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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. 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. 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 We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signals We apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelines The seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity

Identifying the observables that warn of volcanic eruptions is a major challenge in natural hazard management. A potentially important observable is the release of heat through volcano surfaces, which represents a major energy source at quiescent volcanoes. However, it remains unclear whether surface heat emissions respond to pre-eruptive processes and vary before eruption. Here we show through a statistical analysis of satellite-based long-wavelength (10.780–11.280 μm) infrared data that the last magmatic and phreatic eruptions of five different volcanoes were preceded by subtle but significant long-term (years), large-scale (tens of square kilometres) increases in their radiant heat flux (up to ~1 °C in median radiant temperature). Large-scale thermal unrest is detected even before eruptions that were not anticipated from other volcano monitoring methods, such as the 2014 phreatic eruption of Ontake (Japan) and the 2015 magmatic eruption of Calbuco (Chile). We attribute large-scale thermal unrest to the enhancement of underground hydrothermal activity, and suggest that such analysis of satellite-based infrared observations can improve constraints on the thermal budget of volcanoes, early detection of pre-eruptive conditions and assessments of volcanic alert levels.

Linking volcano‐seismic signals with subsurface processes is crucial to improve the forecasting of volcanic eruptions. One of the most enigmatic signals is shallow volcanic tremor, a highly periodic ground vibration that is typically sourced beneath the crater of active volcanoes, is long lasting (from minutes to years), appears during unrest periods, and frequently precedes eruptions. In this paper, we demonstrate that shallow tremor can be produced by periodic pressure oscillations emerging spontaneously beneath permeable media (e.g., fractured magma caps). These pressure oscillations are the result of three concurrent processes: (a) the transient porous flow of gases through the permeable cap; (b) the temporary accumulation of these gases beneath the cap, forming a gas pocket; and (c) the random supply of volatiles from deeper levels. For highly permeable media (≥10−12m2; realistic for shallow volcanic edifices), the pressure in subsurface gas pockets is governed by the equation of a linear oscillator; hence, under proper conditions, periodic pressure oscillations emerge during the transfer of gases from the Earth's interior to the surface. Our model is consistent with geophysical data showing that the tremor source is localized at a fixed region and can explain the main characteristics of ground vibrations, including frequency gliding, variations of seismic amplitude prior to eruptions, and the different types of tremor observed. For thin permeable caps (<100 m), we find that different eruption mechanisms (e.g., magma ascent vs. cap sealing) leave distinct footprints in the tremor properties, thus opening new perspectives to forecast the type and style of impending eruptions. Volcanic tremor can emerge spontaneously during volcanic degassing if gas accumulates beneath permeable mediaOur model provides a framework to explore the link between shallow volcanic tremor and the processes that ultimately trigger eruptionsUnderstanding the pressure‐outgassing coupling is crucial to interpret geophysical observables and volcanic unrest

Many active volcanoes around the world release passively large amounts of gas between eruptions. Monitoring how these gas emissions fluctuate over time is crucial to infer the physical processes occurring within volcanic conduits and reservoirs. Here we report a new method to capture remotely the spectral properties of the emissions of H2O, the major component of most volcanic plumes. The method is based on a new theoretical model that correlates the volcanogenic water content of condensed volcanic plumes with the intensity of the light scattered by the droplets moving with the gas. In turn, we show that light intensity of the plume, and thus steam pulses time series, can be obtained with a proper analysis of digital images. The model is experimentally validated by generating condensed plumes with an ultrasonic humidifier, and then the method is applied to the gas plumes of Erebus and Mayon volcanoes. Our analysis reveals three main features: (1) H2O time series are composed of numerous periodic components of finite duration; (2) some periodic components are common in H2O and SO2time series, but others are not; and (3) the frequency spectra of the H2O emissions follow a well‐defined fractal distribution, that is, amplitude (Δ) and frequency (ν) are correlated by means of power laws (Δ ∝ νγ), with exponent γ≈ − 1 for Erebus and for Mayon. These findings suggest that quiescent degassing emerges from the complex coupling between different processes occurring within magma plumbing systems. Our method is ideal for real‐time monitoring of high‐frequency H2O cycles at active volcanoes. A new method to monitor the emission cycles of volcanic water vaporThe frequency spectra of H2O time series are fractalH2O and SO2time series can have different spectral content

