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Abstract:
Distributed Dynamic Strain Sensing (DDSS), a.k.a. Distributed Acoustic Sensing (DAS), is becoming a popular tool for volcano monitoring. The sensing method relies on measuring the phase-shift of Rayleigh back-scattered light throughout the fibre due to strain variations in the fibre glass. This provides distributed strain-rate measurements at fine temporal and spatial sampling intervals. During 3 months in 2019, we recorded signals from thousands of mild volcanic explosions from Mt. Etna using a multi-instrument network deployed in an area at ca. 2.5 km distance from the active craters. Infrasound sensors were laying at the surface with a dense array of broadband seismometers (BB). Two types of fibres were also buried ca. 30 cm depth in the non-consolidated scoria from the area. First fibre was a 1.5 km long standard fibre, interrogated with an iDAS unit. The second fibre was a 0.5 km long engineered fibre, interrogated with a Carina unit. Relation between infrasound and DDSS data suggests a ground response of the loose scoria due to the acoustic pressure waves from explosions. Further analysis suggests a non-linear relationship between acoustic pressure and strain-rate data. However, signal saturation is encounter in some of the strain-rate data, which affects the interpretation of the non-linear relation. Therefore, we present an algorithm to correct the signal artefacts, allowing us to restore the true strain-rate signal and exceed the dynamic range limited by the initial DDSS recording parameters. The outcome includes strategies in the selection of acquisition parameters prior to DDSS campaigns to avoid signal saturation.
