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 Discuss., https://doi.org/10.5194/essd-2024-337,https://doi.org/10.5194/essd-2024-337, 2024
Preprint under review for ESSD
Short summary

Related subject area

Solid Earth
ShellSet v1.1.0 parallel dynamic neotectonic modelling: a case study using Earth5-049
Jon B. May, Peter Bird, and Michele M. C. Carafa
Geosci. Model Dev., 17, 6153–6171, https://doi.org/10.5194/gmd-17-6153-2024,https://doi.org/10.5194/gmd-17-6153-2024, 2024
Short summary
FastIsostasy v1.0 – a regional, accelerated 2D glacial isostatic adjustment (GIA) model accounting for the lateral variability of the solid Earth
Jan Swierczek-Jereczek, Marisa Montoya, Konstantin Latychev, Alexander Robinson, Jorge Alvarez-Solas, and Jerry Mitrovica
Geosci. Model Dev., 17, 5263–5290, https://doi.org/10.5194/gmd-17-5263-2024,https://doi.org/10.5194/gmd-17-5263-2024, 2024
Short summary
Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time
Sia Ghelichkhan, Angus Gibson, D. Rhodri Davies, Stephan C. Kramer, and David A. Ham
Geosci. Model Dev., 17, 5057–5086, https://doi.org/10.5194/gmd-17-5057-2024,https://doi.org/10.5194/gmd-17-5057-2024, 2024
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-1893,https://doi.org/10.5194/egusphere-2024-1893, 2024
Short summary
Benchmarking the accuracy of higher-order particle methods in geodynamic models of transient flow
Rene Gassmöller, Juliane Dannberg, Wolfgang Bangerth, Elbridge Gerry Puckett, and Cedric Thieulot
Geosci. Model Dev., 17, 4115–4134, https://doi.org/10.5194/gmd-17-4115-2024,https://doi.org/10.5194/gmd-17-4115-2024, 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.