Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application

Naruse, Hajime; Nakao, Kento

doi:https://doi.org/10.5194/esurf-9-1091-2021

Articles | Volume 9, issue 5

https://doi.org/10.5194/esurf-9-1091-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-9-1091-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 5

Research article

|

03 Sep 2021

Research article |

| 03 Sep 2021

Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application

Hajime Naruse and Kento Nakao

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Final revised paper (published on 03 Sep 2021)
Preprint (discussion started on 19 Nov 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Referee Comment', Anonymous Referee #1, 02 Jan 2021
- AC1: 'Reply on RC1', Hajime Naruse, 28 Feb 2021
RC2: 'Review', Anonymous Referee #2, 22 Feb 2021
- AC2: 'Reply on RC2', Hajime Naruse, 28 Feb 2021

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Hajime Naruse on behalf of the Authors (28 Jun 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (08 Jul 2021) by Paola Passalacqua

RR by Anonymous Referee #1 (26 Jul 2021)

ED: Publish as is (30 Jul 2021) by Paola Passalacqua

ED: Publish subject to technical corrections (06 Aug 2021) by Niels Hovius (Editor)

AR by Hajime Naruse on behalf of the Authors (07 Aug 2021) Author's response Manuscript

Short summary

This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.