10:45 AM - 12:15 PM
[SSS03-P04] Estimation of amplitude noise reduction and SNR variation as a function of depth recorded by a deep vertical array
Keywords:background seismic noise, Signal to Noise ratio, microseismic monitoring
To design an efficient seismic monitoring infrastructure, the characterization of the background seismic noise level of each potential seismic station installation site is one of the most important data-quality metrics used to evaluate the suitability of such sites to host the seismic network. The background seismic noise can be generated by different sources such as, ocean waves (microseisms), atmospheric disturbances (strong wind and storms), and anthropogenic activities, temperature changes and magnetic field variations. Such disturbances are characterized by specific frequency bands, time-occurrence (diurnal and seasonal variation), and site location (close to populated area or to the coasts). Reducing the effect of these noise sources is one of the main challenges to face for designing seismic monitoring networks and, more specifically, when selecting the hosting site of a seismic stations. A solution to attenuate the seismic noise effect is obtained by deploying seismic stations in boreholes. In addition, a general law estimating the sufficient depth to gain to detect even low seismic events, highly masked by background noise, is fundamental for defining the capability of microseismic network. In this study, we analyse the continuous seismic noise level at S. Potito-Cotignola gas storage in the Po Valley (Northern Italy) recorded from January 2019 to December 2021 by a broadband (BB) seismic station at surface and a vertical array composed by 6-short period 3-components seismometers installed at depth ranging between 35 to 285 m in borehole. We aim to characterize the seismic noise by computing the amplitude noise reduction in terms of dB as a function of depth for different frequencies and the SNR depth variation by selecting 18 seismic events, with different hypocentral (from 12.9 to 37.2 km) distance and magnitude (from -0.1 to 2.9). Our Results show that 1) the dependence of noise level with depth follows a logarithmic empirical trend, 2) the SNR trend increases according to seismometer depth and from the lower to the larger magnitude events and it seems independent by selected hypocentral distances. The empirical relationships we estimated can be used to help the design microseismic monitoring networks in similar geological settings.