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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]

The dual frequency Global Navigation Satellite System (GNSS) observations could determine the total electron content (TEC) in the ionosphere. In this study, GNSS TEC was applied to detect traveling ionospheric disturbances (TIDs) after the eruption of Hunga Tonga–Hunga Ha'apai (HTHH) on 15 January 2022. The eruption caused two types of tsunamis, first is tsunami generated by atmospheric wave (meteo‐tsunami) and second is caused by eruption induces water displacement or tsunami classic. At the same time with former tsunami, the atmospheric wave (shock and lamb waves) also caused TIDs at a speed of approximately ∼0.3 km/s. We found moderate correlation between this TIDs amplitude and the tsunami wave height model from tide gauge stations in New Zealand (0.64) and Australia (0.65). Further we attempted to reveal 3D structure of the TIDs in New Zealand, South Australia, and Philippines using 3D tomography. The tomography was set up > 1,170 blocks, as large as 1.0° (east–west) × 1.0° (north–south) × 100 km (vertical), up to 600 km altitude over selected regions. Tomogram shows beautiful concentric directivity of the first TIDs generated by atmospheric wave (AW). Key Points: 3D ionospheric disturbances during the 2022 Tonga eruption was investigated with spatial and temporal directionThe highest concentration of the electron disturbances was observed at an altitude of 200–300 km, which decreased at 500–600 kmA correlation between ionospheric disturbances and the height of tsunami associated with eruption was observed [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 tsunami that occurred after the Hunga Tonga-Hunga Ha'apai volcano eruption on 15 January 2022 was unusually fast and large, particularly at large distances from the source. Here we use an observation-calibrated air-wave model to generate ocean waves using a tsunami model. We used pressure data observed at 94 stations in Niue, the Cook Islands, and New Zealand's main and outer islands to obtain a simple air-wave model. The modelled air-wave travels at an approximated constant speed of 317 m/s with an amplitude that decays with distance from the volcano. We then simulated the generation and propagation of tsunami due to the propagating air-wave in the atmosphere above the ocean. The leading sea surface displacement excited by the pressure disturbances travels at the same speed as the air-wave. This leading wave is then followed by subsequent water waves that travel in the same direction as the leading wave but at the conventional tsunami propagation speed. In the model, the air-wave was more effective at generating a tsunami when it travelled over a deep bathymetric feature like the Kermadec-Tonga Trench. The tsunami amplitudes observed at gauges do not decay as rapidly with distance from the volcano as would be expected for a localized tsunami source. This is due to the continuous excitation of the tsunami as the air-wave propagates across the ocean. In shallow water, the leading water surface displacement can often be much smaller than the later waves that were most likely to have been generated in the deep ocean. A better understanding of the complexity of tsunami generation and propagation of this kind is important to help improve future tsunami disaster mitigation from such sources. [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]

We investigated the effects of fault parameter uncertainty on the deterministic assessment of tsunami hazards for the submarine and coastal active faults in the Sea of Japan that were recently modeled by the Integrated Research Project on Seismic and Tsunami Hazards around the Sea of Japan. A key parameter in scenario-based tsunami assessment is the fault slip amount, which is usually calculated from empirical scaling relations that relate the fault size to the slip. We examined four methods to estimate the fault slip amounts and compared the coastal tsunami heights from the slip amounts obtained by two different empirical relations. The resultant coastal tsunami heights were strongly affected by the choice of scaling relation, particularly the fault aspect ratio (fault length/fault width). The geometric means of the coastal tsunami heights calculated from the two methods ranged from 0.69 to 4.30 with an average of 2.01. We also evaluated the effects of fault slip angles, which are also important parameters for controlling coastal tsunami heights, by changing the slip angles for faults in the southwestern and central parts of the Sea of Japan, where the strike-slip faults are concentrated. The effects of uncertainty of the fault slip angles (± 30° from the standard) on the coastal tsunami heights were revealed to be equal to or greater than those resulting from the choice of scaling relations; the geometric means of the coastal tsunami heights from the modified fault slip angles relative to the standard fault slip angles ranged from 0.23 to 5.88. Another important characteristic is that the locations of the maximum coastal tsunami height and the spatial pattern of the coastal tsunami heights can change with varying fault slip angles. [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]

