zhang (10.1016/j.oregeorev.2022.104916_b0765) 2021; 30 Liu (10.1016/j.oregeorev.2022.104916_b0410) 2018; 27 Carranza (10.1016/j.oregeorev.2022.104916_b0100) 2010; 38 Luo (10.1016/j.oregeorev.2022.104916_b0425) 2020; 122 Chen (10.1016/j.oregeorev.2022.104916_b0190) 2021; 382-383 Li (10.1016/j.oregeorev.2022.104916_b0360) 2018; 312–313 10.1016/j.oregeorev.2022.104916_b0525 Harris (10.1016/j.oregeorev.2022.104916_b0280) 2003; 12 Porwal (10.1016/j.oregeorev.2022.104916_b0540) 2006; 15 Breiman (10.1016/j.oregeorev.2022.104916_b0050) 2001; 45 Li (10.1016/j.oregeorev.2022.104916_b0395) 2016; 79 Ding (10.1016/j.oregeorev.2022.104916_b0220) 2016; 77 Zhang (10.1016/j.oregeorev.2022.104916_b0770) 2016; 59 10.1016/j.oregeorev.2022.104916_b0250 Xiong (10.1016/j.oregeorev.2022.104916_b0680) 2021; 147 Chen (10.1016/j.oregeorev.2022.104916_b9005) 2019; 28 Leite (10.1016/j.oregeorev.2022.104916_b0330) 2009; 57 Cheng (10.1016/j.oregeorev.2022.104916_b0200) 2011; 37 Carranza (10.1016/j.oregeorev.2022.104916_b0115) 2015; 74 Guo (10.1016/j.oregeorev.2022.104916_b0270) 2015; 109 Ziaii (10.1016/j.oregeorev.2022.104916_b0745) 2009; 100 Carranza (10.1016/j.oregeorev.2022.104916_b0105) 2010; 60 Yousefi (10.1016/j.oregeorev.2022.104916_b0705) 2015; 74 Brown (10.1016/j.oregeorev.2022.104916_b0065) 2000; 47 Zuo (10.1016/j.oregeorev.2022.104916_b0790) 2014; 139 Wang (10.1016/j.oregeorev.2022.104916_b0645) 2013; 134 Parsa (10.1016/j.oregeorev.2022.104916_b0500) 2018; 92 10.1016/j.oregeorev.2022.104916_b0415 Zuo (10.1016/j.oregeorev.2022.104916_b0815) 2021; 30 Abedi (10.1016/j.oregeorev.2022.104916_b0020) 2013; 21 Li (10.1016/j.oregeorev.2022.104916_b0390) 2020; 29 Xu (10.1016/j.oregeorev.2022.104916_b0685) 2021; 104316 Mao (10.1016/j.oregeorev.2022.104916_b0450) 2021; 49 Mojaddadi (10.1016/j.oregeorev.2022.104916_b0465) 2017; 8 Sevastjanova (10.1016/j.oregeorev.2022.104916_b0600) 2012; 280 Ding (10.1016/j.oregeorev.2022.104916_b0225) 2018; 95 Liu (10.1016/j.oregeorev.2022.104916_b0420) 2020; 12 Budholiya (10.1016/j.oregeorev.2022.104916_b0070) 2020 Zuo (10.1016/j.oregeorev.2022.104916_b0800) 2019; 192 Oh (10.1016/j.oregeorev.2022.104916_b0495) 2010; 19 Wong (10.1016/j.oregeorev.2022.104916_b0655) 2015; 48 Ford (10.1016/j.oregeorev.2022.104916_b0255) 2008; 55 Zuo (10.1016/j.oregeorev.2022.104916_b0810) 2020; 29 10.1016/j.oregeorev.2022.104916_b0305 Chen (10.1016/j.oregeorev.2022.104916_b0140) 2016 10.1016/j.oregeorev.2022.104916_b0145 10.1016/j.oregeorev.2022.104916_b0300 Zuo (10.1016/j.oregeorev.2022.104916_b0775) 2011; 37 10.1016/j.oregeorev.2022.104916_b0785 Yousefi (10.1016/j.oregeorev.2022.104916_b0710) 2015; 79 Kreuzer (10.1016/j.oregeorev.2022.104916_b0320) 2007; 32 Shahriari (10.1016/j.oregeorev.2022.104916_b0605) 2015; 104 Porwal (10.1016/j.oregeorev.2022.104916_b0530) 2003; 12 Li (10.1016/j.oregeorev.2022.104916_b0345) 2007; 97 Skabar (10.1016/j.oregeorev.2022.104916_b0620) 2007; 39 Chen (10.1016/j.oregeorev.2022.104916_b0155) 2015; 71 Schill (10.1016/j.oregeorev.2022.104916_b0595) 1993; 80 Zhao (10.1016/j.oregeorev.2022.104916_b0760) 2019; 112 Prado (10.1016/j.oregeorev.2022.104916_b0550) 2020; 124 Rigol-Sanchez (10.1016/j.oregeorev.2022.104916_b0560) 2003; 24 Carranza (10.1016/j.oregeorev.2022.104916_b0085) 2008; 33 Chen (10.1016/j.oregeorev.2022.104916_b0150) 2014; 140 Wang (10.1016/j.oregeorev.2022.104916_b0640) 2011; 37 Almasi (10.1016/j.oregeorev.2022.104916_b0025) 2017; 91 Joly (10.1016/j.oregeorev.2022.104916_b9025) 2012; 48 Li (10.1016/j.oregeorev.2022.104916_b0350) 2015; 363 10.1016/j.oregeorev.2022.104916_b0315 Kiangala (10.1016/j.oregeorev.2022.104916_b0430) 2021; 4 Ford (10.1016/j.oregeorev.2022.104916_b9020) 2020; 29 Meng (10.1016/j.oregeorev.2022.104916_b0455) 1994; 16 Hariharan (10.1016/j.oregeorev.2022.104916_b0275) 2017; 26 Parsa (10.1016/j.oregeorev.2022.104916_b0505) 2022; 31 Shu (10.1016/j.oregeorev.2022.104916_b0610) 2006; 12 10.1016/j.oregeorev.2022.104916_b0675 10.1016/j.oregeorev.2022.104916_b0670 Brandmeier (10.1016/j.oregeorev.2022.104916_b9000) 2020; 29 Carranza (10.1016/j.oregeorev.2022.104916_b0075) 2001; 10 Saljoughi (10.1016/j.oregeorev.2022.104916_b0590) 2016; 10 Wu (10.1016/j.oregeorev.2022.104916_b0660) 2018; 102 Rahimi (10.1016/j.oregeorev.2022.104916_b0555) 2021; 128 Chen (10.1016/j.oregeorev.2022.104916_b0160) 2017; 64 Cheng (10.1016/j.oregeorev.2022.104916_b9015) 2007; 32 Nie (10.1016/j.oregeorev.2022.104916_b0480) 2013; 381–382 Sun (10.1016/j.oregeorev.2022.104916_b0630) 2020; 10 Yin (10.1016/j.oregeorev.2022.104916_b0695) 2021 Sahin (10.1016/j.oregeorev.2022.104916_b0580) 2020; 2 10.1016/j.oregeorev.2022.104916_b0725 Li (10.1016/j.oregeorev.2022.104916_b0385) 2021; 30 Nykänen (10.1016/j.oregeorev.2022.104916_b0485) 2008; 17 Dong (10.1016/j.oregeorev.2022.104916_b0235) 2020; 27 Hosseiny (10.1016/j.oregeorev.2022.104916_b0290) 2020; 10 10.1016/j.oregeorev.2022.104916_b0445 Carranza (10.1016/j.oregeorev.2022.104916_b0095) 2009; 35 Cascalho (10.1016/j.oregeorev.2022.104916_b0130) 2016; 334 Abedi (10.1016/j.oregeorev.2022.104916_b0010) 2012; 46 McCuaig (10.1016/j.oregeorev.2022.104916_b9035) 2010; 38 Zuo (10.1016/j.oregeorev.2022.104916_b0780) 2013; 28 McMillan (10.1016/j.oregeorev.2022.104916_b0440) 2021; 40 Ding (10.1016/j.oregeorev.2022.104916_b0230) 2018; 94 Zuo (10.1016/j.oregeorev.2022.104916_b0795) 2017; 26 Behnia (10.1016/j.oregeorev.2022.104916_b0040) 2007; 16 Chen (10.1016/j.oregeorev.2022.104916_b0135) 2005; 48 Li (10.1016/j.oregeorev.2022.104916_b0375) 2021; 32 Breslow (10.1016/j.oregeorev.2022.104916_b0055) 1988; 75 Carranza (10.1016/j.oregeorev.2022.104916_b0125) 2016; 25 Lee (10.1016/j.oregeorev.2022.104916_b0325) 2019; 9 Mohebi (10.1016/j.oregeorev.2022.104916_b0460) 2015; 69 Porwal (10.1016/j.oregeorev.2022.104916_b0545) 2006; 32 Peng (10.1016/j.oregeorev.2022.104916_b0515) 2006; 41 10.1016/j.oregeorev.2022.104916_b0615 Yousefi (10.1016/j.oregeorev.2022.104916_b0720) 2019; 111 Leite (10.1016/j.oregeorev.2022.104916_b0335) 2009; 35 Lisitsin (10.1016/j.oregeorev.2022.104916_b0380) 2013; 52 10.1016/j.oregeorev.2022.104916_b0730 Amano (10.1016/j.oregeorev.2022.104916_b0030) 1992; 20 10.1016/j.oregeorev.2022.104916_b0295 Roy (10.1016/j.oregeorev.2022.104916_b0575) 2006; 29 Li (10.1016/j.oregeorev.2022.104916_b0355) 2016; 79 Barak (10.1016/j.oregeorev.2022.104916_b0035) 2018; 9 10.1016/j.oregeorev.2022.104916_b0060 Ahneman (10.1016/j.oregeorev.2022.104916_b0005) 2018; 360 Chung (10.1016/j.oregeorev.2022.104916_b0205) 1993; 2 10.1016/j.oregeorev.2022.104916_b0180 Tayebi (10.1016/j.oregeorev.2022.104916_b0635) 2014; 23 Abedi (10.1016/j.oregeorev.2022.104916_b0015) 2013; 54 Wu (10.1016/j.oregeorev.2022.104916_b0665) 2017; 53 Zhang (10.1016/j.oregeorev.2022.104916_b9070) 2021; 136 Sagi (10.1016/j.oregeorev.2022.104916_b0585) 2018; 8 Yang (10.1016/j.oregeorev.2022.104916_b0690) 2021 Xiong (10.1016/j.oregeorev.2022.104916_b9050) 2016; 86 Li (10.1016/j.oregeorev.2022.104916_b0370) 2016; 89 Peng (10.1016/j.oregeorev.2022.104916_b0510) 2003; 200 10.1016/j.oregeorev.2022.104916_b9040 Yang (10.1016/j.oregeorev.2022.104916_b9065) 2022; 31 10.1016/j.oregeorev.2022.104916_b0625 Carranza (10.1016/j.oregeorev.2022.104916_b0090) 2009; 35 Chen (10.1016/j.oregeorev.2022.104916_b0175) 2022; 41 10.1016/j.oregeorev.2022.104916_b0740 Pirajno (10.1016/j.oregeorev.2022.104916_b0520) 2010; 50 King (10.1016/j.oregeorev.2022.104916_b0310) 2001; 9 Zhang (10.1016/j.oregeorev.2022.104916_b0750) 2016; 59 Morton (10.1016/j.oregeorev.2022.104916_b0470) 1999; 124 Carranza (10.1016/j.oregeorev.2022.104916_b0120) 2015; 71 Lisitsin (10.1016/j.oregeorev.2022.104916_b0400) 2015; 71 Egozcue (10.1016/j.oregeorev.2022.104916_b0240) 2003; 35 Luo (10.1016/j.oregeorev.2022.104916_b9030) 2021; 131 Rokach (10.1016/j.oregeorev.2022.104916_b0570) 2019; 85 Wang (10.1016/j.oregeorev.2022.104916_b0650) 2015; 527 Rodriguez-Galiano (10.1016/j.oregeorev.2022.104916_b0565) 2014; 28 Li (10.1016/j.oregeorev.2022.104916_b0365) 2020; 122 Nanni (10.1016/j.oregeorev.2022.104916_b0475) 2009; 36 Gudiyangada (10.1016/j.oregeorev.2022.104916_b0265) 2020; 590 Hallsworth (10.1016/j.oregeorev.2022.104916_b0285) 2008; 203 |
| References_xml | – volume: 36 start-page: 3028 year: 2009 end-page: 3033 ident: b0475 article-title: An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring publication-title: Expert Syst. Appl. – year: 2008 ident: b0185 article-title: GIS-Based Integrated Quantitative Assessments of Mineral Resources – volume: 104 start-page: 148 year: 2015 end-page: 175 ident: b0605 article-title: Taking the human out of the loop: a review of Bayesian optimization publication-title: Proc. IEEE – volume: 79 start-page: 69 year: 2015 end-page: 81 ident: b0710 article-title: Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling publication-title: Comput. Geosci. – volume: 9 start-page: 19 year: 2018 end-page: 39 ident: b0035 article-title: Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data publication-title: J. Min. Environ. – volume: 12 start-page: 155 year: 2003 end-page: 171 ident: b0530 article-title: Artificial neural networks for mineral potential mapping publication-title: Nat. Resour. Res. – volume: 59 start-page: 556 year: 2016 end-page: 572 ident: b0750 article-title: 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 publication-title: Science China, Earth Sciences – volume: 12 start-page: 241 year: 2003 end-page: 255 ident: b0280 article-title: A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression publication-title: Nat. Resour. Res. – volume: 37 start-page: 1967 year: 2011 end-page: 1975 ident: b0775 article-title: Support vector machine: A tool for mapping mineral prospectivity publication-title: Comput. Geosci. – volume: 32 start-page: 327 year: 2021 end-page: 347 ident: b0375 article-title: Mineral prospectivity prediction via convolutional neural networks based on geological big data publication-title: J. Earth Sci. – volume: 75 start-page: 16 year: 2016 end-page: 28 ident: b0260 article-title: Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China publication-title: Ore Geol. Rev. – volume: 36 start-page: 803 year: 2004 end-page: 826 ident: b0535 article-title: A hybrid neuro-fuzzy model for mineral potential mapping publication-title: Math. Geol. – year: 2016 ident: b0140 article-title: Genera-tion of late meosozic qianlishan A2-type granite in nanling range, South China: implications for shizhuyuan W-Sn mineralization and tectonic evolution publication-title: Lithos – volume: 71 start-page: 703 year: 2015 end-page: 718 ident: b0120 article-title: Predictive mapping of prospectivity for orogenic gold, Giyani greenstone belt (South Africa) publication-title: Ore Geol. Rev. – volume: 55 start-page: 13 year: 2008 end-page: 23 ident: b0255 article-title: Evaluating geological complexity and complexity gradients as controls on copper mineralization, Mt Isa Inlier publication-title: Aust. J. Earth Sci. – volume: 8 start-page: 1249 year: 2018 ident: b0585 article-title: Ensemble learning: a survey publication-title: WIREs Data Min. Knowledge Discovery – volume: 91 start-page: 1066 year: 2017 end-page: 1080 ident: b0025 article-title: Prospectivity analysis of orogenic gold deposits in saqez-sardasht goldfield, Zagros orogen publication-title: Iran. Ore Geol. Rev. – volume: 10 start-page: 229 year: 2016 end-page: 256 ident: b0590 article-title: A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran publication-title: Appl. Geomatics – reference: Chen,T., Guestrin, C., 2016.XGBoost:A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference. 