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SUMMARY The eruption of the submarine Hunga Tonga-Hunga Haʻapai (Hunga Tonga) volcano on 15 January 2022, was one of the largest volcanic explosions recorded by modern geophysical instrumentation. The eruption was notable for the broad range of atmospheric wave phenomena it generated and for their unusual coupling with the oceans and solid Earth. The event was recorded worldwide across the Global Seismographic Network (GSN) by seismometers, microbarographs and infrasound sensors. The broad-band instrumentation in the GSN allows us to make high fidelity observations of spheroidal solid Earth normal modes from this event at frequencies near 3.7 and 4.4 mHz. Similar normal mode excitations were reported following the 1991 Pinatubo (Volcanic Explosivity Index of 6) eruption and were predicted, by theory, to arise from the excitation of mesosphere-scale acoustic modes of the atmosphere coupling with the solid Earth. Here, we compare observations for the Hunga Tonga and Pinatubo eruptions and find that both strongly excited the solid Earth normal mode 0S29 (3.72 mHz). However, the mean modal amplitude was roughly 11 times larger for the 2022 Hunga Tonga eruption. Estimates of attenuation (Q) for 0S29 across the GSN from temporal modal decay give Q = 332 ± 101, which is higher than estimates of Q for this mode using earthquake data (Q = 186.9 ± 5). Two microbarographs located at regional distances (<1000 km) to the volcano provide direct observations of the fundamental acoustic mode of the atmosphere. These pressure oscillations, first observed approximately 40 min after the onset of the eruption, are in phase with the seismic Rayleigh wave excitation and are recorded only by microbarographs in proximity (<1500 km) to the eruption. We infer that excitation of fundamental atmospheric modes occurs within a limited area close to the site of the eruption, where they excite select solid Earth fundamental spheroidal modes of similar frequencies that are globally recorded and have a higher apparent Q due to the extended duration of atmospheric oscillations. 

The International Monitoring System (IMS) infrasound network has been established to detect nuclear explosions and other signals of interest embedded in the station‐specific ambient noise. The ambient noise can be separated into coherent infrasound (e.g., real infrasonic signals) and incoherent noise (such as that caused by wind turbulence). Previous work statistically and systematically characterized coherent infrasound recorded by the IMS. This paper expands on this analysis of the coherent ambient infrasound by including updated IMS data sets with data up to the end of 2020 for all 53 of the currently certified IMS infrasound stations using an updated configuration of the Progressive Multi‐Channel Correlation (PMCC) method. This paper presents monthly station‐dependent reference curves for the back azimuth, trace velocity, and root mean squared amplitude, which provides a means to determine the deviation from the nominal monthly behavior. In addition, a daily Ambient Noise Stationarity (ANS) factor based on deviations from the reference curves is determined for a quick reference to the coherent signal quality compared to the nominal situations. Newly presented histograms provide a higher resolution spectrum, including the observations of the microbarom peak, as well as additional peaks reflecting station‐dependent environmental noise. The aim of these reference curves is to identify periods of suboptimal operation (e.g., nonoperational sensor) or instances of strong abnormal signals of interest.

 

Mount Michael stratovolcano, South Sandwich Islands is extremely remote and challenging to observe, but eruptive activity has been sporadically observed since 1820 and captured by satellite methods since 1989. We identify long‐range infrasound signals recorded by the International Monitoring System attributable to episodes of persistent eruptive activity at Mount Michael. Analysis of multi‐year (2004–2020) infrasound array data at station IS27, Antarctica (range 1,672 km) reveals candidate signals especially from May 2005 to January 2008 and from May 2016 to April 2018. By combining ray‐tracing with empirical climatologies and atmospheric specifications, we show that systematic variations in the observed backazimuth of the signals (at IS27) are broadly consistent with annual variability in stratospheric propagation conditions for a source at Mount Michael. Observed signal amplitudes combined with transmission loss estimates are consistent with moderate explosive eruption. We highlight a selection of infrasound signals that correspond to satellite observation of eruptions.

 

Infrasound (low‐frequency acoustic waves) has proven useful to detect and characterize subaerial volcanic activity, but understanding the infrasonic source during sustained eruptions is still an area of active research. Preliminary comparison between acoustic eruption spectra and the jet noise similarity spectra suggests that volcanoes can produce an infrasonic form of jet noise from turbulence. The jet noise similarity spectra, empirically derived from audible laboratory jets, consist of two noise sources: large‐scale turbulence (LST) and fine‐scale turbulence (FST). We fit the similarity spectra quantitatively to eruptions of Mount St. Helens in 2005, Tungurahua in 2006, and Kīlauea in 2018 using nonlinear least squares fitting. By fitting over a wide infrasonic frequency band (0.05–10 Hz) and restricting the peak frequency above 0.15 Hz, we observe a better fit during times of eruption versus non‐eruptive background noise. Fitting smaller overlapping frequency bands highlights changes in the fit of LST and FST spectra, which aligns with observed changes in eruption dynamics. Our results indicate that future quantitative spectral fitting of eruption data will help identify changes in eruption source parameters such as velocity, jet diameter, and ash content which are critical for effective hazard monitoring and response.

 

Acoustic waveform inversions can provide estimates of volume flow rate and erupted mass, enhancing the ability to estimate volcanic emissions. Previous studies have generally assumed a simple acoustic source (monopole); however, more complex and accurate source reconstructions are possible with a combination of equivalent sources (multipole). We deployed a high‐density acoustic network around Yasur volcano, Vanuatu, including acoustic sensors on a tethered aerostat that was moved every ∼15–60 min. Using this unique data set we invert for the acoustic multipole source mechanism using a grid search approach for 80 events to examine volume flow rates and dipole strengths. Our method utilizes finite‐difference time‐domain modeling to obtain the full 3‐D Green's functions that account for topography. Inversion results are compared using a monopole‐only, multipole (monopole and dipole), simulations that do not include topography, and those that use a subset of sensors. We find that the monopole source is a good approximation when topography is considered. However, initial compression amplitude is not fully captured by a monopole source so source directionality cannot be ruled out. The monopole solution is stable regardless of whether a monopole‐only or multipole inversion is performed. Inversions for the dipole components produce estimates consistent with observed source directionality, though these inversions are somewhat unstable given station configurations of typical deployments. Our results suggest that infrasound waveform inversion shows promise for realistic quantitative source estimates, but additional work is necessary to fully explore inversion stability, uncertainty, and robustness.