Multi-Mode Rayleigh Wave Dispersion Spectrum Inversion Using Wasserstein Distance Coupled with Bayesian Optimization

We propose a new automatic framework for non-destructive multi-channel analysis of surface waves (MASW) that combines multi-mode dispersion spectrum matching and the finite element method (FEM)-based inversion to enhance the accuracy of subsurface profiling in site investigation activities. This framework eliminates the need for manual identification of the Rayleigh wave energy component and multi-mode assignment, reducing the dependence on operator experience and judgment. The dispersion spectrum is generated through a FEM model that simulates 2D seismic wave propagation, taking into account the actual acquisition layout and lateral variations in the subsurface. We introduce the Wasserstein distance (WD) for evaluating the difference between observed and simulated spectra, and incorporate Bayesian optimization for efficiently inverting shear wave velocity profiles. The effectiveness of the proposed framework is demonstrated through synthetic data examples, and the superiority of the WD-based objective function is illustrated by comparing it with the conventional mean square error (MSE)-based objective function. Subsequently, we conduct a field test on a reclaimed landfill to validate the proposed framework. This test confirms the ability of framework to retrieve multi-mode Rayleigh waves and demonstrates its effectiveness in providing high-resolution shear wave profiles of the shallow subsurface.