Historical records have proven that the southern coasts of Java, Indonesia, are prone to tsunamis. The existing tsunami observing system using bottom pressure gauges, also known as tsunameters, composes a global network but is too sparse for regional tsunami forecasts. The nearest tsunameter to Indonesia is located approximately 500‐km offshore, which is not very useful for the area, particularly for a tsunami source in eastern part of the Sunda megathrust. Here we propose a methodology to optimally place offshore tsunameters with the main consideration of operational costs, which is proportional to the number of observation points. We use a stochastic slip model of earthquakes with Mw 8.8–9.2 to generate multiple tsunami source realizations representing possible future events in the region. Based on the sources, we then identify a potential zone where an early tsunami detection is feasible. Furthermore, empirical orthogonal functions derived from tsunami simulations are used to determine the initial locations for tsunameter placement inside the specified zone. Finally, we apply an optimization scheme to improve the initial locations that facilitates both tsunami forecasts and source characterizations through a tsunami waveform inversion analysis. Our result indicates that six tsunameters are sufficient to efficiently cover the major seismogenic region in the study area. Key Points: The study proposes an efficient design of tsunami observation network off southern coast of Java, IndonesiaWe utilize optimizations to determine the best observation locations for multiple earthquake models (Mw 8.8‐9.2)The result suggests locations of six tsunameters that can provide accurate tsunami forecasts and source characterizations [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]

Abstract The grain size distribution of a tsunami deposit may have a correlation with the tsunami inundation process, and further with the tsunami source characteristics. We test this hypothesis using thickness and grain size distribution data of the 2011 Tohoku tsunami deposit in Numanohama coast, Iwate Prefecture, Japan. Here, we build and validate a tsunami sediment transport model that can simulate deposit thickness and grain size distribution. Our numerical model has three layers: parent (bed), deposit (bed), and suspended load layers. The two bed-layers contain information about the grain size distribution. This numerical model can handle a wide range of grain sizes from 0.063 (4ϕ) to 5.657 mm (−2.5ϕ). The grain size distributions at 12 sample points along a 900 m transect from the beach, through a marsh, and up to the inundation limit, are used to validate the tsunami sediment transport model. We adopt a reference tsunami source model that can well reproduce the observed tsunami run-up heights ranging from 16 to 35 m along the steep valley during the 2011 tsunami. The simulated sand thickness distribution along the transect is consistent with the observed thickness ranging from 3 to 23 cm. The computed net erosion and deposition suggest that most of the sand deposit was originated from the near shore. The shapes of the simulated grain size distributions represented by their sorting, skewness, kurtosis, and mean at most of the sample sites are similar to the observations. The differences between the observed and simulated peak of grain size distributions are less than 1ϕ. To evaluate the sensitivity to the tsunami source model, we test five tsunami scenarios which are modified from the reference source model. While the tsunami scenario with 120% of the reference amplitude can also reproduce the thickness and grain size distribution, the scenarios with amplitudes smaller than 80% of the reference or with wave periods shorter than 50% of the reference source model underestimate the thickness and cannot reproduce the grain size distributions. Our simulation results suggest that it is possible to estimate tsunami wave amplitude and wave period from sediment deposit thickness and grain size distribution data. Highlights • Development of a numerical method to simulate the grain size distribution of a tsunami deposit. • Tsunami sediment transport model validation using high-quality data collected in Numanohama, Iwate, Japan. • It is possible to estimate tsunami amplitude and period from sediment deposit thickness and grain size distribution data. [ABSTRACT FROM AUTHOR]

Highlights • A method for forecasting near-field tsunami inundation using data assimilation was developed. • A synthetic forecast test for the 2011 Tohoku-oki tsunami was well preformed. Abstract An approach for forecasting near-field tsunami inundation was developed by combining two methods. The first method computes tsunami by assimilating pressure data observed at numerous ocean bottom sensors without tsunami source information, and the second method forecasts near-field tsunami inundation by selecting a site-specific scenario from a precomputed tsunami inundation database. In order to evaluate the validity of this combined method, we performed a synthetic forecast test for the 2011 Tohoku-oki tsunami along the Pacific coast in Japan. A tsunami computation test performed using the assimilation of synthetic pressure data reveals that the method reproduced well the tsunami field for the 2011 Tohoku-oki tsunami. A synthetic near-field tsunami inundation forecast at four sites, Kamaishi, Rikuzentakata, Minamisanriku, and the Sendai Plain for the 2011 Tohoku-oki tsunami also worked. The results indicate that an accurate tsunami inundation forecast method by this combined approach using pressure data from numerous ocean bottom sensors is now available. [ABSTRACT FROM AUTHOR]

