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The 2024 Noto Peninsula earthquake (M JMA 7.6) occurred, followed by a tsunami on January 1, 2024. The earthquake caused considerable damage by strong ground motion over a wide area centered on the Noto Peninsula. The tsunami also damaged coastal areas. Japan Agency for Marine–Earth Science and Technology (JAMSTEC), Earthquake Research Institute (ERI) and Atmosphere and Ocean Research Institute (AORI) of the University of Tokyo conducted emergency survey cruises with various observations such as ocean bottom seismometers (OBSs) deployment and recovery operations to identify aftershock activity promptly and acquisition of multibeam bathymetry data from the northeast area of the Noto Peninsula and Toyama Bay. Our survey mainly covered the northeast part of the aftershock area and the downstream part of the Toyama Deep–Sea Channel (TDSC) in the Toyama Bay and the southern Toyama Trough. Survey lines in the northern survey area generally trend NNE–SSW, roughly parallel to the channel; some lines in the NNW–SSE direction cross the active faults. We compiled a bathymetric map (with 25 m grid spacing), using all available multibeam echo sounder (MBES) data. We identified some important bathymetric features, such as gentle wavy topography developed over the levee and horseshoe-shaped landforms. In addition, we conducted a dense survey to detect depth differences before and after the earthquake in the area along TDSC in the middle of our survey area. Results revealed four small-scale landslide areas in the dense survey area. Obtaining detailed topographic data using modern multibeam sonar is extremely important to assess the risk of tsunami damage for various marine infrastructures as well as the potential occurrence of submarine landslides and slope failures. Graphical Abstract

The Noto Peninsula, extending northward into the Sea of Japan, features a narrow, elongated shape, complex coastal topography, and numerous active faults along its coastline. Since December 2020, intense earthquake swarms accompanied by crustal deformation have occurred in the northeastern peninsula, likely caused by fluid upwelling from deep underground. The largest event, a Magnitude 7.6 earthquake, struck on January 1, 2024, with aftershock distributions indicating multiple faults ruptured over approximately 150 km. This study aimed to clarify the temporal and spatial variation in seismic wave radiation and investigate the source process of the M7.6 event using the back-projection method. This method estimates the origin of wave packets recorded by a seismic array. In Japan, seismic networks operated by local governments often include densely distributed stations to evaluate seismic intensity. We used these dense sites as a seismic array complemented by strong ground motion data from NIED K-NET and KiK-net. The analysis assumed three fault planes, based on previous studies. Velocity waveforms in two frequency bands (0.05–2.0 Hz and 0.5–5.0 Hz) were used to estimate areas of strong radiation intensity, representing the sources of seismic waves. In the low-frequency band, strong radiation intensity was observed near the rupture initiation point and in shallow regions of the northern Noto Peninsula, corresponding to large fault slips that caused the uplift of the coastline. In contrast, no strong radiation intensity was detected off the northeast coast of the Noto Peninsula in the low-frequency band, suggesting the absence of a significant slip. High-frequency analysis revealed distributions of strong radiation intensities complementary to those in the low-frequency band. A subevent occurring around 20 s after the rupture initiation was found to originate near the northern coast of the Noto Peninsula. Graphical Abstract

The 2024 Mw 7.5 Noto Peninsula earthquake was the largest inland earthquake recorded by dense K-NET and KiK-net strong-motion seismographs in Japan after their establishment in the second half of the 1990s. The earthquake caused the death of hundreds of people and brought heavy damage to buildings and lifeline infrastructures in the region. Extensive ground deformation related to liquefaction occurred, and large amplitude spiky waveforms were recorded at several sites near the liquefied areas. Peak ground accelerations (PGAs) exceeding the acceleration due to gravity and peak ground velocities (PGVs) exceeding 100 cm/s were recorded at several stations within 20 km of the fault-rupture distances. From the viewpoint of mitigating future earthquake disasters, we compared the observed PGAs and PGVs with the most commonly used ground motion prediction equations and found that the PGAs and PGVs were generally typical of crustal earthquakes, reminding us of earthquake vulnerabilities of buildings and infrastructures, in the region. Analysis of nonlinear site responses during strong shakings is crucial for safe and optimized design of engineering structures at soil sites, because soil behavior depends on level of input motions. We examined the features of nonlinear site responses at sixty K-NET and KiK-net sites combined, where PGVs were approximately 10 cm/s or larger, by comparing S-wave spectral ratios between weak motions and those from the Noto Peninsula earthquake. We found that the spectral ratios for the Noto Peninsula earthquake were reduced at higher frequencies over about 3 Hz at several sites, and significant shifts of the predominant frequencies to lower ones were also observed at some of the sites, where relatively higher PGVs were observed. The comparison also indicated that degree of nonlinearity depended on the level of input motions and average S-wave velocity in the upper 30 m (Vs30) of the soil layers, but not at all sites. Plots of PGA and PGV/Vs30 as proxies to stress and strain elicited probable trends of nonlinear behaviors at the soil sites. Graphical Abstract

