Direct 3-D variational Bayesian surface wave inversion and its application to ambient noise tomography beneath Great Britain

SUMMARY We present a new, variational, fully nonlinear, probabilistic ambient noise tomography method, which estimates subsurface structure and quantifies the corresponding uncertainties directly in 3-D from inter-receiver seismic surface wave dispersion data. We use the method to invert for high resolution 3-D seismic velocity models of the upper crust beneath Great Britain using seismic ambient noise data recorded around the region—a task that proved too high-dimensional and hence computationally demanding for Monte Carlo sampling to converge to a stable solution. We compare the inversion results from the new method to those obtained from two standard, indirect inversion methods, in which 2-D (geographical) surface wave velocity maps and 1-D (depth) shear velocity profiles are estimated in two separate, consecutive steps. The results show that the direct 3-D scheme preserves better lateral continuity and produces better data fit than the two-step methods, and provides information about lateral correlations that is absent from the two-step solutions. The inversion results are consistent with large-scale geology of Great Britain, and for the first time provide seismologically imaged evidence of the Great Glen Fault and other major tectonic faults. We therefore propose that direct 3-D inversion schemes should be used where possible for surface wave inversion as they provide improved results at little additional computational cost.