Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3447-2025
https://doi.org/10.5194/essd-17-3447-2025
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: 3,340 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,185 827 328 3,340 45 78 117
  • HTML: 2,185
  • PDF: 827
  • XML: 328
  • Total: 3,340
  • Supplement: 45
  • BibTeX: 78
  • EndNote: 117
Views and downloads (calculated since 25 Jul 2024)
Cumulative views and downloads (calculated since 25 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,340 (including HTML, PDF, and XML) Thereof 3,247 with geography defined and 93 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 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.

Share
Altmetrics
Final-revised paper
Preprint