Volcanic CO2 tracks the incubation period of basaltic paroxysms

Monitoring underwater volcano degassing using fiber-optic sensing

Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change.

Excess degassing drives long-term volcanic unrest at Nevado del Ruiz

This study combines volcanic gas compositions, SO2 flux and satellite thermal data collected at Nevado del Ruiz between 2018 and 2021. We find the Nevado del Ruiz plume to have exhibited relatively steady, high CO2 compositions (avg. CO2/ST ratios of 5.4 ± 1.9) throughout. Our degassing models support that the CO2/ST ratio variability derives from volatile exsolution from andesitic magma stored in the 1-4 km depth range. Separate ascent of CO2-rich gas bubbles through shallow (< 1 km depth), viscous, conduit resident magma causes the observed excess degassing. We infer that degassing of ~ 974 mm3 of shallow (1-4 km) stored magma has sourced the elevated SO2 degassing recorded during 2018-2021 (average flux ~ 1548 t/d). Of this, only < 1 mm3 of magma have been erupted through dome extrusion, highlighting a large imbalance between erupted and degassed magma. Escalating deep CO2 gas flushing, combined with the disruption of passive degassing, through sudden accumulation and pressurization of bubbles due to lithostatic pressure, may accelerate volcanic unrest and eventually lead to a major eruption.

Magma recharge and mush rejuvenation drive paroxysmal activity at Stromboli volcano

Open-conduit basaltic volcanoes can be characterised by sudden large explosive events (paroxysms) that interrupt normal effusive and mild explosive activity. In June-August 2019, one major explosion and two paroxysms occurred at Stromboli volcano (Italy) within only 64 days. Here, via a multifaceted approach using clinopyroxene, we show arrival of mafic recharges up to a few days before the onset of these events and their effects on the eruption pattern at Stromboli, as a prime example of a persistently active, open-conduit basaltic volcano. Our data indicate a rejuvenated Stromboli plumbing system where the extant crystal mush is efficiently permeated by recharge magmas with minimum remobilisation promoting a direct linkage between the deeper and the shallow reservoirs that sustains the currently observed larger variability of eruptive behaviour. Our approach provides vital insights into magma dynamics and their effects on monitoring signals demonstrating the power of petrological studies in interpreting patterns of surficial activity.

The electrical signature of mafic explosive eruptions at Stromboli volcano, Italy

Volcanic lightning is commonly observed in explosive volcanic eruptions of Volcanic Explosivity Index (VEI) > 2 and can be detected remotely providing real-time volcano monitoring information. However, little is known about the electrical activity accompanying the lower-magnitude spectrum of explosive eruptions, often involving mafic magmas. We narrow this gap in knowledge by presenting the electrical signature of the explosive activity (VEI ≤ 1) of Stromboli volcano (Italy) recorded by an electrostatic thunderstorm detector. The persistent eruptive activity of mild Strombolian explosions is occasionally interrupted by larger-scale major explosions and paroxysmal events.Here, we present electrical observations of three major explosions and unprecedented measurements of the 3 July 2019 paroxysm. The electrical signals of the major explosions show apparent similarities, with movements of charge and tens of electrical discharges, arising the question of whether these observations could be used to supplement the classification scheme of explosions on Stromboli. The electrical signals from the 3 July 2019 paroxysm exceed those from the major explosions in amplitude, discharge rate and complexity, showing characteristic variations during different phases of the eruption.These results show that also impulsive lower-magnitude explosions generate detectable electrical activity, which holds promise for monitoring low VEI activity at mafic volcanoes.

Changes in the Eruptive Style of Stromboli Volcano before the 2019 Paroxysmal Phase Discovered through SOM Clustering of Seismo-Acoustic Features Compared with Camera Images and GBInSAR Data

Two paroxysmal explosions occurred at Stromboli on July 3 and August 28, 2019, the first of which caused the death of a young tourist. After the first paroxysm an effusive activity began from the summit vents and affected the NW flank of the island for the entire period between the two paroxysms. We carried out an unsupervised analysis of seismic and infrasonic data of Strombolian explosions over 10 months (15 November 2018–15 September 2019) using a Self-Organizing Map (SOM) neural network to recognize changes in the eruptive patterns of Stromboli that preceded the paroxysms. We used a dataset of 14,289 events. The SOM analysis identified three main clusters that showed different occurrences with time indicating a clear change in Stromboli’s eruptive style before the paroxysm of 3 July 2019. We compared the main clusters with the recordings of the fixed monitoring cameras and with the Ground-Based Interferometric Synthetic Aperture Radar measurements, and found that the clusters are associated with different types of Strombolian explosions and different deformation patterns of the summit area. Our findings provide new insights into Strombolian eruptive mechanisms and new perspectives to improve the monitoring of Stromboli and other open conduit volcanoes.