2016: 785-794. 10.1145/2939672.2939785. – volume: 27 start-page: 299 year: 2018 end-page: 313 ident: b0410 article-title: A MaxEnt model for mineral prospectivity mapping publication-title: Nat. Resour. Res. – volume: 80 start-page: 200 year: 2017 end-page: 213 ident: b0165 article-title: Mapping mineral prospectivity using an extreme learning machine regression publication-title: Ore Geol. Rev. – volume: 94 start-page: 438 year: 2008 end-page: 452 ident: b0210 article-title: Predicting landslides for risk analysis spatial models tested by a cross-validation technique publication-title: Geomorphology – volume: 122 year: 2020 ident: b0365 article-title: Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian-Zhangbaling area, Anhui Province, China publication-title: Appl. Geochem. – volume: 33 start-page: 536 year: 2008 end-page: 558 ident: b0085 article-title: Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping publication-title: Ore Geol. Rev. – year: 2021 ident: b0695 article-title: Mineral prospectivity mapping via gated recurrent unit model publication-title: Nat. Resour. Res. – volume: 54 start-page: 145 year: 2013 end-page: 164 ident: b0015 article-title: Application of fuzzy-AHP method to integrate geophysical data in a prospect scale, a case study: seridune copper deposit publication-title: Boll. Geofis. Teor. Appl. – volume: 79 start-page: 1 year: 2016 end-page: 25 ident: b0355 article-title: Genesis of the huangshaping W-Mo-Cu-Pb-Zn polymetallic deposit in southeastern Hunan Province, China: constraints from fluid inclusions, trace elements, and isotopes publication-title: Ore Geol. Rev. – volume: 140 start-page: 56 year: 2014 end-page: 63 ident: b0150 article-title: Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly publication-title: J. Geochem. Explor. – reference: Liu, W., Zhang, M., Luo, Z., Cai, Y., 2017. An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access, 5,24417-24425. http://dx.doi.org/10.1109/ACCESS.2017.2766203. – volume: 50 start-page: 325 year: 2010 end-page: 346 ident: b0520 article-title: Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: examples from NW China and Altay-Sayan (Siberia) publication-title: J. Geodyn. – volume: 29 start-page: 3415 year: 2020 end-page: 3424 ident: b0805 article-title: Geodata science-based mineral prospectivity mapping: a review publication-title: Nat. Resour. Res. – volume: 94 start-page: 193 year: 2018 end-page: 211 ident: b0230 article-title: Garnet and scheelite as indicators of multi-stage tungsten mineralization in the Huangshaping deposit, southern Hunan province, China publication-title: Ore Geol. Rev. – reference: Mao, J.W., Pirajno, F., Cook, N., 2011. Mesozoic metallogeny in East China and corresponding geodynamic settings-an introduction to the special issue. Ore Geol. Rev. 43, 1-7. – volume: 9 start-page: 1 year: 2019 end-page: 9 ident: b0325 article-title: CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network publication-title: Sci. Rep. – volume: 35 start-page: 2032 year: 2009 end-page: 2046 ident: b0095 article-title: Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity publication-title: Comput. Geosci. – volume: 28 start-page: 202 year: 2013 end-page: 211 ident: b0780 article-title: Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization publication-title: Appl. Geochem. – volume: 97 start-page: 161 year: 2007 end-page: 193 ident: b0345 article-title: He, Pb and S isotopic constraints on the relationship between the A-type Qitianling granite and the Furong tin deposit, Hunan Province, China publication-title: Lithos – reference: Carranza, E.J.M., 2008.Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Handbook of Exploration and Environmental Geochemistry, vol. 11 Elsevier, Amsterdam. – reference: Polikar, R., 2012.Ensemble learning. In: Ensemble Machine Learning, Springer, pp. 1-34. – volume: 48 start-page: 912 year: 2005 end-page: 924 ident: b0135 article-title: Petrogenesis and significance of early Yanshanian syenite-granite complex in eastern Nanling Range publication-title: Sci. China, Ser. D Earth Sci. – volume: 312–313 start-page: 1 year: 2018 end-page: 20 ident: b0360 article-title: Zircon geochronology and geochemistry of the Xianghualing A-type granitic rocks: Insights into multi-stage Sn-polymetallic mineralization publication-title: Lithos – volume: 29 start-page: 71 year: 2020 end-page: 88 ident: b9000 article-title: Boosting for mineral prospectivity modeling: A new GIS toolbox publication-title: Nat. Resour. Res. – volume: 79 start-page: 1 year: 2016 end-page: 25 ident: b0395 article-title: Genesis of the Huangshaping WMo-Cu-Pb-Zn polymetallic deposit in southeastern Hunan Province, China: constraints from fluid inclusions, trace elements, and isotopes publication-title: Ore Geol. Rev. – volume: 38 start-page: 219 year: 2010 end-page: 241 ident: b0100 article-title: Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden) publication-title: Ore Geol. Rev. – volume: 28 start-page: 31 year: 2019 end-page: 46 ident: b9005 article-title: Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency publication-title: Nat. Resour. Res. – volume: 10 start-page: 102 year: 2020 ident: b0630 article-title: Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: a case study from southern Jiangxi Province, China publication-title: Minerals – volume: 35 start-page: 675 year: 2009 end-page: 687 ident: b0335 article-title: Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil publication-title: Comput. Geosci. – volume: 75 start-page: 11 year: 1988 end-page: 20 ident: b0055 article-title: Logistic regression for two stage case-control data publication-title: Biometrika – volume: 74 start-page: 60 year: 2015 end-page: 70 ident: b0115 article-title: Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) publication-title: Comput. Geosci. – volume: 74 start-page: 97 year: 2015 end-page: 109 ident: b0705 article-title: Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping publication-title: Comput. Geosci. – volume: 38 start-page: 128 year: 2010 end-page: 138 ident: b9035 article-title: Translating the mineral systems approach into an effective exploration targeting system publication-title: Ore Geol. Rev. – volume: 92 start-page: 97 year: 2018 end-page: 112 ident: b0500 article-title: Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran publication-title: Ore Geol. Rev. – volume: 280 start-page: 179 year: 2012 end-page: 194 ident: b0600 article-title: A detrital heavy mineral viewpoint on sediment provenance and tropical weathering in SE Asia publication-title: Sed. Geol. – volume: 32 start-page: 1 year: 2006 end-page: 16 ident: b0545 article-title: Bayesian network classifiers for mineral potential mapping publication-title: Comput. Geosci. – volume: 10 year: 2020 ident: b0290 article-title: A framework for modeling flood depth using a hybrid of hydraulics and machine learning publication-title: Sci. Rep. – volume: 29 start-page: 260 year: 2006 end-page: 286 ident: b0575 article-title: 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 publication-title: Ore Geol. Rev. – volume: 527 start-page: 1130 year: 2015 end-page: 1141 ident: b0650 article-title: Flood hazard risk assessment model based on random forest publication-title: J. Hydrol. – volume: 136 start-page: 1 year: 2021 end-page: 8 ident: b9070 article-title: Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network publication-title: Ore Geol. Rev. – year: 1994 ident: b0045 article-title: Geographic Information Systems for Geoscientists, Modelling with GIS – reference: Yuan, S.D., Mao, J., Cook, N.J., Wang, X., Liu, X., Yuan, Y., 2015. A Late Cretaceous tin metallogenic event in Nanling W-Sn metallogenic province: constraints from U-Pb, Ar-Ar geochronology at the Jiepailing Sn-Be-F deposit, Hunan, China. Ore Geol. Rev. 65, 283-293. – volume: 26 start-page: 489 year: 2017 end-page: 507 ident: b0275 article-title: Random forest-based prospectivity modelling of Greenfield Terrains using sparse deposit data: An example from the Tanami Region, Western Australia publication-title: Nat. Resour. Res. – reference: Hu, X.L., Gong, Y.J., Pi, D.H., Zhang, Z.J., Zeng, G.P., Xiong, S.F., Yao, S.Z., 2017. Jurassic magmatism related Pb-Zn-W-Mo polymetallic mineralization in the central Nanling Range, South China: geochronologic, geochemical, and isotopic evidence from the Huangshaping deposit. Ore Geol. Rev. 91, 877-895. – reference: Feurer, M., Klein, A., Eggensperger K, Springenberg JT, Blum M, Hutter F., 2019. Auto-sklearn: efficient and robust automated machine learning, part of the springer series on challenges in machine learning book series (SSCML). 