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Campi Flegrei caldera is an Italian high-risk volcano experiencing a progressively more intense long-term uplift, accompanied by increasing seismicity and geochemical emissions over the last two decades. Ground deformation shows an axisymmetric bell-shaped pattern, with a maximum uplift of about 120 cm, from 2005, in the caldera central area. We analyzed Sentinel-1 and COSMO-SkyMed Multi-Temporal DInSAR measurements and GNSS data to reveal and investigate a geodetic anomaly that has clearly manifested since 2021, locally deviating from the typical bell-shaped deformation pattern. This anomaly is located east of Pozzuoli town, in the Mt. Olibano–Accademia area, covers an area of about 1.3 km2 and shows, in comparison to surrounding areas, a maximum uplift deficit of about 9 cm between 2021 and 2023. To investigate the anomaly causes, we analyzed the caldera seismicity and inverted the DInSAR data to determine the primary source of the ground deformation pattern, which is consistent with a penny-shaped source located approximately 3800 m beneath the Pozzuoli town, with a radius of about 1200 m. We also found that the time evolution of the uplift deficit in the geodetic anomaly area correlates well with the earthquake occurrence, with the greater magnitude events clustering in this area. These considerations suggest the geodetic anomaly is a local response to the tensile stress regime produced by the inflating primary deformation source. This phenomenon can be influenced by the Mt. Olibano–Accademia lava domes lithological heterogeneities that may induce a localized reaction to ground deformation during the inflationary phase. Our interpretation aligns with the concentration of earthquakes and hydrothermal fluid emissions in this area, indicating the presence of faults, fractures, and fluid circulation. Accordingly, the geodetic anomaly area represents a zone of crustal weakness that requires careful monitoring and study.

Abstract The Millennium Eruption of Paektu volcano, on the border of China and North Korea, generated tephra deposits that extend >1000 km from the vent, making it one of the largest eruptions in historical times. Based on observed thicknesses and compositions of the deposits, the widespread tephra dispersal is attributed to two eruption phases fuelled by chemically distinct magmas that produced both pyroclastic flows and fallout deposits. We used an ensemble-based method with a dual step inversion, in combination with the FALL3D atmospheric tephra transport model, to constrain these two different phases. The volume of the two distinct phases has been calculated. The results indicate that about 3-16 km3 (with a best estimate of 7.2 km3) and 4-20 km3 (with a best estimate of 9.3 km3) of magma were erupted during the comendite and trachyte phases of the eruption, respectively. Eruption rates of up to 4 × 108 kg/s generated plumes that extended 30-40 km up into the stratosphere during each phase.

Abstract Lava overflows are highly hazardous phenomena that can occur at Stromboli. They can destabilize the crater area and the “Sciara del Fuoco” unstable slope, formed by several sector collapses, which can generate potentially tsunamigenic landslides. In this study, we have identified precursors of the October-November 2022 effusive crisis through seismic and thermal camera measurements. We analyzed the lava overflow on October 9, which was preceded by a crater-rim collapse, and the overflow on November 16. In both cases, seismic precursors anticipating the overflow onset have been observed. The analysis of the seismic and thermal data led to the conclusion that the seismic precursors were caused by an escalating degassing process from the eruptive vent, which climaxed with the overflows. Volcano deformation derived from ground-based InSAR and strainmeter data showed that inflation of the crater area accompanied the escalating degassing process up to the beginning of the lava overflows. The inflation of the crater area was especially evident in the October 9 episode, which also showed a longer seismic precursor compared to the November 16 event (58 and 40 min respectively). These results are important for understanding Stromboli’s eruptive mechanisms and open a perspective for early warning of potentially dangerous phenomena.

Abstract Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 μm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.

