Kyoto University · 지구·행성과학
Rimali Mitra 교수의 연구실은 해일 화분과 유체역학적 조건을 고도로 정교하게 재구성하는 데 초점을 맞춘 기초 및 응용 연구를 수행하고 있습니다. 특히, 깊이 신경망(DNN)을 활용한 역모델링 기법을 통해 고대 해일의 침수 거리, 유속, 침하두께, 퇄성물 농도 등을 정밀하게 추정하는 데 전문성을 기르고 있으며, 인도양, 일본 동북부 해안 등 실제 해일 피해 지역을 대상으로 한 사례 연구를 통해 재해 예측 및 대비 전략 수립에 기여하고 있습니다. 연구는 환경 변화와 해일의 상호작용을 이해하는 데에도 응용되고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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 mod
Abstract. 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 reconstruc
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 inundati
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 mod
Abstract. The 2011 Tohoku-oki tsunami inundated the Joban coastal area in the Odaka region of Minamisoma City, up to 2,818 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 dis
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).
<strong class="journal-contentHeaderColor">Abstract.</strong> The 2011 Tohoku-oki tsunami inundated the Joban coastal area in the Odaka region of Minamisoma City, up to 2,818 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 th
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 mod