Before the M7.6 Noto Peninsula earthquake on 1 January 2024, which caused severe damage, an earthquake swarm started from May 2018 and became very active after December 2020 in the northeastern tip of the Noto Peninsula. It is widely considered that the swarm activity was triggered by upward migration of fluids with a large volume, as exemplified by Global Navigation Satellite System (GNSS) data that showed horizontal inflation and uplift around above the swarm area. However, the cause of the upward fluid migration has hardly been discussed. In this study, we consider this problem focusing on geodetic data. By applying a geodetic data inversion method based on basis function expansion to GNSS data in central Japan, we estimate the temporal change of strain-rate fields before and after the 2011 Tohoku-oki earthquake. Because of the postseismic deformation of the 2011 Tohoku-oki earthquake, the estimated strain-rate fields show a drastic change before and after the 2011 Tohoku-oki earthquake: dilatation rates and EW contraction rates reversed from contraction to extension in a wide area including the Noto Peninsula. The obtained strain-rate fields are further converted to the stress-rate fields under the condition of an isotropic elastic medium. The resulting extensive stress-rate field is likely to have facilitated the upward fluid migration, which would trigger the swarm activity in the Noto Peninsula since 2018. Graphical Abstract

This study reports sedimentological and paleontological features of deposits left by the 2024 Noto Peninsula tsunami in Suzu City, Japan. Tsunami deposits were found up to 70 m inland from the post-tsunami shoreline along our transect. The tsunami deposits were collected at five locations for observation by Soft X-ray and CT images, grain-size analysis, and diatom analysis. Soft X-ray and CT images identified that the five stratigraphic units (Units 1–5) at the most seaward location (SZ1) and deposits with faint laminae at the other locations (SZ2–4). Grain-size analysis showed that the tsunami deposits generally composed of fine to very fine sand at all sampled locations. At SZ1, Unit 3 exhibits climbing ripples with their leeside seaward. The ripple tops were probably dragged seaward. The eroded upper contact of Unit 4 implies yet another current at SZ1. Diatom assemblages within the tsunami deposits are dominated by marine and brackish species, except Unit 4 at SZ1 with more than 30% freshwater terrestrial species. Diatom assemblages in the tsunami deposits, vented sediments, and beach sand suggest that the SZ1 tsunami deposit was derived from both terrestrial and marine sources, while the main source was the coastal beach at the other locations.

To examine the challenges and practical realities of providing end-of-life care in welfare evacuation centers following the Noto Peninsula earthquake in Japan, and to identify lessons for improving disaster preparedness in similar settings. Case 1: A man in his late 90s was transferred to a welfare evacuation center after contracting COVID-19 in a general shelter. He arrived with fever and marked physical decline. Acetaminophen was administered to relieve his fever and provide comfort. His condition gradually worsened, and eight days after arriving at the evacuation shelter, he died peacefully while being closely observed by medical staff. Case 2: A man in his 60s with a history of smoking and alcohol use was found bedridden and incontinent at home and was subsequently moved to a welfare evacuation center. Two days after evacuation, he complained of leg and back pain, which was suspected to be due to arterial occlusion. He was monitored and provided with supportive care at the center, however, pain control remained inadequate. Four days after evacuation, he was found in respiratory arrest and was confirmed dead. These cases underscore the need for establishing unified guidelines and external support frameworks for end-of-life care in disaster settings. In a disaster-prone country like Japan, scenario-based training and the integration of trained volunteers are essential to ensuring dignified care for vulnerable evacuees.

