Full wavefield surface wave analysis with Bayesian Evidential Learning

Surface waves such as Rayleigh, Love and Scholte waves can exhibit dispersion, that is, variations in phase velocity with wavelength as a function of frequency. This property enables the inversion of 1-D models of seismic velocity and density in the subsurface. Conventional deterministic and stochastic inversion schemes are widely applied to surface wave data but face two main challenges. The first is the identification of dispersion curves for fundamental and higher modes on wavefield-transformed images, which is often done manually. The second is the quantification of uncertainty, which can be computationally expensive in stochastic approaches or limited to data-propagated uncertainty in deterministic inversions. Our objectives are to (1) eliminate the need for manual or automatic dispersion curve picking, and (2) directly infer ensembles of 1-D velocity models—and their associated uncertainties—from the full velocity spectrum, that is, the complete dispersion image containing all modes. To this end, we employ Bayesian Evidential Learning, a predictive framework that reproduces experimental data from prior information while allowing prior falsification. In our application, ensembles of prior Earth models are sampled to predict 1-D subsurface structures in terms of seismic velocity and, where applicable, attenuation from near-surface seismic wave data. This approach bypasses traditional inversion schemes and provides a computationally efficient tool for uncertainty quantification.