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Traces of past landslides were found on the seabed of Palabuhanratu Bay, West Java. This landslide is thought to have generated a tsunami, but has never been investigated before. This bay is located around the western part of the Cimandiri Fault which is an active horizontal fault with a length of 100 km. Therefore, it is necessary to study the potential impact of a tsunami in the Palabuhanratu Bay area caused by a combination of local earthquakes and underwater landslides around the bay. Evidence of past landslides was revealed through side-scan sonar data from the underwater research vessel Baruna Jaya IV in Palabuhanratu Bay, Indonesia, in 2020. The data from this survey provides evidence of debris flows (historical landslide data) at the survey site. We simulated 29 tsunami scenarios from combined landslide earthquake sources by solving shallow water nonlinear equations numerically. Tsunami sources from earthquakes are classified into three types, e.g., land faults, sea faults, and combinations of land and sea faults. While the source of the tsunami from the landslide is divided by volume. Combination of the earthquake magnitudes range from M6.80 to M7.85, and the landslide volume ranged from 3.06 × 105 m3 to 2.5 × 108 m3. This study concludes that in our scenario, the M8.12 type T7 earthquake generates the largest tsunami in the study area, followed by the T6L5 scenario with M7.85 from the Cimandiri Fault and landslide with a total volume of 2.5 × 108 m3. [ABSTRACT FROM AUTHOR]

This paper presents the first example of how to systematically identify the submarine landslide source of a tsunami using an innovative hybrid approach. This ground-breaking method is developed to resolve the puzzle around the source mechanism of the mysterious tsunami observed on 6th February 2023 in the Eastern Mediterranean Sea. The tsunami followed the two inland Mw 7.8 and Mw 7.5 Türkiye–Syria earthquakes, which occurred consequently with a 9 h interval on this day. The first earthquake (Mw 7.8) had an epicentral distance of 90 km from the nearest coast, which is closer than the second one (Mw 7.5) to the coast and yet its crustal deformation was almost entirely limited to inland. Therefore, the co-seismic surface displacement generated by the earthquake was ruled out as the source of the tsunami, confirmed by numerical modelling. Here, we hypothesized that the tsunami was most likely generated by a submarine landslide triggered by the earthquake. Analysis of tide gauge observations revealed that the waves arrived from 27 min to 48 min after the first earthquake (Mw 7.8) at different coastal locations, implying that the potential submarine landslide was triggered by the first earthquake (Mw 7.8). Backward tsunami travel time mapping using tide gauge observations guided us to constrain the area of the potential landslide. We approximated the dimensions of the landslide using spectral analysis of the tsunami observations. Consequently, an iterative trial-and-error approach was employed to confirm the landslide source of the tsunami by defining various informed alternative landslide scenarios and applying numerical modeling. Modelling showed that a submarine landslide can reproduce the tsunami observations reasonably well. It is located on a steep slope of the seafloor approximately 50 km from Arsuz. The submarine landslide is estimated to have caused a seafloor deformation measuring approximately 16 km in length and 4.0 km in width. [ABSTRACT FROM AUTHOR]

