Preprints
https://doi.org/10.5194/egusphere-2023-369
https://doi.org/10.5194/egusphere-2023-369
24 Apr 2023
 | 24 Apr 2023

Understanding flow characteristics from tsunami deposits at Odaka, Joban coast, using a DNN inverse model

Rimali Mitra, Hajime Naruse, and Tomoya Abe

Abstract. The 2011 Tohoku-oki tsunami inundated the Joban coastal area in the Odaka region of Minamisoma City, up to 2,818 m from the shoreline. In this study, the flow characteristics of the tsunami were reconstructed from deposits using the DNN (deep neural network) inverse model, suggesting that the tsunami inundation occurred in the Froude-supercritical condition. The DNN inverse model effectively estimated the tsunami flow parameters in the Odaka region, including the maximum inundation distance, flow velocity, maximum flow depth, and sediment concentration. Despite having a few topographical instabilities that caused the flow height to fluctuate greatly, the reconstructed maximum flow depth and flow velocity were reasonable and close to the values reported in the field observations. The reconstructed data around the Odaka region were characterized by an extremely high velocity (12.1 m/s). This study suggests that the large fluctuation of flow depths at the Joban coast compared with the stable flow depths at the Sendai plain can be explained by the inundation in the supercritical flow condition.

Journal article(s) based on this preprint

08 Feb 2024
Understanding flow characteristics from tsunami deposits at Odaka, Joban Coast, using a deep neural network (DNN) inverse model
Rimali Mitra, Hajime Naruse, and Tomoya Abe
Nat. Hazards Earth Syst. Sci., 24, 429–444, https://doi.org/10.5194/nhess-24-429-2024,https://doi.org/10.5194/nhess-24-429-2024, 2024
Short summary
Rimali Mitra, Hajime Naruse, and Tomoya Abe

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-369', Anonymous Referee #1, 01 Jun 2023
    • CC1: 'Reply on RC1', Rimali Mitra, 29 Jun 2023
    • AC1: 'Reply on RC1', Hajime Naruse, 16 Jul 2023
  • RC2: 'Comment on egusphere-2023-369', Masaki Yamada, 20 Jun 2023
    • CC2: 'Reply on RC2', Rimali Mitra, 29 Jun 2023
    • AC2: 'Reply on RC2', Hajime Naruse, 16 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-369', Anonymous Referee #1, 01 Jun 2023
    • CC1: 'Reply on RC1', Rimali Mitra, 29 Jun 2023
    • AC1: 'Reply on RC1', Hajime Naruse, 16 Jul 2023
  • RC2: 'Comment on egusphere-2023-369', Masaki Yamada, 20 Jun 2023
    • CC2: 'Reply on RC2', Rimali Mitra, 29 Jun 2023
    • AC2: 'Reply on RC2', Hajime Naruse, 16 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (30 Sep 2023) by Rachid Omira
AR by Hajime Naruse on behalf of the Authors (27 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (20 Dec 2023) by Rachid Omira
AR by Hajime Naruse on behalf of the Authors (22 Dec 2023)

Journal article(s) based on this preprint

08 Feb 2024
Understanding flow characteristics from tsunami deposits at Odaka, Joban Coast, using a deep neural network (DNN) inverse model
Rimali Mitra, Hajime Naruse, and Tomoya Abe
Nat. Hazards Earth Syst. Sci., 24, 429–444, https://doi.org/10.5194/nhess-24-429-2024,https://doi.org/10.5194/nhess-24-429-2024, 2024
Short summary
Rimali Mitra, Hajime Naruse, and Tomoya Abe

Data sets

Odaka_DNN_inverse_2011_tsunami: DNN inverse model for 2011 Tohoku-oki tsunami at Odaka, Japan Rimali Mitra, Hajime Naruse, and Tomoya Abe https://doi.org/10.5281/zenodo.4764153

Model code and software

DNN inverse model for 2011 Tohoku-oki tsunami at Odaka, Japan Rimali Mitra, Hajime Naruse, and Tomoya Abe https://doi.org/10.5281/zenodo.4764153

Rimali Mitra, Hajime Naruse, and Tomoya Abe

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This study estimates the behavior of the 2011 Tohoku-oki tsunami from its deposit distributed in the Joban coastal area. In this study, the flow characteristics of the tsunami were reconstructed using the DNN (deep neural network) inverse model, suggesting that the tsunami inundation occurred in the very high-velocity condition.