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1. A Pilot Experiment on Infrasonic Lahar Detection at Mount Adams, Cascades: Ambient Infrasound and Wind-Noise Characterization at a Quiescent Stratovolcano

   https://doi.org/10.1785/0220200361  

   Sanderson, Richard W. ; Matoza, Robin S. ; Haymon, Rachel M. ; Steidl, Jamison H. ( May 2021 , Seismological Research Letters)

   Abstract Erosion, hydrothermal activity, and magmatism at volcanoes can cause large and unexpected mass wasting events. Large fluidized debris flows have occurred within the past 6000 yr at Mount Adams, Washington, and present a hazard to communities downstream. In August 2017, we began a pilot experiment to investigate the potential of infrasound arrays for detecting and tracking debris flows at Mount Adams. We deployed a telemetered four-element infrasound array (BEAR, 85 m aperture), ~11 km from a geologically unstable area where mass wasting has repeatedly originated. We present a preliminary analysis of BEAR data, representing a survey of the ambient infrasound and noise environment at this quiescent stratovolcano. Array processing reveals near continuous and persistent infrasound signals arriving from the direction of Mount Adams, which we hypothesize are fluvial sounds from the steep drainages on the southwest flank. We interpret observed fluctuations in the detectability of these signals as resulting from a combination of (1) wind-noise variations at the array, (2) changes in local infrasound propagation conditions associated with atmospheric boundary layer variability, and (3) changing water flow speeds and volumes in the channels due to freezing, thawing, and precipitation events. Suspected mass movement events during the study period are small (volumes <105  m3 and durations <2 min),more »with one of five visually confirmed events detected infrasonically at BEAR. We locate this small event, which satellite imagery suggests was a glacial avalanche, using three additional temporary arrays operating for five days in August 2018. Events large enough to threaten downstream communities would likely produce stronger infrasonic signals detectable at BEAR. In complement to recent literature demonstrating the potential for infrasonic detection of volcano mass movements (Allstadt et al., 2018), this study highlights the practical and computational challenges involved in identifying signals of interest in the expected noisy background environment of volcanic topography and drainages.« less
2. Narrow-Band Least-Squares Infrasound Array Processing

   https://doi.org/10.1785/0220220042  

   Iezzi, Alexandra M. ; Matoza, Robin S. ; Bishop, Jordan W. ; Bhetanabhotla, Sneha ; Fee, David ( June 2022 , Seismological Research Letters)

   Abstract Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. We introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau,more »στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.« less
3. A neural network for online spike classification that improves decoding accuracy

   https://doi.org/10.1152/jn.00641.2019  

   Issar, Deepa ; Williamson, Ryan C. ; Khanna, Sanjeev B. ; Smith, Matthew A. ( April 2020 , Journal of Neurophysiology)

   Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network’s stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network’s labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decodingmore »with our network’s classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding. NEW & NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.« less
4. One hundred years of advances in volcano seismology and acoustics

   https://doi.org/10.1007/s00445-022-01586-0  

   Matoza, Robin S. ; Roman, Diana C. ( August 2022 , Bulletin of Volcanology)

   Since the 1919 foundation of the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI), the fields of volcano seismology and acoustics have seen dramatic advances in instrumentation and techniques, and have undergone paradigm shifts in the understanding of volcanic seismo-acoustic source processes and internal volcanic structure. Some early twentieth-century volcanological studies gave equal emphasis to barograph (infrasound and acoustic-gravity wave) and seismograph observations, but volcano seismology rapidly outpaced volcano acoustics and became the standard geophysical volcano-monitoring tool. Permanent seismic networks were established on volcanoes (for example) in Japan, the Philippines, Russia, and Hawai‘i by the 1950s, and in Alaska by the 1970s. Large eruptions with societal consequences generally catalyzed the implementation of new seismic instrumentation and led to operationalization of research methodologies. Seismic data now form the backbone of most local ground-based volcano monitoring networks worldwide and play a critical role in understanding how volcanoes work. The computer revolution enabled increasingly sophisticated data processing and source modeling, and facilitated the transition to continuous digital waveform recording by about the 1990s. In the 1970s and 1980s, quantitative models emerged for long-period (LP) event and tremor sources in fluid-driven cracks and conduits. Beginning in the 1970s, early models formore »volcano-tectonic (VT) earthquake swarms invoking crack tip stresses expanded to involve stress transfer into the wall rocks of pressurized dikes. The first deployments of broadband seismic instrumentation and infrasound sensors on volcanoes in the 1990s led to discoveries of new signals and phenomena. Rapid advances in infrasound technology; signal processing, analysis, and inversion; and atmospheric propagation modeling have now established the role of regional (15–250 km) and remote (> 250 km) ground-based acoustic systems in volcano monitoring. Long-term records of volcano-seismic unrest through full eruptive cycles are providing insight into magma transport and eruption processes and increasingly sophisticated forecasts. Laboratory and numerical experiments are elucidating seismo-acoustic source processes in volcanic fluid systems, and are observationally constrained by increasingly dense geophysical field deployments taking advantage of low-power, compact broadband, and nodal technologies. In recent years, the fields of volcano geodesy, seismology, and acoustics (both atmospheric infrasound and ocean hydroacoustics) are increasingly merging. Despite vast progress over the past century, major questions remain regarding source processes, patterns of volcano-seismic unrest, internal volcanic structure, and the relationship between seismic unrest and volcanic processes.

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5. Seismo-acoustic characterisation of the 2018 Ambae (Manaro Voui) eruption, Vanuatu

   https://doi.org/10.1007/s00445-021-01474-z  

   Park, Iseul ; Jolly, Arthur ; Matoza, Robin S. ; Kennedy, Ben ; Kilgour, Geoff ; Johnson, Richard ; Garaebiti, Esline ; Cevuard, Sandrine ( September 2021 , Bulletin of Volcanology)

   Abstract A new episode of unrest and phreatic/phreatomagmatic/magmatic eruptions occurred at Ambae volcano, Vanuatu, in 2017–2018. We installed a multi-station seismo-acoustic network consisting of seven 3-component broadband seismic stations and four 3-element (26–62 m maximum inter-element separation) infrasound arrays during the last phase of the 2018 eruption episode, capturing at least six reported major explosions towards the end of the eruption episode. The observed volcanic seismic signals are generally in the passband 0.5–10 Hz during the eruptive activity, but the corresponding acoustic signals have relatively low frequencies (< 1 Hz). Apparent very-long-period (< 0.2 Hz) seismic signals are also observed during the eruptive episode, but we show that they are generated as ground-coupled airwaves and propagate with atmospheric acoustic velocity. We observe strongly coherent infrasound waves at all acoustic arrays during the eruptions. Using waveform similarity of the acoustic signals, we detect previously unreported volcanic explosions at the summit vent region based on constant-celerity reverse-time-migration (RTM) analysis. The detected acoustic bursts are temporally related to shallow seismic volcanic tremor (frequency content of 5–10 Hz), which we characterise using a simplified amplitude ratio method at a seismic station pair with different distances from the vent. The amplitude ratio increased at the onset of large explosions and then decreased,more »which is interpreted as the seismic source ascent and descent. The ratio change is potentially useful to recognise volcanic unrest using only two seismic stations quickly. This study reiterates the value of joint seismo-acoustic data for improving interpretation of volcanic activity and reducing ambiguity in geophysical monitoring.« less