Siwei Yu

1.5k total citations · 2 hit papers
30 papers, 1.1k citations indexed

About

Siwei Yu is a scholar working on Geophysics, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Siwei Yu has authored 30 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Geophysics, 12 papers in Computer Vision and Pattern Recognition and 7 papers in Computational Mechanics. Recurrent topics in Siwei Yu's work include Seismic Imaging and Inversion Techniques (25 papers), Image and Signal Denoising Methods (11 papers) and Seismic Waves and Analysis (10 papers). Siwei Yu is often cited by papers focused on Seismic Imaging and Inversion Techniques (25 papers), Image and Signal Denoising Methods (11 papers) and Seismic Waves and Analysis (10 papers). Siwei Yu collaborates with scholars based in China, United States and Norway. Siwei Yu's co-authors include Jianwei Ma, Wenlong Wang, Xiaoqun Zhang, Stanley Osher, Mauricio D. Sacchi, Yongna Jia, Lina Liu, Zuoqiang Shi, Mathini Sellathurai and Xiaojing Wang and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Signal Processing and Reviews of Geophysics.

In The Last Decade

Siwei Yu

28 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning for Geophysics: Current and Future Trends

2021 335 citations Siwei Yu, Jianwei Ma Reviews of Geophysics profile →
Deep learning for denoising

2019 281 citations Siwei Yu, Jianwei Ma et al. Geophysics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Siwei Yu China	11	864	382	302	221	153	30	1.1k
Runqiu Wang China	18	951 1.1×	296 0.8×	384 1.3×	199 0.9×	135 0.9×	60	1.2k
Amin Roshandel Kahoo Iran	18	756 0.9×	296 0.8×	317 1.0×	142 0.6×	145 0.9×	56	962
Danilo R. Velis Argentina	18	838 1.0×	352 0.9×	159 0.5×	240 1.1×	164 1.1×	61	1.0k
Shuwei Gan China	21	1.5k 1.8×	523 1.4×	595 2.0×	180 0.8×	232 1.5×	37	1.7k
Xiaohong Chen China	18	717 0.8×	440 1.2×	196 0.6×	151 0.7×	239 1.6×	64	994
Milton J. Porsani Brazil	16	635 0.7×	293 0.8×	184 0.6×	74 0.3×	111 0.7×	132	890
Youzuo Lin United States	21	909 1.1×	620 1.6×	155 0.5×	357 1.6×	278 1.8×	103	1.6k
Ray Abma United States	18	1.4k 1.6×	641 1.7×	410 1.4×	112 0.5×	235 1.5×	64	1.6k
Umair bin Waheed Saudi Arabia	19	889 1.0×	377 1.0×	70 0.2×	371 1.7×	194 1.3×	117	1.3k
Xintong Dong China	18	874 1.0×	380 1.0×	304 1.0×	253 1.1×	89 0.6×	67	1.1k

Countries citing papers authored by Siwei Yu

Since Specialization

Citations

This map shows the geographic impact of Siwei Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Siwei Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siwei Yu more than expected).

Fields of papers citing papers by Siwei Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Siwei Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Siwei Yu. The network helps show where Siwei Yu may publish in the future.

Co-authorship network of co-authors of Siwei Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Siwei Yu. A scholar is included among the top collaborators of Siwei Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Siwei Yu. Siwei Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Yu, Siwei, et al.. (2024). Regularized Full-Waveform Inversion With Shearlet Transform and Total Generalized Variation. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–15. 2 indexed citations

2.

Yu, Siwei, et al.. (2024). Seismic Registration With a Deep Neural Network Constraint. IEEE Geoscience and Remote Sensing Letters. 21. 1–5.

3.

Yu, Siwei, et al.. (2024). Dealiased seismic data interpolation by dynamic matching. Geophysics. 89(5). V361–V376. 1 indexed citations

4.

Chen, Yao, et al.. (2024). Self-Supervised Transfer Learning POCS-Net for Seismic Data Interpolation. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–18. 2 indexed citations

5.

Yu, Siwei, et al.. (2024). Track-to-Track Association Algorithm with Spatio-Temporal Features. 1–6. 1 indexed citations

6.

Yu, Siwei & Jianwei Ma. (2023). Simultaneous off-the-grid regularization and reconstruction for 3D seismic data by a new combined sampling operator. Geophysics. 88(4). V291–V302. 1 indexed citations

7.

Yu, Siwei, et al.. (2023). A projection-onto-convex-sets network for 3D seismic data interpolation. Geophysics. 88(3). V249–V265. 14 indexed citations

8.

Yu, Siwei, et al.. (2023). An adaptive linear-mode decomposition for effective separation of linear and nonlinear seismic events, ground roll, and random noise. Geophysics. 88(4). V303–V315. 4 indexed citations

9.

Jayaram, Vikram, et al.. (2023). Using geometric mode decomposition for the background noise suppression on microseismic data. Geophysical Prospecting. 71(8). 1420–1437. 3 indexed citations

10.

Yu, Siwei & Jianwei Ma. (2021). Deep Learning for Geophysics: Current and Future Trends. Reviews of Geophysics. 59(3). 335 indexed citations breakdown →

11.

Wang, Xiaojing & Siwei Yu. (2021). Seismic Data Regularization on Nonequispaced Grid via a Joint Sparsity-Promotion Method. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 2 indexed citations

12.

Yu, Siwei, Jianwei Ma, & Wenlong Wang. (2019). Deep learning for denoising. Geophysics. 84(6). V333–V350. 281 indexed citations breakdown →

13.

Yu, Siwei, et al.. (2019). Off-the-grid vertical seismic profile data regularization by a compressive sensing method. Geophysics. 85(2). V157–V168. 9 indexed citations

14.

Yu, Siwei, et al.. (2019). Earthquake accelerogram denoising by wavelet-based variational mode decomposition. Journal of Seismology. 23(4). 649–663. 13 indexed citations

15.

Yu, Siwei, et al.. (2018). Deep learning tutorial for denoising.. arXiv (Cornell University). 5 indexed citations

16.

Wang, Fei, Siwei Yu, Chenguang Shi, & Mathini Sellathurai. (2018). LPI time‐based TMS against high‐sensitivity ESM. IET Radar Sonar & Navigation. 12(12). 1509–1516. 2 indexed citations

17.

Ma, Jianwei & Siwei Yu. (2017). Sparsity in compressive sensing. The Leading Edge. 36(8). 646–652. 8 indexed citations

18.

Yu, Siwei, Jianwei Ma, & Stanley Osher. (2016). Monte Carlo data-driven tight frame for seismic data recovery. Geophysics. 81(4). V327–V340. 70 indexed citations

19.

Yu, Siwei, Jianwei Ma, Xiaoqun Zhang, & Mauricio D. Sacchi. (2015). Interpolation and denoising of high-dimensional seismic data by learning a tight frame. Geophysics. 80(5). V119–V132. 136 indexed citations

20.

Yu, Siwei. (2012). Compressive Sensing and its Application: from Sparse to Low-rank Regularized Optimization. Signal Processing. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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