10.1007/978-3-030-05318-5_6. – volume: 16 start-page: 147 year: 2007 end-page: 155 ident: b0040 article-title: Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran publication-title: Nat. Resour. Res. – volume: 6 start-page: 21020 year: 2018 end-page: 21031 ident: b0755 article-title: A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE publication-title: Access – volume: 95 start-page: 65 year: 2018 end-page: 78 ident: b0225 article-title: Magnetite as an indicator of mixed sources for W-Mo-Pb-Zn mineralization in the Huangshaping polymetallic deposit, southern Hunan Province, China publication-title: Ore Geol. Rev. – volume: 89 start-page: 161 year: 2016 end-page: 173 ident: b0370 article-title: GeoCube: a 3D mineral resources quantitative prediction and assessment system publication-title: Comput. Geosci. – volume: 40 start-page: 99 year: 2021 end-page: 105 ident: b0440 article-title: Mineral prospectivity mapping using a VNet convolutional neural network publication-title: Lead. Edge – volume: 80 start-page: 339 year: 1993 end-page: 352 ident: b0595 article-title: Logistic analysis in case-control studies under validation sampling publication-title: Biometrika – volume: 39 start-page: 439 year: 2007 end-page: 451 ident: b0620 article-title: Mineral potential mapping using Bayesian learning for multilayer perceptrons publication-title: Math. Geol. – volume: 17 start-page: 29 year: 2008 end-page: 48 ident: b0485 article-title: Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the central lapland greenstone belt, northern Fennoscandian Shield publication-title: Nat. Resour. Res. – volume: 26 start-page: 457 year: 2017 end-page: 464 ident: b0795 article-title: Machine learning of mineralization-related geochemical anomalies: a review of potential methods publication-title: Nat. Resour. Res. – reference: Hu, X., Gong, Y., Pi, D., Zhang, Z., Zeng, G., Xiong, S., Yao, S., 2017. Jurassic magmatism related Pb-Zn-W-Mo polymetallic mineralization in the central Nanling range, South China: geochronologic, geochemical, and isotopic evidence from the Huangshaping deposit. Ore Geol. Rev. 10.1016/j.oregeorev.2017.08.016. – volume: 360 start-page: 186 year: 2018 end-page: 190 ident: b0005 article-title: Predicting reaction performance in C-N cross-coupling using machine learning publication-title: Science – volume: 46 start-page: 272 year: 2012 end-page: 283 ident: b0010 article-title: Support vector machine for multi-classification of mineral prospectivity areas publication-title: Comput. Geosci. – reference: Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. Int. Joint Conf. Artif. Intel. 14 (2),1137-1145. – reference: Xiong, Y., Zuo, R., 2018.GIS-based rare events logistic regression for mineral prospectivity mapping. Comput. Geosci., 111, 18-25. – volume: 147 year: 2021 ident: b0680 article-title: A positive and unlabeled learning algorithm for mineral prospectivity mapping publication-title: Comput. Geosci. – volume: 30 start-page: 27 year: 2021 end-page: 38 ident: b0385 article-title: Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping publication-title: Nat. Resour. Res. – volume: 382-383 start-page: 105952 year: 2021 ident: b0190 article-title: Extraction of fractionated interstitial melt from a crystal mush system generating the late jurassic high-silica granites from the qitianling composite pluton, South China: implications for greisen-type tin mineralization publication-title: Lithos – volume: 64 start-page: 639 year: 2017 end-page: 651 ident: b0160 article-title: Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data publication-title: Aust. J. Earth Sci. – reference: Zuo, R., Xia, Q., Zhang, D., 2013. A comparison study of the C-A and S-A models with singularity analysis to identify geochemical anomalies in covered areas. Appl. Geochem. 33, 165-172. – reference: Yousefi, M., Carranza, E.J.M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., Mihalasky, M.J.,2021. Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook. J. Geochem. Explor.,1-12. – volume: 381–382 start-page: 110 year: 2013 end-page: 118 ident: b0480 article-title: Controlling factors on heavy mineral assemblages in Chinese loess and Red Clay publication-title: Palaeogeogr. Palaeoclimatol. Palaeoecol. – volume: 100 start-page: 133 year: 2017 end-page: 147 ident: b0405 article-title: Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar, China publication-title: Ore Geol. Rev. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0050 article-title: Random forests publication-title: Machine Learn. – volume: 590 year: 2020 ident: b0265 article-title: Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using dempster shafer theory publication-title: J. Hydrol. – volume: 21 start-page: 556 year: 2013 end-page: 567 ident: b0020 article-title: Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 203 start-page: 196 year: 2008 end-page: 212 ident: b0285 article-title: Provenance of late Carboniferous sandstones in the Pennine Basin (UK) from combined heavy mineral, garnet geochemistry and palaeocurrent studies publication-title: Sed. Geol. – volume: 35 start-page: 383 year: 2009 end-page: 400 ident: b0090 article-title: Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features publication-title: Ore Geol. Rev. – volume: 28 start-page: 1336 year: 2014 end-page: 1354 ident: b0565 article-title: Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain publication-title: Int. J. Geograph. Inf. Sci. – volume: 71 start-page: 749 year: 2015 end-page: 760 ident: b0155 article-title: Mineral potential mapping with a restricted Boltzmann machine publication-title: Ore Geol. Rev. – reference: Ji, S.,Wang, X., Zhao, W., Guo, D., 2019. An application of a three-stage XGBoost-based model to sales forecasting of a cross-border e-commerce enterprise. Math. Problems Eng. – reference: Yu, C.W., N., 2009. Regional metallogenic zonation in Nanling area: time-space synchronization in complex metallogenic system. Beijing: Geological Publishing House. – volume: 41 start-page: 1 year: 2022 end-page: 12 ident: b0175 article-title: Dictionary learning for integration of evidential layers for mineral prospectivity modeling publication-title: Ore Geol. Rev. – volume: 48 start-page: 2839 year: 2015 end-page: 2846 ident: b0655 article-title: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation publication-title: Pattern Recogn. – volume: 200 start-page: 129 year: 2003 end-page: 136 ident: b0510 article-title: Samarium-neodymium isotope systematics of hydrothermal calcites from the Xikuangshan antimony deposit (Hunan, China): the potential of calcite as a geochronometer publication-title: Chem. Geol. – volume: 26 start-page: 1 year: 2017 end-page: 14 ident: b0490 article-title: Optimizing a knowledge-driven prospectivity model for gold deposits within Perapohja Belt, Northern Finland publication-title: Nat. Resour. Res. – volume: 28 start-page: 931 year: 2019 end-page: 951 ident: b0245 article-title: Risk-based analysis in mineral potential mapping: application of quantifier-guided ordered weighted averaging method publication-title: Nat. Resour. Res. – volume: 10 start-page: 165 year: 2001 end-page: 175 ident: b0075 article-title: Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines publication-title: Explor. Min. Geol. – volume: 334 start-page: 21 year: 2016 end-page: 33 ident: b0130 article-title: Heavy mineral assemblages of the storegga tsunami deposit publication-title: Sed. Geol. – volume: 59 start-page: 556 year: 2016 end-page: 572 ident: b0770 article-title: 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 publication-title: Sci. China Earth Sci. – volume: 35 start-page: 279 year: 2003 end-page: 300 ident: b0240 article-title: Isometric logratio transformations for compositional data analysis publication-title: Math. Geol. – volume: 12 start-page: 418 year: 2006 end-page: 431 ident: b0610 article-title: Pre-Devonian tectonic evolution of South China: from Cathaysian Block to Caledonian period folded orogenic belt publication-title: Geol. J. China Univ. – volume: 112 year: 2019 ident: b0760 article-title: Controls on and prospectivity mapping of volcanic-type uranium mineralization in the Pucheng district, NW Fujian, China publication-title: Ore Geol. Rev. – volume: 27 start-page: 171 year: 2020 end-page: 178 ident: b0235 article-title: Genetic mineralogy of natural heavy placer minerals and its effectiveness in mineral prospecting publication-title: Earth Sci. Front. – volume: 192 start-page: 1 year: 2019 end-page: 14 ident: b0800 article-title: Deep learning and its application in geochemical mapping publication-title: Earth-Sci. Rev. – volume: 29 start-page: 3443 year: 2020 end-page: 3455 ident: b0810 article-title: Effects of random negative training samples on mineral prospectivity mapping publication-title: Nat. Resour. Res. – volume: 30 start-page: 1 year: 2021 end-page: 21 ident: b0815 article-title: Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions publication-title: Nat. Resour. Res. – reference: Skabar, A.A., 2003.Mineral potential mapping using feed-forward neural networks. In Proceedings of the international joint conference on neural networks, 3, 1814-1819, Portland, OR, the United States, IEEE Press. – volume: 60 start-page: 129 year: 2010 end-page: 149 ident: b0105 article-title: Improved wildcat modelling of mineral prospectivity publication-title: Resour. Geol. – volume: 135 start-page: 1066 year: 2021 end-page: 1080 ident: b0170 article-title: Mineral exploration targeting by combination of recursive indicator elimination with the ?2-regularization logistic regression based on geochemical data publication-title: Ore Geol. Rev. – volume: 31 start-page: 37 year: 2022 end-page: 50 ident: b0505 article-title: Deep GMDH neural networks for predictive mapping of mineral prospectivity in terrains hosting few but large mineral deposits publication-title: Nat. Resour. Res. – volume: 25 start-page: 35 year: 2016 end-page: 50 ident: b0125 article-title: Data-driven predictive modeling of mineral prospectivity using Random Forests: a case study in Catanduanes Island (Philippines) publication-title: Nat. Resour. Res. – volume: 37 start-page: 662 year: 2011 end-page: 669 ident: b0200 article-title: A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan publication-title: China. Comput. Geosci. – volume: 12 start-page: 385 year: 2020 end-page: 393 ident: b0420 article-title: Modelling of shallow landslides with Machine Learning algorithms publication-title: Geosci. Front. – volume: 247 start-page: 124 year: 2015 end-page: 136 ident: b0340 article-title: Comparisoning state-of-the-art classification algorithms for credit scoring: an update of research publication-title: Eur. J. Oper. Res. – volume: 15 start-page: 1 year: 2006 end-page: 14 ident: b0540 article-title: A hybrid fuzzy weights-of-evidence model for mineral potential mapping publication-title: Nat. Resour. Res. – volume: 20 start-page: 391 year: 1992 end-page: 394 ident: b0030 article-title: Two-phase uplift of higher Himalayas since 17 Ma publication-title: Geology – volume: 164 start-page: 94 year: 2016 end-page: 106 ident: b0715 article-title: Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping publication-title: J. Geochem. Explor. – reference: Sun, T., Chen, F., Zhong, L.X., Liu, W.M., Wang, Y., 2019. GIS-based mineral prospectivity mapping using machine learning methods: a case study from Tongling ore district, eastern China. Ore Geol. Rev. 109, 26-49. https://10.1016/j.oregeorev.201 9.04.003. – volume: 73 start-page: 198 year: 2014 end-page: 207 ident: b0215 article-title: Regression trees for modeling geochemical data-an application to Late Jurassic carbonates (Ammonitico Rosso) publication-title: Comput. Geosci. – volume: 7 start-page: 3019 year: 2010 end-page: 3025 ident: b0195 article-title: Density/area powerlaw models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China publication-title: Biogeosciences – volume: 2 start-page: 122 year: 1993 end-page: 139 ident: b0205 article-title: The representation of geoscience information for data integration publication-title: Nonrenewable Resources – reference: Brochu, E., Cora, V., de Freitas, N. 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599v1 [cs.LG]. – volume: 139 start-page: 170 year: 2014 end-page: 176 ident: b0790 article-title: Identification of geochemical anomalies associated with mineralizationin the Fanshan district, Fujian, China publication-title: J. Geochem. Explor. – volume: 4 start-page: 1 year: 2021 end-page: 14 ident: b0430 article-title: An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment publication-title: Machine Learn. Appl. – volume: 69 start-page: 187 year: 2015 end-page: 198 ident: b0460 article-title: Controls on porphyry Cu mineralization around Hanza Mountain, south-east of Iran: an analysis of structural evolution from remote sensing, geophysical, geochemical and geological data publication-title: Ore Geol. Rev. – volume: 102 start-page: 220 year: 2018 end-page: 239 ident: b0660 article-title: Genesis of the Xianghualing Sn-Pb-Zn deposit, South China: a multi-method zircon study publication-title: Ore Geol. Rev. – volume: 19 start-page: 103 year: 2010 end-page: 124 ident: b0495 article-title: Application of artificial neural network for gold-silver deposits potential mapping: a case study of Korea publication-title: Nat. Resour. Res. – reference: Wyborn, L.A.I., Heinrich, C.A., Jaques, A.L., 1994. Australian Proterozoic mineral systems: essential ingredients and mappable criteria. In: The AusIMM Annual Conference, vol. 1994. AusIMM Darwin, pp. 109-115. – volume: 30 start-page: 1011 year: 2021 end-page: 1031 ident: b0765 article-title: Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional autoencoder network and supervised convolutional neural network publication-title: Nat. Resour. Res. – volume: 134 start-page: 27 year: 2013 end-page: 37 ident: b0645 article-title: Fault trace-oriented singularity mapping technique to characterize anisotropic geochemical signatures in Gejiu mineral district, China publication-title: J. Geochem. Explor. – volume: 104316 year: 2021 ident: b0685 article-title: Mineral prospectivity mapping by deep learning method in YawanDaqiao area, Gansu publication-title: Ore Geol. Rev. – volume: 111 year: 2019 ident: b0720 article-title: Exploration information systems-a proposal for the future use of GIS in mineral exploration targeting publication-title: Ore Geol. Rev. – volume: 131 year: 2021 ident: b9030 article-title: Detection of geochemical anomalies related to mineralization using the GANomaly network publication-title: Appl. Geochem. – volume: 49 start-page: 592 year: 2021 end-page: 596 ident: b0450 article-title: Recognition of a Middle-Late Jurassic are-related porphyry copper belt along the Southeast China coast: geological characteristics and metallogenic implications publication-title: Geology – start-page: 1 year: 2021 end-page: 19 ident: b0690 article-title: A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation publication-title: Nat. Resour. Res. – volume: 32 start-page: 314 year: 2007 end-page: 324 ident: b9015 article-title: Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China publication-title: Ore. Geol. Rev. – volume: 120 start-page: 1 year: 2020 end-page: 20 ident: b0700 article-title: Mineral prospectivity analysis for BIF iron deposits: a case study in the Anshan-Benxi area, Liaoning province, North-East China publication-title: Ore Geol. Rev. – volume: 85 year: 2019 ident: b0570 article-title: Ensemble learning: pattern classification using ensemble methods publication-title: World Sci. – volume: 77 start-page: 117 year: 2016 end-page: 132 ident: b0220 article-title: S, Pb, and Sr isotope geochemistry and genesis of Pb-Zn mineralization in the Huangshaping polymetallic ore deposit of southern Hunan Province publication-title: China. Ore Geol. Rev. – volume: 9 start-page: 137 year: 2001 end-page: 163 ident: b0310 article-title: Logistic regression in rare events data publication-title: Polit. Anal. – volume: 31 start-page: 1 year: 2022 end-page: 17 ident: b9065 article-title: Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest publication-title: Nat. Resour. Res. – volume: 57 start-page: 1049 year: 2009 end-page: 1065 ident: b0330 article-title: Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil publication-title: Geophys. Prospect. – volume: 37 start-page: 1946 year: 2011 end-page: 1957 ident: b0640 article-title: Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China publication-title: Comput. Geosci. – volume: 86 start-page: 75 year: 2016 end-page: 82 ident: b9050 article-title: Recognition of geochemical anomalies using a deep autoencoder network publication-title: Comput. Geosci. – volume: 23 start-page: 627 year: 2014 end-page: 644 ident: b0635 article-title: Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model publication-title: Turk. J. Earth Sci. – volume: 109 start-page: 253 year: 2015 end-page: 282 ident: b0270 article-title: Geochronological and geochemical constraints on the petrogenesis and geodynamic setting of the Qianlishan granitic pluton, Southeast China publication-title: Mineral. Petrol. – volume: 122 year: 2020 ident: b0425 article-title: Recognition of geochemical anomalies using a deep variational autoencoder network publication-title: Appl. Geochem. – volume: 32 start-page: 37 year: 2007 end-page: 80 ident: b0320 article-title: Ore controls in the Charters Towers goldfield, NE Australia: Constraints from geological, geophysical and numerical analyses publication-title: Ore Geol. Rev. – volume: 128 start-page: 1 year: 2021 end-page: 8 ident: b0555 article-title: Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof publication-title: Appl. Geochem. – volume: 16 start-page: 72 year: 1994 end-page: 76 ident: b0455 article-title: Robust kriging and its application in delineation of geochemical anomalies with scale of 1:50000 publication-title: Comput. Techn. Geophys. Geochem. Explor. – volume: 363 start-page: 112 year: 2015 end-page: 124 ident: b0350 article-title: Provenance of heavy mineral deposits on the northwestern shelf of the South China Sea, evidence from single-mineral chemistry publication-title: Mar. Geol. – volume: 124 start-page: 3 year: 1999 end-page: 29 ident: b0470 article-title: Processes controlling the compositionof heavy mineral assemblages in sandstones publication-title: Sed. Geol. – volume: 29 start-page: 189 year: 2020 end-page: 202 ident: b9045 article-title: Mapping mineral prospectivity via semi-supervised random forest publication-title: Nat. Resour. Res. – volume: 29 start-page: 267 year: 2020 end-page: 283 ident: b9020 article-title: Practical implementation of random forest-based mineral potential mapping for porphyry Cu-Au mineralization in the Eastern Lachlan Orogen, NSW, Australia publication-title: Nat. Resour. Res. – volume: 71 start-page: 861 year: 2015 end-page: 881 ident: b0400 article-title: Spatial data analysis of mineral deposit point patterns: applications to exploration targeting publication-title: Ore Geol. Rev. – volume: 47 start-page: 757 year: 2000 end-page: 770 ident: b0065 article-title: Artificial neural networks: a new method for mineral prospectivity mapping publication-title: Aust. J. Earth Sci. – volume: 29 start-page: 203 year: 2020 end-page: 227 ident: b0390 article-title: Prospectivity mapping for tungsten polymetallic mineral resources, Nanling Metallogenic Belt, South China: Use of Random Forest algorithm from a perspective of data imbalance publication-title: Nat. Resour. Res. – volume: 8 start-page: 1080 year: 2017 end-page: 1102 ident: b0465 article-title: Ensemble machine-learning-based geospatial approach for flood risk assessment using multisensor remote-sensing data and GIS publication-title: Geomatics Natural Hazards Risk – volume: 41 start-page: 661 year: 2006 end-page: 669 ident: b0515 article-title: Precise molybdenite Re-Os and mica Ar-Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China publication-title: Mineral Deposita – volume: 24 start-page: 1151 year: 2003 end-page: 1156 ident: b0560 article-title: Artificial neural networks as a tool for mineral potential mapping with GIS publication-title: Int. J. Remote Sens. – year: 2020 ident: b0070 article-title: An optimized XGBoost based diagnostic system for effective prediction of heart disease publication-title: J. King Saud Univ. – Comput. Inf. Sci. – reference: Carranza, E.J.M., Laborte, A.G., 2015. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: application of Random Forests algorithm. Ore Geol. Rev., 71, 777-787. – volume: 52 start-page: 100 year: 2013 end-page: 112 ident: b0380 article-title: Regional prospectivity analysis for hydrothermal-remobilized nickel mineral systems in western Victoria, Australia publication-title: Ore Geol. Rev. – volume: 100 start-page: 25 year: 2009 end-page: 36 ident: b0745 article-title: Neuro-fuzzy modelling in mining geochemistry: identification of geochemical anomalies publication-title: J. Geochem. Explor. – volume: 124 year: 2020 ident: b0550 article-title: Modeling of Cu-Au prospectivity in the caraja′s mineral province (Brazil) through machine learning, dealing with imbalanced training data publication-title: Ore Geol. Rev. – reference: Snoek, J., Larochelle, H., Adams, R.P., 2012. Practical Bayesian optimization of machine learning algorithms. In: Shawe-Taylor, J, Zemel, R.S., Bartlett, P.L. (Eds.), Proceedings of the 24th International Conference on Neural Information Processing Systems. Curran Associates Inc., New York, United States, pp. 2951–2959. – volume: 81 start-page: 278 year: 2007 end-page: 286 ident: b0735 article-title: 40Ar-39Ar isotopic dating of the Xianghualing Sn-polymetallic orefield in Southern Hunan, China and its geological implications publication-title: Acta Geol. Sinica (Engl. Ed.) – volume: 53 start-page: 89 year: 2017 end-page: 103 ident: b0665 article-title: Mineralogy, fluid inclusion petrography, and stable isotope geochemistry of Pb-Zn-Ag veins at the Shizhuyuan deposit, Hunan Province, southeastern China publication-title: Miner. Depos. – volume: 2 year: 2020 ident: b0580 article-title: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN publication-title: Appl. Sci. – reference: Chen, Y.Q., Chen, J.G., Wang, X.Q., et al., 2008.GIS-Based Integrated Quantitative Assessments of Mineral Resources. Geological Publishing House, Beijing. – volume: 48 start-page: 349 year: 2012 end-page: 383 ident: b9025 article-title: Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: mineral system analysis, targeting model and prospectivity analysis publication-title: Ore Geol. Rev. – ident: 10.1016/j.oregeorev.2022.104916_b0315 – volume: 280 start-page: 179 year: 2012 ident: 10.1016/j.oregeorev.2022.104916_b0600 article-title: A detrital heavy mineral viewpoint on sediment provenance and tropical weathering in SE Asia publication-title: Sed. Geol. doi: 10.1016/j.sedgeo.2012.03.007 – ident: 10.1016/j.oregeorev.2022.104916_b0725 doi: 10.1016/j.gexplo.2021.106839 – volume: 69 start-page: 187 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0460 article-title: Controls on porphyry Cu mineralization around Hanza Mountain, south-east of Iran: an analysis of structural evolution from remote sensing, geophysical, geochemical and geological data publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2015.02.016 – volume: 59 start-page: 556 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0770 article-title: 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 publication-title: Sci. China Earth Sci. doi: 10.1007/s11430-015-5178-3 – ident: 10.1016/j.oregeorev.2022.104916_b9040 – volume: 100 start-page: 25 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0745 article-title: Neuro-fuzzy modelling in mining geochemistry: identification of geochemical anomalies publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2008.03.004 – volume: 12 start-page: 241 issue: 4 year: 2003 ident: 10.1016/j.oregeorev.2022.104916_b0280 article-title: A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression publication-title: Nat. Resour. Res. doi: 10.1023/B:NARR.0000007804.27450.e8 – volume: 17 start-page: 29 issue: 1 year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0485 article-title: Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the central lapland greenstone belt, northern Fennoscandian Shield publication-title: Nat. Resour. Res. doi: 10.1007/s11053-008-9062-0 – volume: 48 start-page: 2839 issue: 9 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0655 article-title: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2015.03.009 – ident: 10.1016/j.oregeorev.2022.104916_b0415 doi: 10.1109/ACCESS.2017.2766203 – volume: 122 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0425 article-title: Recognition of geochemical anomalies using a deep variational autoencoder network publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2020.104710 – volume: 7 start-page: 3019 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b0195 article-title: Density/area powerlaw models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China publication-title: Biogeosciences doi: 10.5194/bg-7-3019-2010 – volume: 334 start-page: 21 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0130 article-title: Heavy mineral assemblages of the storegga tsunami deposit publication-title: Sed. Geol. doi: 10.1016/j.sedgeo.2016.01.007 – volume: 124 start-page: 3 year: 1999 ident: 10.1016/j.oregeorev.2022.104916_b0470 article-title: Processes controlling the compositionof heavy mineral assemblages in sandstones publication-title: Sed. Geol. doi: 10.1016/S0037-0738(98)00118-3 – volume: 54 start-page: 145 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0015 article-title: Application of fuzzy-AHP method to integrate geophysical data in a prospect scale, a case study: seridune copper deposit publication-title: Boll. Geofis. Teor. Appl. – ident: 10.1016/j.oregeorev.2022.104916_b0305 doi: 10.1155/2019/8503252 – volume: 9 start-page: 19 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0035 article-title: Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data publication-title: J. Min. Environ. – volume: 147 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0680 article-title: A positive and unlabeled learning algorithm for mineral prospectivity mapping publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2020.104667 – ident: 10.1016/j.oregeorev.2022.104916_b0250 doi: 10.1007/978-3-030-05318-5_6 – volume: 527 start-page: 1130 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0650 article-title: Flood hazard risk assessment model based on random forest publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.06.008 – volume: 41 start-page: 661 year: 2006 ident: 10.1016/j.oregeorev.2022.104916_b0515 article-title: Precise molybdenite Re-Os and mica Ar-Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China publication-title: Mineral Deposita doi: 10.1007/s00126-006-0084-4 – ident: 10.1016/j.oregeorev.2022.104916_b0295 doi: 10.1016/j.oregeorev.2017.08.016 – volume: 35 start-page: 279 issue: 3 year: 2003 ident: 10.1016/j.oregeorev.2022.104916_b0240 article-title: Isometric logratio transformations for compositional data analysis publication-title: Math. Geol. doi: 10.1023/A:1023818214614 – volume: 10 issue: 1 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0290 article-title: A framework for modeling flood depth using a hybrid of hydraulics and machine learning publication-title: Sci. Rep. doi: 10.1038/s41598-020-65232-5 – volume: 53 start-page: 89 issue: 1 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0665 article-title: Mineralogy, fluid inclusion petrography, and stable isotope geochemistry of Pb-Zn-Ag veins at the Shizhuyuan deposit, Hunan Province, southeastern China publication-title: Miner. Depos. doi: 10.1007/s00126-017-0725-9 – volume: 192 start-page: 1 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0800 article-title: Deep learning and its application in geochemical mapping publication-title: Earth-Sci. Rev. doi: 10.1016/j.earscirev.2019.02.023 – volume: 112 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0760 article-title: Controls on and prospectivity mapping of volcanic-type uranium mineralization in the Pucheng district, NW Fujian, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2019.103028 – volume: 164 start-page: 94 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0715 article-title: Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2015.10.008 – volume: 16 start-page: 72 issue: 1 year: 1994 ident: 10.1016/j.oregeorev.2022.104916_b0455 article-title: Robust kriging and its application in delineation of geochemical anomalies with scale of 1:50000 publication-title: Comput. Techn. Geophys. Geochem. Explor. – volume: 32 start-page: 314 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b9015 article-title: Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China publication-title: Ore. Geol. Rev. doi: 10.1016/j.oregeorev.2006.10.002 – year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0185 – volume: 35 start-page: 383 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0090 article-title: Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2009.01.001 – volume: 79 start-page: 1 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0395 article-title: Genesis of the Huangshaping WMo-Cu-Pb-Zn polymetallic deposit in southeastern Hunan Province, China: constraints from fluid inclusions, trace elements, and isotopes publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2016.04.023 – volume: 36 start-page: 3028 issue: 2 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0475 article-title: An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.01.018 – volume: 200 start-page: 129 year: 2003 ident: 10.1016/j.oregeorev.2022.104916_b0510 article-title: Samarium-neodymium isotope systematics of hydrothermal calcites from the Xikuangshan antimony deposit (Hunan, China): the potential of calcite as a geochronometer publication-title: Chem. Geol. doi: 10.1016/S0009-2541(03)00187-6 – volume: 8 start-page: 1249 issue: 4 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0585 article-title: Ensemble learning: a survey publication-title: WIREs Data Min. Knowledge Discovery doi: 10.1002/widm.1249 – volume: 10 start-page: 229 issue: 3 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0590 article-title: A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran publication-title: Appl. Geomatics doi: 10.1007/s12518-018-0229-z – volume: 12 start-page: 418 year: 2006 ident: 10.1016/j.oregeorev.2022.104916_b0610 article-title: Pre-Devonian tectonic evolution of South China: from Cathaysian Block to Caledonian period folded orogenic belt