Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3447-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3447-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
21 Jul 2025
Data description article |
| 21 Jul 2025
| 21 Jul 2025
cigChannel: a large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation
Guangyu Wang
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Mengcheng, 233500, China
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Mengcheng, 233500, China
Wen Zhang
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Mengcheng, 233500, China
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Short summary
Seismic paleochannel interpretation is vital for georesource exploration and paleoclimate research yet remains time-consuming. While deep learning offers automation potential, it is limited by the lack of labeled data. We present a workflow to simulate geologically reasonable 3D seismic volumes with diverse paleochannels, generating a large-scale labeled dataset. Field applications demonstrate its effectiveness. The dataset and codes are publicly available to support future research.
Seismic paleochannel interpretation is vital for georesource exploration and paleoclimate...
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