Understanding flow characteristics from tsunami deposits at Odaka, Joban Coast, using a deep neural network (DNN) inverse model

Rimali Mitra, Hajime Naruse, Tomoya Abe
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Abstract

Abstract. The 2011 Tohoku-oki tsunami inundated the Joban coastal area in the Odaka region of the city of Minamisoma, up to 2818 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 anthropogenic undulations that caused the inundation 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−1). This study suggests that the large fluctuation in flow depths on the Joban Coast compared with the stable flow depths in the Sendai Plain can be explained by the inundation in the supercritical flow condition.
利用深度神经网络(DNN)反演模型了解常磐海岸小田中海啸沉积物的流动特征
摘要2011 年的东北海啸淹没了南相马市大高地区的常磐沿海地区,距离海岸线长达 2818 米。在这项研究中,利用 DNN(深度神经网络)反模型从沉积物中重建了海啸的流动特征,表明海啸淹没发生在 Froude 超临界条件下。DNN 反演模型有效地估算了小田中地区的海啸流参数,包括最大淹没距离、流速、最大流深和沉积物浓度。尽管有一些地形人为起伏导致淹没高度波动较大,但重建的最大水流深度和水流速度是合理的,与实地观测报告的数值接近。小田中地区周围重建数据的特点是流速极高(12.1 m s-1)。这项研究表明,与仙台平原稳定的水流深度相比,常磐海岸的水流深度波动较大,这可以用超临界水流条件下的淹没来解释。
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