Mereu, Luigi

On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC’s southern and eastern flanks. The monitoring system of the Istituto Nazionale di Geofisica e Vulcanologia testified to its occurrence. We analyzed the seismic and infrasound signals to constrain the temporal evolution of the fountain, which lasted about 5 h. We finally reached Etna’s summit two weeks later and found an unexpected pyroclastic density current (PDC) deposit covering the southern lava flow at its middle portion. We performed unoccupied aerial system and field surveys to reconstruct in 3D the SEC, lava flows, and PDC deposits and to collect some samples. The data allowed for detailed mapping, quantification, and characterization of the products. The resulting lava flows and PDC deposit volumes were (1.54 ± 0.47) × 106 m3 and (1.30 ± 0.26) × 105 m3, respectively. We also analyzed ground-radar and satellite data to evaluate that the plume height ranges between 10 and 15 km. This work is a comprehensive analysis of the fieldwork, UAS, volcanic tremor, infrasound, radar, and satellite data. Our results increase awareness of the volcanic activity and potential dangers for visitors to Etna’s summit area.

Atmospheric injection of volcanic ash during eruptions is a threat to aviation. Reliable forecast of airborne ash dispersal relies on empirical and numerical models. Key inputs into these models are so-called eruption source parameters such as the rate at which pyroclastic material is ejected from the vent and the maximum height of eruptive columns. Here, we use infrasound data recorded during eruptive activity in June 2021 at Mt. Etna, Italy, to demonstrate its potential for assessment of eruption rates in near-real time. We calculate a time series of flow velocity at the vent using data corrected for topographic scattering, and the effect of vent geometry on the acoustic source radiation. We obtain values of flow velocity of 50-125 m/s during a period of sustained, paroxysmal, activity. We use independent estimates from other ground-based remote sensing data to validate our results. Further, we use the infrasound-derived flow velocities as input into a 1D plume model to estimate the maximum height of the eruption column. Our results suggest that infrasound technology holds promise to assess eruption rates and inform modelling of volcanic plumes. We anticipate that implementation of real-time operational workflows based on infrasound data and plume modelling will impact decision-making and risk mitigation at active volcanoes.

The management of multiple hazards simultaneously impacting on a territory is a challenge for effective risk mitigation. This is particularly true on active volcanoes like Mt. Etna, characterized by effusive and explosive eruptions, often coupled with an intense seismic activity. This work aims at presenting the approach of the PANACEA project on the treatment of multi-hazards in terms of risk, which requires a common definition of the exposed elements and their vulnerability. Another aspect emerging from the recent and historical volcanic crises at Etna, is the occurrence of cascading effects and the problem of assessing their short-term interactions. Here we present a risk model taking into account a set of sequences of hazardous events which may result from a volcano unrest to possible impacts to some infrastructural elements. The outcomes of the project are intended to be a significant step towards a more comprehensive resilience to volcanic disasters, leading to a more safe society.

Accurate forecasting of volcanic particle (tephra) dispersal and fallout requires a reliable estimation of key Eruption Source Parameters (ESPs) such as the Mass Eruption Rate (Q M). QM is usually estimated from the Top Plume Height (HTP) using empirical and analytical models. For the first time, we combine estimates of HTP and QM derived from the same sensor (radar) with mean wind velocity values (vW) for lava-fountain fed tephra plumes associated with 32 paroxysms of Mt. Etna (Italy) to develop a new statistical model based on a Markov Chain Monte Carlo approach for model parameter estimation. This model is especially designed for application to radar data to quickly infer QM from observed HTP and vW, and estimate the associated uncertainty. It can be easily applied and adjusted to data retrieved by radars worldwide, improving our capacity to quickly estimate QM and related uncertainties required for the tephra dispersal hazard.

This work presents the first results of the PANACEA project regarding the assessment of different volcano-related hazards at Mt. Etna (lava and pyroclastic flows, tephra fallout and earthquakes) by exploiting data deriving from the volcano’s history with accurate physical–mathematical models. Volcano-related hazards are distributed differently on Etna—from the deserted summit area down to the densely populated flanks—but must be considered together for long-term territorial planning.

