Volcanic eruptions have important social and economic impacts. They can affect local population near active volcanoes (e.g., Horwell et al., 2017), causing loss of life, damage to infrastructure, and disruption to aviation (e.g., Barnard, 2004;Poret, Corradini, et al., 2018;. The fallout of ballistics in areas close to erupting vents represents a hazard to tourists that visit volcanoes every year (e.g., Andronico et al., 2021). Volcanoes with multiple active vents pose additional significant monitoring challenges owing to the rapid changes frequently observed in both the location and style of eruptive activity (e.g.

Microseism is the continuous background seismic signal caused by the interaction between the atmosphere, the hydrosphere and the solid Earth. Several studies have dealt with the relationship between microseisms and the tropical cyclones, but none focused on the small-scale tropical cyclones that occur in the Mediterranean Sea, called Medicanes. In this work, we analysed the Medicane Apollo which impacted the eastern part of Sicily during the period 25 October–5 November 2021 causing heavy rainfall, strong wind gusts and violent sea waves. We investigated the microseism accompanying this extreme Mediterranean weather event, and its relationship with the sea state retrieved from hindcast maps and wave buoys. The spectral and amplitude analyses showed the space–time variation of the microseism amplitude. In addition, we tracked the position of Apollo during the time using two different methods: (i) a grid search method; (ii) an array analysis. We obtained a good match between the real position of Apollo and the location constraint by both methods. This work shows that it is possible to extract information on Medicanes from microseisms for both research and monitoring purposes.

<p>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.<br>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.<br>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.<br>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.<br>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, R<sup>2</sup> 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.<br>Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.</p>

Volcanic activity represents a hazard to population and infrastructure worldwide. The study of acoustic waves in the atmosphere by volcanic activity is growing in popularity as an effective tool to monitor and understand the mechanisms of eruptions. In 2019, we deployed two 6-element infrasound arrays at Mt. Etna, Italy, one of the most active volcanoes in the world. Our experiment captured a range of acoustic signals associated with diverse activity ranging from background degassing to energetic Strombolian explosions, lava flows, and atmospheric injection of volcanic ash. Here, we present a description of this valuable, publicly available, research dataset. We document the design and scope of the experiment, report on data availability, and present a brief summary of the activity observed at Mt. Etna during our deployment aiming to facilitate future use of these valuable data. This dataset is the first example of open data from a multiple infrasound array experiment at Mt. Etna and one of the few available globally.

<p>Microseism is the most continuous and ubiquitous seismic signal on the Earth and is caused by the interaction between the atmosphere, the hydrosphere and the Solid Earth. In literature, there are several studies that deal with the relationship between microseism and cyclonic activity considering in particular hurricanes, tropical cyclones and typhoons. However, the relationships between microseism and the small-scale tropical cyclones that occur in the Mediterranean Sea, called Medicanes, have never been analysed. For this reason, we considered the Medicane Apollo, which developed in the Ionian Sea and impacted the eastern part of Sicily during the period 25th October to 5th November 2021 causing heavy rainfall (> 400 mm/48h), strong wind gusts (104 km/h) and violent sea waves (significant wave height > 3.5 m). Furthermore, the heavy rainfall induced by the presence of Apollo, caused damage to infrastructure and agriculture forcing the Sicilian regional government to declare a state of emergency for 32 municipalities (in the provinces of Catania, Messina, Siracusa and Ragusa) that were mostly affected by the Medicane Apollo.</p><p>In this work, we analysed the microseism signal recorded by 78 seismic stations installed in South Italy, Malta and Greece coastline during the period under investigation. To obtain information about the significant wave heights, we consider the data obtained by hindcast maps and four wavemeters buoys. The spectral and amplitude analysis allowed us to obtain information about the space-time variations of the microseism amplitude and in addition, we were able both to differentiate the seismic stations that perceive Apollo (stations installed close to the Ionian Sea), the seismic stations that do not perceive the medicane (stations installed close to the Tyrrhenian sea) and the microseism bands influenced by the presence of the Medicane Apollo. Moreover, we tracked the position of the Apollo by using two different methods: i) grid search method based on the seismic amplitude decay using the 78 seismic stations first mentioned and ii) array technique by 15 seismic stations installed on Etna which may be considered an array thanks to their spatial distribution and geometry. We obtain a good match between the real positions of the Medicane Apollo derived from satellite images and the positions computed by the two analysis methods. This work shows that it is possible to extract information about the Mediterranean extreme meteo-marine events from microseism, a seismic signal that until not long ago was considered as noise, both for monitoring and research purposes.</p>