Peng Jiang

2.0k total citations · 2 hit papers
62 papers, 1.4k citations indexed

About

Peng Jiang is a scholar working on Ocean Engineering, Geophysics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Peng Jiang has authored 62 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Ocean Engineering, 26 papers in Geophysics and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Peng Jiang's work include Seismic Imaging and Inversion Techniques (24 papers), Geophysical Methods and Applications (21 papers) and Seismic Waves and Analysis (13 papers). Peng Jiang is often cited by papers focused on Seismic Imaging and Inversion Techniques (24 papers), Geophysical Methods and Applications (21 papers) and Seismic Waves and Analysis (13 papers). Peng Jiang collaborates with scholars based in China, United States and United Kingdom. Peng Jiang's co-authors include Yuxiao Ren, Bin Liu, Senlin Yang, Jingliang Peng, Haibin Ling, Yangkang Chen, Jingyi Yu, Yunhai Wang, Shucai Li and Zhengfang Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and Construction and Building Materials.

In The Last Decade

Peng Jiang

57 papers receiving 1.4k citations

Hit Papers

Deep-Learning Inversion of Seismic Data 2019 2026 2021 2023 2019 2021 50 100 150 200 250
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.

  1. Deep-Learning Inversion of Seismic Data
    2019 293 citations Bin Liu, Yuxiao Ren et al. IEEE Transactions on Geoscience and Remote Sensing profile →
  2. GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel Linings
    2021 119 citations Bin Liu, Yuxiao Ren et al. IEEE Transactions on Geoscience and Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peng Jiang China 18 692 672 396 201 184 62 1.4k
Luming Liang United States 16 564 0.8× 790 1.2× 516 1.3× 307 1.5× 26 0.1× 42 1.5k
Eun‐Jung Holden Australia 21 205 0.3× 355 0.5× 355 0.9× 239 1.2× 70 0.4× 94 1.7k
Hannes Hofmann Germany 25 643 0.9× 718 1.1× 93 0.2× 900 4.5× 222 1.2× 104 2.2k
Rui‐Sheng Jia China 19 75 0.1× 144 0.2× 449 1.1× 44 0.2× 37 0.2× 85 1.0k
Yunzhi Shi United States 14 988 1.4× 1.4k 2.1× 135 0.3× 501 2.5× 22 0.1× 29 1.6k
Liguan Wang China 16 128 0.2× 189 0.3× 136 0.3× 92 0.5× 50 0.3× 89 778
Haibin Di United States 24 900 1.3× 1.2k 1.7× 77 0.2× 588 2.9× 19 0.1× 87 1.5k
Masahiro Ishii Japan 14 188 0.3× 51 0.1× 70 0.2× 284 1.4× 37 0.2× 74 950
Chuan He China 16 97 0.1× 111 0.2× 327 0.8× 70 0.3× 32 0.2× 83 775

Countries citing papers authored by Peng Jiang

Since Specialization
Citations

This map shows the geographic impact of Peng Jiang'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 Peng Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Jiang more than expected).

Fields of papers citing papers by Peng Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peng Jiang. 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 Peng Jiang. The network helps show where Peng Jiang may publish in the future.

Co-authorship network of co-authors of Peng Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Jiang. A scholar is included among the top collaborators of Peng Jiang 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 Peng Jiang. Peng Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jiang, Peng, et al.. (2025). Smoothing Objective Function for 3-D Electrical Resistivity Inversion by CNNs Regularizer. IEEE Sensors Letters. 9(4). 1–4.
2.
Jiang, Peng, et al.. (2024). Deep Learning-Based Optimization Framework for Full-Waveform Inversion in Tunnels. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–11. 3 indexed citations
3.
Jiang, Peng, et al.. (2024). Deep Learning-Based Rock Muck Detection on Point Clouds During TBM Excavation. IEEE Sensors Journal. 24(19). 31347–31356. 1 indexed citations
4.
Ren, Yuxiao, et al.. (2024). Joint Inversion of Seismic and Resistivity Data Powered by Deep Learning. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 1 indexed citations
5.
Jiang, Peng, et al.. (2024). GPR Full-Waveform Inversion Through Adaptive Filtering of Model Parameters and Gradients Using CNN. IEEE Sensors Journal. 25(3). 5878–5888.
6.
Liu, Bin, et al.. (2023). Physics-Driven Deep Learning Inversion for Direct Current Resistivity Survey Data. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–11. 12 indexed citations
7.
Jiang, Peng, et al.. (2023). Gradient optimization method for tunnel resistivity and chargeability joint inversion based on deep learning. Tunnelling and Underground Space Technology. 144. 105513–105513. 5 indexed citations
8.
Jiang, Peng, et al.. (2023). Deep Learning Joint Inversion of Electrical Data for Ahead-Prospecting in Tunneling. Advances in Civil Engineering. 2023. 1–10.
9.
Guo, Qian, et al.. (2023). Deep learning inversion method of tunnel resistivity synthetic data based on modelling data. Near Surface Geophysics. 21(4). 249–260. 4 indexed citations
10.
Jiang, Peng, et al.. (2022). Deep Learning Inversion of Electrical Resistivity Data by One-Sided Mapping. IEEE Signal Processing Letters. 29. 2248–2252. 9 indexed citations
11.
Liu, Bin, et al.. (2022). Physics-driven self-supervised learning system for seismic velocity inversion. Geophysics. 88(2). R145–R161. 26 indexed citations
12.
Mu, Xidong, Sichen Wang, Peng Jiang, & Yanlan Wu. (2022). Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model. Journal of Environmental Sciences. 132. 122–133. 10 indexed citations
13.
Wang, Jing, Kefu Chen, Hanchi Liu, et al.. (2021). Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images. IEEE Access. 9. 87207–87218. 28 indexed citations
14.
Ren, Yuxiao, Bin Liu, Senlin Yang, Duo Li, & Peng Jiang. (2021). Seismic data inversion with acquisition adaptive convolutional neural network for geologic forward prospecting in tunnels. Geophysics. 86(5). R659–R670. 17 indexed citations
15.
Wang, Zhengfang, Jing Wang, Anthony G. Cohn, et al.. (2021). Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network. Construction and Building Materials. 319. 125658–125658. 39 indexed citations
16.
Guo, Qian, et al.. (2020). Adaptive Convolution Neural Networks for Electrical Resistivity Inversion. IEEE Sensors Journal. 21(2). 2055–2066. 18 indexed citations
17.
Huang, Hai, Yan He, Daizhou Zhang, et al.. (2020). Evidence for immortality and autonomy in animal cancer models is often not provided, which causes confusion on key issues of cancer biology. Journal of Cancer. 11(10). 2887–2920. 6 indexed citations
18.
Dong, Yanan, Jing Wang, Zhengfang Wang, et al.. (2019). A Deep-Learning-Based Multiple Defect Detection Method for Tunnel Lining Damages. IEEE Access. 7. 182643–182657. 55 indexed citations
19.
20.
Jiang, Peng & Michael T. Heath. (2013). Pattern Discovery in High Dimensional Binary Data. 474–481. 1 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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