Japan Earthquake Research Articles

Emergency Management in Japan: Human Decision‐Making Strategy Analysis During Large‐Scale Earthquake

ABSTRACTIn the context of unexpected disasters, comprehending individual decision‐making strategy is essential for governmental policy formulation and evacuation planning. The main challenges stem from the absence of large‐scale mobility data and the complex influences of multiple factors on personal strategies. To address this, this study focuses on the 2011 Tohoku earthquake in Japan as a case, which struck at 14:46 on March 11, a typical working day. Decision‐making strategies regarding how people return home from their workplaces have emerged as a primary concern. Therefore, we construct a large human mobility database for the Greater Tokyo area, and develop an empirical prediction for decision‐making strategies following earthquakes with the consideration of multiple factors. Except for users' location‐based information, we extract the grid‐based stay areas from historical stay records (normal days before disasters) and discover the functions of these areas combined with points of interest located within them. Experimental results indicate the effectiveness of our proposed framework in strategy prediction. Besides, we conduct an explainable feature importance analysis of key factors, which also provides insights for understanding human decisions during disasters.

Deep vein thrombosis in response to stress induced by earthquakes in Japan: a meta-analysis of possible exacerbating factors

The principal goal of this study was to assess factors associated with deep vein thrombosis (DVT) in the aftermath of earthquakes in Japan. We searched PubMed, Google Scholar, Web of Science, and Cochrane Library for articles published in English or Japanese regarding indicators for DVT associated with Japanese earthquakes. We calculated pooled odds ratios (OR) or mean differences (MD) with 95% confidence intervals (CIs) for patients with DVT (the DVT group) as compared with the non-DVT group for potential predictors. Ultimately, 7 articles were included in the analysis, comprising 6,637 subjects (DVT, 895; and non-DVT, 5,742). The following factors proved statistically significant: female gender (OR = 1.48, 95% CI; 1.20–1.81, p = 0.0002), greater age (MD = 4.44, 95% CI; 1.62–7.25, p = 0.002), hypertension (OR = 1.44, 95% CI; 1.18–1.75, p = 0.0003), heart disease (OR = 1.45, 95% CI; 1.07–1.97, p = 0.02), previous history of DVT (OR = 9.23, 95% CI; 2.94–28.91, p = 0.0001), sleeping pill use (OR = 1.81, 95% CI; 1.37–2.38, p = 0.0001), lower leg varix (OR = 1.63, 95% CI; 1.17–2.28, p = 0.004), and soleal vein dilatation ≥ 8 mm (OR = 2.13, 95% CI; 1.32–3.42, p = 0.002). Our findings furnish insights that can aid in making informed choices about healthcare and policy in the context of earthquake-induced stress.

A Six-Year (2014–2020) Statistical Correlation Study of VLF Terminator Time Shift with Earthquakes in Japan

In this study, we present a six-year (2014–2020) statistical analysis of VLF subionospheric propagation data at 19 VLF receivers from the VLF transmitter with call name JJI and frequency 22.2 kHz, all located in Japan. Moderate and strong earthquakes (EQs) (ML≥4.5 and depth ≤ 50 km) that occurred in the wider area around Japan during the same time period with the available VLF data are investigated. The terminator times’ (TT) shift in VLF amplitude data as a possible precursor of an EQ are statistically examined, focusing on the correlation with seismic activity. The concept of the effective EQ magnitude (Meff) is used in order to define the total EQ energy possibly affecting the midpoint of each path for each day. It is important to note that dates when geomagnetic storms or solar flares occurred as well as dates corresponding to the already known winter effect on TT statistics in the north–south direction were excluded. The cross-correlation between TT statistical anomalies and seismic activity, represented by Meff, was extracted. Maximum cross-correlation values were found for most of the cases prior to the subsequent seismic activity, indicating a link between the ionospheric anomalies and the subsequent seismicity. Finally, the wide temporal range of the cross-correlation maxima temporal locations is justified by the inhomogeneity of the lower ionosphere, coupled with the anisotropy of the preseismic effect of the impending seismicity, highlighting the complexity of the EQ preparation processes.

