Inverse modelling as a powerful tool for gas flow emission rate estimation from stationary hydrothermal vents  (Vulcano Island, Aeolian archipelago, Italy)

Submarine hydrothermal systems attract increasing interest from the scientific community, as they emit huge amounts of both elements and energy. However, in relation to their extreme environmental conditions (e.g. high temperature and pressure, low Ph) direct measurements can be challenging to perform. In this context, passive hydroacoustics may represent a powerful technique for both short- and long-term monitoring as the typical source mechanisms of the hydrothermal fields, directly related to ascending fluids, radiate sound pressure following different acoustic modes.Here we present preliminary results obtained by using a spectral approach for estimating the gas flow emission rate starting from the acoustic dataset collected between 22nd and 26th August 2022 on a stationary flux vent located at ~1.8 metres depth, inside the shallow hydrothermal field at Baia di Levante in Vulcano island (Aeolian Islands, Italy). To carry out the estimation of the gas flow emission rate emitted by the hydrothermal vent, we implemented a customised inverse modelling algorithm based on a spectral method founded upon the assumption that the acoustic signature of a single bubble event evolves over time as a sinusoid that exponentially decays. According to this approach, we refined  the formulation of a forward model for the sound radiated by a stationary, high-flux  bubbles’ plume, then the path was backward analysed through the proposed inversion algorithm, which allowed us to obtain the estimated value of the flow emission rate.High-resolution audio frames were recorded by using hydrophones [1 - 12800] Hz, that were deployed in the proximity of the investigated vent, collecting a total amount of  7  bursts of  ~10 hour-long each.  Preliminary analyses of the Power Spectral Density (PSD) and Pressure Power Spectrum highlighted the presence of different persistent energetic frequency peaks over the environmental background noise coherently with the dynamics of the hydrothermal field. The most energetic ones, likely due to the acoustic signal radiated by a large, resonant bubble plume, consistently confirmed the coupling of the estimated radius with direct observations.  The performed analysis allowed us to identify the main features of the vent, characterised by bubbles radii up to 0.03 m that produce the main energetic peak centred at ~100 Hz, along with smaller bubbles generating less energetic peaks up to 2 kHz. Therefore, the algorithm was set to work in a wide frequency range, spanning from 60 Hz to 2060 Hz, in order to estimate all the gas released by the vent. The estimated flow emission rate  for the investigated period retrieved values spanning from 3.31 to 6.98 litres per minute, with a mean value of 4.97 litres per minute, in good agreement with the direct observations. These results confirm that passive acoustic methods represent a valid and robust tool for both monitoring and research activity in submarine hydrothermal fields, providing a long-lasting instrument able to detect the fluctuations connected to the variations of such natural systems.