This study explored closed-circuit television (CCTV) networks in northeastern Toyama Prefecture, Japan, as a new data source for tsunami detection following the 2024 Noto Peninsula earthquake. We analyzed CCTV footage and extracted time-series water level fluctuations at Yokoyama, Shimoiino, and Ekko. Spectral analysis of these waveforms revealed several long-period peaks (more than 100 s) in power spectral density (PSD), suggesting the presence of tsunami components. Notably, relatively large PSD peaks at approximately 5–10 min were observed at all CCTV locations in this study and at offshore wave observation points (Tanaka and Toyama). At Yokoyama, a maximum run-up of approximately 3 m was confirmed around 16:28. Although water level fluctuations at Shimoiino and Ekko were detected, identifying tsunami components proved challenging due to their small magnitude compared to other wave components. Despite these challenges, this study demonstrates the potential of CCTV networks for tsunami detection, and further research is needed to achieve real-time detection.

On January 1, 2024, an earthquake of magnitude 7.6 struck the Noto Peninsula in Ishikawa Prefecture, Japan. Tsunamis were recorded along nearby coasts following the earthquake, with the early tsunami arrival time at the Toyama tide station—located far from the earthquake epicenter—indicating potential submarine landslides in Toyama Bay. To identify these potential submarine landslides by detecting changes in seafloor depth, we collected new bathymetric data using a multibeam echo sounder in January and February 2024, and then compared them with data collected in 2010. This bathymetric comparison revealed submarine landslides along a submarine canyon off the Jinzu River, covering an area measuring 3.5 km × 1 km at depths of 40–370 m. Slide relief ranged from several meters up to 40 m, with some slides displaying distinct head scarps. Seafloor observations in areas with minor depth changes confirmed the presence of cliffs, disturbed seabed, and a redox boundary on the disturbed oxide layer, indicating recent landslides. Given the slide distribution within the estimated tsunami source area of the 2024 Noto Peninsula Earthquake, it is likely these slides were triggered by the earthquake.

A comprehensive analysis of modern tsunami deposits offers a valuable opportunity to elucidate the characteristics of paleo-tsunami deposits. On 1 January 2024, a tsunami was generated by a magnitude 7.6 seismic event and subsequently struck the Noto Peninsula in central Japan. In order to create a facies model of the tsunami deposits in terrestrial and riverine environments, field surveys were conducted on both the onshore and sandbars within the river channel in the Nunoura area on the northeastern Noto Peninsula. Terrestrial tsunami deposits were observed up to several hundred meters inland, with a slight decrease in thickness of several centimeters with distance from the shoreline. In terrestrial settings, the presence of a substantial silty layer overlying a graded sandy layer is indicative of ponded stagnant water from the tsunami wave. In contrast, riverine tsunami deposits are thicker and more extensive than terrestrial sediments, containing both gravels and shell fragments. An erosional surface develops between deposits of run-up and backwash flows, but a mud drape is not observed.

The MJMA 7.6 (Mw 7.5) Noto Peninsula Earthquake of 1 January 2024 in Japan triggered widespread slope failures across northern Noto region, but their spatial controls and susceptibility patterns remain poorly quantified. Most previous studies have focused mainly on fault rupture, ground deformation, and tsunami impacts, leaving a clear gap in machine learning based assessment of earthquake-induced slope failures. This study integrates 2323 mapped landslides with eleven conditioning factors to develop the first data-driven susceptibility framework for the 2024 event. Spatial analysis shows that 75% of the landslides are smaller than 3220 m2 and nearly half occurred within about 23 km of the epicenter, reflecting concentrated ground shaking beyond the rupture zone. Terrain variables such as slope (mean 31.8°), southwest-facing aspects, and elevations of 100–300 m influenced the failure patterns, along with peak ground acceleration values of 0.8–1.1 g and proximity to roads and rivers. Six supervised machine learning models were trained, with Random Forest and Gradient Boosting achieving the highest accuracies (AUC = 0.95 and 0.94, respectively). Explainable AI using SHapley Additive exPlanations (SHAP) identified slope, epicentral distance, and peak ground acceleration as the dominant predictors. The resulting susceptibility maps align well with observed failures and provide an interpretable foundation for post-earthquake hazard assessment and regional risk reduction. Further work should integrate post-seismic rainfall, multi-temporal inventories, and InSAR deformation to support dynamic hazard assessment and improved early warning.