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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.

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.

Mount Etna, Europe’s largest and most active volcano is situated close to the Metropolitan area of Catania with about 1 Million inhabitants. Continuous monitoring has therefore been carried out for decades. Among the various disciplines infrasound recordings play an important role in this context. Explosive activity near or above ground as well as shallow tremor processes are easier to identify with airborne sound waves than with seismic waves that are significantly scattered and refracted in the volcanic edifice. However, infrasound signals are often affected by noise, especially by wind noise in the summit area.At Mount Etna five summit craters are currently known with fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns interfered by changing noise levels. Manual distinction of noisy data from real volcanogenic signals brings along a considerable effort and requires expert knowledge. We therefore apply unsupervised pattern recognition techniques for this task. Extracting features from the amplitude spectrum we are able to distinguish different infrasound regimes with Self-Organizing maps (SOMs). SOMs allow to color-code the results for an intuitive interpretation and evidence the presence of transitional activity regimes. We define a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible to train the SOM. This enables a straight forward interpretation of new data.

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.