Tsunami damage to ports: cataloguing damage to create fragility functions from the 2011 Tohoku event

Modern tsunami events have highlighted the vulnerability of port structures to these high-impact but infrequent occurrences. However, port planning rarely includes adaptation measures to address tsunami hazards. The 2011 Tohoku tsunami presented us with an opportunity to characterise the vulnerability of port industries to tsunami impacts. Here, we provide a spatial assessment and photographic interpretation of freely available data sources. Approximately 5000 port structures were assessed for damage and stored in a database. Using the newly developed damage database, tsunami damage is quantified statistically for the first time, through the development of damage fragility functions for eight common port industries. In contrast to tsunami damage fragility functions produced for buildings from an existing damage database, our fragility functions showed higher prediction accuracies (up to 75 % accuracy). Pre-tsunami earthquake damage was also assessed in this study and was found to influence overall damage assessment. The damage database and fragility functions for port industries can inform structural improvements and mitigation plans for ports against future events.

Rapid Assessment of Tsunami Offshore Propagation and Inundation with D-FLOW Flexible Mesh and SFINCS for the 2011 Tōhoku Tsunami in Japan

This study demonstrates the skills of D-FLOW Flexible Mesh (FM) and SFINCS (Super-Fast INundation of CoastS) in combination with the Delft Dashboard Tsunami Toolbox to numerically simulate tsunami offshore propagation and inundation based on the example of the 2011 Tōhoku tsunami in Japan. Caused by a megathrust earthquake, this is one of the most severe tsunami events in recent history, resulting in vast inundation and devastation of the Japanese coast. The comparison of the simulated with the measured offshore water levels at four DART buoys located in the Northwestern Pacific Ocean shows that especially the FM but also the SFINCS model accurately reproduce the observed tsunami propagation. The inundation observed at the Sendai coast is well reproduced by both models. All in all, the model outcomes are consistent with the findings gained in earlier simulation studies. Depending on the specific needs of future tsunami simulations, different possibilities for the application of both models are conceivable: (i) the exclusive use of FM to achieve high accuracy of the tsunami offshore propagation, with the option to use an all-in-one model domain (no nesting required) and to add tsunami sediment dynamics, (ii) the combined use of FM for the accurate simulation of the tsunami propagation and of SFINCS for the accurate and time efficient simulation of the onshore inundation and (iii) the exclusive use of SFINCS to get a reliable picture of the tsunami propagation and accurate results for the onshore inundation within seconds of computational time. This manuscript demonstrates the suitability of FM and SFINCS for the rapid and reliable assessment of tsunami propagation and inundation and discusses use cases of the three model combinations that form an important base for tsunami risk management.

Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks

Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

Progress and remaining problems in subduction paleoseismology along the Pacific coast of northeast Japan

<p>In the last two decades, tsunami geology in northeast Japan (Hokkaido and Tohoku) has focused on extending the record of tsunamis beyond the range of historical documents in the region. In Hokkaido facing to southern Kuril trench, recurrent sandy deposits interbedded with peat are regarded as evidence of historical and prehistoric tsunamis. Distribution of one of the sand layers just below a historic tephra (Ko-c2; 1694 CE), so-called 17th-century tsunami deposit, exceeds historical and recent tsunami inundations in eastern Hokkaido. Numerical simulations to reproduce the distributions first suggested a multi-segment fault model with unimodal slip (Mw > 8.4; Nanayama et al., 2003 in Nature), and later with variable slip (Mw > 8.8; Ioki and Tanioka, 2016 in EPSL). Tohoku region, facing to Japan trench, has longer historical record than Hokkaido and the oldest historical earthquake is the Jogan event in 869 CE. Numerical simulations constrained by spatial distributions of the tsunami deposits, coastal submergence, and observation of the 2011 Tohoku tsunami deposit suggest that the 869 event was a plate-boundary rupture at least 200 km long along the Japan Trench (Mw > 8.3–8.6). After the 2011 Tohoku event, a large tsunami in 1454 CE (Kyotoku event) became reexamined and considered to have been generated by a rupture area including the Miyagi-oki region (part of the Jogan rupture). If the 869. 1454, and 2011 events were similar, recurrence of earthquakes in Japan trench is more periodic than southern Kuril trench.   This presentation is based on descriptions and discussion in Sawai (2020) in Earth Science Reviews.  </p>