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ABSTRACT 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. 

Abstract Volcano infrasound data contain a wealth of information about eruptive patterns, for which machine learning (ML) is an emerging analysis tool. Although global catalogs of labeled infrasound events exist, the application of supervised ML to local (<15 km) volcano infrasound signals has been limited by a lack of robust labeled datasets. Here, we automatically generate a labeled dataset of >7500 explosions recorded by a five-station infrasound network at the highly active Yasur Volcano, Vanuatu. Explosions are located via backprojection and associated with one of Yasur’s two summit subcraters. We then apply a supervised ML approach to classify the subcrater of origin. When trained and tested on data from the same station, our chosen algorithm is >95% accurate; when training and testing on different stations, accuracy drops to about 75%. The choice of waveform features provided to the algorithm strongly influences classification performance. 

Abstract 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. 

Abstract Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over the next decade. The increased availability of infrasound sensors will expand observations of varied eruption styles, and the associated increase in data volume will make machine learning workflows more feasible. More sophisticated modeling will be applied to examine infrasound source and propagation effects from local to global distances, leading to improved infrasound-derived estimates of eruption properties. Future work will use infrasound to detect, locate, and characterize moving flows, such as pyroclastic density currents, lahars, rockfalls, lava flows, and avalanches. Infrasound observations will be further integrated with other data streams, such as seismic, ground- and satellite-based thermal and visual imagery, geodetic, lightning, and gas data. The volcano infrasound community should continue efforts to make data and codes accessible and to improve diversity, equity, and inclusion in the field. In summary, the next decade of volcano infrasound research will continue to advance our understanding of complex volcano processes through increased data availability, sensor technologies, enhanced modeling capabilities, and novel data analysis methods that will improve hazard detection and mitigation.