A tsunamigenic earthquake with thrust faulting mechanism occurred southeast of the Loyalty Islands, New Caledonia, in the Southern Vanuatu subduction zone on the 10th of February 2021. The tsunami was observed at coastal gauges in the surrounding islands and in New Zealand. The tsunami was also recorded at a new DART network designed to enhance the tsunami forecasting capability of the Southwestern Pacific. We used the tsunami waveforms in an inversion to estimate the fault slip distribution. The estimated major slip region is located near the trench with maximum slip of 4 m. This source model with an assumed rupture velocity of 1.0 km/s can reproduce the observed seismic waves. We evaluated two tsunami forecasting approaches for coastal regions in New Zealand: selecting a pre‐computed scenario, and interpolating between two pre‐computed scenarios. For the evaluation, we made a reference map of tsunami threat levels in New Zealand using the estimated source model. The results show that the threat level maps from the pre‐computed Mw 7.7 scenario located closest to the epicenter, and from an interpolation of two scenarios, match the reference threat levels in most coastal regions. Further improvements to enhance the system toward more robust warnings include expansion of scenario database and incorporation of tsunami observation around tsunami source regions. We also report on utilization of the coastal gauge and DART station data for updating forecasts in real‐time during the event and discuss the differences between the rapid‐response forecast and post‐event retrospective forecasts. Plain Language Summary: We estimated the tsunami source of the 2021 Loyalty Islands earthquake from inversion of tsunami waveforms recorded at offshore DART and coastal stations. These DART stations are part of a new DART network that was designed to enhance the tsunami forecasting capability of New Zealand and the Southwestern Pacific region. The inversion result suggest that the earthquake ruptured the plate interface with relatively large slip near the trench. Our source model can well reproduce the observed tsunami and seismic waveforms. The tsunami threat level map for New Zealand coastal regions produced from the source model is then used as a reference map to evaluate two techniques for rapid tsunami forecasting. Both techniques utilize pre‐computed earthquake scenarios. The first technique is using the epicenter and magnitude of the earthquake to select the nearest earthquake scenario. The second technique interpolates pre‐computed results of two earthquake scenarios around the epicenter. The tsunami hindcast accuracies from the two techniques are high as the resulting tsunami threat levels matched the reference ones at most of the warning regions. Potential improvements to enhance the system toward more robust warnings include expansion of the scenario database and incorporation of tsunami observations around the source regions. Key Points: Significant near trench slip on the plate interface of the 2021 Loyalty Islands earthquake was estimated by inverting coastal and offshore tsunami waveformsAccurate tsunami forecast can be obtained from interpolating results of precomputed earthquake scenariosThe new New Zealand DART network is essential in improving the tsunami warning capability for countries in the South West Pacific [ABSTRACT FROM AUTHOR]

The 2019 Mw 7.0 Banten, Indonesia, earthquake occurred at a 49 km depth in a relatively unknown region, where the geological structure did not clearly show the fault. In this study, we use the Global Navigation Satellite System data to analyse the fault source of the earthquake. Following the earthquake's focal mechanism, we modelled a total of four fault models using two possible fault strikes, with each of the fault strikes investigated for shallow top depth and deeper top depth. This study also utilises the tide gauge data to confirm the tsunami waveform, modelled using the estimated coseismic slip. We present evidence of the shallow rupture of the 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake from an ENE-WSW fault direction. The tsunami modelling of a shallow top depth of an ENE-WSW fault direction is a better fit in predicting the tide gauge waveform. We also present evidence that the 2019 Banten intraslab earthquake generated very few aftershocks for a magnitude 7-class earthquake. The stress transfer of a shallow rupture ENE- WSW fault model was able to explain the relocated two weeks of aftershocks. [ABSTRACT FROM AUTHOR]

An unusual devastating tsunami occurred on September 28, 2018 after a strike‐slip faulting earthquake in Sulawesi, Indonesia. The induced tsunami struck Palu city with ∼4‐m flow depth. We performed two analyses to investigate the source of the tsunami. We first conducted the teleseismic source inversion and obtained the overall slip distribution of the strike‐slip fault. Our tsunami simulation from the coseismic deformation of the seismically estimated strike‐slip faulting produced a tsunami comparable to the leading part of the observation at Pantoloan. In order to reconstruct the detailed slip distribution on the fault plane, we then jointly utilized the tsunami waveform and Synthetic Aperture Radar (SAR) data. Because of the lack of SAR data in the bay, the tsunami data is necessary to constrain the offshore slip distribution, which directly induces the tsunami. The inverted source model shows a strike‐slip fault which consists of three segments extending from the epicenter to the south of 1.4°S with two bends and two asperities around Palu city. The joint inversion model accurately reconstructs the observed surface displacements and the leading part of the tsunami waveform. Our result exhibits the significant contribution of the strike‐slip faulting to the tsunami, but it also suggests additional tsunami sources, such as landslides, for the high inundations near Palu bay. The result also indicates that regional devastating tsunamis can be generated by an onshore strike‐slip fault with localized large dip slip. Plain Language Summary: The Palu, Indonesia, earthquake of September 28, 2018 produced tsunami flooding and damage in Palu city. Because of its strike‐slip mechanism, which is typically not efficient to produce tsunamis, multiple submarine landslides have been speculated as a tsunami source. We found that the fault model estimated by teleseismic waves (recorded globally outside Indonesia) can reproduce the tsunami recorded at the Pantoloan station in Palu bay. This indicates that the source of tsunami recoded at Pantoloan is mostly the fault motion due to the earthquake. We then combined the displacements measured by Synthetic Aperture Radar images and the Pantoloan tsunami waveform to estimate the detailed slip distribution on the fault. The slip model shows a strike‐slip fault with two large slip areas located near Palu city between two bends. This model well reproduces the Pantoloan tsunami but fails to fully reproduce the inundations in Palu city, suggesting that additional tsunami sources, such as landslides, should be responsible for the large inundations in Palu city. Our result suggests that a strike‐slip fault can induce a devastating local tsunami. Key Points: Two analyses utilizing teleseismic, Synthetic Aperture Radar, and tsunami data retrieve an earthquake source model that explains the observed tsunamiA strike‐slip fault with localized large dip slip of the 2018 Palu earthquake induced the devastating regional tsunamiOnshore strike‐slip faults should also be regarded as a potential source of tsunamis for regional areas [ABSTRACT FROM AUTHOR]

