Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2495-2023
https://doi.org/10.5194/gmd-16-2495-2023
Development and technical paper
 | 
09 May 2023
Development and technical paper |  | 09 May 2023

ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation

Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi

Related authors

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
Earth Syst. Sci. Data, 17, 595–609, https://doi.org/10.5194/essd-17-595-2025,https://doi.org/10.5194/essd-17-595-2025, 2025
Short summary

Related subject area

Solid Earth
Accelerated pseudo-transient method for elastic, viscoelastic, and coupled hydromechanical problems with applications
Yury Alkhimenkov and Yury Y. Podladchikov
Geosci. Model Dev., 18, 563–583, https://doi.org/10.5194/gmd-18-563-2025,https://doi.org/10.5194/gmd-18-563-2025, 2025
Short summary
Reconciling surface deflections from simulations of global mantle convection
Conor P. B. O'Malley, Gareth G. Roberts, James Panton, Fred D. Richards, J. Huw Davies, Victoria M. Fernandes, and Sia Ghelichkhan
Geosci. Model Dev., 17, 9023–9049, https://doi.org/10.5194/gmd-17-9023-2024,https://doi.org/10.5194/gmd-17-9023-2024, 2024
Short summary
Three-dimensional analytical solution of self-potential from regularly polarized bodies in a layered seafloor model
Pengfei Zhang, Yi-an Cui, Jing Xie, Youjun Guo, Jianxin Liu, and Jieran Liu
Geosci. Model Dev., 17, 8521–8533, https://doi.org/10.5194/gmd-17-8521-2024,https://doi.org/10.5194/gmd-17-8521-2024, 2024
Short summary
A fast surrogate model for 3D Earth glacial isostatic adjustment using Tensorflow (v2.8.0) artificial neural networks
Ryan Love, Glenn A. Milne, Parviz Ajourlou, Soran Parang, Lev Tarasov, and Konstantin Latychev
Geosci. Model Dev., 17, 8535–8551, https://doi.org/10.5194/gmd-17-8535-2024,https://doi.org/10.5194/gmd-17-8535-2024, 2024
Short summary
CitcomSVE 3.0: A Three-dimensional Finite Element Software Package for Modeling Load-induced Deformation for an Earth with Viscoelastic and Compressible Mantle
Tao Yuan, Shijie Zhong, and Geruo A
EGUsphere, https://doi.org/10.5194/egusphere-2024-3200,https://doi.org/10.5194/egusphere-2024-3200, 2024
Short summary

Cited articles

Adams, E. W. and Schlager, W.: Basic types of submarine slope curvature, J. Sediment. Res., 70, 814–828, https://doi.org/10.1306/2DC4093A-0E47-11D7-8643000102C1865D, 2000. a
Araya-Polo, M., Jennings, J., Adler, A., and Dahlke, T.: Deep-learning tomography, The Leading Edge, 37, 58–66, 2018. a
Asquith, D.: Depositional topography and major marine environments, Late Cretaceous, Wyoming, AAPG Bull., 54, 1184–1224, 1970. a
Athy, L. F.: Density, porosity, and compaction of sedimentary rocks, AAPG Bull., 14, 1–24, 1930. a
Badrinarayanan, V., Kendall, A., and Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE T. Pattern Anal., 39, 2481–2495, 2017. a, b
Download
Short summary
We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Share