Unusually high SO2 emissions and plume height from Piton de la Fournaise volcano during the April 2020 eruption.

Piton de la Fournaise volcano, La Réunion, France, erupted between the 2 and 6 April 2020, one of a series of eruptive phases which occur typically two or three times per year. Here, we use back trajectory analysis of satellite data from the TROPOMI instrument to determine that gas emissions during the June 2020 eruption were of unusually high intensity and altitude, producing 34.9 ± 17.4 kt of SO2 and plume heights up to 5 km a.s.l. The early stages of the eruption (2-4 April 2020) were characterised by relatively low SO2 emission rates despite strong low frequency tremor (LFT); the latter phase followed an increase in intensity and explosivity in the early hours of 5 April 2020. This period included lava fountaining, significantly increased SO2 emission rates, increased high frequency tremor (HFT) and decreased LFT. Using the PlumeTraj back trajectory analysis toolkit, we found the peak SO2 emission rate was 284 ± 130 kg/s on the 6 April. The plume altitude peaked at ~ 5 km a.s.l. on 5 April, in the hours following a sudden increase in explosivity, producing one of the tallest eruption columns recorded at Piton de la Fournaise. PlumeTraj allowed us to discriminate each day's SO2, which otherwise would have led to a mass overestimate due to the plumes remaining visible for more than 24 h. The eruption exhibited a remarkable decoupling and anti-correlation between the intensity of the LFT signal and that of the magma and gas emission rates. LFT intensity peaked during the first phase with low magma and SO2 emissions, but quickly decreased during the second phase, replaced by unusually strong HFT. We conclude that the observation of strong HFT is associated with higher intensity of eruption, degassing, and greater height of neutral buoyancy of the plume, which may provide an alert to the presence of greater hazards produced by higher intensity eruptive activity. This might be particularly useful when direct visual observation is prevented by meteorological conditions. This eruption shows the importance of combining multiple data sets when monitoring volcanoes. Combining gas and seismic data sets allowed for a much more accurate assessment of the eruption than either could have done alone. Supplementary Information: The online version contains supplementary material available at 10.1007/s00445-023-01628-1.

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano.

Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption as well as the closing of the eruptive vent during the September-November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.

Constraining Spatiotemporal Characteristics of Magma Migration at Piton De La Fournaise Volcano From Pre-eruptive Seismicity.

Volcano-tectonic seismicity has been recorded for decades on various volcanoes and is linked with the magma transport and reservoir pressurization. Yet earthquakes often appear broadly distributed such that magma movement is difficult to infer from its analysis. We explore the seismicity that occurred before eruptions at Piton de la Fournaise in the last 5 years. Using template matching and relocation techniques, we produce a refined image of the summit seismicity, demonstrating that most earthquakes are located on a ring structure. However, only a portion of this structure is activated before each eruption, which provides an indication as to the direction of the future eruptive site. Furthermore, we show that the delay between the pre-eruptive swarm and the eruption onset is proportional to the distance of the eruptive fissures relative to the summit cone. This reveals that the beginning of the intrusion already bears information regarding the future eruption location.

Very- and ultra-long-period seismic signals prior to and during caldera formation on La Réunion Island.

Early detection of the onset of a caldera collapse can provide crucial information to understand their formation and thus to minimize risks for the nearby population and visitors. Here, we analyse the 2007 caldera collapse of Piton de la Fournaise on La Réunion Island recorded by a broadband seismic station. We show that this instrument recorded ultra-long period (ULP) signals with frequencies in the range (0.003-0.01 Hz) accompanied by very-long period (VLP) signals (between 0.02 and 0.50 Hz) prior to and during the caldera formation suggesting it is possible to detect the beginning of the collapse at depth and anticipate its surface formation. Interestingly, VLP wave packets with a similar duration of 20 s are identified prior to and during the caldera formation. We propose that these events could result from repeating piston-like successive collapses occurring through a ring-fault structure surrounding a magma reservoir from the following arguments: the source mechanism from the main collapse, the observations of slow source processes as well as observations from the field and the characteristic ring-fault seismicity.