Two repeating compensated linear vector dipole (CLVD) earthquakes occurred in 2009 and 2017 on the Kermadec Ridge near the Curtis submarine volcano. The tsunami and seismic waveforms of both events are almost identical. Simulated tsunami and seismic waveforms are compared with observations of the 2017 event to estimate the location and geometry of the source. A CLVD source model at 8–14 km depth is consistent with the global CMT solution but generates no tsunami. The tsunami waveforms can be well reproduced if the source model is about 1.5 km deep. However, a seismic source at this depth is not consistent with the observed seismic waves. This suggests that two sources were involved with different depths. The main, shallow, tsunami source could be due to hydrofracturing of heated water in a shallow sediment layer triggered by a deeper earthquake. Plain Language Summary: Two different earthquakes that generate almost identical tsunami and seismic waves are extremely rare. However, this occurred in 2009 (Mw 6.0) and 2017 (Mw 5.9) in the Kermadec Ridge, New Zealand. Both tsunamis were also much larger than expected from their moment tensor solutions. This kind of tsunami is very dangerous because the tsunami observation network is sparse and the tsunami threat could be underpredicted. We use computer codes to simulate the tsunami and seismic waves from the 2017 event and compare a suite of observations to the models to study the source mechanism. First, we use tsunami simulation results to find the exact location of the source. Further tsunami and seismic wave simulations are then used to further estimate the earthquake source model. We find that the observations can best be explained by two sources within the Curtis submarine volcano. The deeper source is responsible for most of the seismic energy while the shallow source appears to be mostly responsible for the tsunami. Key Points: Tsunami and seismic waveforms suggest that the 2009 and 2017 events are repeating tsunami earthquakes, with similar magnitudes and mechanismThe source of the 2017 tsunami is located at Curtis Island with sea surface displacement peak of 3.0 mThe estimated source model for the 2017 earthquake consists of two sources at 8 km (Mw 5.8) and 1.5 km (Mw 5.3) depth [ABSTRACT FROM AUTHOR]

In the subduction zone off the west coast of central Sumatra, two great earthquakes, the 2007 great Bengkulu earthquake (Mw 8.4) and the 2010 Mentawai tsunami earthquake (Mw 7.8), occurred along the plate interface. Although the moment magnitude of the 2010 earthquake was much smaller than that of the 2007 earthquake, the tsunami heights resulting from the former 2010 earthquake were higher than those resulting from the latter 2007 earthquake, indicating that tsunami heights are difficult to forecast. An advanced method for determining appropriate source models that can explain the tsunami heights along coastal areas is needed for tsunami warning purposes. In this study, fault parameters were estimated from the W-phase inversion, and fault length and width were calculated from suitable scaling relations between those and the magnitude for the 2007 and 2010 earthquakes. Tsunami numerical simulations were conducted using various slip amounts or corresponding rigidities. The best slip amount or corresponding rigidity was selected by comparing the measured and computed tsunami heights. For the 2007 Bengkulu earthquake, the measured tsunami heights are well explained using a rigidity of 3.0 × 1010 Nm−2 (7.59-m slip amount). For the 2010 Mentawai tsunami earthquake, the measured tsunami heights are well explained using a rigidity of 1.5 × 1010 Nm−2 (8.17-m slip amount). From those results, we determined the depth-dependent rigidity relation for Central Sumatra to estimate appropriate source models in our tsunami height forecasting method. [ABSTRACT FROM AUTHOR]

