The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

中文翻译：

---

使用深度神经网络逆模型重建帕通岛 2004 年印度洋海啸沉积物的流动条件

2004年印度洋海啸给沿海地区造成重大经济损失和大量人员死亡。使用逆模型估计海啸流量条件已成为减灾和管理的一个基本方面。在这里，使用结合了深度神经网络 (DNN) 的逆向建模，对泰国受到 2004 年印度洋海啸影响的帕通岛进行了案例研究。DNN逆向分析利用后海啸沉积物的厚度和粒度分布重建了最大淹没距离、流速和最大流深等流动条件值，以及五个粒度级的含沙量。 -帕通岛周围的海啸调查。还使用折刀法报告了不确定性的量化。使用以前应用于帕通岛及其周围地区的其他模型，将预测的流动条件与报告的观测值和模拟结果进行比较。估算的单位面积体积、粒度分布等沉积特征与实地调查的实测值一致。这些定性和定量比较表明，DNN 逆模型是估计现代海啸物理特征的潜在工具。估算的单位面积体积、粒度分布等沉积特征与实地调查的实测值一致。这些定性和定量比较表明，DNN 逆模型是估计现代海啸物理特征的潜在工具。估算的单位面积体积、粒度分布等沉积特征与实地调查的实测值一致。这些定性和定量比较表明，DNN 逆模型是估计现代海啸物理特征的潜在工具。