Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-595-2025
https://doi.org/10.5194/essd-17-595-2025
Data description article
 | 
10 Feb 2025
Data description article |  | 10 Feb 2025

cigFacies: a massive-scale benchmark dataset of seismic facies and its application

Hui Gao, Xinming Wu, Xiaoming Sun, Mingcai Hou, Hang Gao, Guangyu Wang, and Hanlin Sheng

Related authors

Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
Yimin Dou, Xinming Wu, Hui Gao, and Zhengfa Bi
EGUsphere, https://doi.org/10.48550/arXiv.2605.01273,https://doi.org/10.48550/arXiv.2605.01273, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
LFD (v1.0): Latent-Compression-Free Generative Diffusion with Geological Priors and Geophysical Regularization for Implicit Structural Modeling
Zhixiang Guo, Xinming Wu, Yimin Dou, Hui Gao, and Guillaume Caumon
EGUsphere, https://doi.org/10.5194/egusphere-2026-1087,https://doi.org/10.5194/egusphere-2026-1087, 2026
Short summary
ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023,https://doi.org/10.5194/gmd-16-2495-2023, 2023
Short summary

Cited articles

Chen, L., Lu, Y.-C., Guo, T.-L., and Deng, L.-S.: Growth characteristics of Changhsingian (Late Permian) carbonate platform margin reef complexes in Yuanba gas Field, northeastern Sichuan Basin, China, Geol. J., 47, 524–536, 2012. a
Duan, Y., Zheng, X., Hu, L., and Sun, L.: Seismic facies analysis based on deep convolutional embedded clustering, Geophysics, 84, IM87–IM97, 2019. a
Dunham, M., Malcolm, A., and Welford, J.: Toward a semisupervised machine learning application to seismic facies classification, in: EAGE 2020 Annual Conference & Exhibition Online, 2020, 1–5, European Association of Geoscientists & Engineers, 2020. a
Fensel, D., Simsek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., and Wahler, A.: Knowledge graphs, Springer, https://doi.org/10.1007/978-3-030-37439-6, 2020. a
Gao, H., Wu, X., Sun, X., and Hou, M.: cigFacies datasets: the massive-scale benchmark dataset of seismic facies, Zenodo [data set], https://doi.org/10.5281/zenodo.10777460, 2024a. a, b, c
Download
Short summary
We propose three strategies for field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a massive-scale, feature-rich, and high-realism benchmark dataset of seismic facies and evaluate its effectiveness in training a deep-learning model for automatic seismic facies classification.
Share
Altmetrics
Final-revised paper
Preprint