Surface wave analysis using DAS and dense nodal array recordings in near-surface active surveying

Summary Distributed Acoustic Sensing (DAS) captures seismic wavefields through precise measurement of phase changes in back-scattered laser pulse signals in an optical fiber that reflect distributed strains or strain rates over continuous fiber segments. The limitation of the DAS wavefield measurement for near surface imaging remains unclear and is worthy of verification with conventional dense nodal arrays. This study compares co-located DAS and dense nodal array recordings in near-surface active surveys conducted in Shenzhen, China. Our field experiment reveals the spatially fixed, step-like distortions of coherent signals in DAS wavefield recordings. These artifacts are attributed to the gauge-length spatial average acting upon fiber segments with heterogeneous sensitivity, which can arise from variations in backscatter intensity and cable coupling conditions. These distortions cause partial deviations between waveforms of DAS and co-located seismometer from their theoretical relationship. A statistical comparison of phase differences between DAS and seismometer waveforms across all nodal points reveals a normal distribution, indicating the prevalence of such distortions in DAS recordings. Moreover, the distortion becomes more severe as frequency increases, resulting in overly dispersed phase difference distributions between DAS and seismometers. This leads to discrepancies in the dispersion curves extracted from them at high frequencies, as dispersion extraction depends on the relative energy peaks contributed by the dominant channels. This study demonstrates that within the frequency range not severely affected by distortion, DAS can effectively extract comparable dispersion curves and velocity structure profiles in place of the nodal array for near-surface imaging applications, as validated through full-profile comparisons. But the DAS wavefield distortions limit advanced applications of the complete wavefield for better subsurface characterization, and imply that DAS data simulation and instrument response analysis should consider channel ensembles.