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 paper
 | 
10 Feb 2025
Data description paper |  | 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

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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
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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.
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