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