Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

Abstract. The 2004 Indian Ocean tsunami caused major topographic changes that resulted in 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, in relation to the 2004 Indian Ocean tsunami, a case study involving the Phra Thong island in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The inverse analysis reconstructed the values of flow conditions such as maximum inundation length, flow velocity and maximum flow depth, sediment concentration from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other 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 characteristics of modern tsunamis.