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

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Cited articles

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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.

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