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1. 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. (September 2022, Bulletin of Volcanology)

   Abstract 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 for 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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2. Evaluating the applicability of a screen diffraction approximation to local volcano infrasound

   https://doi.org/10.30909/vol.04.01.6785  

   Maher, Sean; Matoza, Robin; de Groot-Hedlin, Catherine; Kim, Keehoon; Gee, Kent (January 2021, Volcanica)

   null (Ed.)

   Atmospheric acoustic waves from volcanoes at infrasonic frequencies (0.01–20 Hz) can be used to estimate source parameters for hazard modeling, but signals are often distorted by wavefield interactions with topography, even at local recording distances (<15 km). We present new developments toward a simple empirical approach to estimate attenuation by topographic diffraction at reduced computational cost. We investigate the applicability of a thin screen diffraction relationship developed by Maekawa [1968, doi: https://doi.org/10.1016/0003-682X(68)90020- 0]. We use a 2D axisymmetric finite-difference method to show that this relationship accurately predicts power losses for infrasound diffraction over an idealized kilometer-scale screen; thus validating the scaling to infrasonic wavelengths. However, the Maekawa relationship overestimates attenuation for realistic volcano topography (using Sakurajima Volcano as an example). The attenuating effect of diffraction may be counteracted by constructive interference of multiple reflections along concave volcano slopes. We conclude that the Maekawa relationship is insufficient as formulated for volcano infrasound, and suggest modifications that may improve the prediction capability. 

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3. Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

   https://doi.org/10.3389/feart.2021.620813  

   Fee, David; Toney, Liam; Kim, Keehoon; Sanderson, Richard W.; Iezzi, Alexandra M.; Matoza, Robin S.; De Angelis, Silvio; Jolly, Arthur D.; Lyons, John J.; Haney, Matthew M. (February 2021, Frontiers in Earth Science)

   null (Ed.)

   Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (<15 km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ∼20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability. 

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4. Evidence for near-source nonlinear propagation of volcano infrasound from Strombolian explosions at Yasur Volcano, Vanuatu

   https://doi.org/10.1007/s00445-022-01552-w  

   Maher, Sean P.; Matoza, Robin S.; Jolly, Arthur; de Groot-Hedlin, Catherine; Gee, Kent L.; Fee, David; Iezzi, Alexandra M. (April 2022, Bulletin of Volcanology)

   Abstract Volcanic eruption source parameters may be estimated from acoustic pressure recordings dominant at infrasonic frequencies (< 20 Hz), yet uncertainties may be high due in part to poorly understood propagation dynamics. Linear acoustic propagation of volcano infrasound is commonly assumed, but nonlinear processes such as wave steepening may distort waveforms and obscure the sourcing process in recorded waveforms. Here we use a previously developed frequency-domain nonlinearity indicator to quantify spectral changes due to nonlinear propagation primarily in 80 signals from explosions at Yasur Volcano, Vanuatu. We find evidence for $$\le$$ ≤ 10 −3  dB/m spectral energy transfer in the band 3–9 Hz for signals with amplitude on the order of several hundred Pa at 200–400 m range. The clarity of the nonlinear spectral signature increases with waveform amplitude, suggesting stronger nonlinear changes for greater source pressures. We observe similar results in application to synthetics generated through finite-difference wavefield simulations of nonlinear propagation, although limitations of the model complicate direct comparison to the observations. Our results provide quantitative evidence for nonlinear propagation that confirms previous interpretations made on the basis of qualitative observations of asymmetric waveforms. 

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5. Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

   https://doi.org/10.1029/2024JB029194  

   Tan, Darren; Fee, David; Witsil, Alex; Girona, Társilo; Haney, Matthew; Wech, Aaron; Waythomas, Chris; Lopez, Taryn (June 2024, Journal of Geophysical Research: Solid Earth)

   Abstract 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. 

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