Emerging place governance to lost places for communities: Government planning and citizen-driven placemaking in the disaster memorial parks after the 2011 Japan tsunami

Physical destruction, land use management, residential buyouts, and residential relocation following disasters disconnects people and places. Previous studies have revealed the unintended negative effects of residential buyouts and displacements, such as the reproduction of vulnerability, along with social injustice and also suggested that places determine the critical elements of long-term community recovery. We explored how government place-based planning and citizen-driven placemaking transform place identity of lost places after the 2011 Japan tsunami. We selected tsunami reconstruction memorial parks as lost places for local communities, and analyzed government planning reports, interviewed citizen-driven placemakers, and conducted questionnaire survey to clarify residents’ perception of place. Our results showed that the sense of belonging was determined by their sense of place in relation to memorial parks, as “part of the everyday landscape,” “nostalgia,” and “symbol of reconstruction.” We also found that citizen-driven placemaking embodied the concept of disaster memorial parks developed by government planning: requiem, commemoration, symbol of reconstruction. These results suggest that citizen-driven placemaking can potentially serve as a driving force to transform a sense of place that enhances the sense of belonging to lost places. We conclude that local authorities must pay attention to the fluctuation in the meaning of lost places that may influence long-term community recovery. Governments, civic organizations, and locals need to promote collaboration and pursue multi-stakeholder land use governance which can enhance community resilience.

A Resolution of the Variability Discrepancy Between Peak Ground Acceleration and Spectral Stress Drop

Abstract It has been recognized that the between-event variability of peak ground acceleration (PGA) is significantly smaller than the variability of the stress drop calculated from corner frequency (the spectral stress drop). Resolving this discrepancy is indispensable for improving seismic hazard assessment because the spectral stress drop is considered a fundamental parameter for predicting high-frequency ground motions. This study addresses this paradox for Mw 3.6–7.1 crustal earthquakes in Japan. Two factors are essential for resolving the problem: (1) calculating the spectral stress drop using a high-frequency-fitted corner frequency (called the stress parameter Δσfch) and (2) considering the magnitude dependence of Δσfch. To estimate Δσfch, the source spectra for crustal earthquakes in Japan are obtained using the two-stage spectral ratio method, which enables the estimation of double-corner-frequency spectra. This two-stage approach is more effective for accurately estimating Δσfch than the standard spectral ratio approach that assumes the single-corner frequency model. This study shows that Δσfch increases with increasing magnitude up to Mw∼5.5 and then becomes constant. The variability of Δσfch calculated by considering this magnitude dependence of Δσfch aligns with the between-event variability of PGA. Incorporating a double-corner-frequency model is crucial for predicting ground-motion variability and enhancing seismic hazard assessments.

The Sofu Seamount Submarine Volcano Present in the Source Area of the October 2023 Earthquakes and Tsunamis in Japan

AbstractOn 8 October 2023 (UTC), unique earthquakes occurred in the Izu‐Ogasawara Arc, Japan, in which the P‐ and S‐phases were barely visible and only the T‐phases were evident, followed by tsunamis that reached islands in the Izu‐Ogasawara Arc and a wide area of the Pacific coast of southwest Japan. Our estimated T‐phase source area coincides with the Sofu Seamount, which was previously unrecognized as an active submarine volcano. A bathymetric survey of the seamount conducted 1 month after the event revealed characteristics of the seamount with a caldera and a central cone. Compared to the bathymetry in 1987, the topography in the caldera had changed significantly such as a crater forming in the central cone. This seamount is likely to be an active volcano. The topographic changes on the caldera‐sized scale that occurred at the caldera can be explained as a source of the October tsunami.

Long-period ground motion simulation and pulse probability distribution characteristics of the Kumamoto MW 7.1 earthquake in Japan

