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Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy

Urheber*innen

Currenti,  Gilda
External Organizations;

Allegra,  Martina
External Organizations;

Cannavò,  Flavio
External Organizations;

/persons/resource/pjousset

Jousset,  P.
2.2 Geophysical Imaging of the Subsurface, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Prestifilippo,  Michele
External Organizations;

Napoli,  Rosalba
External Organizations;

Sciotto,  Mariangela
External Organizations;

Di Grazia,  Giuseppe
External Organizations;

Privitera,  Eugenio
External Organizations;

Palazzo,  Simone
External Organizations;

/persons/resource/lotte

Krawczyk,  C.M.
2.2 Geophysical Imaging of the Subsurface, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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5022764.pdf
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Zitation

Currenti, G., Allegra, M., Cannavò, F., Jousset, P., Prestifilippo, M., Napoli, R., Sciotto, M., Di Grazia, G., Privitera, E., Palazzo, S., Krawczyk, C. (2023): Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy. - Scientific Reports, 13, 4641.
https://doi.org/10.1038/s41598-023-31779-2


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5022764
Zusammenfassung
Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO2 degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.