| Support vector machine: A tool for mapping mineral prospectivity R Zuo, EJM Carranza Computers & Geosciences 37 (12), 1967-1975, 2011 | 458 | 2011 |
| Fractal/multifractal modeling of geochemical data: A review R Zuo, J Wang Journal of Geochemical Exploration 164, 33-41, 2016 | 339 | 2016 |
| Deep learning and its application in geochemical mapping R Zuo, Y Xiong, J Wang, EJM Carranza Earth-science reviews 192, 1-14, 2019 | 317 | 2019 |
| Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt … R Zuo Journal of Geochemical Exploration 111 (1-2), 13-22, 2011 | 301 | 2011 |
| Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China R Zuo, Q Cheng, FP Agterberg, Q Xia Journal of Geochemical Exploration 101 (3), 225-235, 2009 | 262 | 2009 |
| Recognition of geochemical anomalies using a deep autoencoder network Y Xiong, R Zuo Computers & Geosciences 86, 75-82, 2016 | 261 | 2016 |
| Machine learning of mineralization-related geochemical anomalies: A review of potential methods R Zuo Natural Resources Research 26, 457-464, 2017 | 200 | 2017 |
| Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization R Zuo, Q Xia, H Wang Applied geochemistry 28, 202-211, 2013 | 187 | 2013 |
| Application of fractal models to characterization of vertical distribution of geochemical element concentration R Zuo, Q Cheng, Q Xia Journal of Geochemical Exploration 102 (1), 37-43, 2009 | 184 | 2009 |
| Mapping mineral prospectivity through big data analytics and a deep learning algorithm Y Xiong, R Zuo, EJM Carranza Ore Geology Reviews 102, 811-817, 2018 | 177 | 2018 |
| A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas R Zuo, Q Xia, D Zhang Applied geochemistry 33, 165-172, 2013 | 176 | 2013 |
| Big data analytics of identifying geochemical anomalies supported by machine learning methods R Zuo, Y Xiong Natural Resources Research 27, 5-13, 2018 | 164 | 2018 |
| Spatial analysis and visualization of exploration geochemical data R Zuo, EJM Carranza, J Wang Earth-Science Reviews 158, 9-18, 2016 | 158 | 2016 |
| Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China R Zuo Applied geochemistry 26, S271-S273, 2011 | 142 | 2011 |
| Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn-type Fe deposits in Southwestern Fujian Province, China R Zuo, Z Zhang, D Zhang, EJM Carranza, H Wang Ore Geology Reviews 71, 502-515, 2015 | 139 | 2015 |
| Identification of weak anomalies: A multifractal perspective R Zuo, J Wang, G Chen, M Yang Journal of Geochemical Exploration 148, 12-24, 2015 | 137 | 2015 |
| Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping T Li, R Zuo, Y Xiong, Y Peng Natural Resources Research 30, 27-38, 2021 | 134 | 2021 |
| Geodata Science-Based Mineral Prospectivity Mapping: A Review R Zuo Natural Resources Research 29 (6), 3415–3424, 2020 | 131 | 2020 |
| Fractal/multifractal modelling of geochemical exploration data R Zuo, EJM Carranza, Q Cheng Journal of Geochemical Exploration 122, 1-3, 2012 | 125 | 2012 |
| GIS-based rare events logistic regression for mineral prospectivity mapping Y Xiong, R Zuo Computers & Geosciences 111, 18-25, 2018 | 120 | 2018 |
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