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: 3,158 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,057 773 328 3,158 32 75 92
  • HTML: 2,057
  • PDF: 773
  • XML: 328
  • Total: 3,158
  • Supplement: 32
  • BibTeX: 75
  • EndNote: 92
Views and downloads (calculated since 25 Jul 2024)
Cumulative views and downloads (calculated since 25 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,158 (including HTML, PDF, and XML) Thereof 3,072 with geography defined and 86 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Oct 2025
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