Abstract Ongoing resurgence affects Campi Flegrei caldera (Italy) via bradyseism, i.e. a series of ground deformation episodes accompanied by increases in shallow seismicity. In this study, we perform a mathematical analysis of the GPS and seismic data in the instrumental catalogs from 2000 to 2020, and a comparison of them to the preceding data from 1983 to 1999. We clearly identify and characterize two overlying trends, i.e. a decennial-like acceleration and cyclic oscillations with various periods. In particular, we show that all the signals have been accelerating since 2005, and 90–97% of their increase has occurred since 2011, 40–80% since 2018. Nevertheless, the seismic and ground deformation signals evolved differently—the seismic count increased faster than the GPS data since 2011, and even more so since 2015, growing faster than an exponential function The ground deformation has a linearized rate slope, i.e. acceleration, of 0.6 cm/yr2 and 0.3 cm/yr2 from 2000 to 2020, respectively for the vertical (RITE GPS) and the horizontal (ACAE GPS) components. In addition, all annual rates show alternating speed-ups and slow-downs, consistent between the signals. We find seven major rate maxima since 2000, one every 2.8–3.5 years, with secondary maxima at fractions of the intervals. A cycle with longer period of 6.5–9 years is also identified. Finally, we apply the probabilistic failure forecast method, a nonlinear regression that calculates the theoretical time limit of the signals going to infinity (interpreted here as a critical state potentially reached by the volcano), conditional on the continuation of the observed nonlinear accelerations. Since 2000, we perform a retrospective analysis of the temporal evolution of these forecasts which highlight the periods of more intense acceleration. The failure forecast method applied on the seismic count from 2001 to 2020 produces upper time limits of [0, 3, 11] years (corresponding to the 5th, 50th and 95th percentiles, respectively), significantly shorter than those based on the GPS data, e.g. [0, 6, 21] years. Such estimates, only valid under the model assumption of continuation of the ongoing decennial-like acceleration, warn to keep the guard up on the future evolution of Campi Flegrei caldera.

For active volcanoes, knowledge about probabilities of eruption and impacted areas becomes valuable information for decision-makers to develop short- and long-term emergency plans, for which probabilistic volcanic hazard assessment (PVHA) is needed. High-resolution or spatially extended PVHA requires extreme-scale high-performance computing systems. Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth; www.cheese-coe.eu), an effort was made to generate exascale-suitable codes and workflows to collect and process in some hours the large amount of data that a quality PVHA requires. To this end, we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop PVHA for a volcano. This tool uses the Bayesian event tree methodology to calculate eruption probabilities, vent-opening location(s), and eruptive source parameters (ESPs) based on volcano history, monitoring system data, and meteorological conditions. Then, the tool interacts with the chosen hazard model, performing a simulation for each ESP set or volcanic scenario (VS). Finally, the resulting information is processed by proof-of-concept-subjected high-performance data analytics (HPDA) scripts, producing the hazard maps which describe the probability over time of exceeding critical thresholds at each location in the investigated geographical domain. Although PVHA_HPC-WF can be adapted to other hazards, we focus here on tephra (i.e., lapilli and ash) transport and deposition. As an application, we performed PVHA for Campi Flegrei (CF), Italy, an active volcano located in one of the most densely inhabited areas in Europe and under busy air traffic routes. CF is currently in unrest, classified as being in an attention level by the Italian Civil Protection. We consider an approximate 2,000 × 2,000 × 40 km computational domain with 2 km grid resolution in the horizontal and 40 vertical levels, centered in CF. To explore the natural variability and uncertainty of the eruptive conditions, we consider a large number of VSs allowing us to include those of low probability but high impact, and simulations of tephra dispersal are performed for each of them using the FALL3D model. Results show the potential of HPC to timely execute a vast range of simulations of complex numerical models in large high-resolution computational domains and analyze great volumes of data to obtain quality hazard maps.

In recent decades, the Campi Flegrei caldera (Italy) showed unrest characterized by increases in seismicity, ground uplift, and hydrothermal activity. Currently, the seismic and hydrothermal phenomena are mostly concentrated in the Solfatara–Pisciarelli area, which presents a wide fumarolic field and mud emissions. The main fumarole in Pisciarelli is associated with a boiling mud pool. Recently, episodes of a sudden increase in hydrothermal activity and expansion of mud and gas emissions occurred in this area. During these episodes, which occurred in December 2018 and September 2020, Short Duration Events (SDEs), related to the intensity of mud pool boiling, were recorded in the fumarolic seismic tremor. We applied a Self-Organizing Map (SOM) neural network to recognize the occurrence of SDEs in the fumarolic tremor of Campi Flegrei, which provides important information on the state of activity of the hydrothermal system and about the possible phreatic activity. Our method, based on an ad hoc feature extraction procedure, effectively clustered the seismic signals containing SDEs and separated them from those representing the normal fumarolic tremor. This result is useful for improving the monitoring of the Solfatara–Pisciarelli hydrothermal area which is a high-risk zone in Campi Flegrei.

