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Over the last 20 years, infrasound signals have been used to investigate and monitor active volcanoes during eruptive and degassing activity. In particular, infrasound amplitude information has been used to estimate eruptive parameters such as plume height, magma discharge rate and lava fountain height. Active volcanoes are characterized by pronounced topography and, during eruptive activity, the topography can change rapidly, affecting the observed infrasound amplitudes. While the interaction of infrasonic signals with topography has been investigated by several authors over the past decade, the impact of changing topography on the infrasonic amplitudes has not yet been explored. In this work, the infrasonic signals accompanying 57 lava fountain paroxysms at Mount Etna (Italy) during 2021 were analyzed. In particular, the temporal and spatial variations of the infrasound amplitudes were investigated. During 2021, significant changes in the topography around the most active crater (the South-East Crater) took place and were reconstructed in detail through unoccupied aerial system surveys. Through analysis of the observed infrasound signals and numerical simulations of the acoustic wavefield, we demonstrate that the observed spatial and temporal variation in the infrasound signals can be explained by the combined effects of changes in the location of the acoustic source and changes in the near-vent topography. This work demonstrates the importance of accurate source locations and high-resolution topographic information, particularly in the near-vent region where the topography is most likely to change rapidly. Changing topography should be considered when interpreting local infrasound observations over long time-scales.

Rotational sensors have been deployed on several volcanoes worldwide including Kilauea, Stromboli, Etna and a few volcanoes in Iceland. Within this presentation we focus on our first experiment using a rotational sensor on Etna in Italy. We recorded the volcanic activity including degassing and vigorous strombolian activity in August to September 2019. We compare our results using a rotational sensor with a normal Trillium Compact seismometer and the seismic network maintained by the INGV. We detect LP events, VT events and volcanic tremor, study the wavefield and back azimuths of the events and derive phase velocities of the ground. Luckily, we were able to assess the quality of our results using the detailed earthquake catalogues and locations derived at the INGV. We can easily detect changes in the wavefield e. g. when strong strombolian activity kicks in and are looking forward to applications on other volcanoes where details of the volcanic activity or changes might go unnoticed if no rotational sensor is present.

Since September 2021, signs of unrest at Vulcano Island have been noticed after four years of quiescence, along with CO2 degassing and the occurrence of long-period and very long-period events. With the intention of improving the monitoring activities, a submarine fiber optic telecommunications cable linking Vulcano Island to Sicily was interrogated from 15 January to 14 February 2022. Of particular interest has been the recording of 1488 events with wide range of waveforms made up of two main frequency bands (from 3 to 5 Hz and from 0.1 to 0.2 Hz).With the aim of the automatic detection of seismic-volcanic events, different approaches were explored, particularly investigating whether the application of machine learning could provide the same performance as conventional techniques. Unlike many traditional algorithms, deep learning manages to guarantee a generalized approach by automatically and hierarchically extracting the relevant features from the raw data. Due to their spatio-temporal density, the data acquired by the DAS can be assimilated to a sequence of images; this property has been exploited by re-designing deep learning techniques for image processing, specifically employing Convolutional Neural Networks.The results demonstrate that deep learning not only achives good performance but that it even outperforms classical algorithms. Despite providing a generalized approach, Convolutional Neural Networks have been shown to be more effective than traditional tecniques in expoiting the high spatial and temporal sampling of the acquired data.

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation. Different metrics, such as MAE, R2 and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

On 13 May 2008, a strong eruption took place along a fracture opened on Mt. Etna's eastern flank accompanied by significant seismic activity. Recent studies show that seismic ambient noise is an effective method to observe the fluctuations of seismic wave velocities due to volcanic and seismic activities. In this study, we analyze the temporal seismic velocity changes (dv/v) from January 2007 to June 2008 at Mt. Etna by using seismic ambient noise. We retrieve a large dataset to calculate dv/v because of the large number of permanent seismic stations located at Mt. Etna. Daily cross-correlation functions (CCFs) are computed at 15 stations for 105 interstation pairs in the 1-2 Hz frequency band by using vertical components. We observe that the dv/v displays a gradual reduction and reaches down to -0.3% associated with the aforementioned volcanic activity. We compared our results with volcano-tectonic earthquakes and volcanic tremor. Our study shows a significant relationship between the magma intrusion and decrease of the dv/v.

Etna volcano in Italy is one of the most active volcanoes in Europe. We recorded the volcanic activity including degassing and vigorous strombolian activity using a seismometer and a rotational sensor in August to September 2019. We test the newly developed rotational sensor in the field in comparison to the broadband seismometer and seismic-network-based locations using the INGV network. We demonstrate that a single rotational sensor co-located with a seismometer can be used to identify specific seismic wave types, to estimate the back azimuth of wave arrivals and the local seismic phase velocities.Using the rotational sensor, we easily detected the dominant SH-type waves composing volcanic tremor during weak volcanic activity and the recorded VLP/ LP events. Changes in the composition of the tremor wavefield caused by the onset of vigorous volcanic activity are obvious and can be detected in near real-time if data is streamed. We discuss the changes in the wavefield composition from SH-type waves to a mixed wavefield in the context of the volcanic activity, the back azimuth of the signals and associated phase velocities. Our findings are consistent with observations by INGV and hence the rotational sensor reliably enlarges our sensor portfolio in volcanic environments. In fact, wavefield and ground properties can be derived using just one sensor instead of a sensor network, which makes experiments in remote areas cheaper and easier to maintain. In addition, you can observe phenomena that otherwise go unnoticed, like near vent block rotation.

During the COVID-19 pandemic, most countries worldwide put in place social interventions, consisting of restricting the mobility of citizens, aimed at slowing and mitigating the spread of the epidemic. In particular, Italy, as the first European country violently struck by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce both human mobility and human-to-human contacts from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in the densely populated Eastern Sicily, characterised by the presence of a permanent seismic network used for both seismic and volcanic monitoring. We specifically emphasize how the amount of the amplitude reduction of anthropogenic seismic noise (reaching ~50–60%), its temporal pattern and spectral content are strongly station-dependent. As for the latter, we exhibited that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. Finally, we found an impressive similarity between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile phone-derived data shared by Google, Facebook and Apple. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.