GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 3.3 ______ ___ GNGTS 2023 signal-to-noise ratio (SNR) compared to standard seismic sensors (Li & Zhan, 2018; Walter et al., 2020). Moreover, signal incoherencies are common, due to site effects and poor ground-fiber coupling (Van den Ende & Ampuero, 2021). This work focuses on the potential of DAS for seismological monitoring. Standard seismic arrays are usually evaluated on (a) their geometry and (b) the SNR of the recorded events. The potential of DAS arrays instead, is conditioned by the following factors: a) the signal directivity, i.e. the strain/strain-rate are measured only for their longitudinal components along the fiber direction; b) the differences in FOC coupling with the ground, and c) the higher susceptibility to local rock elasticity variations (Ajo-Franklin et al., 2019; Van den Ende & Ampuero, 2021). Indeed, all these factors affect the signal amplitudes and their spatial coherence. Therefore, arrival times estimated on DAS data show complex noise characteristics, which in turn might influence the uncertainty of the event locations. FOCs are the backbone of the global telecommunication network and show a worldwide distribution (e.g., urban, ocean seafloor environments). Despite various successful case-studies (Fichtner et al., 2022; Jousset et al., 2018; Klaasen et al., 2021; Lellouch et al., 2020; Lindsey et al., 2017; Nishimura et al., 2021; Van den Ende & Ampuero, 2021; Walter et al., 2020; Zhu & Stensrud, 2019), a more general exploration on the influence of geometry and installation environment on their seismic monitoring potential is still lacking. Therefore, in this work, DAS is tested for an operative monitoring task, investigating the influence of different sources of noise in events’ arrival times estimated with DAS and exploiting an unprecedently various database, including different typologies of seismic events (earthquakes, ice-quakes, volcanic earthquakes) in numerous installation contexts. Data and methods Diversified DAS datasets have been collected from online open access databases and from direct agreements with researchers responsible for the specific acquisitions. Both telecommunication cables, repurposed for DAS monitoring tasks, and suited installations, are included in the study (Figure 1). Each DAS dataset is provided with the recording of at least a local seismic event (< 100 km from the array barycenter). A reference event is then selected for the study and an independent location is provided, from the analysis of seismological catalogs or “expert” analysis of local seismic arrays/selected DAS channels. A basic pre-processing is adopted to increase the SNRs of the recorded events, including linear de-trending, cosine tapering and bandpass filtering. Moreover, spatial subsampling is embraced when the DAS gauge length exceeds the channel spacing. This procedure can help increase the SNRs (Piana Agostinetti et al., 2022), especially in poorly coupled FOCs portions. A coherent automatic picking procedure (Baer & Kradolfer, 1987) is adopted to pick the first onsets at the DAS channels of each pre-processed event. Then, a Markov Chain Monte Carlo (McMC) approach is used for estimating hypocentral parameters (Riva and Piana Agostinetti, 2022). Beside the estimated locations, based on observed arrival times, four synthetic tests are implemented to simulate different sources of noise in DAS arrival times and to measure how such