The 2016 Kumamoto MW 7.1 earthquake in Japan caused severe structural damage. It produced valuable seismic records that offered an opportunity to investigate the characteristics of long-period ground motion and pulse probability distribution. This study works in three main parts. First, we use the finite difference method (FDM) to reproduce the long-period ground motions of the 2016 Kumamoto MW 7.1 earthquake. That provides a crucial foundation for the subsequent phases. We identify velocity pulses within both synthetic and observed waveforms. By rigorously analyzing these pulses, we unveiled a wealth of insights into the characteristics of velocity pulses, strengthening the influence of the rupture directivity effect. In particular, the northeast region of the rupture exhibited a significant concentration of pulsed attributes. Finally, we further focus on establishing statistical relationships between different pulse parameters. The study revealed that the pulse probability distribution model fit well with the observed pulse distribution in the parallel to the fault (FP) and normal to the fault (FN) directions. However, the model also faced challenges in the FN direction due to the mechanism of velocity pulses and altered rupture propagation direction. Crucially, the study established that velocity pulses primarily cluster within a fault distance of less than 30 km, and the relationship between pulse peak value and fault distance followed an exponential trend. The study underscores the imperative of further refining the probability distribution model of velocity pulses, making it more robust and widely applicable. Furthermore, the correlation between velocity pulses and seismic parameters requires further validation through seismic records. This study sets the stage for a deeper understanding of earthquake-induced ground motion and pulse characteristics, contributing to the ongoing efforts in seismic risk assessment.

Towards multi-variable tsunami damage modeling for coastal roads: Insights from the application of explainable machine learning to the 2011 Great East Japan Event

The accurate assessment of tsunami-induced damage to coastal roads is crucial for effective disaster risk management. Traditional approaches, reliant on univariate fragility functions, often fail to capture the complex interplay of variables influencing road damage during tsunami events. This study addresses this limitation by employing machine learning techniques on an extensive dataset compiled after the 2011 Great East Japan tsunami. The dataset, enriched with additional explicative variables accounting for the hydraulic features of the event and the physical characteristics at roads’ location, enables a comprehensive analysis of road damage mechanisms. Results indicate that while inundation depth remains a significant predictor, factors such as wave approach angle, road orientation and potential overflow from inland watercourses also play critical roles.

An end-to-end framework for fire following earthquake simulation at regional scale: A case study on the 2024 Japan Noto Peninsula earthquake

Fire following earthquakes remains a significant and threatening hazard to communities in urban regions. Addressing this critical issue requires an effective simulation method that is suitable for widespread adoption. This paper presents a systematic end-to-end framework for simulating fire following earthquakes at a regional scale by integrating models and methods available in existing literature. Relevant theories and models are summarized and incorporated into the calculation workflow. The framework encompasses three key features: (1) GIS-based regional building data management, (2) physics-based fire simulation, and (3) high-fidelity visualization. The implementation of the framework is presented in a sequential and detailed manner, with a focus on its practicality and replicability. Each component of the workflow is designed to be adaptable, allowing for easy customization and improvement to accommodate unique site characteristics or the integration of new models through the introduction of sub-modules. To demonstrate the practicality of the proposed framework, a case study based on the recent 2024 Noto Peninsula earthquake in Japan is presented. The simulation results correspond well with the observed actual damage at the fire following earthquake disaster site, with an accuracy of 87.8 %, which validates the framework's accuracy and reliability in simulating fire following earthquakes.

Building a Sustainable and Resilient U.S. Disaster Response and Relief Logistics System: Lessons from Domestic and International Events

The United States (U.S.) has experienced frequent and severe natural disasters in recent time. This has in turn uncovered significant challenges in disaster relief and emergency response systems, particularly in the area of logistics. These challenges often lead to delays, inefficiencies and environmental consequences. Hence, creating a two-sided challenge, that requires both sustainability initiatives as well as a resilient Logistics system to fix. The lessons learned from prior domestic and international occurrences such as Hurricane Katrina, the 2011 Japan tsunami, and the Haiti earthquake presents the basis for the paper to examine the weaknesses and develop means of improving the current system. Therefore, this paper focuses on how the U.S. can improve its disaster response and relief system while building a resilient logistics system through unique strategies and recommendations inspired by the lessons learned from previous happenings. The recommendations for improvement include the implementation of green technologies, the decentralization of the system, improved public-private partnerships, and the optimization of the supply chain through the uses of predictive analytics and blockchain technology (Ahmed et al., 2024; Bealt et al., 2016; Hoshiba & Ozaki, 2014).

