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ABSTRACT Seismic data contains a continuous record of wind influenced by different factors across the frequency spectrum. To assess the influences of wind on ground motion, we use colocated wind and seismic data from 110 stations in the Alaska component of the EarthScope Transportable Array. We compare seismic probability power spectral densities and wind speed and direction during 2018 to develop a quantitative measure of the seismic sensitivity to wind. We observe a pronounced increase in seismic energy as a function of wind speed for almost all stations. At frequencies below the microseism band, our observations agree with previous authors in finding that sensor emplacement and ground materials are important, and that much of the wind influence likely comes from associated changes in barometric pressure. Wind has the least influence in the microseism band, but that is only because its contribution to noise is much smaller than the ubiquitous microseism background. At frequencies above the microseism band, we find that wind sensitivity is correlated with land cover type, increasing with vegetation height. This sensitivity varies seasonally, which we attribute to snow insulation, the burial of vegetation and objects around the station, and potentially the role of frozen ground. Wind direction also manifests in seismic data, which we attribute to turbulent air on the lee side of station huts coupling with the ground and the seismometer borehole cap. We find some dependence on bedrock type, with a greater seismic response in unconsolidated sediment. These results provide guidance on site selection and construction, and make it possible to forecast seismic network performance under different wind conditions. When we examine the factors at work in a warming climate, we find reason to anticipate increasing seismic noise from wind in the Arctic over the decades to come. 

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 Earthquakes generate infrasound in multiple ways. Acoustic coupling at the surface from vertical seismic velocity, termed local infrasound, is often recorded by infrasound sensors but has seen relatively little study. Over 140 infrasound stations have recently been deployed in Alaska. Most of these stations have single sensors, rather than arrays, and were originally installed as part of the EarthScope Transportable Array. The single sensor nature, paucity of ground-truth signals, and remoteness makes evaluating their data quality and utility challenging. In addition, despite notable recent advances, infrasound calibration and frequency response evaluation remains challenging, particularly for large networks and retrospective analysis of sensors already installed. Here, we examine local seismoacoustic coupling on colocated seismic and infrasound stations in Alaska. Numerous large earthquakes across the region in recent years generated considerable vertical seismic velocity and local infrasound that were recorded on colocated sensors. We build on previous work and evaluate the full infrasound station frequency response using seismoacoustic coupled waves. By employing targeted signal processing techniques, we show that a single seismometer may be sufficient for characterizing the response of an entire nearby infrasound array. We find that good low frequency (<1 Hz) infrasound station response estimates can be derived from large (Mw>7) earthquakes out to at least 1500 km. High infrasound noise levels at some stations and seismic-wave energy focused at low frequencies limit our response estimates. The response of multiple stations in Alaska is found to differ considerably from their metadata and are related to improper installation and erroneous metadata. Our method provides a robust way to remotely examine infrasound station frequency response and examine seismoacoustic coupling, which is being increasingly used in airborne infrasound observations, earthquake magnitude estimation, and other applications. 

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.