publication-title: Geol. J. China Univ. – volume: 59 start-page: 556 issue: 3 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0750 article-title: 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 publication-title: Science China, Earth Sciences doi: 10.1007/s11430-015-5178-3 – volume: 75 start-page: 16 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0260 article-title: Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2015.12.005 – volume: 104 start-page: 148 issue: 1 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0605 article-title: Taking the human out of the loop: a review of Bayesian optimization publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2494218 – ident: 10.1016/j.oregeorev.2022.104916_b0740 doi: 10.1016/j.oregeorev.2014.10.006 – volume: 590 issue: 125275 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0265 article-title: Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using dempster shafer theory publication-title: J. Hydrol. – volume: 203 start-page: 196 year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0285 article-title: Provenance of late Carboniferous sandstones in the Pennine Basin (UK) from combined heavy mineral, garnet geochemistry and palaeocurrent studies publication-title: Sed. Geol. doi: 10.1016/j.sedgeo.2007.11.002 – volume: 29 start-page: 3443 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0810 article-title: Effects of random negative training samples on mineral prospectivity mapping publication-title: Nat. Resour. Res. doi: 10.1007/s11053-020-09668-6 – volume: 79 start-page: 1 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0355 article-title: Genesis of the huangshaping W-Mo-Cu-Pb-Zn polymetallic deposit in southeastern Hunan Province, China: constraints from fluid inclusions, trace elements, and isotopes publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2016.04.023 – volume: 120 start-page: 1 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0700 article-title: Mineral prospectivity analysis for BIF iron deposits: a case study in the Anshan-Benxi area, Liaoning province, North-East China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2018.11.019 – volume: 102 start-page: 220 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0660 article-title: Genesis of the Xianghualing Sn-Pb-Zn deposit, South China: a multi-method zircon study publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2018.09.005 – volume: 26 start-page: 457 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0795 article-title: Machine learning of mineralization-related geochemical anomalies: a review of potential methods publication-title: Nat. Resour. Res. doi: 10.1007/s11053-017-9345-4 – volume: 32 start-page: 37 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b0320 article-title: Ore controls in the Charters Towers goldfield, NE Australia: Constraints from geological, geophysical and numerical analyses publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2006.12.001 – volume: 10 start-page: 102 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0630 article-title: Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: a case study from southern Jiangxi Province, China publication-title: Minerals doi: 10.3390/min10020102 – volume: 381–382 start-page: 110 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0480 article-title: Controlling factors on heavy mineral assemblages in Chinese loess and Red Clay publication-title: Palaeogeogr. Palaeoclimatol. Palaeoecol. doi: 10.1016/j.palaeo.2013.04.020 – volume: 23 start-page: 627 issue: 6 year: 2014 ident: 10.1016/j.oregeorev.2022.104916_b0635 article-title: Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model publication-title: Turk. J. Earth Sci. doi: 10.3906/yer-1401-9 – volume: 140 start-page: 56 year: 2014 ident: 10.1016/j.oregeorev.2022.104916_b0150 article-title: Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2014.02.013 – volume: 29 start-page: 203 issue: 1 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0390 article-title: Prospectivity mapping for tungsten polymetallic mineral resources, Nanling Metallogenic Belt, South China: Use of Random Forest algorithm from a perspective of data imbalance publication-title: Nat. Resour. Res. doi: 10.1007/s11053-019-09564-8 – volume: 94 start-page: 193 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0230 article-title: Garnet and scheelite as indicators of multi-stage tungsten mineralization in the Huangshaping deposit, southern Hunan province, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2018.01.029 – ident: 10.1016/j.oregeorev.2022.104916_b0525 doi: 10.1007/978-1-4419-9326-7_1 – volume: 46 start-page: 272 issue: 2 year: 2012 ident: 10.1016/j.oregeorev.2022.104916_b0010 article-title: Support vector machine for multi-classification of mineral prospectivity areas publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.12.014 – ident: 10.1016/j.oregeorev.2022.104916_b0675 doi: 10.1016/j.cageo.2017.10.005 – volume: 31 start-page: 1 year: 2022 ident: 10.1016/j.oregeorev.2022.104916_b9065 article-title: Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest publication-title: Nat. Resour. Res. doi: 10.1007/s11053-022-10038-7 – volume: 9 start-page: 137 issue: 2 year: 2001 ident: 10.1016/j.oregeorev.2022.104916_b0310 article-title: Logistic regression in rare events data publication-title: Polit. Anal. doi: 10.1093/oxfordjournals.pan.a004868 – volume: 75 start-page: 11 issue: 1 year: 1988 ident: 10.1016/j.oregeorev.2022.104916_b0055 article-title: Logistic regression for two stage case-control data publication-title: Biometrika doi: 10.1093/biomet/75.1.11 – volume: 71 start-page: 749 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0155 article-title: Mineral potential mapping with a restricted Boltzmann machine publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2014.08.012 – volume: 95 start-page: 65 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0225 article-title: Magnetite as an indicator of mixed sources for W-Mo-Pb-Zn mineralization in the Huangshaping polymetallic deposit, southern Hunan Province, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2018.02.019 – volume: 36 start-page: 803 year: 2004 ident: 10.1016/j.oregeorev.2022.104916_b0535 article-title: A hybrid neuro-fuzzy model for mineral potential mapping publication-title: Math. Geol. doi: 10.1023/B:MATG.0000041180.34176.65 – volume: 38 start-page: 219 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b0100 article-title: Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden) publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2010.02.003 – volume: 124 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0550 article-title: Modeling of Cu-Au prospectivity in the caraja′s mineral province (Brazil) through machine learning, dealing with imbalanced training data publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2020.103611 – volume: 47 start-page: 757 issue: 4 year: 2000 ident: 10.1016/j.oregeorev.2022.104916_b0065 article-title: Artificial neural networks: a new method for mineral prospectivity mapping publication-title: Aust. J. Earth Sci. doi: 10.1046/j.1440-0952.2000.00807.x – volume: 12 start-page: 385 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0420 article-title: Modelling of shallow landslides with Machine Learning algorithms publication-title: Geosci. Front. doi: 10.1016/j.gsf.2020.04.014 – volume: 57 start-page: 1049 issue: 6 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0330 article-title: Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil publication-title: Geophys. Prospect. doi: 10.1111/j.1365-2478.2008.00779.x – volume: 312–313 start-page: 1 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0360 article-title: Zircon geochronology and geochemistry of the Xianghualing A-type granitic rocks: Insights into multi-stage Sn-polymetallic mineralization publication-title: Lithos doi: 10.1016/j.lithos.2018.05.001 – volume: 247 start-page: 124 issue: 1 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0340 article-title: Comparisoning state-of-the-art classification algorithms for credit scoring: an update of research publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2015.05.030 – volume: 92 start-page: 97 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0500 article-title: Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2017.11.013 – volume: 2 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0580 article-title: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN publication-title: Appl. Sci. – volume: 30 start-page: 27 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0385 article-title: Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping publication-title: Nat. Resour. Res. doi: 10.1007/s11053-020-09742-z – ident: 10.1016/j.oregeorev.2022.104916_b0180 doi: 10.1145/2939672.2939785 – volume: 74 start-page: 60 issue: 1 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0115 article-title: Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2014.10.004 – volume: 32 start-page: 1 issue: 1 year: 2006 ident: 10.1016/j.oregeorev.2022.104916_b0545 article-title: Bayesian network classifiers for mineral potential mapping publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2005.03.018 – volume: 19 start-page: 103 issue: 2 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b0495 article-title: Application of artificial neural network for gold-silver deposits potential mapping: a case study of Korea publication-title: Nat. Resour. Res. doi: 10.1007/s11053-010-9112-2 – ident: 10.1016/j.oregeorev.2022.104916_b0060 – volume: 38 start-page: 128 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b9035 article-title: Translating the mineral systems approach into an effective exploration targeting system publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2010.05.008 – volume: 37 start-page: 662 issue: 5 year: 2011 ident: 10.1016/j.oregeorev.2022.104916_b0200 article-title: A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan publication-title: China. Comput. Geosci. doi: 10.1016/j.cageo.2010.11.001 – volume: 89 start-page: 161 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0370 