Haibin Di

2.0k total citations
87 papers, 1.5k citations indexed

About

Haibin Di is a scholar working on Geophysics, Ocean Engineering and Mechanical Engineering. According to data from OpenAlex, Haibin Di has authored 87 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Geophysics, 55 papers in Ocean Engineering and 36 papers in Mechanical Engineering. Recurrent topics in Haibin Di's work include Seismic Imaging and Inversion Techniques (73 papers), Drilling and Well Engineering (43 papers) and Hydraulic Fracturing and Reservoir Analysis (30 papers). Haibin Di is often cited by papers focused on Seismic Imaging and Inversion Techniques (73 papers), Drilling and Well Engineering (43 papers) and Hydraulic Fracturing and Reservoir Analysis (30 papers). Haibin Di collaborates with scholars based in United States, British Virgin Islands and China. Haibin Di's co-authors include Ghassan AlRegib, Dengliang Gao, Aria Abubakar, Zhen Wang, Zhun Li, Muhammad Shafiq, Yazeed Alaudah, Zijie J. Wang, W. Z. Hu and Jian Kang and has published in prestigious journals such as Materials Science and Engineering A, Geophysics and Geophysical Journal International.

In The Last Decade

Haibin Di

82 papers receiving 1.5k citations

Peers

Haibin Di
A. Manuello Italy
Zhicheng Geng United States
Yunzhi Shi United States
Gianni Niccolini Italy
Behzad Tokhmechi Iran
Xingyao Yin China
Guoqiang Xue China
Lukas Mosser United Kingdom
Shuki Ronen United States
Xiaokai Wang China
A. Manuello Italy
Haibin Di
Citations per year, relative to Haibin Di Haibin Di (= 1×) peers A. Manuello

Countries citing papers authored by Haibin Di

Since Specialization
Citations

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

Fields of papers citing papers by Haibin Di

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haibin Di

This figure shows the co-authorship network connecting the top 25 collaborators of Haibin Di. A scholar is included among the top collaborators of Haibin Di 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 Haibin Di. Haibin Di 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.
Wang, Meng, et al.. (2025). Enhanced stability of superhydrophobic surfaces using in situ gas injection. International Journal of Mechanical Sciences. 294. 110266–110266. 2 indexed citations
2.
Ovcharenko, Oleg, Haibin Di, Umair bin Waheed, & Vladimir Kazei. (2025). Introduction to this special section: Generative and physics-informed AI. The Leading Edge. 44(2). 78–78.
3.
Liu, Qiang, Zhiyang Yuan, Haibin Di, & Shouli Sun. (2025). Global calibration of an on-machine measurement system using a chromatic confocal sensor. Precision Engineering. 96. 864–873. 1 indexed citations
4.
Zhao, Tao, Haibin Di, & Aria Abubakar. (2024). Evaluating machine learning-predicted subsurface properties via seismic data reconstruction. Geophysics. 89(6). R509–R519. 2 indexed citations
5.
Waheed, Umair bin, Haibin Di, Jiajia Sun, & Doug Angus. (2023). Introduction to special issue on machine learning applications in geophysical exploration and monitoring. Geophysical Prospecting. 72(1). 3–6.
6.
Di, Haibin & Auwal Bala Abubakar. (2023). An Integrated Workflow for Windfarm Site Characterization by Deep Learning. 1–6. 1 indexed citations
7.
Di, Haibin, Nam Pham, & Aria Abubakar. (2023). Estimating geotechnical parameters using semisupervised learning: An example from the Dutch offshore wind farm zone. Geophysics. 89(1). WA85–WA93. 2 indexed citations
8.
Di, Haibin, et al.. (2023). Latest advancements in machine learning for geophysics — Introduction. Geophysics. 89(1). WAi–WAii. 2 indexed citations
9.
Abubakar, Aria, et al.. (2022). Deep learning for end-to-end subsurface modeling and interpretation: An example from the Groningen gas field. The Leading Edge. 41(4). 259–267. 7 indexed citations
10.
Di, Haibin & Aria Abubakar. (2022). Automated active learning in seismic image interpretation. The Leading Edge. 41(9). 628–635. 3 indexed citations
11.
Di, Haibin, Cen Li, Stewart W. Smith, Zhun Li, & Aria Abubakar. (2021). Imposing interpretational constraints on a seismic interpretation convolutional neural network. Geophysics. 86(3). IM63–IM71. 22 indexed citations
12.
Di, Haibin & Aria Abubakar. (2021). Estimating subsurface properties using a semisupervised neural network approach. Geophysics. 87(1). IM1–IM10. 22 indexed citations
13.
Di, Haibin, Zhun Li, & Aria Abubakar. (2021). Using relative geologic time to constrain convolutional neural network-based seismic interpretation and property estimation. Geophysics. 87(2). IM25–IM35. 18 indexed citations
14.
Di, Haibin, et al.. (2020). Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow. Geophysics. 85(4). WA77–WA86. 65 indexed citations
15.
Di, Haibin, et al.. (2020). Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand. The Leading Edge. 39(10). 727–733. 22 indexed citations
16.
Di, Haibin, Cen Li, Stewart W. Smith, & Aria Abubakar. (2019). Machine learning-assisted seismic interpretation with geologic constraints. 5360–5364. 9 indexed citations
17.
Wang, Zhen, et al.. (2018). Successful leveraging of image processing and machine learning in seismic structural interpretation: A review. The Leading Edge. 37(6). 451–461. 99 indexed citations
18.
AlRegib, Ghassan, Mohamed Deriche, Zhiling Long, et al.. (2018). Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective. IEEE Signal Processing Magazine. 35(2). 82–98. 57 indexed citations
19.
Di, Haibin & Dengliang Gao. (2016). Improved estimates of seismic curvature and flexure based on 3D surface rotation in the presence of structure dip. Geophysics. 81(2). IM13–IM23. 27 indexed citations
20.

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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