Sustaining an accurate, timely, and global tsunami forecast system remains a challenge for scientific communities. To this end, various viable geophysical monitoring devices have been deployed. However, it is difficult to implement new observation networks in other regions and maintaining the existing systems is costly. This study proposes a new and complementary approach to monitoring the tsunami using existing moving platforms. The proposed system consists of a radar altimeter, Global Navigation Satellite Systems receiver, and an adequate communication link on airborne platforms such as commercial airplanes, drones, or dedicated high-speed aircraft, and a data assimilation module with a deterministic model. We demonstrated, through twin-data experiment, the feasibility of the proposed system in forecasting tsunami at the Nankai Trough of Japan. Our results demonstrated the potential of an airborne tsunami observation as a viable future technology through proxy observations and rigorous numerical experiments. The wide coverage of the tsunamigenic regions without a new observation network is an advantage while various regulatory constraints need to be overcome. This study offered a novel perspective on the developments in tsunami detection and forecasting technology. Such multi-purpose observation using existing platforms provides a promising and practical solution in establishing sustainable observational networks. [ABSTRACT FROM AUTHOR]

On 28 September 2018, a strike‐slip earthquake occurred in Palu, Indonesia, and was followed by a series of tsunami waves that devastated the coast of Palu Bay. The tsunami was recorded at the Pantoloan tide gauge station with a peak amplitude of ~2 m above the water level and struck at high tide. We use the Pantoloan tsunami waveform and synthetic aperture rada displacement data in a joint inversion to estimate the vertical displacement around the narrow bay. Our inversion result suggests that the middle of the bay was uplifted up to 0.8 m, while the other parts of the bay subsided by up to 1 m. However, this seafloor displacement model alone cannot fully explain the observed tsunami inundation. The observed tsunami inundation heights and extents could be reproduced by a tsunami inundation simulation with a source model that combined the estimated vertical displacement with multiple subaerial‐submarine landslides. Plain Language Summary: The tsunami that devastated Palu and other coastal towns inside Palu Bay on 28 September 2018 was recorded at a sea level monitoring station in Pantoloan. The recorded tsunami wave data are used in an inversion method to estimate the source of the tsunami in the form of an initial seafloor displacement. We found that the seafloor displacement was the main cause of the large tsunami. Satellite images and field survey data suggest that landslides around multiple river deltas also generated local tsunami waves. Our numerical simulations of the tsunami inundation show that the disaster was caused by a combination of the sudden ground and seafloor changes from the earthquake, landslides, and the high tide at the time of the event. Key Points: The series of tsunami waves that created the disaster in Palu was caused by a combination of seafloor uplift and multiple landslidesThe seafloor vertical displacement was estimated using tsunami waveform and SAR data and is evaluated to be the source the largest tsunamiGround subsidence of up to 1 m and a tide level of 1 m during the event enhanced the tsunami impact in Palu city [ABSTRACT FROM AUTHOR]

The 2016 Kaikoura, New Zealand, earthquake was one of the most complex ruptures ever recorded. The epicentre was located well inland, but the rupture area extended offshore and generated a modest tsunami which was recorded by tide gauges. Here, we present a detailed estimate of seafloor vertical displacement during the earthquake sequence by a joint inversion of tsunami waveforms and vertical displacement data observed at GPS stations and obtained by field surveys. The combined dataset provides a solution with good resolution, capable of resolving test sources of 20 km of characteristic diameter throughout the study area. We found two seafloor uplift regions which are located very close to the coast, one is located offshore of the Kaikoura peninsula and the other larger uplift region is located near the Kekerengu and Needles faults. To estimate crustal deformation with a complete spatial coverage of the event, the estimated seafloor vertical displacement was combined with the inland vertical displacement from InSAR and GPS datasets. This vertical displacement is then inverted for the fault slip distributions of the Needles, Jordan-Kekerengu, Papatea, Hundalee, Hump faults, and a newly identified fault beneath Kaikoura. We also found that the Needles fault is probably an offshore extension of the Kekerengu fault. The seismic moment calculated from the fault slip distributions by assuming a rigidity of 2.7 × 1010 N/m2, is 5.19 × 1020 Nm or equivalent to Mw 7.8. This total seismic moment estimate is consistent with that of the Global Centroid Moment Tensor solution. The tsunami potential energy calculated from the seafloor vertical displacement is 9.40 × 1012 J, of which about 70% is attributed to movement on the faults known to have ruptured, suggesting a secondary source for tsunami generation. [ABSTRACT FROM AUTHOR]