Unlike in the deep ocean, simulating the propagation of tsunamis in the coastal zone requires a non-linear model with a fine resolution bathymetry/topography to cope with the non-linearity and complex coastal morphology. Consequently, it raises considerable computational burdens that may not be suitable for a real-time tsunami forecasting. To overcome the issue, we utilize a precomputed tsunami database comprised of pairs of low- and high-resolution maximum tsunami elevations and flow depths originating from various hypothetical earthquake scenarios. In the actual tsunami event, our algorithm requires only a low-resolution simulation result from a linear model as the input to generate the corresponding high-resolution inundation map. Therefore, the use of the non-linear model in the real-time computation can be circumvented. A dimensionality reduction or a projection to an optimal subspace is necessary to speed up the computation and possibly improve the accuracy. To that end, we construct a projection matrix based on a principal component analysis, commonly used in the computer vision field for pattern recognitions. We apply the proposed method to the 2011 Tohoku tsunami event and select the Rikuzentakata bay and Otsuchi bay as the study sites. The proposed algorithm produces the high-resolution inundation map within seconds using the low-resolution linear simulation result obtained in ∼5 min, whereas a comparable inundation forecast accuracy by the direct non-linear forward model using a nested grid system takes ∼40 min. [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 15 January 2022 Tonga volcanic tsunami was a unique event as it was the only event after the 1883 Krakatau volcanic tsunami that created waves by a dual-mechanism generation process comprising atmospheric pressure waves and eruption-induced water displacements. Here, we study 22 tide gauge waveforms, eight DART (Deep-ocean Assessment and Reporting of Tsunamis) records, eight air pressure time series, apply spectral analysis, and conduct numerical modelling to develop a source model. Our source model accounts only for the contribution of eruption-induced water displacement. The maximum overall coastal tide gauge amplitudes were in the range of 4.2–148.8 cm, whereas DARTs registered maximum amplitudes of 3.6–21.4 cm. We identified the dominant tsunami periods due to the localized water displacement mechanism as 10–17 min and 4–7 min. The waves generated by atmospheric pressure waves had a period of 7–10 min and an amplitude of 9–19 cm on coastal tide gauges; the corresponding values for DARTs were 30–60 min and 4.2–15.7 cm. Modelling showed that the eruption-induced water displacement source had a characteristic initial length of 12 km, a maximum initial amplitude of 90 m, and a volume of 6.60 × 109 m3. • 2022 Tonga tsunami generated by atmospheric pressure wave and volcano mass movement. • The length of tsunami source was 12 km and maximum initial amplitude was 90 m. • The volume of displaced water at volcano source was 6,600 million m3. [ABSTRACT FROM AUTHOR]

Abstract: The slip distribution of the 11 March 2011 Tohoku earthquake is inferred from tsunami waveforms, GPS data, and seafloor crustal deformation data. The major slip region extends all the way to the trench, and the large slip area extends 300km long and 160km wide. The largest slip of 44m is located up-dip of the hypocenter. The large slip amount, about 41m, ruptured the plate interface near the trench. The seismic moment calculated from the estimated slip distribution is 5.5×1022 Nm (Mw 9.1). The large tsunami due to the 2011 Tohoku earthquake is generated from those large slip areas near the trench. The additional uplift at the sedimentary wedge as suggested for the 1896 Sanriku earthquake may have occurred during the 2011 Tohoku earthquake, too. [Copyright &y& Elsevier]

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]

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]

The 2011 Tohoku-oki earthquake generated a large tsunami that caused catastrophic damage along the Pacific coast of Japan. The major portion of the damage along the Pacific coast of Tohoku in Japan was mainly caused by the first few cycles of tsunami waves. However, the largest phase of the tsunami arriving surprisingly late in Hakodate in Hokkaido, Japan; that is, approximately 9 h after the origin time of the earthquake. It is important to understand the generation mechanism of this large later phase. The tsunami was numerically computed by solving both linear shallow water equations and non-linear shallow water equations with moving boundary conditions throughout the computational area. The later tsunami phases observed on southern Hokkaido can be much better explained by tsunami waveforms computed by solving the non-linear equations than by those computed by solving the linear equations. This suggests that the later tsunami waves arrived at the Hokkaido coast after propagating along the Pacific coast of the Tohoku region with repeated inundations far inland or reflecting from the coast of Tohoku after the inundation. The spectral analysis of the observed waveform at Hakodate tide gauge shows that the later tsunami that arrived between 7.5 and 9.5 h after the earthquake mainly contains a period of 45–50 min. The normal modes of Hakodate Bay were also computed to obtain the eigen periods, eigenfunctions, and spatial distribution of water heights. The computed tsunami height distributions near Hakodate and the fundamental mode of Hakodate Bay indicate that the large later phases are mainly caused by the resonance of the bay, which has a period of approximately 50 min. The results also indicate that the tsunami wave heights near the Hakodate port area, the most populated area in Hakodate, are the largest in the bay because of the resonance of the fundamental mode of the bay. The results of this study suggest that large future tsunamis might excite the fundamental mode of Hakodate Bay and cause large later phases near the Hakodate port. [ABSTRACT FROM AUTHOR]

