On 16 September 2015, a moment magnitude (Mw) 8.3 earthquake struck off the coast of central Chile, generating a large tsunami with nearby coastal wave heights observed on tide gauges in Chile and Peru of up to 4.7 m and distal observations of over 40 cm in the Kuril Islands across the Pacific Ocean. Through a transcoastal geodetic study, including tsunami time series recorded at open ocean pressure gauges, subaerial deformation observed through interferometric synthetic aperture radar from the Sentinel‐1 A satellite and continuous GPS, we identify the location and extent of coseismic slip. We find that most coseismic slip was concentrated in a patch immediately offshore, with little modeled slip near the trench. This result satisfies the tsunami waveforms measured in the deep ocean north of the rupture area, with wave heights up to 10 cm. While the event exhibits some features of a slow tsunami earthquake (moderately large tsunami and possible slow second‐stage rupture), our inversion results do not require substantial near‐trench rupture. However, the prevalence of large and shallow thrust along subduction megathrusts along central Chile raises the question of the likelihood of future such events and the implications for future hazardous tsunamigenic earthquakes.

On 28 September 2018, Indonesia was struck by an MW 7.5 strike-slip earthquake. An unexpected tsunami followed, inundating nearby coastlines leading to extensive damage. Given the traditionally non-tsunamigenic mechanism, it is important to ascertain if the source of the tsunami is indeed from coseismic deformation, or something else, such as shaking induced landsliding. Here we determine the leading cause of the tsunami is a complex combination of both. We constrain the coseismic slip from the earthquake using static offsets from geodetic observations and validate the resultant “coseismic-only” tsunami to observations from tide gauge and survey data. This model alone, although fitting some localized run-up measurements, overall fails to reproduce both the timing and scale of the tsunami. We also model coastal collapses identified through rapidly acquired satellite imagery and video footage as well as explore the possibility of submarine landsliding using tsunami raytracing. The tsunami model results from the landslide sources, in conjunction with the coseismic-generated tsunami, show a greatly improved fit to both tide gauge and field survey data. Our results highlight a case of a damaging tsunami the source of which is a complex mix of coseismic deformation and landsliding. Tsunamis of this nature are difficult to provide warning for and are underrepresented in regional tsunami hazard analysis.

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For coastal regions on the margin of a subduction zone, near-field megathrust earthquakes are the source of the most extreme tsunami hazards, and are important to handle properly as one aspect of any Probabilistic Tsunami Hazard Assessment. Typically, great variability in inundation depth at any point is possible due to the extreme variation in extent and pattern of slip over the fault surface. In this context, we present an approach to estimating inundation depth probabilities (in the form of hazard curves at a set of coastal locations) that consists of two components. The first component uses a Karhunen-Loève expansion to express the probability density function (PDF) for all possible events, with PDF parameters that are geophysically reasonable for the Cascadia Subduction Zone. It is then easy and computationally cheap to generate a large N number of samples from this PDF; doing so and performing a full tsunami inundation simulation for each provides a brute force approach to estimating probabilities of inundation. However, to obtain reasonable results, particularly for extreme flooding due to rare events, N would have to be so large as to make the tsunami simulations prohibitively expensive. The second component tackles this difficulty by using importance sampling techniques to adequately sample the tails of the distribution and properly re-weight the probability assigned to the resulting realizations, and by grouping the realizations into a small number of clusters that we believe will give similar inundation patterns in the region of interest. In this approach, only one fine-grid tsunami simulation need be computed from a representative member of each cluster. We discuss clustering based on proxy quantities that are cheap to compute over a large number of realizations, but that can identify a smaller number of clusters of realizations that will have similar inundation depths. The fine-grid simulations for each cluster representative can also be used to develop an improved strategy, in which these are combined with cheaper coarse-grid simulations of other members of the cluster. We illustrate the methodology by considering two coastal locations: Crescent City, CA and Westport, WA.

Tsunamis are one of the most destructive effects of subduction zone earthquakes. Directly observing and understanding the generation and propagation of tsunamis remains challenging due to limited offshore instrumentation and a sparse catalog of large events. This makes linking characteristics of the earthquake rupture to their effect on tsunami generation difficult. While past studies explored how varying earthquake source geometries affect tsunami nucleation, little has been done to examine the role of the kinematic component of rupture on the tsunami; we explore these effects in this study. While past studies have examined the kinematic effect using coastal tide gauge data, we expand this examination to more recent pressure gauges. We identify a consistent rotation of the main beam of tsunami energy when using a kinematic model, affecting far-field hazards. We also identify a delay in tsunami arrival times at both coastal and open-ocean gauges that can be as long as the total source duration. For large earthquakes, this delay introduces nonnegligible mapping errors when employing open-ocean tsunami data for source characterizations. As a result of our findings we recommend including a kinematic component to tsunami modeling when studying events with source durations over 120 s and using recordings from open-ocean pressure gauges. We also find that when focusing purely on coastal gauge data and near-source hazards, the kinematic component is a much smaller contribution to the source uncertainty and can be ignored.