We present an approach to enhance the development of Near-Field Tsunami Inundation Forecasting (NearTIF). We combine the inversion method with the original NearTIF to obtain a more accurate tsunami source, utilizing Ocean Bottom Pressure Gauge (OBPG) data. We use Mw 8.5 hypothetical earthquake located in the Java megathrust to assess the optimum time window of OBPG data for inversion with a smoothing factor. The 15-min time series data obtained a good-fit fault slip distribution, indicated by high correlation and low RMSE. Additionally, Green's function based on tsunami waveform predictions is the substitute for the low-resolution forward modeling part of the original NearTIF to acquire inundation forecasts with a reduced computational time. The 330 pre-computed tsunami scenario, using a non-linear numerical model, is employed for matching and shifting between the predicted and pre-computed waveform in the 15 virtual observation point along the Kulon Progo coast, the area of interest, using five hypothetical sources varying from Mw 8.5 to Mw 8.9. The Aida number (K) implies that the developed NearTIF gives accuracy in the acceptable range (0.6 < K < 1.4) in less than 2 min after the tsunami recorded in the OBPG. The technique presented in this paper could provide near real-time and accurate inundation forecast. • A combination of inversion method and original NearTIF to provide a rapid tsunami inundation forecast. • Utilizing 15 minutes tsunami waveform at OBPG can obtain a reliable slip distribution for tsunami early warning purposes. • Utilizing the Akaike Bayesian Information Criteria (ABIC) for the inversion process with a smoothing factor. • Analysing the accuracy of tsunami inundation using Aida number (K) and its corresponding standard deviation (κ). [ABSTRACT FROM AUTHOR]

The method was numerically tested for two large underthrust fault models, a giant earthquake ( I M i SB w sb 8.8) and the Nemuro-oki earthquake ( I M i SB w sb 8.0) models along the Kurile arc subduction zone. Katsumata and Nakatani ([4]) tested the seismic quiescence hypothesis through retrospective trials of alarm-based earthquake prediction in the Kurile-Japan subduction zone. 10.1186/s40623-020-01293-0 4 Katsumata K, Nakatani M. Testing the seismic quiescence hypothesis through retrospective trials of alarm-based earthquake prediction in the Kurile-Japan subduction zone. [Extracted from the article]

The January 2024 Noto Peninsula earthquake (M w 7.6) generated a destructive tsunami in the Sea of Japan region. Locally, inside Toyama Bay, the tsunami arrived at the Toyama wave and tide gauges within 2–3 min of the earthquake. Such an early tsunami arrival is inconsistent with the epicentral distance of ∼60 km. The travel time of the earthquake-induced tsunami is anticipated to be around 20 min, which is confirmed through tsunami simulations. Based on spectral and wavelet analyses of the Toyama tide and wave gauge records, we found 5 min, 14 min, and 32 min dominant wave periods. The shorter wave periods of 5 and 14 min are likely associated with a submarine landslide due to their early arrivals, as evidenced in our wavelet analysis. We then identified a potential submarine landslide location using the tsunami back-propagated travel time technique and visual inspection of the bathymetric profile. Conducting several simulations and comparing simulated and observed waveforms, we identified a seafloor landslide length of approximately 3000 m located ∼4 km offshore Toyama City. Our combined earthquake-landslide source model better reproduces the tsunami observations, indicating the contribution of the submarine landslide to the January 2024 Noto Peninsula tsunami. • Earthquake source models cannot explain the early arrival of tsunami at Toyama city during the 2024 Noto Peninsula event. • An additional source is needed to approximate the observed tsunami at Toyama tide and wave gauges. • A submarine landslide of length ∼3000 km occurred 4 km offshore Toyama city potentially caused the early tsunami arrivals. • These findings suggest serious implications for future tsunami disaster mitigation in the region. [ABSTRACT FROM AUTHOR]