We propose an advanced two‐step tsunami source inversion method with adaptive Green's functions by applying an optimization and a reciprocity principle. The method first reconstructs the sea surface displacement from observed tsunami waveforms based on a superposition of Gaussian‐shaped unit sources. In the first step, we optimize the unit source locations that give the best fit of waveforms to observations. This leads to nonequidistantly distributed unit sources, in which the synthetic waveforms from such sources to the observation points are generated using the reciprocity principle to save computational efforts of Green's functions. In the second step, the reconstructed sea surface displacement is used to estimate the slip on a finite fault plane. We also optimize the fault parameters that produce the closest displacement pattern to the first step result. Therefore, our method provides best fitting of waveforms and optimum fault parameters with automation for the process. The current method improves our previous studies, in terms of the construction of tsunami Green's functions using the reciprocity principle and determination of fault parameters through the optimization. We test the proposed method using tsunami data of the 2004 Kii Peninsula, Japan event generated by an Mw 7.4 intraplate earthquake. The overall waveform fit that resulted from our method shows much better agreement with the observations compared to that of the conventional approach, and the estimated fault depth is more consistent with the relocated aftershock distribution. Key Points: We developed a two‐step tsunami source inversion method to infer the sea surface displacement and fault slipAn optimization method is used to determine the optimal unit source locations and fault parametersA reciprocity principle is used to efficiently construct the tsunami Green's function [ABSTRACT FROM AUTHOR]

We examined the effect of the coastal forest on tsunami sediment movement on the Sendai Plain, Japan, during the 2011 Tohoku tsunami using a numerical simulation of sediment transport. To account for the variable roughness coefficient in the coastal forest, a composite equivalent roughness coefficient was adopted. The simulated mean concentration of suspended sediment, wave height, flow velocity, and inundation limit were significantly reduced by the coastal forest. For fine to medium sand, the volumes of maximum deposition and erosion were reduced to 57–83% of those in the simulation without the coastal forest; these reductions were particularly notable for fine sand. The maximum extent and thickness of the sand layer were also reduced. The simulated sediment thickness and extent with the coastal forest for finer grain sizes roughly agreed with the observations, while the simulated results for coarser sand underestimated the observations. The reduction of sediment transport due to the coastal forest suggests that land use conditions and the vegetation environment at the time of a tsunami should be considered when studying historical and paleo-tsunamis using the geological evidence. [ABSTRACT FROM AUTHOR]

The December 2004 Sumatra-Andaman tsunami prompted an unprecedented research effort to find ancient precursors and quantify the recurrence time of such a deadly natural disaster. This effort, however, has focused primarily along the northern and eastern Indian Ocean coastlines, in proximal areas hardest hit by the tsunami. No studies have been made to quantify the recurrence of tsunamis along the coastlines of the western Indian Ocean, leading to an underestimation of the tsunami risk in East Africa. Here, we document a 1000-yr- old sand layer hosting archaeological remains of an ancient coastal Swahili settlement in Tanzania. The sedimentary facies, grain-size distribution, and faunal assemblages indicate a tsunami wave as the most likely cause for the deposition of this sand layer. The tsunami in Tanzania is coeval with analogous deposits discovered at eastern Indian Ocean coastal sites. Numerical simulations of tsunami wave propagation indicate a megathrust earthquake generated by a large rupture of the Sumatra-Andaman subduction zone as the likely tsunami source. Our findings provide evidence that teletsunamis represent a serious threat to coastal societies along the western Indian Ocean, with implications for future tsunami hazard and risk assessments in East Africa. [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]