Tephra dispersal and fallout resulting from explosive activity of Mt. Etna (Italy) represent a significant threat to the surrounding inhabited areas as well as to aviation operations. An earlywarning system aimed at foreseeing the onset of paroxysmal activity has been developed, combining a thermal infrared camera, infrasonic network, and a weather radar. In this way, it is possible to identify the onset of a lava fountain as well as to determine the associated mass eruption rate (MER) and top plume height (HTP). The new methodology, defined as the paroxysmal early-warning (PEW) alert system, is based on the analysis of some explosive eruptions that occurred between 2011 and 2021 at Etna, simultaneously observed by the thermal camera and infrasound systems dislocated around the summit eruptive craters, and by the weather radar, located at about 32 km from the summit craters. This work represents an important step towards the mitigation of the potential impact associated with the tephra dispersal and fallout during paroxysms at Etna, which can be applied to other volcanoes with similar activity and monitoring strategies.

This work was born from a wish of remembering the fundamental contribution of Prof. Frank Silvio Marzano to the field of physical volcanology. In fact, for the last fifteen years and in the context of several European projects, Prof. Marzano collaborated with many volcanologists as well as scientists from different fields and wrote many scientific articles aimed at studying the dynamics of explosive eruptions. He left his imprinting in this research sector laying the foundations of radar volcanology in Italy, and extended his studies to other sensors. His work is relevant for the analysis of the main eruption source parameters needed to characterize the eruptive events. Here we show how remote sensing instruments applied to analyze explosive activity of different volcanoes worldwide, are going to increase the knowledge in this multidisciplinary research area and the awareness from the scientific community of the potential of these sensors at various wavelengths.

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 10 11 kg (MLE method) and 1.32 ± 0.47 10 11 kg (NN method) for Kelud; 4.48 ± 1.61 10 10 kg (MLE method) and 4.32 ± 1.56 10 10 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.

Between 2020 and 2022, more than sixty lava fountains occurred at Mt. Etna (Italy), which formed high eruption columns rising up to 15 km above sea level (a.s.l.). During those events, several ballistics fell around the summit craters, sometimes reaching touristic areas. The rather frequent activity poses questions on how the impact associated with the fallout of those particles, can be estimated. In this work, we present field data collected soon after the lava fountain on 21 February 2022. This event produced a volcanic plume of about 10 km a.s.l. which was directed toward the southeast. Several ballistics fell in the area of the Barbagallo Craters (just southeast of the summit area at around 2900 m a.s.l.), which is one of the most popular touristic areas on Etna. Hence, we collected several samples and performed laboratory analyses in order to retrieve their size, shape and density. Those values together with a quantitative analysis of the lava fountain were compared with results obtained by a free-available calculator of ballistic trajectories named the ‘Eject!’. A similar approach was hence applied to other lava fountains of the 2020–2022 sequence for which the fallout of large clasts was reported. This work is a first step to identifying in near real-time the area affected by the fallout of ballistics during Etna lava fountains and quantifying their hazard.

- During explosive eruptions, the real-time estimation of the mass eruption rate (MER) is challenging although crucial to mitigate the impact of erupted tephra. Microwave radar techniques at L and/or X bands, as well as thermal infrared imagery, can provide a reliable MER estimation in real time. Using lava fountains of 3-5 December 2015 at Mt. Etna (Italy) as test cases, we investigate the differences among all these remote sensing methods and introduce a new approach, called the Near Source Approach (NSA) using only X-band radar data. We also extend the Volcanic Advanced Radar Retrieval (VARR) methodology to estimate the gas-tephra mixture density near the volcanic crater. The analysis of uncertainty is carried out comparing the NSA with the Mass Continuity Approach (MCA), Top Plume Approach (TPA) and Surface Flux approach (SFA), already used to estimate the MER of other Etna explosive events. The analysis allows us to identify the optimal real-time MER retrieval strategy, showing the potential and limitations of each method. We show that the MCA method, entirely based on the Xband radar data processing, is the best s trategy with a percentageuncertainty in the MER estimation of 22.3%, whereas other approaches exhibit e a higher uncertainty (26.4% for NSA, 30% for TPA and 31.6% for SFA).