Noise analysis of Distributed Acoustic Sensing (DAS) systems in borehole installations

Abstract

In recent years, there has been an increasing interest in Distributed Acoustic Sensing (DAS) technology for microseismic monitoring, especially in operations involving borehole installations. Despite the widespread adoption of DAS systems in such contexts, many questions regarding the data quality of the recordings are still open. Is the DAS self-noise higher than traditional systems? How does the ambient noise recorded by a DAS system attenuate with the depth as observed with traditional geophones? It is known that various noise types, including optical, thermal, and mechanical noise coupled with the fiber, affect DAS data. Additionally, the noise frequency band often overlaps with the signal frequency band, making frequency filtering alone inadequate for denoising. Therefore, specialized noise reduction workflows, such as FK Filtering and SVD, are necessary. Mitigating the impact of noise on DAS data remains a primary challenge for the seismological and geophysical community. This study aims to examine and characterize the noise influencing DAS data collected in borehole installations, with a specific focus on the data recorded at the Frontier Observatory for Research in Geothermal Energy site in Utah, USA. We use Power Spectral Density analysis to assess depth-dependent noise reduction and its temporal variations. Furthermore, the depth dependence of the signal-to-noise ratio for various microseismic events is evaluated. Finally, a comparison is drawn with geophones data colocated with the fiber, offering a comprehensive exploration of the advantages and disadvantages of the two data acquisition technologies.