Characterizing non-linear ground response using signal classification from acoustic signals and Distributed Dynamic Strain Sensing (DDSS) at Mt. Etna volcano, Sicily.

During 2018, a study was conducted to understand the response of new seismic instrumentation to the complex seismo-acoustic wavefield of Mt. Etna. The study consisted on deploying a multi-instrumental network at Pizzi Deneri (PDN) observatory, near the main craters of Mt. Etna. The multi-instrumental network comprised infrasound sensors, broad-band seismometers (BB) and a fiber optic cable buried within the local loosed scoria surface. The cable was connected to a Distributed Dynamic Strain (DDSS) interrogator. Part of the collected data reveals, what is believed to be, a case of a non-linear ground response from an air-to-ground coupling from an acoustic wave. An infrasound sensor registered the arrival of a signal from a volcanic explosion with a main frequency of ~2 Hz. Immediately, a BB and fibre optic virtual sensor (DDSS channel), co-located with the infrasound sensor, registered a signal linked to the acoustic arrival. However, the dominant frequencies captured by the BB and DDSS range between 15 and 20 Hz. To further study this phenomenon, a second experiment was conducted in 2019 in the same place (PDN) and using the same type of instrumentation, but in a different spatial arrangement. In a total of three months, we obtained more than 65000 examples of acoustic signals linked to volcanic explosions. Embedded in the examples, there are cases of non-linear ground response. However, the dataset also contains cases with no ground response triggered by acoustic signals. To understand which acoustic inputs could trigger the ground response, we performed a classification of the acoustic signals based on waveform similarity. In addition, to understand the resulted ground response, we extended the waveform similarity classification to the DDSS records to achieve spatial-temporal characterization of the phenomenon in study. The outcomes of this method allows us to understand the spatial effect of acoustic signals on the ground, monitor temporal variations, and discriminate between reliable data and DDSS signal artifacts such as saturation.