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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]

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]

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]

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 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]

We present a method of tsunami data assimilation using a linear dispersive model in order to provide an accurate tsunami early warning. To speed up the assimilation process, we use the Green’s function-based tsunami data assimilation, in which the Green’s functions are calculated in advance with linear dispersive tsunami propagation models. We demonstrate a test case in the Nankai Trough off southwest Japan, with a source model similar to the main shock of the 2004 off the Kii Peninsula earthquake (M7.4) which generated tsunamis with dispersive characteristics. We show that assimilation of existing ocean bottom pressure gauge data can rapidly forecast the tsunami arrival time and the maximum height of the first tsunami peak along the coast of Shikoku and Kyushu Islands. Both the linear long-wave model and the linear dispersive model can accurately forecast the tsunami height, but the linear dispersive model can predict the tsunami arrival time more accurately for the tested earthquake. [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]