Correction to: Earth Planets Space (2021) 73:122 https://doi.org/10.1186/s40623-021-01439-8 Following publication of the original article (Tanioka et al. [1]), the authors reported an error in the third reference. References 1 Tanioka Y, Uchida N, Gusman AR, Shishikura M, Nishimura T. Special issue, "Kurile arc subduction zone: View of great earthquake generation and disaster mitigation of related phenomena". [Extracted from the article]

We analyze the regional tsunami hazard along the Sea of Japan coast associated with 60 active faults beneath the eastern margin of the Sea of Japan. We generate stochastic slip distribution using a Monte Carlo approach at each fault, and the total number of required earthquake samples is determined based on convergence analysis of maximum coastal tsunami heights. The earthquake recurrence interval on each fault is estimated from observed seismicity. The variance parameter representing aleatory uncertainty for probabilistic tsunami hazard analysis is determined from comparison with the four historical tsunamis, and a logic-tree is used for the choice of the values. Using nearshore tsunami heights at the 50 m isobath and an amplification factor by the Green's law, hazard curves are constructed at 154 locations for coastal municipalities along the Sea of Japan coast. The highest maximum coastal tsunamis are expected to be approximately 3.7, 7.7, and 11.5 m for the return periods of 100-, 400-, and 1000-year, respectively. The results indicate that the hazard level generally increases from southwest to northeast, which is consistent with the number and type of the identified fault systems. Furthermore, the deaggregation of hazard suggests that tsunamis in the northeast are predominated by local sources, while the southwest parts are likely affected by several regional sources. [ABSTRACT FROM AUTHOR]

The January 1, 2024 Noto Peninsula M w 7.5 tsunamigenic earthquake, with at least 241 deaths, was the most destructive event in Japan following the March 2011 M w 9.0 catastrophic event. We conducted field surveys in the affected area 24 days after the event to document tsunami heights, runups, coseismic coastal uplift and damage to coastal structures. Here, we present the results of the surveys and analyze tsunami height distribution and associated damage. In our survey of 29 locations, tsunami runups and heights varied in the ranges of 4.4–6.2 m, and 1.0–4.4 m, respectively. Notably, Joetsu, the farthest location from the epicenter in our field survey, recorded the highest runup of 6.2 m, which is attributed to the directivity effect of tsunami waves. The maximum surveyed runup of 6.2 m closely aligns with the reported maximum fault slip of 4–6 m for this earthquake, confirming a long-established rule of thumb. Coastal crustal uplifts of up to 1.6 m have rendered many ports unsuitable for use. We identified three failure mechanisms affecting coastal structures: overturning caused by tsunami backwash, damage resulting from tsunami inundation currents and wave pressure, and impacts from floating objects and debris. • Maximum runup of the Jan. 2024 Noto Peninsula (Japan Sea) tsunami was 6.2 m. • Coastal crustal uplifts of up to 1.6 m have rendered many ports unsuitable for use. • Maximum tsunami runup (6.2 m) was comparable to the maximum fault slip (4–6 m). • Three failure mechanisms observed: tsunami backwash, wave pressure, debris impacts. [ABSTRACT FROM AUTHOR]

We estimate the fault model of the 2 September 2009 Tasikmalaya intraslab earthquake based on the GPS data available in western Java, Indonesia. The focal mechanism of the earthquake was used to help construct two possible fault models: a west-dipping fault with a strike of 160.8° and an east-dipping fault with a strike of 34.0°. In this study, vertical information from GPS data is crucial for constructing the top depth of the fault. The subsidence information from GPS data located near the epicenter suggests that the earthquake involved a deeper fault model. While the amount of the moment release of the east-dipping fault (Model dipE) is equivalent to Mw 6.9, the moment release of the west-dipping fault (Model dipW) is equivalent to Mw 6.8. The GPS data inversion indicates that Model dipW produces a better fit than Model dipE. The tsunami simulation indicates that the tsunami height generated by the east-dipping fault is smaller than that generated by the west-dipping fault, implying that the maximum tsunami height of the latter is closer to agreement with the reported one. Unlike Model dipE, the stress transfer analysis of Model dipW indicates that most of the aftershocks were located in the region where ΔCFF is positive, suggesting positive stress from the ruptured triggered aftershocks. The combined analysis of GPS data, tsunami simulation, and stress transfer suggests that the fault ruptured during the 2009 earthquake was dipping westward with a steep dip angle. • GPS data, tsunami simulation, and stress transfer for analysing crustal deformation related to earthquake occurrences. • The 2009 Tasikmalaya intraslab earthquake ruptured at a west-dipping fault. • Positive stress from the mainshock triggering the aftershocks explain by the at a west-dipping fault. [ABSTRACT FROM AUTHOR]