article-title: GeoCube: a 3D mineral resources quantitative prediction and assessment system publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2016.01.012 – start-page: 1 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0690 article-title: A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation publication-title: Nat. Resour. Res. – volume: 111 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0720 article-title: Exploration information systems-a proposal for the future use of GIS in mineral exploration targeting publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2019.103005 – volume: 71 start-page: 703 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0120 article-title: Predictive mapping of prospectivity for orogenic gold, Giyani greenstone belt (South Africa) publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2014.10.030 – volume: 29 start-page: 260 year: 2006 ident: 10.1016/j.oregeorev.2022.104916_b0575 article-title: 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 publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2005.10.002 – volume: 128 start-page: 1 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0555 article-title: Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2021.104940 – volume: 25 start-page: 35 issue: 1 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0125 article-title: Data-driven predictive modeling of mineral prospectivity using Random Forests: a case study in Catanduanes Island (Philippines) publication-title: Nat. Resour. Res. doi: 10.1007/s11053-015-9268-x – volume: 26 start-page: 489 issue: 4 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0275 article-title: Random forest-based prospectivity modelling of Greenfield Terrains using sparse deposit data: An example from the Tanami Region, Western Australia publication-title: Nat. Resour. Res. doi: 10.1007/s11053-017-9335-6 – ident: 10.1016/j.oregeorev.2022.104916_b0080 – volume: 15 start-page: 1 year: 2006 ident: 10.1016/j.oregeorev.2022.104916_b0540 article-title: A hybrid fuzzy weights-of-evidence model for mineral potential mapping publication-title: Nat. Resour. Res. doi: 10.1007/s11053-006-9012-7 – volume: 80 start-page: 339 issue: 2 year: 1993 ident: 10.1016/j.oregeorev.2022.104916_b0595 article-title: Logistic analysis in case-control studies under validation sampling publication-title: Biometrika doi: 10.1093/biomet/80.2.339 – volume: 50 start-page: 325 issue: 3–4 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b0520 article-title: Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: examples from NW China and Altay-Sayan (Siberia) publication-title: J. Geodyn. doi: 10.1016/j.jog.2010.01.018 – volume: 28 start-page: 202 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0780 article-title: Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2012.10.031 – volume: 48 start-page: 912 year: 2005 ident: 10.1016/j.oregeorev.2022.104916_b0135 article-title: Petrogenesis and significance of early Yanshanian syenite-granite complex in eastern Nanling Range publication-title: Sci. China, Ser. D Earth Sci. doi: 10.1360/03yd0384 – volume: 37 start-page: 1946 year: 2011 ident: 10.1016/j.oregeorev.2022.104916_b0640 article-title: Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.06.023 – volume: 6 start-page: 21020 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0755 article-title: A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE publication-title: Access doi: 10.1109/ACCESS.2018.2818678 – volume: 21 start-page: 556 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0020 article-title: Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping publication-title: Int. J. Appl. Earth Obs. Geoinf. – ident: 10.1016/j.oregeorev.2022.104916_b0145 – volume: 52 start-page: 100 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0380 article-title: Regional prospectivity analysis for hydrothermal-remobilized nickel mineral systems in western Victoria, Australia publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2012.04.001 – volume: 40 start-page: 99 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0440 article-title: Mineral prospectivity mapping using a VNet convolutional neural network publication-title: Lead. Edge doi: 10.1190/tle40020099.1 – volume: 97 start-page: 161 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b0345 article-title: He, Pb and S isotopic constraints on the relationship between the A-type Qitianling granite and the Furong tin deposit, Hunan Province, China publication-title: Lithos doi: 10.1016/j.lithos.2006.12.009 – ident: 10.1016/j.oregeorev.2022.104916_b0625 doi: 10.1016/j.oregeorev.2019.04.003 – volume: 131 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b9030 article-title: Detection of geochemical anomalies related to mineralization using the GANomaly network publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2021.105043 – volume: 64 start-page: 639 issue: 5 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0160 article-title: Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data publication-title: Aust. J. Earth Sci. doi: 10.1080/08120099.2017.1328705 – volume: 26 start-page: 1 issue: 4 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0490 article-title: Optimizing a knowledge-driven prospectivity model for gold deposits within Perapohja Belt, Northern Finland publication-title: Nat. Resour. Res. – volume: 37 start-page: 1967 issue: 12 year: 2011 ident: 10.1016/j.oregeorev.2022.104916_b0775 article-title: Support vector machine: A tool for mapping mineral prospectivity publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2010.09.014 – volume: 27 start-page: 299 issue: 3 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0410 article-title: A MaxEnt model for mineral prospectivity mapping publication-title: Nat. Resour. Res. doi: 10.1007/s11053-017-9355-2 – volume: 29 start-page: 267 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b9020 article-title: Practical implementation of random forest-based mineral potential mapping for porphyry Cu-Au mineralization in the Eastern Lachlan Orogen, NSW, Australia publication-title: Nat. Resour. Res. doi: 10.1007/s11053-019-09598-y – volume: 136 start-page: 1 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b9070 article-title: Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2021.104264 – volume: 41 start-page: 1 year: 2022 ident: 10.1016/j.oregeorev.2022.104916_b0175 article-title: Dictionary learning for integration of evidential layers for mineral prospectivity modeling publication-title: Ore Geol. Rev. – volume: 16 start-page: 147 issue: 2 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b0040 article-title: Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran publication-title: Nat. Resour. Res. doi: 10.1007/s11053-007-9036-7 – volume: 29 start-page: 71 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b9000 article-title: Boosting for mineral prospectivity modeling: A new GIS toolbox publication-title: Nat. Resour. Res. doi: 10.1007/s11053-019-09483-8 – volume: 33 start-page: 536 year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0085 article-title: Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2007.07.001 – volume: 35 start-page: 2032 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0095 article-title: Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2009.02.008 – volume: 139 start-page: 170 year: 2014 ident: 10.1016/j.oregeorev.2022.104916_b0790 article-title: Identification of geochemical anomalies associated with mineralizationin the Fanshan district, Fujian, China publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2013.08.013 – volume: 60 start-page: 129 year: 2010 ident: 10.1016/j.oregeorev.2022.104916_b0105 article-title: Improved wildcat modelling of mineral prospectivity publication-title: Resour. Geol. doi: 10.1111/j.1751-3928.2010.00121.x – volume: 28 start-page: 1336 issue: 7 year: 2014 ident: 10.1016/j.oregeorev.2022.104916_b0565 article-title: Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain publication-title: Int. J. Geograph. Inf. Sci. doi: 10.1080/13658816.2014.885527 – volume: 109 start-page: 253 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0270 article-title: Geochronological and geochemical constraints on the petrogenesis and geodynamic setting of the Qianlishan granitic pluton, Southeast China publication-title: Mineral. Petrol. doi: 10.1007/s00710-014-0355-1 – volume: 49 start-page: 592 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0450 article-title: Recognition of a Middle-Late Jurassic are-related porphyry copper belt along the Southeast China coast: geological characteristics and metallogenic implications publication-title: Geology doi: 10.1130/G48615.1 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.oregeorev.2022.104916_b0050 article-title: Random forests publication-title: Machine Learn. doi: 10.1023/A:1010933404324 – volume: 71 start-page: 861 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0400 article-title: Spatial data analysis of mineral deposit point patterns: applications to exploration targeting publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2015.05.019 – volume: 104316 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0685 article-title: Mineral prospectivity mapping by deep learning method in YawanDaqiao area, Gansu publication-title: Ore Geol. Rev. – volume: 79 start-page: 69 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0710 article-title: Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2015.03.007 – ident: 10.1016/j.oregeorev.2022.104916_b0300 doi: 10.1016/j.oregeorev.2017.08.016 – volume: 20 start-page: 391 year: 1992 ident: 10.1016/j.oregeorev.2022.104916_b0030 article-title: Two-phase uplift of higher Himalayas since 17 Ma publication-title: Geology doi: 10.1130/0091-7613(1992)020<0391:TPUOHH>2.3.CO;2 – year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0140 article-title: Genera-tion of late meosozic qianlishan A2-type granite in nanling range, South China: implications for shizhuyuan W-Sn mineralization and tectonic evolution publication-title: Lithos doi: 10.1016/j.lithos.2016.10.010 – volume: 382-383 start-page: 105952 