Estimation of Tsunami Characteristics from Deposits: Inverse Modeling Using a Deep‐Learning Neural Network
Estimation of Tsunami Characteristics from Deposits: Inverse Modeling Using a Deep‐Learning Neural Network
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根据沉积物估计海啸特征：使用深度学习神经网络进行逆向建模
DOI：
10.1029/2020jf005583
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Abe Tomoya
中科院分区：
文献类型：
--
作者：
Mitra Rimali;Naruse Hajime;Abe Tomoya
海啸沉积物提供的信息，估计的规模和流动条件的paleotsunamis，和逆模型有可能重建的海啸从他们的存款的水力条件。大多数先前提出的模型是基于过于简化的假设，并具有一定的局限性。我们在FITTNUSS模型的基础上提出了一个新的逆模型，该模型考虑了悬浮泥沙的非均匀和非稳定输运以及湍流混合。本模型使用深度神经网络（DNN）进行反演方法。在该方法中，针对随机初始流动条件（例如，最大淹没长度、流速、最大水流深度和泥沙浓度），以生成沉积特征（如厚度和粒度分布）的人工训练数据集。然后，DNN被训练以基于从正向模型导出的人工数据集建立通用逆模型。使用独立的人工数据集进行的测试表明，经过训练的DNN可以从沉积物的特征中重建原始的流动条件。最后，将该模型应用于2011年东北冲海啸沉积物的数据集。流态预测结果与仙台平原的观测资料进行了验证。采用刀切法估计结果的精密度。不确定性分析后，流速和最大水深的估计结果分别约为5.40.1 m/s和4.10.2 m。DNN显示出从其沉积物重建海啸特征的希望，这将有助于估计古海啸的水力条件。
Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth, and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain‐size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku‐oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.40.1 m/s and 4.10.2 m, respectively, after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.
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DOI：
--
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