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SUMMARY Infrasound sensors are deployed in a variety of spatial configurations and scales for geophysical monitoring, including networks of single sensors and networks of multisensor infrasound arrays. Infrasound signal detection strategies exploiting these data commonly make use of intersensor correlation and coherence (array processing, multichannel correlation); network-based tracking of signal features (e.g. reverse time migration); or a combination of these such as backazimuth cross-bearings for multiple arrays. Single-sensor trace-based denoising techniques offer significant potential to improve all of these various infrasound data processing strategies, but have not previously been investigated in detail. Single-sensor denoising represents a pre-processing step that could reduce the effects of ambient infrasound and wind noise in infrasound signal association and location workflows. We systematically investigate the utility of a range of single-sensor denoising methods for infrasound data processing, including noise gating, non-negative matrix factorization, and data-adaptive Wiener filtering. For the data testbed, we use the relatively dense regional infrasound network in Alaska, which records a high rate of volcanic eruptions with signals varying in power, duration, and waveform and spectral character. We primarily use data from the 2016–2017 Bogoslof volcanic eruption, which included multiple explosions, and synthetics. The Bogoslof volcanic sequence provides an opportunity to investigate regional infrasound detection, association, and location for a set of real sources with varying source spectra subject to anisotropic atmospheric propagation and varying noise levels (both incoherent wind noise and coherent ambient infrasound, primarily microbaroms). We illustrate the advantages and disadvantages of the different denoising methods in categories such as event detection, waveform distortion, the need for manual data labelling, and computational cost. For all approaches, denoising generally performs better for signals with higher signal-to-noise ratios and with less spectral and temporal overlap between signals and noise. Microbaroms are the most globally pervasive and repetitive coherent ambient infrasound noise source, with such noise often referred to as clutter or interference. We find that denoising offers significant potential for microbarom clutter reduction. Single-channel denoising of microbaroms prior to standard array processing enhances both the quantity and bandwidth of detectable volcanic events. We find that reduction of incoherent wind noise is more challenging using the denoising methods we investigate; thus, station hardware (wind noise reduction systems) and site selection remain critical and cannot be replaced by currently available digital denoising methodologies. Overall, we find that adding single-channel denoising as a component in the processing workflow can benefit a variety of infrasound signal detection, association, and location schemes. The denoising methods can also isolate the noise itself, with utility in statistically characterizing ambient infrasound noise.

Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (

Education research has suggested that students who receive a wider variety of sensory inputs during the learning process will be able to make more connections and more deeply understand course content. To this end, three physical hands-on laboratory learning modules were designed for a computational numerical methods course to help students develop intuition for physical systems, to allow for a comparison of real data against numerical solutions of physics equations, and to increase student engagement. These modules were designed to reinforce specific topics covered in the numerical methods curriculum, including numerical differentiation and numerical solution of ordinary differential equations. Pre- and post-tests showed an increase in student knowledge after module completion. In addition, end-of-term surveys showed that a strong majority of students believed modules helped them visualize and understand key numerical methods concepts, and that most would be interested in learning new material with similar modules in the future. [ABSTRACT FROM AUTHOR]

Abstract: We present a new infrasonic signal detection and back azimuth determination technique that requires just one microphone and one three‐component seismometer. Ground‐coupled airwaves (GCAs) occur when an incident atmospheric acoustic wave impinges on the ground surface and is partially transmitted as a seismic wave. GCAs are commonly detected hundreds of kilometers away on seismic networks and are observed to have retrograde particle motion. Horizontally propagating acoustic waves and GCAs have previously been observed on collocated infrasound and seismic sensor pairs as coherent with a 90° phase difference. If the sensors are spatially separated, an additional propagation‐induced phase shift is present. The additional phase shift depends on the direction from which the acoustic wave arrives, as each back azimuth has a different apparent distance between the sensors. We use the additional phase shift, the coherence, and the characteristic particle motion on the three‐component seismometer to determine GCA arrivals and their unique back azimuth. We test this technique with synthetic seismo‐acoustic data generated by a coupled Earth‐atmosphere 3‐D finite difference code, as well as three seismo‐acoustic data sets from Mount St. Helens, Mount Cleveland, and Mount Pagan volcanoes. Results from our technique compare favorably with traditional infrasound array processing and provide robust GCA detection and back azimuth determination. Assuming adequate station spacing and sampling, our technique provides a new and robust method to detect infrasonic signals and determine their back azimuth, and may be of practical benefit where resources are limited and large sensor networks or arrays are not feasible.