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0190 article-title: Extraction of fractionated interstitial melt from a crystal mush system generating the late jurassic high-silica granites from the qitianling composite pluton, South China: implications for greisen-type tin mineralization publication-title: Lithos doi: 10.1016/j.lithos.2020.105952 – volume: 12 start-page: 155 year: 2003 ident: 10.1016/j.oregeorev.2022.104916_b0530 article-title: Artificial neural networks for mineral potential mapping publication-title: Nat. Resour. Res. doi: 10.1023/A:1025171803637 – ident: 10.1016/j.oregeorev.2022.104916_b0445 doi: 10.1016/j.oregeorev.2011.09.003 – volume: 74 start-page: 97 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0705 article-title: Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2014.10.014 – volume: 29 start-page: 3415 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0805 article-title: Geodata science-based mineral prospectivity mapping: a review publication-title: Nat. Resour. Res. doi: 10.1007/s11053-020-09700-9 – volume: 4 start-page: 1 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0430 article-title: An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment publication-title: Machine Learn. Appl. – volume: 30 start-page: 1011 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0765 article-title: Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional autoencoder network and supervised convolutional neural network publication-title: Nat. Resour. Res. doi: 10.1007/s11053-020-09789-y – year: 1994 ident: 10.1016/j.oregeorev.2022.104916_b0045 – volume: 48 start-page: 349 year: 2012 ident: 10.1016/j.oregeorev.2022.104916_b9025 article-title: Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: mineral system analysis, targeting model and prospectivity analysis publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2012.05.004 – year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0695 article-title: Mineral prospectivity mapping via gated recurrent unit model publication-title: Nat. Resour. Res. – volume: 134 start-page: 27 year: 2013 ident: 10.1016/j.oregeorev.2022.104916_b0645 article-title: Fault trace-oriented singularity mapping technique to characterize anisotropic geochemical signatures in Gejiu mineral district, China publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2013.07.009 – volume: 28 start-page: 31 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b9005 article-title: Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency publication-title: Nat. Resour. Res. doi: 10.1007/s11053-018-9375-6 – volume: 85 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0570 article-title: Ensemble learning: pattern classification using ensemble methods publication-title: World Sci. – volume: 135 start-page: 1066 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0170 article-title: Mineral exploration targeting by combination of recursive indicator elimination with the ?2-regularization logistic regression based on geochemical data publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2021.104213 – volume: 2 start-page: 122 year: 1993 ident: 10.1016/j.oregeorev.2022.104916_b0205 article-title: The representation of geoscience information for data integration publication-title: Nonrenewable Resources doi: 10.1007/BF02272809 – volume: 9 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0325 article-title: CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network publication-title: Sci. Rep. – volume: 31 start-page: 37 issue: 1 year: 2022 ident: 10.1016/j.oregeorev.2022.104916_b0505 article-title: Deep GMDH neural networks for predictive mapping of mineral prospectivity in terrains hosting few but large mineral deposits publication-title: Nat. Resour. Res. doi: 10.1007/s11053-021-09984-5 – volume: 94 start-page: 438 year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0210 article-title: Predicting landslides for risk analysis spatial models tested by a cross-validation technique publication-title: Geomorphology doi: 10.1016/j.geomorph.2006.12.036 – volume: 55 start-page: 13 issue: 1 year: 2008 ident: 10.1016/j.oregeorev.2022.104916_b0255 article-title: Evaluating geological complexity and complexity gradients as controls on copper mineralization, Mt Isa Inlier publication-title: Aust. J. Earth Sci. doi: 10.1080/08120090701581364 – volume: 27 start-page: 171 issue: 5 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0235 article-title: Genetic mineralogy of natural heavy placer minerals and its effectiveness in mineral prospecting publication-title: Earth Sci. Front. – volume: 360 start-page: 186 issue: 6385 year: 2018 ident: 10.1016/j.oregeorev.2022.104916_b0005 article-title: Predicting reaction performance in C-N cross-coupling using machine learning publication-title: Science doi: 10.1126/science.aar5169 – ident: 10.1016/j.oregeorev.2022.104916_b0730 – volume: 100 start-page: 133 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0405 article-title: Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2017.04.029 – volume: 28 start-page: 931 year: 2019 ident: 10.1016/j.oregeorev.2022.104916_b0245 article-title: Risk-based analysis in mineral potential mapping: application of quantifier-guided ordered weighted averaging method publication-title: Nat. Resour. Res. doi: 10.1007/s11053-018-9428-x – volume: 81 start-page: 278 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b0735 article-title: 40Ar-39Ar isotopic dating of the Xianghualing Sn-polymetallic orefield in Southern Hunan, China and its geological implications publication-title: Acta Geol. Sinica (Engl. Ed.) doi: 10.1111/j.1755-6724.2007.tb00951.x – volume: 35 start-page: 675 issue: 3 year: 2009 ident: 10.1016/j.oregeorev.2022.104916_b0335 article-title: Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2008.05.003 – ident: 10.1016/j.oregeorev.2022.104916_b0785 doi: 10.1016/j.apgeochem.2013.02.009 – volume: 73 start-page: 198 year: 2014 ident: 10.1016/j.oregeorev.2022.104916_b0215 article-title: Regression trees for modeling geochemical data-an application to Late Jurassic carbonates (Ammonitico Rosso) publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2014.09.007 – volume: 122 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0365 article-title: Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian-Zhangbaling area, Anhui Province, China publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2020.104747 – volume: 80 start-page: 200 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0165 article-title: Mapping mineral prospectivity using an extreme learning machine regression publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2016.06.033 – volume: 363 start-page: 112 year: 2015 ident: 10.1016/j.oregeorev.2022.104916_b0350 article-title: Provenance of heavy mineral deposits on the northwestern shelf of the South China Sea, evidence from single-mineral chemistry publication-title: Mar. Geol. doi: 10.1016/j.margeo.2015.01.015 – volume: 91 start-page: 1066 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0025 article-title: Prospectivity analysis of orogenic gold deposits in saqez-sardasht goldfield, Zagros orogen publication-title: Iran. Ore Geol. Rev. doi: 10.1016/j.oregeorev.2017.11.001 – volume: 77 start-page: 117 issue: 27 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b0220 article-title: S, Pb, and Sr isotope geochemistry and genesis of Pb-Zn mineralization in the Huangshaping polymetallic ore deposit of southern Hunan Province publication-title: China. Ore Geol. Rev. doi: 10.1016/j.oregeorev.2016.02.010 – ident: 10.1016/j.oregeorev.2022.104916_b0670 – volume: 39 start-page: 439 issue: 5 year: 2007 ident: 10.1016/j.oregeorev.2022.104916_b0620 article-title: Mineral potential mapping using Bayesian learning for multilayer perceptrons publication-title: Math. Geol. doi: 10.1007/s11004-007-9106-8 – year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b0070 article-title: An optimized XGBoost based diagnostic system for effective prediction of heart disease publication-title: J. King Saud Univ. – Comput. Inf. Sci. – volume: 10 start-page: 165 year: 2001 ident: 10.1016/j.oregeorev.2022.104916_b0075 article-title: Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines publication-title: Explor. Min. Geol. doi: 10.2113/0100165 – volume: 29 start-page: 189 year: 2020 ident: 10.1016/j.oregeorev.2022.104916_b9045 article-title: Mapping mineral prospectivity via semi-supervised random forest publication-title: Nat. Resour. Res. doi: 10.1007/s11053-019-09510-8 – ident: 10.1016/j.oregeorev.2022.104916_b0615 doi: 10.1109/IJCNN.2003.1223683 – volume: 30 start-page: 1 issue: 5 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0815 article-title: Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions publication-title: Nat. Resour. Res. doi: 10.1007/s11053-021-09871-z – volume: 86 start-page: 75 year: 2016 ident: 10.1016/j.oregeorev.2022.104916_b9050 article-title: Recognition of geochemical anomalies using a deep autoencoder network publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2015.10.006 – volume: 32 start-page: 327 year: 2021 ident: 10.1016/j.oregeorev.2022.104916_b0375 article-title: Mineral prospectivity prediction via convolutional neural networks based on geological big data publication-title: J. Earth Sci. doi: 10.1007/s12583-020-1365-z – volume: 8 start-page: 1080 issue: 2 year: 2017 ident: 10.1016/j.oregeorev.2022.104916_b0465 article-title: Ensemble machine-learning-based geospatial approach for flood risk assessment using multisensor remote-sensing data and GIS publication-title: Geomatics Natural Hazards Risk doi: 10.1080/19475705.2017.1294113 – volume: 24 start-page: 1151 issue: 5 year: 2003 ident: 10.1016/j.oregeorev.2022.104916_b0560 article-title: Artificial neural networks as a tool for mineral potential mapping with GIS publication-title: Int. J. Remote Sens. doi: 10.1080/0143116021000031791 – ident: 10.1016/j.oregeorev.2022.104916_b0110 doi: 10.1016/j.oregeorev.2014.08.010 |
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| Title | Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping |
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