| Deep learning and its application in geochemical mapping R Zuo, Y Xiong, J Wang, EJM Carranza Earth-science reviews 192, 1-14, 2019 | 321 | 2019 |
| Recognition of geochemical anomalies using a deep autoencoder network Y Xiong, R Zuo Computers & Geosciences 86, 75-82, 2016 | 260 | 2016 |
| 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 | 179 | 2018 |
| Big data analytics of identifying geochemical anomalies supported by machine learning methods R Zuo, Y Xiong Natural Resources Research 27, 5-13, 2018 | 163 | 2018 |
| 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 | 137 | 2021 |
| GIS-based rare events logistic regression for mineral prospectivity mapping Y Xiong, R Zuo Computers & Geosciences 111, 18-25, 2018 | 119 | 2018 |
| 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 ZJ Zhang, RG Zuo, YH Xiong Science China Earth Sciences 59, 556-572, 2016 | 117 | 2016 |
| The processing methods of geochemical exploration data: past, present, and future R Zuo, J Wang, Y Xiong, Z Wang Applied Geochemistry 132, 105072, 2021 | 108 | 2021 |
| Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China Y Gao, Z Zhang, Y Xiong, R Zuo Ore Geology Reviews 75, 16-28, 2016 | 102 | 2016 |
| Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine Y Xiong, R Zuo Computers & geosciences 140, 104484, 2020 | 101 | 2020 |
| Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions R Zuo, OP Kreuzer, J Wang, Y Xiong, Z Zhang, Z Wang Natural Resources Research 30, 3059-3079, 2021 | 98 | 2021 |
| Mapping mineral prospectivity via semi-supervised random forest J Wang, R Zuo, Y Xiong Natural Resources Research 29, 189-202, 2020 | 96 | 2020 |
| Recognition of geochemical anomalies using a deep variational autoencoder network Z Luo, Y Xiong, R Zuo Applied Geochemistry 122, 104710, 2020 | 86 | 2020 |
| Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method C Zhang, R Zuo, Y Xiong Applied Geochemistry 130, 104994, 2021 | 85 | 2021 |
| Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder Y Xiong, R Zuo Mathematical Geosciences, 1-22, 2022 | 78 | 2022 |
| Geodata science and geochemical mapping R Zuo, Y Xiong Journal of Geochemical Exploration 209, 106431, 2020 | 67 | 2020 |
| Detection of geochemical anomalies related to mineralization using the GANomaly network Z Luo, R Zuo, Y Xiong, X Wang Applied Geochemistry 131, 105043, 2021 | 60 | 2021 |
| Effects of misclassification costs on mapping mineral prospectivity Y Xiong, R Zuo Ore Geology Reviews 82, 1-9, 2017 | 56 | 2017 |
| A geologically constrained variational autoencoder for mineral prospectivity mapping R Zuo, Z Luo, Y Xiong, B Yin Natural Resources Research 31 (3), 1121-1133, 2022 | 55 | 2022 |
| A spatially constrained multi-autoencoder approach for multivariate geochemical anomaly recognition L Chen, Q Guan, Y Xiong, J Liang, Y Wang, Y Xu Computers & geosciences 125, 43-54, 2019 | 52 | 2019 |
Renguang ZuoChina University of GeosciencesPatvirtintas el. paštas cug.edu.cn