Abstract At Numanohama Marsh on the rocky Sanriku coast in northeast Japan, we identified 17 sand layers with sedimentary structures characteristic of event deposits (i.e., a sharp erosional contact with the underlying unit, a normal and/or inverse grading structure, parallel/cross lamination, mud clasts, and/or a thin mud layer). Abundant marine nannofossils, coccoliths, and the small catchment area in the narrow valley suggest that these event layers were likely formed by inundations during tsunamis or storms. Among the 17 sand layers, nine contained gravels derived from the beach, riverbed, or slope areas. Six event layers contained all three of these gravels, suggesting a strong inflow current that transported round pebbles from the beach, erosion of wall rock in the valley during the inflow and outflow phases, and a backwash current that caused sub-angular riverbed gravel to accumulate in lowland areas. These six layers were traced up to 490 m inland from the coastline (present elevation: 7 m above mean sea level) and were several cm thick at almost every survey point. We concluded that these six event layers are tsunami deposits, and dating results revealed that they correlate with the 2011 Tohoku-oki earthquake; the 1933 and 1896 Sanriku-oki earthquakes; any of the 1763 Aomori-oki, 1793 Miyagi-oki, and 1856 Aomori-oki earthquakes; the 1611 Tohoku-oki earthquake; and the 869 Tohoku-oki (Jogan) earthquake. In addition, changes in the sedimentary environment inferred from diatom assemblages were well correlated with the deposition of three of the event layers. Two historical earthquakes — the 1454 and 1611 Tohoku-oki earthquakes — have been previously suggested as being the immediate predecessors of the 2011 Tohoku-oki earthquake, and our dating results favor the 1611 earthquake. Although the source region of the 869 Tohoku-oki (Jogan) earthquake has been estimated to have been off the Fukushima and Miyagi prefectures, our results suggest that the source region extended further north along the Japan Trench. Highlights • Seventeen event deposits for 2000 years were examined on the Sanriku coast, Japan. • Sources of gravel deposits are classified as beach, riverbed, and slope. • Gravel compositions are used to infer the inundation behavior of event deposits. • Marine nannofossil and diatoms are used to infer origin and environment of deposits. • Event deposits are correlated with 1611, 869 and other historical tsunamis. [ABSTRACT FROM AUTHOR]

A geological survey was conducted in a small valley along the Sanriku coast, and the grain compositions of both autochthonous gravels and the 2011 tsunami deposit from the Tohoku-oki earthquake were investigated to reveal the depositional form of the tsunami deposits on the ria-type coast. The features of autochthonous gravels in the beach, riverbed, and slope areas are characterized by a combination of their rock type and roundness. The roundness values of the beach and slope gravels are large (0.8–0.9) and small (0.1–0.2), respectively, regardless of rock type, and the riverbed gravels are typically granite rocks with an intermediate roundness (0.4–0.5). The 2011 tsunami deposits along the survey transect include the above autochthonous gravels and show several characteristic features, such as a sharp erosional contact with the underlying peaty soil, normal and/or inverse grading structures, mud sheets, and richness in gravels in the lower unit, while the thickness shows no systematic changes with the distance from the coast. The presence of slope gravels suggests high tsunami inundation and flux, which eroded bare rock and talus and transported them to the valley plain by backwash current. The mixture of beach and riverbed deposits implies the formation of both an upstream current from the beach, which transported beach pebbles inland, and a backwash current along the channel, which accumulated sub-angular riverbed gravels around the river mouth. The characteristics of the 2011 tsunami deposit on the Sanriku coast, where narrow valleys are surrounded by high cliffs with steep slopes, are significantly different from those of sandy tsunami deposits in Sendai Plain, where a flat plain develops within several kilometers of the coastline. The differences among sedimentary facies are affected by the topography and autochthonous deposits. [ABSTRACT FROM AUTHOR]