Generative Adversarial Networks-Based Ground-Motion Model for Crustal Earthquakes in Japan Considering Detailed Site Conditions

ABSTRACT We develop a ground-motion model (GMM) for crustal earthquakes in Japan that can directly model the probability distribution of ground-motion acceleration time histories based on generative adversarial networks (GANs). The proposed model can generate ground motions conditioned on moment magnitude, rupture distance, and detailed site conditions defined by the average shear-wave velocity in the top 5, 10, and 20 m (VS5, VS10, and VS20) and the depth to shear-wave velocities of 1.0 km/s and 1.4 km/s (Z1.0 and Z1.4). We construct the neural networks based on styleGAN2 and introduce a novel neural network architecture to generate ground motions considering the effect of source, path, and such detailed site conditions. The resulting 5% damped spectral acceleration from the proposed GMM is consistent with empirical GMMs in terms of magnitude and distance scaling. The proposed GMM can also generate ground motions accounting for the shear-wave velocity profiles of surface soil with different magnitudes and distances and represent characteristics that are not explained solely byVS30.

Machine learning-based prediction of statically equivalent seismic forces in pin-supported cylindrical reticulated shells

Reticulated shells exhibit complex vibrations during earthquakes, encompassing components in horizontal and vertical directions, and multiple vibration modes occur. In particular, single-layer reticulated shells with a small depth relative to their span exhibit many vibration modes, and the shapes of these modes can vary depending on the geometry. The method for rapidly setting equivalent static seismic forces remains unexplored. In response to the above background, this study proposes a novel approach for calculating the seismic forces on single-layer reticulated shells using machine learning techniques. The shells in focus are pin-supported cylindrical reticulated shells, typically for the roofs of gymnasiums used as evacuation facilities during severe earthquakes in Japan. Machine learning uses numerical analysis results for approximately 20,000 shells, with varied spans, half-open angles, and aspect ratios. A method for preprocessing the principal vibration modes as image data is proposed, after which the imaged vibration modes are predicted from the shape parameters of the shell using a neural network. The prediction accuracy is analyzed, and a method for rapidly calculating seismic loads based on combining predicted vibration modes is proposed. These seismic loads are compared with the response spectrum method results, and their effectiveness is discussed.

Dual-initiation ruptures in the 2024 Noto earthquake encircling a fault asperity at a swarm edge.

To reveal the connections between the 2024 moment magnitude (Mw) 7.5 Noto earthquake in Japan and the seismicity swarms that preceded it, we investigated its rupture process through near-source waveform analysis and source imaging techniques, combining seismic and geodetic datasets. We found notable complexity in the initial rupture stages. A strong fault asperity, which remained unbroken in preceding seismic swarms, slowed down the rupture. Then, a second rupture initiated at the opposite edge of the asperity, and the asperity succumbed to double-pincer rupture fronts. The failure of this high-stress drop asperity drove the earthquake into a large-scale event. Our observations help unravel the crucial role of fault asperities in controlling swarm migration and rupture propagation and underscore the need for detailed seismological and interdisciplinary studies to assess seismic risk in swarm-prone regions.

Post-seismic topographic shifts and delayed vegetation recovery in the epicentral area of the 2018 Mw 6.6 Hokkaido Eastern Iburi earthquake

The 2018, Mw 6.6 Hokkaido Eastern Iburi earthquake in Japan triggered over 10,000 landsliding in an area spanning about 500 km2, altering the local topography and leading to the accumulation of loose deposits on hillslopes and in valleys. However, a comprehensive post-seismic landslide inventory and an assessment of topographic changes are lacking, hindering a quantitative hazard assessment. Additionally, the extent of vegetation recovery in areas affected by coseismic landslides, a key indicator of post-seismic debris flow hazard, has not been evaluated. Here, we utilize high-resolution digital elevation models and multi-temporal satellite imagery to analyze topographic changes and vegetation dynamics in the earthquake’s epicentral area (seismic intensity >5.5). We observe that the event roughened the overall gentle topography of the region and made the slopes steeper. Owing to the absence of significant rainstorms and snowmelt post 2018, only a few debris remobilizations (60) and new landslides (80) have occurred in the affected region. Moreover, we noticed a slow vegetation recovery in the post-seismic phase, suggesting that the likelihood of debris flows and gully erosion remains elevated, highlighting the need for continued monitoring and assessment.