Many active volcanoes around the world alternate episodes of unrest and mildly explosive eruptions with quiescent periods dominated by abundant but passive gas emissions. These are the so‐called persistently degassing volcanoes, and well‐known examples are Mayon (Philippines) and Etna (Italy). Here, we develop a new lumped‐parameter model to investigate by how much the gas released during quiescence can decrease the pressure within persistently degassing volcanoes. Our model is driven by the gas fluxes measured with monitoring systems and takes into account the size of the conduit and reservoir, the viscoelastic response of the crust, the magma density change, the bubble exsolution and expansion at depth, and the hydraulic connectivity between reservoirs and deeper magma sources. A key new finding is that, for a vast majority of scenarios, passive degassing reduces the pressure of shallow magma reservoirs by several MPa in only a few months or years, that is, within the intereruptive timescales of persistently degassing volcanoes. Degassing‐induced depressurization could be responsible for the subsidence observed at some volcanoes during quiescence (e.g., at Satsuma‐Iwojima and Asama, in Japan; Masaya, in Nicaragua; and Llaima, in Chile), and could play a crucial role in the onset and development of the physical processes which may in turn culminate in new unrest episodes and eruptions. For example, degassing‐induced depressurization could promote magma replenishment, induce massive and sudden gas exsolution at depth, and trigger the collapse of the crater floor and reservoir roof. Passive degassing depressurizes magma reservoirs by several MPa

The Whakaari/White Island volcano, located ~ 50 km off the east coast of the North Island in New Zealand, has experienced sequences of quiescence, unrest, magmatic and phreatic eruptions over the last decades. For the last 15 years, seismic data have been continuously archived providing potential insight into this frequently active volcano. Here we take advantage of this unusually long time series to retrospectively process the seismic data using ambient noise and tremor-based methodologies. We investigate the time (RSAM) and frequency (Power Spectral Density) evolution of the volcanic tremor, then estimate the changes in the shallow subsurface using the Displacement Seismic Amplitude Ratio (DSAR), relative seismic velocity (dv/v) and decorrelation, and the Luni-Seismic Correlation (LSC). By combining our new set of observations with the long-term evolution of earthquakes, deformation, visual observations and geochemistry, we review the activity of Whakaari/White Island between 2007 and the end of 2018. Our analysis reveals the existence of distinct patterns related to the volcano activity with periods of calm followed by cycles of pressurization and eruptions. We finally put these results in the wider context of forecasting phreatic eruptions using continuous seismic records.

The surfaces of icy moons are covered by fractures, other tectonic features, and active or ancient remains of cryovolcanism. These observations suggest active or recent tectonics, but there is still much unknown about the specific conditions surrounding the formation of these features. One important process leading to the fracture of the ice shell is the freezing and consequent pressurization of its ocean, because water expands upon freezing. However, the influence of dissolved non‐condensable gases (herein referred to as volatiles) on the aforementioned dynamics remains poorly constrained. In this study, we present a new experimental investigation to explore the effect of dissolved volatiles in the internal pressure evolution of 10 cm diameter water spheres subjected to freezing temperatures between ~−60°C and ~−20°C. Our experiments reveal that spheres with a reduced initial amount of volatiles dissolved undergo an abrupt transition with dramatic increase of (a) the time between consecutive ice shell fractures and (b) the pressure required to break the shell. We show from a simple numerical model that this transition occurs when exsolution (i.e., nucleation and growth of bubbles) occurs and the fluid inside the shell becomes significantly more compressible. Exsolution is, in turn, triggered by the gradual thickening of the ice shell, which increases the concentration of dissolved volatiles and eventually leads to saturation. These results suggest that the content of volatiles of icy satellites plays a significant role in their geologic history and potential for habitability. Icy moons have subsurface oceans which may be freezing. As water freezes, it expands. When a subsurface ocean on an icy moon freezes, this expansion of the ice shell increases the pressure in the ocean and can lead to the formation of water‐filled cracks in the icy surface of the moon. To understand how the amount of gas in the ocean water controls when the cracks form, we freeze small spheres of water with different amounts of air under controlled temperature. We find that at some point during the freezing and growth of the ice shell, there is a sharp change in how often cracks occur. This happens because dissolved gases remain in the liquid water during freezing and eventually there is enough gas in the water for bubbles to form. This is important because it tells us how the gas content in the oceans of icy moons might control some of the cracks we observe on the surface of icy moons. The freezing of an ocean world is modeled experimentally by freezing spheres of waterIncreased dissolved volatiles in the water significantly influence the tectonic evolution of the body, leading to a lower frequency of fracture events and thinner ice shell at final failureThese results have implications for the geologic history and habitability of icy moons. Specifically, oceans become more volatile‐rich over time, which in turn decreases the frequency at which the ice shell fractures