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.
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.
the Creative Commons Attribution 4.0 License.
Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application
Graduate School of Science, Kyoto University, Kitashirakawa Oiwakecho, Sakyo-ku, Kyoto, 606-8502 Japan
Kento Nakao
Baseload Power Japan, SHINTORA-DORI CORE 3F, 4-1-1, Shimbashi, Minato-ku, Tokyo, 105-0004 Japan
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Bedrock strength in bedrock river is often seen as controlling incision rates and river profiles, natural changes in rock type do not always match slope changes. In the Abukuma River basin, Japan, we measured bedrock strength and despite large strength differences, slopes were nearly uniform. Numerical tests showed that the model, which includes sediment cover and erosion effects, best explained river profiles. Thus, sediment plays a greater role than bedrock strength in shaping river profiles.
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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.
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We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.
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We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.
Rimali Mitra, Hajime Naruse, and Shigehiro Fujino
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A case study on the 2004 Indian Ocean tsunami was conducted at the Phra Thong island, Thailand, using a deep neural network (DNN) inverse model. The model estimated tsunami characteristics from the deposits at Phra Thong island. The uncertainty quantification of the result was evaluated. The predicted flow conditions and the depositional characteristics were compared with the reported observed values. This DNN model can serve as an essential tool for tsunami hazard mitigation at coastal cities.
Nanako Yamanishi and Hajime Naruse
EGUsphere, https://doi.org/10.5194/egusphere-2025-4283, https://doi.org/10.5194/egusphere-2025-4283, 2025
This preprint is open for discussion and under review for Earth Surface Dynamics (ESurf).
Short summary
Short summary
Bedrock strength in bedrock river is often seen as controlling incision rates and river profiles, natural changes in rock type do not always match slope changes. In the Abukuma River basin, Japan, we measured bedrock strength and despite large strength differences, slopes were nearly uniform. Numerical tests showed that the model, which includes sediment cover and erosion effects, best explained river profiles. Thus, sediment plays a greater role than bedrock strength in shaping river profiles.
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
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.
Koji Ohata, Hajime Naruse, and Norihiro Izumi
Earth Surf. Dynam., 11, 961–977, https://doi.org/10.5194/esurf-11-961-2023, https://doi.org/10.5194/esurf-11-961-2023, 2023
Short summary
Short summary
We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.
Koji Ohata, Hajime Naruse, and Norihiro Izumi
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2021-60, https://doi.org/10.5194/esurf-2021-60, 2021
Publication in ESurf not foreseen
Short summary
Short summary
We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.
Rimali Mitra, Hajime Naruse, and Shigehiro Fujino
Nat. Hazards Earth Syst. Sci., 21, 1667–1683, https://doi.org/10.5194/nhess-21-1667-2021, https://doi.org/10.5194/nhess-21-1667-2021, 2021
Short summary
Short summary
A case study on the 2004 Indian Ocean tsunami was conducted at the Phra Thong island, Thailand, using a deep neural network (DNN) inverse model. The model estimated tsunami characteristics from the deposits at Phra Thong island. The uncertainty quantification of the result was evaluated. The predicted flow conditions and the depositional characteristics were compared with the reported observed values. This DNN model can serve as an essential tool for tsunami hazard mitigation at coastal cities.
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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.
This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from...