Abstract: The two major explosive phases of the 22–23 April 2015 eruption of Calbuco volcano, Chile, produced powerful seismicity and infrasound. The eruption was recorded on seismo‐acoustic stations out to 1,540 km and on five stations (IS02, IS08, IS09, IS27, and IS49) of the International Monitoring System (IMS) infrasound network at distances from 1,525 to 5,122 km. The remote IMS infrasound stations provide an accurate explosion chronology consistent with the regional and local seismo‐acoustic data and with previous studies of lightning and plume observations. We use the IMS network to detect and locate the eruption signals using a brute‐force, grid‐search, cross‐bearings approach. After incorporating azimuth deviation corrections from stratospheric crosswinds using 3‐D ray tracing, the estimated source location is 172 km from true. This case study highlights the significant capability of the IMS infrasound network to provide automated detection, characterization, and timing estimates of global explosive volcanic activity. Augmenting the IMS with regional seismo‐acoustic networks will dramatically enhance volcanic signal detection, reduce latency, and improve discrimination capability.

Volcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co‐eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single‐station ambient noise interferometry at five three‐component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on 26 May 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes. Plain Language Summary: Volcanic eruptions provide important insights into how volcanoes work and the potential risks they pose. This study looks at the ongoing eruption at Great Sitkin Volcano in the central Aleutian volcanic arc to better understand the changes happening beneath the surface. We measure the changes in the velocity of seismic waves, with data from five seismometers on the island. We collected data from July 2019 to July 2023, covering different eruptive stages and multiple lava effusion episodes during the eruption that started on 26 May 2021. Coincident with the lava effusion since late July 2021, we notice a significant increase in seismic velocities, most prominently at a station northwest of the volcano's central crater. We conclude that this increase reflects the structural change in the shallow subsurface due to magma extrusion, with deflation of the edifice and closure of cracks. The findings in this study help to improve our understanding of how volcanoes behave during eruptions. Key Points: We observe prominent, spatially variable increases in seismic velocity during lava effusions at Great Sitkin Volcano since late July 2021The sustained co‐eruptive increase in seismic velocity reflects structural changes with crack closures as the consequence of magma extrusionThe distinct spatial variation of the velocity increase may be controlled by reservoir geometry or the strength of shallow volcaniclastics [ABSTRACT FROM AUTHOR]

The March 2016 eruption of Pavlof Volcano, Alaska, produced an ash plume that caused the cancellation of more than 100 flights in North America. The eruption generated strong tremor that was recorded by seismic and remote low-frequency acoustic (infrasound) stations, including the EarthScope Transportable Array. The relationship between the tremor amplitudes and plume height changes considerably between the waxing and waning portions of the eruption. Similar hysteresis has been observed between seismic river noise and discharge during storms, suggesting that flow and erosional processes in both rivers and volcanoes can produce irreversible structural changes that are detectable in geophysical data. We propose that the time-varying relationship at Pavlof arose from changes in the tremor source related to volcanic vent erosion. This relationship may improve estimates of volcanic emissions and characterization of eruption size and intensity.

Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. Plain Language Summary: Volcanic tremor is a persistent vibration of the ground, atmosphere, or both that can occur before and during volcanic eruptions. Despite its importance in volcano monitoring and eruption forecasting, volcano observatories do not have a reliable way of automatically detecting and identifying tremor due to the variable intensities and frequencies at which it occurs. In order to accomplish this, we develop and test a pair of machine learning models that classify spectrograms (i.e., images representing a signal's frequency content over time) from seismic and low‐frequency acoustic data. The models are trained on manually labeled images derived from the recent 2021–2022 eruption of Pavlof Volcano, Alaska, which demonstrated substantial signal diversity (e.g., different tremor types, earthquakes, explosions and noise). Our models achieve 81.2% and 90.0% accuracy on the seismic and low‐frequency acoustic test sets respectively, and perform well when applied to data recorded from past Pavlof Volcano eruptions. In addition, transitions in tremor sequences identified from our analysis generally coincide with shifts in eruptive patterns from Pavlof Volcano. Our tools can help volcano observatories systematically monitor tremor, and advance tremor research by making catalogs of their occurrences more consistent and accessible. Key Points: We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signalsWe apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelinesThe seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity [ABSTRACT FROM AUTHOR]