Near Surface Imaging with Zig-Zag shaped DAS Arrays Based on Cross Correlation of Ambient Noise

Distributed Acoustic Sensing (DAS) has effectively transformed traditional telecommunication fiber-optic cables into highly efficient, dense seismic arrays. In this paper, we perform cross-correlation and stacking on the signals recorded by DAS fiber arrays in a Zig-Zag pattern, which increases the spatial coverage and improves the ability to detect and analyze various seismic waves modes. We observed that the resulting signals include both Rayleigh and Love waves. Additionally, the propagation characteristics of the wave field exhibit a Zig-Zag distribution pattern, consistent with the spatial distribution of the fibers. The challenge arises from the need to distinguish and accurately interpret the overlapping signals of Rayleigh and Love waves, which have different propagation characteristics. To address these challenges, we propose an inversion method specifically tailored for the near surface imaging with DAS array data when the fiber-optic cables are not laid in straight lines. We also conducted an on-site experiment using Zig-Zag shaped DAS arrays with known subsurface velocity model. This experiment was designed to record ambient noise signals over a week and analyze the propagation characteristics of both Rayleigh and Love waves within the surface waves captured by the DAS system. The inversion results obtained from the analysis of the recorded data showed a high degree of consistency with the ground truth subsurface structure of the test area. This demonstrates the effectiveness of the proposed method in overcoming the limitations of traditional dispersion curve-based inversion techniques, particularly in the context of non-linear fiber layouts. This research provides strong support for the practical application of dark fibers in near surface imaging, demonstrating the potential of dark fibers.