Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 7
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.
https://doi.org/10.5194/essd-17-3447-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 7
Data description paper
 | 
21 Jul 2025
Data description paper |  | 21 Jul 2025

cigChannel: a large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation

Guangyu Wang, Xinming Wu, and Wen Zhang

Viewed

Total article views: 5,065 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,129 1,579 357 5,065 78 92 144
  • HTML: 3,129
  • PDF: 1,579
  • XML: 357
  • Total: 5,065
  • Supplement: 78
  • BibTeX: 92
  • EndNote: 144
Views and downloads (calculated since 25 Jul 2024)
Cumulative views and downloads (calculated since 25 Jul 2024)

Viewed (geographical distribution)

Total article views: 5,065 (including HTML, PDF, and XML) Thereof 5,003 with geography defined and 62 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Jan 2026
Download
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.

Read more
Share
Altmetrics
Final-revised paper
Preprint