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Volcano seismicity is often detected and classified based on its spectral properties. However, the wide variety of volcano seismic signals and increasing amounts of data make accurate, consistent, and efficient detection and classification challenging. Machine learning (ML) has proven very effective at detecting and classifying tectonic seismicity, particularly using Convolutional Neural Networks (CNNs) and leveraging labeled datasets from regional seismic networks. Progress has been made applying ML to volcano seismicity, but efforts have typically been focused on a single volcano and are often hampered by the limited availability of training data. We build on the method of Tan et al. [2024] (10.1029/2024JB029194) to generalize a spectrogram-based CNN termed the VOlcano Infrasound and Seismic Spectrogram Neural Network (VOISS-Net) to detect and classify volcano seismicity at any volcano. We use a diverse training dataset of over 270,000 spectrograms from multiple volcanoes: Pavlof, Semisopochnoi, Tanaga, Takawangha, and Redoubt volcanoes\replaced (Alaska, USA); Mt. Etna (Italy); and Kīlauea, Hawai`i (USA). These volcanoes present a wide range of volcano seismic signals, source-receiver distances, and eruption styles. Our generalized VOISS-Net model achieves an accuracy of 87 % on the test set. We apply this model to continuous data from several volcanoes and eruptions included within and outside our training set, and find that multiple types of tremor, explosions, earthquakes, long-period events, and noise are successfully detected and classified. The model occasionally confuses transient signals such as earthquakes and explosions and misclassifies seismicity not included in the training dataset (e.g. teleseismic earthquakes). We envision the generalized VOISS-Net model to be applicable in both research and operational volcano monitoring settings. 

Abstract Infrasound is increasing applied as a tool to investigate magma dynamics at active volcanoes, especially at open-vent volcanoes, such as Mt. Etna (Italy), which are prodigious sources of infrasound. Harmonic infrasound signals have been used to constrain crater dimensions and track the movement of magma within the shallow plumbing system. This study interprets the remarkable systematic change in monotonic infrasound signals preceding a lava fountaining episode at Mt. Etna on 20 February 2021. We model the changing tones (0.7 to 3 Hz fundamental frequency) as a rise in the magma column from 172 ± 25 m below the crater rim to 78 ± 8 m over the course of 24 h. The infrasonic gliding disappears approximately 4 h before the onset of lava fountaining as the magma column approaches the flare of the crater and acoustic resonance is no longer supported. The featured 20 February event was just one of 52 lava fountain episodes that occurred at Mt. Etna over the course of 9 months in 2021 and was the only lava fountain episode where dramatic gliding was observed as a subsequent partial collapse of the crater prevented future resonance. The results presented here demonstrate that analysis of infrasonic gliding can be used to track the position of the magma free surface and hence may provide information on the processes taking place within the plumbing system before eruptive activity. 

Climate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland (Kalaallit Nunaat), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning–induced rock-ice avalanche of 25 × 10⁶cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 10¹¹newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere. 

Abstract In December 2018, Mount Etna (Italy) experienced a period of increased eruptive activity that culminated in a fissure eruption on the southeast flank. After the onset of the flank eruption, the peak frequency of the summit infrasound signals decreased while resonance increased. We invert infrasound observations for crater geometry and show that crater depth and radius increased during the eruption, which suggests that the flank eruption drained magma from the summit and that eruptive activity led to erosion of the crater wall. By inverting the entire infrasound amplitude spectra rather than just the peak frequency, we are able to place additional constraints on the crater geometry and invert for, rather than assume, the crater shape. This work illustrates how harmonic infrasound observations can be used to obtain high‐temporal‐resolution information about crater geometry and can place constraints on complex processes occurring in the inaccessible crater region during eruptive activity.