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We present the first two-dimensional (2-D) spatial distribution of seismic scattering and intrinsic attenuation beneath the Aeolian Islands arc. The Aeolian Islands archipelago represents one of the best examples of a small dimension volcanic island arc characterised by the alternation of different structural domains. Using the seismic wave diffusion model as the basis for the analysis, and using data from an active seismic experiment (TOMO-ETNA), we analysed more than 76,700 seismic paths marked by epicentre-seismic station pairs. Based on frequencies of 4–24 Hz, we identified high regional attenuation, comparable with other volcanic areas of the world. We used two different seismogram lengths, reflecting two different sampling depths, which allowed us to observe two different attenuative behaviours. As in most volcanic regions, scattering attenuation predominates over intrinsic attenuation, but some characteristics are area-specific. Volcanic structures present the highest contribution to scattering, especially in the low frequency range. This behaviour is interpreted to reflect the small size of the islands and the potentially relatively small size of individual magmatic feeding systems. In addition, strong scattering observed in one zone is associated with the northernmost part of the so-called Aeolian-Tindari-Letojanni fault system. In contrast, away from the volcanic islands, intrinsic attenuation dominates over scattering attenuation. We interpret this shift in attenuative behaviour as reflecting the large volume of sedimentary material deposited on the seabed. Owing to their poorly consolidated nature, sediments facilitate intrinsic attenuation via energy dissipation, but in general present high structural homogeneity that is reflected by low levels of scattering. Our results show that this region is not underlain by a large volcanic structural complex such as that beneath nearby Mt. Etna volcano. Instead, we observe dimensionally smaller and isolated subsurface volcanic structures. The identification of such features facilitates improved geological interpretation; we can now separate consolidated marine structures from independent subsurface volcanic elements. The results of this study provide a model for new research in similar regions around the world.

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.

In this work, we investigated the microseism recorded by a network of broadband seismic stations along the coastline of Eastern Sicily. Microseism is the most continuous and ubiquitous seismic signal on Earth and is mostly generated by the ocean–solid earth interaction. On the basis of spectral content, it is possible to distinguish three types of microseism: primary, secondary, and short-period secondary microseism (SPSM). We showed how most of the microseism energy recorded in Eastern Sicily is contained in the secondary and SPSM bands. This energy exhibits strong seasonal patterns, with maxima during the winters. By applying array techniques, we observed how the SPSM sources are located in areas of extended shallow water depth: the Catania Gulf and a part of the Northern Sicily coastlines. Finally, by using the significant wave height data recorded by two buoys installed in the Ionian and Tyrrhenian Seas, we developed an innovative method, selected among up-to-date machine learning techniques (MLTs), able to reconstruct the time series of sea wave parameters from microseism recorded in the three microseism period bands by distinct seismic stations. In particular, the developed model, based on random forest regression, allowed estimating the significant wave height with a low average error (∼0.14–0.18 m). The regression analysis suggests that the closer the seismic station to the sea, the more information concerning the sea state are contained in the recorded microseism. This is particularly important for the future development of an experimental monitoring system of the sea state conditions based on microseism recordings.

On active volcanoes, ambient noise-based seismic interferometry can be a very useful monitoring tool as it allows to detect very slight variations in seismic velocity associated with magma transported toward the surface. However, the classical cross-station approach occasionally fails to detect seismic velocity changes related to eruptive activity, even on very active, well-instrumented volcanoes such as Mt. Etna. In this work, we explored an improved ambient noise-based monitoring strategy by performing the autocorrelation of seismic noise recorded at Mt. Etna volcano, by three stations located close to the active summit craters, during April 2013–October 2014. Such an interval was chosen because of the number and variety of eruptions. In place of the classical cross-correlation, we implemented the phase cross-correlation of each component with itself, which does not require normalization of the signals. The detected seismic velocity variations were very consistent for all three stations throughout the study period, mainly ranging between 0.3 and −0.2%, and were time-related to both sequences of paroxysmal eruptions and more effusive activities. In particular, we observed seismic velocity decreases accompanying paroxysmal eruptions, suggesting an intense pressurization within the plumbing system, which created an area of extensional strain with crack openings. In addition, seismic velocity variations over time were analyzed in the light of ground deformation data recorded by GPS stations and volcanic tremor centroid locations and displayed a particularly strong correlation with the former. Finally, we showed that, although the investigated frequency band (1–2 Hz) contained most of the volcanic tremor energy, our results did not indicate a particular contamination of seismic velocity variation measurements by variations of tremor sources. Ultimately, our investigation highlights a better way to implement noise-based seismic monitoring techniques. The near-field sensitivity of the autocorrelation helped improve our understanding of the relationship between variations of seismic velocity, ground deformation and the pressurization dynamics of volcanic plumbing systems which, in turn, allows for better monitoring implementations of seismic interferometry on other volcanoes.