Ciancitto, Francesco

Volcanic hazards associated with lava flows advancing on snow cover are often underrated, although sudden explosions related to different processes of lava-snow/ice contact can occur rapidly and are only preceded by small, easily underrated precursors. On 16 March 2017, during a mildly effusive and explosive eruption at Mount Etna, Italy, a slowly advancing lava lobe interacted with the snow cover to produce a sudden, brief sequence of explosions. White vapor, brown ash, and coarse material were suddenly ejected, and the products struck a group of people, injuring some of them. The proximal deposit formed a continuous mantle of ash, lapilli, and decimeter-sized bombs, while the ballistic material travelled up to 200 m from the lava edge. The deposit was estimated to have a mass of 7.1 ± 0.8 × 104 kg, which corresponds to a volume of 32.0 ± 3.6 m3 of lava being removed by the explosion. Data related to the texture and morphology of the ejected clasts were used to constrain a model of lava-snow interaction. The results suggest that the mechanism causing the explosions was the progressive build-up of pressure due to vapor accumulation under the lava flow, while no evidence was found for the occurrence of fuel-coolant interaction processes. Although these low-intensity explosions are not particularly frequent, the data set collected provides, for the first time, quantitative information about the processes involved and the associated hazard and suggests that mitigation measures should be established to prevent potentially dramatic accidents at worldwide volcanoes frequented by tourists and with fairly easy access, such as Etna.

Open conduit volcanoes like Stromboli can display elusive changes in activity before major eruptive events. Starting on December 2020, Stromboli volcano displayed an increasing eruptive activity, that on 19 May 2021 led to a crater-rim collapse, with pyroclastic density currents (PDCs) that spread along the barren NWflank, entered the sea and ran across it for more than 1 km. This episode was followed by lava flow output from the crater rim lasting a few hours, followed by another phase of lava flow in June 2021. These episodes are potentially very dangerous on island volcanoes since a landslide of hot material that turns into a pyroclastic density current and spreads on the sea surface can threaten mariners and coastal communities, as happened at Stromboli on 3 July and 28 August 2019. In addition, on entering the sea, if their volume is large enough, landslides may trigger tsunamis, as occurred at Stromboli on 30 December 2002. In this paper, we present an integration of multidisciplinary monitoring data, including thermal and visible camera images, ground deformation data gathered from GNSS, tilt, strainmeter and GBInSAR, seismicity, SO2 plume and CO2 ground fluxes and thermal data from the ground and satellite imagery, together with petrological analyses of the erupted products compared with samples from previous similar events. We aim at characterizing the preparatory phase of the volcano that began on December 2020 and led to the May–June 2021 eruptive activity, distinguishing this small intrusion of magma from the much greater 2019 eruptive phase, which was fed by gas-rich magma responsible for the paroxysmal explosive and effusive phases of July–August 2019. These complex eruption scenarios have important implications for hazard assessment and the lessons learned at Stromboli volcano may prove useful for other open conduit active basaltic volcanoes.

In July and August 2019, Stromboli volcano underwent two dangerous paroxysms previously considered “unexpected” because of the absence of significant changes in usually monitored parameters. We applied a multidisciplinary approach to search for signals able to indicate the possibility of larger explosive activity and to devise a model to explain the observed variations. We analysed geodetic data, satellite thermal data, images from remote cameras and seismic data in a timespan crossing the eruptive period of 2019 to identify precursors of the two paroxysms on a medium-term time span (months) and to perform an in-depth analysis of the signals recorded on a short time scale (hours, minutes) before the paroxysm. We developed a model that explains the observations. We call the model “push and go” where the uppermost feeding system of Stromboli is made up of a lower section occupied by a low viscosity, low density magma that is largely composed of gases and a shallower section occupied by the accumulated melt. We hypothesize that the paroxysms are triggered when an overpressure in the lower section is built up; the explosion will occur at the very moment such overpressure overcomes the confining pressure of the highly viscous magma above it.

In 2019, Stromboli volcano experienced one of the most violent eruptive crises in the last hundred years. Two paroxysmal explosions interrupted the 'normal' mild explosive activity during the tourist season. Here we integrate visual and field observations, textural and chemical data of eruptive products, and numerical simulations to analyze the eruptive patterns leading to the paroxysmal explosions. Heralded by 24 days of intensified normal activity and 45 min of lava outpouring, on 3 July a paroxysm ejected ~6 × 107 kg of bombs, lapilli and ash up to 6 km high, damaging the monitoring network and falling towards SW on the inhabited areas. Intensified activity continued until the less energetic, 28 August paroxysm, which dispersed tephra mainly towards NE. We argue that all paroxysms at Stromboli share a common pre-eruptive weeks-to months-long unrest phase, marking the perturbation of the magmatic system. Our analysis points to an urgent implementation of volcanic monitoring at Stromboli to detect such long-term precursors.

A lengthy period of eruptive activity fromthe summit craters ofMt. Etna started in January 2011. It culminated in early December 2015 with a spectacular sequence of intense eruptive events involving all four summit craters (Voragine, Bocca Nuova,NewSoutheast Crater, and Northeast Crater). The activity consisted of high eruption columns, Strombolian explosions, lava flows andwidespread ash falls that repeatedly interferedwith air traffic. The most powerful episode occurred on 3 December 2015 from the Voragine. After three further potent episodes fromthe Voragine, activity shifted to the NewSoutheast Crater on 6 December 2015, where Strombolian activity and lava flow emission lasted for two days and were fed by the most primitive magma of the study period. Activity once more shifted to the Northeast Crater, where ash emission and weak Strombolian activity took place for several days. Sporadic ash emissions from all craters continued until 18 December, when all activity ceased. Although resembling the summit eruptions of 1998–1999, which also involved all four summit craters, thismultifaceted eruptive sequence occurred in an exceptionally short time window of less than three days, unprecedented in the recent activity of Mt. Etna. It also produced important morphostructural changes of the summit area with the coalescence of Voragine and Bocca Nuova in a single large crater, the “Central Crater”, reproducing themorphological setting of the summit cone before the formation of Bocca Nuova in 1968. The December 2015 volcanic crisis was followed closely by the staff of the Etna Observatory to monitor the on-going activity and forecast its evolution, in accordance with protocols agreed with the Italian Civil Protection Department.

