Extracting surface wave dispersion curves with deep learning

Multi-channel Analysis of Surface Waves (MASW) is a seismic method employed to obtain useful information about the shear-wave velocities of the subsurface. A fundamental step in the methodology is the extraction of dispersion curves from dispersion spectra obtained after applying specific processing algorithms; to some extent, this extraction can be automated. However, it still requires extensive quality control, which can be time-demanding in large dataset scenarios. We present a novel approach that leverages deep learning to automatically identify a direct mapping between seismic shot gathers at the associated dispersion curves. Given a site of interest, a set of 1D velocity models is created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh-wave phase dispersion curves are then numerically modeled and used to train a simplified residual network. The proposed approach is shown to achieve satisfactory predictions of dispersion curves on a synthetic test dataset and is ultimately deployed on a field dataset. The predicted dispersion curves are finally inverted, and the resulting shear-wave velocity model is plausible and consistent with prior geological knowledge of the area.