Estimating the final fatalities using early reported death count from the 2023 Kahramanmaraş, Türkiye, MS 8.0–7.9 earthquake doublet and revising the estimates over time

Earthquakes can cause significant damage and loss of life, necessitating immediate assessment of the resulting fatalities. Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decision-making. This study using the February 6, 2023, MS 8.0 and MS 7.9 Kahramanmaraş, Türkiye earthquakes as an example to estimate the ultimate number of fatalities. An early Quick Rough Estimate (QRE) based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye (AFAD) is conducted, and it dynamically adjusts these estimates as new data becomes available. The range of estimates of the final number of deaths can be calculated as 31,384–56,475 based on the "the QRE of the second day multiplied by 2–3″ rule, which incorporates the reported final deaths 50,500. The Quasi-Linear and Adaptive Estimation (QLAE) ​method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths. The correct order of magnitude of the final death toll can be estimated as early as 13 ​hr after the MS 8.0 earthquake. In addition, additional earthquakes such as May 12, 2008, MS 8.1 Wenchuan earthquake (China), September 8, 2023, MS 7.2 Al Haouz earthquake (Morocco), November 3, 2023, MS 5.8 Mid-Western Nepal earthquake, December 18, 2023, MS 6.1 Jishishan earthquake (China), January 1, 2024, MS 7.2 Noto Peninsula earthquake (Japan) and August 8, 2023, Maui, Hawaii, fires are added again to verified the correctness of the model. The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an MS 7.4 earthquake. These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation (QLARM) and Prompt Assessment of Global.

Properties of boiled sand samples from various liquefied ground sites caused by recent great earthquakes in japan and worldwide

Properties of boiled sand samples from various liquefied ground sites caused by recent great earthquakes in japan and worldwide

Dataset of Post-Event Survey of the 2024 Noto Peninsula Earthquake Tsunami in Japan

An earthquake with a moment magnitude of 7.5 (Mw) struck the northern Noto Peninsula, Ishikawa Prefecture, Japan, at 16:10 local time on January 1, 2024. This earthquake triggered a tsunami that propagated along the coastline of Ishikawa, Toyama, and Niigata Prefectures facing the Sea of Japan and significantly damaged coastal communities and infrastructure. Approximately 70 researchers from 23 universities or other institutes throughout Japan formed a joint research group to conduct a post-tsunami survey along a 340 km stretch of the coast. Based on the watermarks and traces of the tsunami, the inundation and run-up heights were surveyed using total stations, automatic optical levels, laser range finders, and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The tidal correction was adjusted using astronomical tidal tables. In total, 303 survey records have been compiled, generating the NP2024TS (Noto Peninsula 2024 Tsunami Survey) dataset. This dataset provides comprehensive information on the inundation and run-up heights of the tsunami, which is useful for understanding tsunami characteristics and validating numerical tsunami models.

Assessing the Predictive Power of GPS-Based Ground Deformation Data for Aftershock Forecasting

Abstract We present a machine learning approach for aftershock forecasting of the Japanese earthquakes catalog. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations on the day of the mainshock to predict aftershock location. The quality of data heavily relies on the density of GPS stations: the predictive power is lost when the mainshocks occur far from measurement stations, as in offshore regions. Despite this fact and the small number of samples and the large number of parameters, we are able to limit overfitting, which shows that this new approach is very promising.

Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku-oki M9.

Rock strength has long been linked to lithospheric deformation and seismicity. However, independent constraints on the related elastic heterogeneity are missing, yet could provide key information for solid Earth dynamics. Using coseismic Global Navigation Satellite Systems (GNSS) data for the 2011 M9 Tohoku-oki earthquake in Japan, we apply an inverse method to infer elastic structure and fault slip simultaneously. We find compliant material beneath the volcanic arc and in the mantle wedge within the partial melt generation zone inferred to lie above ~100 km slab depth. We also identify low-rigidity material closer to the trench matching seismicity patterns, likely associated with accretionary wedge structure. Along with traditional seismic and electromagnetic methods, our approach opens up avenues for multiphysics inversions. Those have the potential to advance earthquake and volcano science, and in particular once expanded to InSAR type constraints, may lead to a better understanding of transient lithospheric deformation across scales.

Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment

Coastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.