Transdimensional surface wave tomography of the near-surface: Application to DAS data

Abstract: Distributed Acoustic Sensing (DAS) is a novel technology that allows sampling of the seismic wavefield densely over a broad frequency band. This makes it an ideal tool for surface wave studies. In this study, we evaluate the potential of DAS to image the near-surface using synthetic data and active-source field DAS data recorded with straight fibers in Groningen, the Netherlands. First, we recover the laterally varying surface wave phase velocities (i.e., local dispersion curves) from the fundamental-mode surface waves. We utilize the Multi Offset Phase Analysis (MOPA) for the recovery of the laterally varying phase velocities. In this way, we take into account the lateral variability of the subsurface structures. Then, instead of inverting each local dispersion curve independently, we propose to use a novel 2D transdimensional surface wave tomography algorithm to image the subsurface. In this approach, we parameterize the model space using 2D Voronoi cells and invert all the local dispersion curves simultaneously to consider the lateral spatial correlation of the inversion result. Additionally, this approach reduces the solution nonuniqueness of the inversion problem. The proposed methodology successfully recovered the shear-wave velocity of the synthetic data. Application to the field data also confirms the reliability of the proposed algorithm. The recovered 2D shear-wave velocity profile is compared to shear-wave velocity logs obtained at the location of two boreholes, which shows a good agreement.