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The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.

The addition of 108 infrasound sensors—a legacy of the temporary USArray Transportable Array (TA) deployment—to the Alaska regional network provides an unprecedented opportunity to quantify the effects of a diverse set of site conditions on ambient infrasound noise levels. TA station locations were not chosen to optimize infrasound performance, and consequently span a dramatic range of land cover types, from temperate rain forest to exposed tundra. In this study, we compute power spectral densities for 2020 data and compile new ambient infrasound low- and high-noise models for the region. In addition, we compare time series of root-mean-squared (rms) amplitudes with wind data and high-resolution land cover data to derive noise–wind speed relationships for several land cover categories. We observe that noise levels for the network are dominated by wind, and that network noise is generally higher in the winter months when storms are more frequent and the microbarom is more pronounced. Wind direction also exerts control on noise levels, likely as a result of infrasound ports being systematically located on the east side of the station huts. We find that rms amplitudes correlate with site land cover type, and that knowledge of both land cover type and wind speed can help predict infrasound noise levels. Our results show that land cover data can be used to inform infrasound station site selection, and that wind–noise models that incorporate station land cover type are useful tools for understanding general station noise performance.

Earthquake ground motions in the vicinity of receivers couple with the atmosphere to generate pressure perturbations that are detectable by infrasound sensors. These so-called local infrasound signals traverse very short source-to-receiver paths, so that they often exhibit a remarkable correlation with seismic velocity waveforms at collocated seismic stations, and there exists a simple relationship between vertical seismic velocity and pressure time series. This study leverages the large regional network of infrasound sensors in Alaska to examine local infrasound from several light to great Alaska earthquakes. We estimate seismic velocity time series from infrasound pressure records and use these converted infrasound recordings to compute earthquake magnitudes. This technique has potential utility beyond the novelty of recording seismic velocities on pressure sensors. Because local infrasound amplitudes from ground motions are small, it is possible to recover seismic velocities at collocated sites where the broadband seismometers have clipped. Infrasound-derived earthquake magnitudes exhibit good agreement with seismically derived values. This proof-of-concept demonstration of computing seismic magnitudes from infrasound sensors illustrates that infrasound sensors may be utilized as proxy vertical-component seismometers, making a new data set available for existing seismic techniques. Because single-sensor infrasound stations are relatively inexpensive and are becoming ubiquitous, this technique could be used to augment existing regional seismic networks using a readily available sensor platform.

The 23 June 2014 Mw=7.9 earthquake that struck the Rat Islands region of Alaska was a rare intermediate-depth event followed by a vigorous aftershock sequence. The earthquake's location within a regional network has allowed us to study it in rich detail. Double-difference relocations of aftershocks and static stress modeling reveal some intriguing features; most aftershocks are not located on the main shock rupture plane, but are concentrated in the adjacent Wadati-Benioff zone in areas of increased stress. Further, a secondary plane of seismicity aligns well with a moderately dipping nodal plane reported by the Global Centroid Moment Tensor Project, and the separation of these locations and the main shock hypocenter from the slab indicates that the main shock ruptured into the oceanic mantle.

We estimated the crustal thickness and velocity structure beneath the five stations comprising the Republic of Singapore’s seismic network. Our data set was composed of 697 teleseismic receiver functions and 7 months of broad-band data that was cross-correlated to produce inter-station Green’s functions. Surface wave group velocities were extracted from the Green’s functions to obtain dispersion data for a path from central Sumatra to Singapore in order to provide a complimentary data set to the receiver functions. Crustal thickness was estimated via an H − k stacking technique, and high-resolution 1D P -wave velocity profiles were generated beneath each station by jointly inverting receiver function stacks and the group velocity data using a linearised time-domain inversion scheme. Crustal thickness beneath four stations was found to be between 28.0 km and 32.0 km, while one station in the northeast of Singapore indicates 24.0 km thick crust. This implies a significant crustal thinning beneath Singapore over the lateral extent of 50.0 km. Inversion results exhibit several crustal features that are observable in the derived models at all five stations, indicating that they exist across Singapore as a whole. There appears to be an upper-crustal high-velocity zone beneath Singapore, underlain by a velocity inversion. Station NTU shows slower near-surface velocities than the other stations, consistent with its situation above the sedimentary Jurong formation. These results expand the available global velocity data set, as well as being useful for assessing the seismic hazard in Singapore.

We employed a 3D velocity model and 3D wave propagation code to simulate long-period ground motions in the upper Mississippi embayment. This region is at risk from large earthquakes in the New Madrid seismic zone (NMSZ) and observational data are sparse, making simulation a valuable tool for predicting the effects of large events. We undertook these simulations to estimate the magnitude of shaking likely to occur and to investigate the influence of the 3D embayment structure and finite-fault mechanics on ground motions. There exist three primary fault zones in the NMSZ, each of which was likely associated with one of the main shocks of the 1811–12 earthquake triplet. For this study, three simulations have been conducted on each major segment, exploring the impact of different epicentral locations and rupture directions on ground motions. The full wave field up to a frequency of 0.5 Hz is computed on a 200 × 200 × 50-km3 volume using a staggered-grid finite-difference code. Peak horizontal velocity and bracketed durations were calculated at the free surface. The NMSZ simulations indicate that for the considered bandwidth, finite-fault mechanics such as fault proximity, directivity effect, and slip distribution exert the most control on ground motions. The 3D geologic structure of the upper Mississippi embayment also influences ground motion with indications that amplification is induced by the sharp velocity contrast at the basin edge.

[1] The Mw 7.8 October 2010Mentawai, Indonesia, earthquake was a“tsunami earthquake,” a rare type of earthquake that generates a tsunami much larger than expected based on the seismicmagnitude.Itproducedalocallydevastatingtsunami,withrunupcommonlyinexcess of 6 m. We examine this event using a combination of high-rate GPS data, from instruments located on the nearby islands, and a tsunami field survey. The GPS displacement time series are deficient in high-frequency energy, and show small coseismic displacements ( 16 m. Our modeling results show that the combination of the small GPS displacements and large tsunami can only be explained by high fault slip at very shallow depths, far from the islands and close to the oceanic trench. Inelastic uplift of trench sediments likely contributed to the size of the tsunami. Recent results for the 2011 Mw 9.0 Tohoko-Oki earthquake have also shown shallow fault slip, but the results from our study, which involves a smaller earthquake, provide much stronger constraints on how shallow the rupture can be, with the majority of slip for the Mentawai earthquake occurring at depths of

We estimated the velocity structure beneath four three-component broad-band seismic stations in the Sumatra region by the joint inversion of teleseismic receiver functions and surface wave group velocities. The stations, part of GEOFON network GE, are located in diverse geologic settings including backarc basins, forearc basins, and the forearc ridge. The stations are distributed roughly trench-parallel, providing samples of conditions along the length of Sumatra. 143 receiver functions were computed by employing an iterative, time-domain deconvolution in order to minimize acausal noise. The teleseismic observations at each station were grouped by back azimuth and ray parameter and then stacked to increase the signal to noise ratios. Surface wave group velocity dispersion was measured for paths across the Sunda block from events to the northeast of the stations. P-wave velocity profiles were derived by inverting the stacks and group velocity data using a linearized time-domain inversion scheme with an interpolated CRUST2.0 global model for input. The results differ significantly from CRUST2.0, and reflect the diversity of geologic environments within which the stations are located. Crustal thicknesses beneath the stations range from 16.0 km at the forearc to 30.0 km at a backarc basin. These results add to the available velocity and crustal thickness data for the Sumatra region.