Studying seismicity in a volcanic environment provides important information on the state of activity of volcanoes. The seismicity of the Neapolitan volcanoes, Campi Flegrei, Vesuvius, and Ischia, shows distinctive characteristics for each volcano, covering a wide range of patterns and types. In this study we relocated some significant volcano-tectonic earthquake swarms that occurred in Campi Flegrei and Vesuvius. Moreover, we compared the earthquake occurrence evolution, the magnitude and the seismic energy release of the three volcanoes. Also, we considered the results of seismic analysis in the light of geochemical and ground deformation data that contribute to defining the state of activity of volcanoes. In Campi Flegrei, which is experiencing a long term unrest, we identified a seismogenic structure at shallow depth in Pisciarelli zone that has been activated repeatedly. The increasing seismicity accompanies an escalation of the hydrothermal activity and a ground uplift phase. At Vesuvius a very shallow seismicity is recorded, which in recent years has shown an increase in terms of the number of events per year. Earthquakes are usually located right beneath the crater axis. They are concentrated in a volume affected by the hydrothermal system. Finally, Ischia generally shows a low level of seismicity, however, in Casamicciola area events with a moderate magnitude can occur and these are potentially capable of causing severe damage to the town and population, due to their small hypocentral depth (typically < 2.5 km). After the seismic crisis of August 21, 2017 (mainshock magnitude M = 4), the seismicity returned to a low level in terms of occurrence rate and magnitude of earthquakes. The seismicity of these three different volcanic areas shows some common aspects that highlight a relevant role of hydrothermal processes in the seismogenesis of volcanic areas. However, while the main swarms in Campi Flegrei and most of the Vesuvian earthquakes are distributed along conduit-like structures, the seismicity of Ischia is mainly located along faults. Furthermore, the temporal evolution of seismicity in Neapolitan volcanic area suggests a concomitant increase in the occurrence of earthquakes both in Campi Flegrei and Vesuvius in recent years.

The analogue experiments that produce seismo-acoustic events are relevant for understanding the degassing processes of a volcanic system. The aim of this work is to design an unsupervised neural network for clustering experimental seismo-acoustic events in order to investigate the possible cause-effect relationships between the obtained signals and the processes. We focused on two tasks: 1) identify an appropriate strategy for parameterizing experimental seismo-acoustic events recorded during analogue experiments devoted to the study of degassing behavior at basaltic volcanoes; 2) define the set up of the selected neural network, the Self-Organizing Map (SOM), suitable for clustering the features extracted from the experimental events. The seismo-acoustic events were generated using an ad hoc experimental setup under different physical conditions of the analogue magma (variable viscosity), injected gas flux (variable flux velocity) and conduit surface (variable surface roughness). We tested the SOMs ability to group the experimental seismo-acoustic events generated under controlled conditions and conduit geometry of the analogue volcanic system. We used 616 seismo-acoustic events characterized by different analogue magma viscosity (10, 100, 1000 Pa s), gas flux (5, 10, 30, 60, 90, 120, 150, 180 × 10−3 l/s) and conduit roughness (i.e. different fractal dimension corresponding to 2, 2.18, 2.99). We parameterized the seismo-acoustic events in the frequency domain by applying the Linear Predictive Coding to both accelerometric and acoustic signals generated by the dynamics of various degassing regimes, and in the time domain, applying a waveform function. Then we applied the SOM algorithm to cluster the feature vectors extracted from the seismo-acoustic data through the parameterization phase, and identified four main clusters. The results were consistent with the experimental findings on the role of viscosity, flux velocity and conduit roughness on the degassing regime. The neural network is capable to separate events generated under different experimental conditions. This suggests that the SOM is appropriate for clustering natural events such as the seismo-acoustic transients accompanying Strombolian explosions and that the adopted parameterization strategy may be suitable to extract the significant features of the seismo-acoustic (and/or infrasound) signals linked to the physical conditions of the volcanic system.

We present 4 years of continuous seafloor deformation measurements carried out in the Campi Flegrei caldera (Southern Italy), one of the most hazardous and populated volcanic areas in the world. The seafloor sector of the caldera has been monitored since early 2016 by the MEDUSA marine research infrastructure, consisting of four instrumented buoys installed where sea depth is less than 100 m. Each MEDUSA buoy is equipped with a cabled, seafloor module with geophysical and oceanographic sensors and a subaerial GPS station providing seafloor deformation and other environmental measures. Since April 2016, the GPS vertical displacements at the four buoys show a continuous uplift of the seafloor with cumulative measured uplift ranging between 8 and 20 cm. Despite the data being affected by environmental noise associated with sea and meteorological conditions, the horizontal GPS displacements on the buoys show a trend coherent with a radial deformation pattern. We use jointly the GPS horizontal and vertical velocities of seafloor and on-land deformations for modeling the volcanic source, finding that a spherical source fits best the GPS data. The geodetic data produced by MEDUSA has now been integrated with the data flow of other monitoring networks deployed on land at Campi Flegrei.

Two paroxysmal explosions occurred at Stromboli on 3 July and 28 August 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.