Explosive Activity at Stromboli Volcano (Aeolian Islands) is continuously monitored by INGV-OE in order to analyze its eruptive dynamics and specific scenarios. In particular, the images acquired from thermal cameras represent a big collection of data. In order to extract useful information from thermal image sequences, we need an efficient way to explore and retrieve information from a huge amount of data. In this work, a novel framework capable of fast data retrieval, using the "metric space" concept, is shown. In the light of it, we implemented an indexing algorithm related to similarity laws. The focal point is finding objects of a set that are “close” in relation to a given query, according to a similarity criterion. In order to perform this task, we performed morphological image processing techniques to each video frame, in order to map the shape area of each explosion into a closed curve, representing the explosion contour itself. In order to constitute a metric space, we chose a certain number of features obtained from parameters related to this closed curve and used them as objects of this metric space where similarity can be evaluated, using an appropriate “metric” function to calculate the distances. Unfortunately, this approach has to deal with an intrinsic issue involving the complexity and the number of distance functions to be calculated on a large amount of data. To overcome this drawback, we used a novel abstract data structure called "K-Pole Tree", having the property of minimizing the number of distances to be calculated among objects. Our method allows for fast retrieval of similar objects using an euclidean distance function among the features of the metric space. Thus, we can cluster explosions related to different kinds of volcanic activity, using "pivot" items. For example, given a known image sequence related to a particular type of explosion, it is possible to quickly and easily find all the image sequences that contain only similar explosions. Our framework is able to both classify each new explosion and dynamically insert the corresponding object into our tree data structure. This approach is able to cluster the entire data space, ensuring that objects with similar features are grouped and classified together.

Il monitoraggio e la sorveglianza vulcanologica con telecamere fisse sull'isola di Stromboli è iniziata circa 20 anni fa con la prima telecamera fissa al Pizzo. Nel 1994 fu istallata la prima telecamera e nel 96 dello stesso anno fu danneggiata dal parossismo del 4 Settembre 1996.

This work is aimed at improving the performance of the ground NEtwork of Thermal and VIsible and cameras located on Mt. Etna volcano (Etna_NETVIS) by optimizing its observational capability on lava flows evolution and by developing dedicated tools for systematically measuring quantitative parameters of known accuracy. The first goal will be achieved through the analysis of the geometrical configuration and its improvement by means of the establishment of additional observation sites to be equipped with mobile stations, depending on the area of interest. This will increment the spatial coverage and improve the observation of the most active areas for surface sin-eruptive processes. For the second objective we will implement new processing tools to permit a reliable quantitative use of the data collected by the surveillance sensors of NETVIS, extending their capability in monitor the lava flow thermal and spatial evolution and by providing georeferenced data for rapid mapping scope. The tool will be used to automatically pre-process multitemporal datasets and will be tested on both simulated and real scenarios. Thanks to data collected and archive by the NETVIS INGV team, we will have the opportunity to develop and test the procedure in different operational conditions selected among the large number of lava flows coupled to lava fountan events occurred between 2011 and 2013. Additionally, Etna_NETVIS data can be used to downscale the information derived from satellite data and/or to integrate the satellite datasets in case of incomplete coverage or missing acquisitions (both due to low revisiting time or bad geometrical conditions). Therefore an additional goal is that of comparing/integrating quantitative data derived from visible and radar satellite sensors with the maps obtained using Etna_NETVIS. The procedure will take into account the discrepancy among the different datasets in terms of accuracy and resolution and will attempt to provide a combined approach (based on error analysis and data weighting) to evaluate the final results reliability. Preliminary results on the procedure and algorithm adopted for geometric and radiometric sensor calibration, definition of optimized configurations through simulation and for extracting updated mapping data from multi-temporal dataset will be presented. This work is developed in the framework of the EU-FP7 project “MED-SUV” (MEDiterranean SUpersite Volcanoes).

Since 1994 a video-surveillance camera located on a peak just above the active volcanic vents of Stromboli island records the explosive activity of one of the few volcanoes on the world performing a persistent eruptive activity. From 2003, after one of the larger lava flow eruption of the last century, the video- surveillance system was enhanced with more stations having both thermal and visual cameras. The video-surveillance helps volcanologists to characterize the mild explosive activity of Stromboli named Strombolian and to distinguish between the frequent “ordinary” Strombolian explosions and the occasional “extraordinary” strong Strombolian explosions that periodically occur. A new class of extraordinary explosions was discovered filling the gap between the ordinary activity and the strong explosions named major explosions when the tephra fallout covers large areas on the volcano summit and paroxysmal ones when the bombs fall down to the inhabited area along the coast of the island. In order to quantify the trend of the ordinary Strombolian explosions and to understand the occurring of the extraordinary strong Strombolian explosions a computer assisted image analysis was developed to process the huge amount of thermal and visual images recorded in several years. The results of this complex analysis allow us to clarify the processes occurring in the upper plumbing system where the pockets/trains of bubbles coalesce and move into the active vent conduits producing the ordinary Strombolian activity, and to infer the process into the deeper part of the plumbing system where new magma supply and its evolution lead to the formation of the extraordinary strong Strombolian explosions.