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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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.