[S-SS04] New trends in data acquisition, analysis and interpretation of seismicity

[SSS04-P07] Noise analysis of Distributed Acoustic Sensing (DAS) systems in borehole installations

*Davide Pecci¹, Giacomo Rapagnani¹, Sonja Gaviano¹, Eusebio Stucchi¹, Renato Iannelli¹, Simone Cesca², Francesco Grigoli¹ (1.University of Pisa, 2.Deutsche GeoForschungsZentrum (GFZ), Potsdam)

Keywords:Distributed Acoustic Sensing, Enhanced Geothermal System, Power Spectral Density, Noise Analysis

In recent years, Distributed Acoustic Sensing (DAS) technology has garnered increasing attention, particularly for microseismic monitoring in borehole installations. Despite the widespread adoption of DAS systems in such applications, numerous inquiries persist regarding the quality of recorded data. Questions arise regarding the comparative self-noise levels of DAS versus traditional systems and how ambient noise recorded by DAS systems attenuates with depth in contrast to observations with traditional geophones. Various sources of noise, including optical, thermal, and mechanical factors coupled with the fiber, impact DAS data. Moreover, the frequency bands of noise often overlap with those of the signal, rendering frequency filtering alone insufficient for effective denoising. Hence, specialized noise reduction techniques like FK Filtering and SVD are indispensable. Tackling noise's impact on DAS data remains a central challenge for the seismological and geophysical community.

This study aims to scrutinize and characterize the noise affecting DAS data gathered from borehole installations, with a specific emphasis on recordings obtained at the Frontier Observatory for Research in Geothermal Energy site in Utah, USA. Utilizing Power Spectral Density analysis, we assess noise reduction relative to depth and its temporal fluctuations. Additionally, we evaluate the depth-dependent signal-to-noise ratio across various microseismic events. Finally, we juxtapose the findings with data from colocated geophones, offering a thorough exploration of the strengths and weaknesses of both data acquisition technologies.