| Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering G Li, X Xiao, JT Tang, J Li, HJ Zhu, C Zhou, FB Yan Applied Geophysics 14 (4), 581-589, 2017 | 59 | 2017 |
| Signal-noise identification of magnetotelluric signals using fractal-entropy and clustering algorithm for targeted de-noising J Li, X Zhang, J Gong, J Tang, Z Ren, G Li, Y Deng, J Cai Fractals 26 (02), 1840011, 2018 | 43 | 2018 |
| De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation G Li, X Liu, J Tang, J Li, Z Ren, C Chen Journal of Applied Geophysics 172, 103919, 2020 | 39 | 2020 |
| Dictionary learning and shift-invariant sparse coding denoising for controlled-source electromagnetic data combined with complementary ensemble empirical mode decomposition G Li, Z He, J Tang, J Deng, X Liu, H Zhu Geophysics 86 (3), E185-E198, 2021 | 34 | 2021 |
| Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data G Li, X Liu, J Tang, J Deng, S Hu, C Zhou, C Chen, W Tang Earth, Planets and Space 72, 1-15, 2020 | 34 | 2020 |
| Denoising AMT data based on dictionary learning JT TANG, G LI, C ZHOU, ZY REN, X Xiao, ZJ LIU Chinese Journal of Geophysics 61 (9), 3835-3850, 2018 | 26 | 2018 |
| Strong noise separation for magnetotelluric data based on a signal reconstruction algorithm of compressive sensing JT TANG, G LI, X Xiao, J LI, C ZHOU, HJ ZHU Chinese Journal of Geophysics 60 (9), 3642-3654, 2017 | 26 | 2017 |
| IncepTCN: A new deep temporal convolutional network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic data G Li, S Wu, H Cai, Z He, X Liu, C Zhou, J Tang Geophysics 88 (4), E107-E122, 2023 | 25 | 2023 |
| Magnetotelluric noise suppression based on matching pursuit and genetic algorithm J LI, H YAN, JT TANG, X ZHANG, G LI, HJ ZHU Chinese Journal of Geophysics 61 (7), 3086-3101, 2018 | 22 | 2018 |
| Magnetotelluric noise suppression based on impulsive atoms and NPSO-OMP algorithm J Li, X Liu, G Li, J Tang Pure and Applied Geophysics 177, 5275-5297, 2020 | 17 | 2020 |
| Power-line interference suppression of MT data based on frequency domain sparse decomposition J Tang, G Li, C Zhou, J Li, X Liu, H Zhu Journal of Central South University 25 (9), 2150-2163, 2018 | 14 | 2018 |
| The stability analysis of systems with nonlinear feedback expressed by a quadratic program G Li, WP Heath, B Lennox Proceedings of the 45th IEEE Conference on Decision and Control, 4247-4252, 2006 | 14 | 2006 |
| Magnetotelluric signal-noise separation method based on SVM–CEEMDWT J Li, J Cai, JT Tang, G Li, X Zhang, ZM Xu Applied Geophysics 16 (2), 160-170, 2019 | 11 | 2019 |
| Identification and suppression of magnetotelluric noise via a deep residual network L Zhang, Z Ren, X Xiao, J Tang, G Li Minerals 12 (6), 766, 2022 | 10 | 2022 |
| Assessment of the learning curve of supercapsular percutaneously assisted total hip arthroplasty in an asian population P Lei, Z Liao, J Peng, G Li, Q Zhou, X Xiao, C Yang BioMed Research International 2020 (1), 5180458, 2020 | 9 | 2020 |
| Multi-type geomagnetic noise removal via an improved U-Net deep learning network G Li, X Zhou, C Chen, L Xu, F Zhou, F Shi, J Tang IEEE Transactions on Geoscience and Remote Sensing, 2023 | 8 | 2023 |
| Groundwater resources survey of tongchuan city using the audio magnetotelluric method Z Xu, J Tang, G Li, HC Xin, Z Xu, X Tan, J Li Applied Geophysics 17 (5), 660-671, 2020 | 8 | 2020 |
| Robust CSEM data processing by unsupervised machine learning G Li, Z He, J Deng, J Tang, Y Fu, X Liu, C Shen Journal of Applied Geophysics 186, 104262, 2021 | 7 | 2021 |
| Magnetotelluric noise attenuation using a deep residual shrinkage network G Zuo, Z Ren, X Xiao, J Tang, L Zhang, G Li Minerals 12 (9), 1086, 2022 | 6 | 2022 |
| Audio magnetotelluric denoising via variational mode decomposition and adaptive dictionary learning L Zhang, J Tang, G Li, W Chen Journal of Applied Geophysics 204, 104748, 2022 | 5 | 2022 |
Zhengyong RenCentral South University, Changsha, China在 csu.edu.cn 的电子邮件经过验证
