Articles | Volume 15, issue 17
Geosci. Model Dev., 15, 6841–6861, 2022
https://doi.org/10.5194/gmd-15-6841-2022
Geosci. Model Dev., 15, 6841–6861, 2022
https://doi.org/10.5194/gmd-15-6841-2022
Development and technical paper
08 Sep 2022
Development and technical paper | 08 Sep 2022

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

Zhengfa Bi et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-117', Anonymous Referee #1, 31 May 2022
    • AC1: 'Reply on RC1', Xinming Wu, 27 Jun 2022
  • RC2: 'Comment on gmd-2022-117', Anonymous Referee #2, 01 Jun 2022
    • AC2: 'Reply on RC2', Xinming Wu, 27 Jun 2022
  • CEC1: 'Comment on gmd-2022-117', Juan Antonio Añel, 15 Jun 2022
    • AC3: 'Reply on CEC1', Xinming Wu, 27 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xinming Wu on behalf of the Authors (02 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (05 Jul 2022) by Thomas Poulet
RR by Anonymous Referee #2 (12 Jul 2022)
RR by Anonymous Referee #1 (21 Jul 2022)
ED: Publish subject to minor revisions (review by editor) (22 Jul 2022) by Thomas Poulet
AR by Xinming Wu on behalf of the Authors (24 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (26 Jul 2022) by Thomas Poulet
AR by Xinming Wu on behalf of the Authors (27 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Jul 2022) by Thomas Poulet
Download
Short summary
We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.