Jinghe Li
Guilin University of Technology
10 Papers
19 Citations
Jinghe Li is an academic researcher from Guilin University of Technology. The author has contributed to research in topics: Computer science & Wavelet. The author has an hindex of 4, co-authored 6 publications. Previous affiliations of Jinghe Li include China University of Geosciences (Wuhan) & China National Petroleum Corporation.
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Papers
Joint Inversion of Electromagnetic and Seismic Data Based on Structural Constraints Using Variational Born Iteration Method
TL;DR: An efficient 2-D joint full-waveform inversion method for electromagnetic and seismic data in a layered medium background is developed and numerical simulation results show that joint inversion can improve the inversion results when compared with those from the separate inversion.
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Wavelet-Based Higher Order Correlative Stacking for Seismic Data Denoising in the Curvelet Domain
TL;DR: The proposed hybrid denoising scheme of wavelet-based higher order correlative stacking (HOCS) in the curvelet domain improves noisy seismic data significantly with respect to both signal-to-noise ratio and fidelity.
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Transfer-Learning-Based SVM Method for Seismic Phase Picking With Insufficient Training Samples
Jinghe Li,Mengkun Ran,Weike Tong,Yehui Cao,Luxi Cai,Xiaoyi Ou,Tingwei Yang +6 more
TL;DR: In this paper , a transductive transfer learning-based support vector machine (TTL-SVM) algorithm was proposed for seismic phase picking when the seismic dataset possesses insufficient training samples.
6
Multiple Frequency Contrast Source Inversion Method for Vertical Electromagnetic Profiling: 2D Simulation Results and Analyses
TL;DR: The inversion technique with CSI combines the efficient FFT algorithm to speed up the matrix–vector multiplication and the stable convergence of the simultaneous multiple frequency CSI in the iteration process to make quantitative conductivity image reconstruction effectively for large-scale electromagnetic oil exploration problems.
5
SimCLR-based data-driven tight frame for seismic denoising and interpolation
Jinghe Li,Wu Xiangling +1 more
TL;DR: The numerical results demonstrate that the SimCLR-DDTF method can intelligently select training patches with more effective information, greatly improve the training efficiency of the DDTF, and obtain high-quality denoising and interpolation results.
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