Articles | Volume 16, issue 9
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-245', Xuesong Ding, 17 Jan 2023
    • AC1: 'Reply on RC1', Xinming Wu, 28 Feb 2023
  • RC2: 'Comment on gmd-2022-245', Mark Jessell, 23 Jan 2023
    • AC2: 'Reply on RC2', Xinming Wu, 28 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Xinming Wu on behalf of the Authors (02 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (31 Mar 2023) by Lutz Gross
RR by Mark Jessell (04 Apr 2023)
RR by Xuesong Ding (08 Apr 2023)
ED: Publish as is (11 Apr 2023) by Lutz Gross
AR by Xinming Wu on behalf of the Authors (12 Apr 2023)  Manuscript 
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.