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020.

Between 28 March and 1 April 2020, Stromboli volcano erupted, with overflows from the NE crater rim spreading along the barren Sciara del Fuoco slope and reaching the sea along the NW coast of the island. Poor weather conditions did not allow a detailed observation of the crater zone through the cameras monitoring network, but a clear view of the lower slope and the flows expanding in the area allowed us to characterize the flow features. This evidence was integrated with satellite, GBInSAR, and seismic data, thus enabling a reconstruction of the whole volcanic event, which involved several small collapses of the summit cone and the generation of pyroclastic density currents (PDCs) spreading along the slope and on the sea surface. Satellite monitoring allowed for the mapping of the lava flow field and the quantification of the erupted volume, and GBInSAR continuous measurements detected the crater widening and the deflation of the summit cone caused by the last overflow. The characterization of the seismicity made it possible to identify the signals that are associated with the propagation of PDCs along the volcano flank and, for the first time, to recognize the signal that is produced by the impact of the PDCs on the coast.

Many calderas show remarkable unrest, which often does not culminate in eruptions (non-eruptive unrest). In this context the interpretation of the geophysical data collected by the monitoring networks is difficult. When the unrest is eruptive, a vent opening process occurs, which leads to an eruption. In calderas, vent locations typically are scattered over a large area and monogenic cones form. The resulting pattern is characterized by a wide dispersion of eruptive vents, therefore, the location of the future vent is not easily predictable. We propose an interpretation of the deformation associated to unrest and vent pattern commonly observed at calderas, based on a physical model that simulates the intrusion and the expansion of a sill. The model can explain both the uplift and any subsequent subsidence through a single process. Considering that the stress mainly controls the vent opening process, we try to gain insight on the vent opening in calderas through the study of the stress field produced by the intrusion of an expanding sill. We find that the tensile stress in the rock above the sill is concentrated at the sill edge in a ring-shaped area with radius depending on the physical properties of magma and rock, the feeding rate and the magma cooling rate. This stress field is consistent with widely dispersed eruptive vents and monogenic cone formation, which are often observed in the calderas. However, considering the mechanical properties of the elastic plate and the rheology of magma, we show that remarkable deformations may be associated with low values of stress in the rock at the top of the intrusion, thereby resulting in non-eruptive unrest. Moreover, we have found that, under the assumption of isothermal conditions, the stress values decrease over time during the intrusion process. This result may explain why the long-term unrest, in general, do not culminate in an eruption. The proposed approach concerns a general process and is applicable to many calderas. In particular, we simulate the vertical displacement as occurred in the centre of Campi Flegrei caldera during the last decades, and we obtain good agreement with the data of a leveling benchmark near the center of the caldera.

After a period of mild eruptive activity, Stromboli showed between 2017 and 2018 a reawakening phase, with an increase in the eruptive activity starting in May 2017. The alert level of the volcano was raised from “green” (base) to “yellow” (attention) on 7 December 2017, and a small lava overflowed the crater rim on 15 December 2017. Between July 2017 and August 2018 the monitoring networks recorded nine major explosions, which are a serious hazard for Stromboli because they affect the summit area, crowded by tourists. We studied the 2017−2018 eruptive phase through the analysis of multidisciplinary data comprising thermal video-camera images, seismic, geodetic and geochemical data. We focused on the major explosion mechanism analyzing the well-recorded 1 December 2017 major explosion as a case study. We found that the 2017−2018 eruptive phase is consistent with a greater gas-rich magma supply in the shallow system. Furthermore, through the analysis of the case study major explosion, we identified precursory phases in the strainmeter and seismic data occurring 77 and 38 s before the explosive jet reached the eruptive vent, respectively. On the basis of these short-term precursors, we propose an automatic timely alarm system for major explosions at Stromboli volcano.

Quiescent volcanoes dissipate a large part of their thermal energy through hot soils and ground degassing mainly in restricted areas called Diffuse Degassing Structures. La Solfatara crater represents the main spot of thermal release for the Campi Flegrei volcano (Italy) despite its reduced dimensions with regards to the whole caldera. The purpose of this study was to develop a method to measure thermal energy release extrapolating it from the ground surface temperature. We used imaging from thermal cameras at short distances (1 m) to obtain a mapping of areas with thermal anomalies and a measure of their temperatures. We built a conceptual model of the energy release from the ground to atmosphere, which well fits the experimental data taken in the La Solfatara crater. Using our model and data, we could estimate the average heat flux in a portion of the crater as q a v g = 220 ± 40 W / m 2 , compatible with other measurements in literature.