Data-driven characterization of near-surface velocity in the San Francisco bay area: A stationary and spatially varying approach

This study presents the formulation of two new sedimentary velocity models (SVMs) applied to the San Francisco Bay Area (SFBA) to improve the near-surface representation of shear-wave velocity ( V S ) for large-scale, broadband numerical simulations, with the ultimate goal of enhancing the representation of the sedimentary layers in community velocity model. The first velocity model is stationary and is based solely on the time-average shear-wave velocity of the top 30 m ( V S 30 ); the second velocity model is spatially varying and has location-specific adjustments. They were developed using a dataset of 200 measured V S profiles. Both models were formulated within a hierarchical Bayesian framework, using a parameterization that ensures robust scaling. The spatially varying model includes a slope adjustment term modeled as a Gaussian process to capture site-specific effects based on location. Residual analysis shows that both models are unbiased for V S values up to 1000 m/s. Along-depth variability models were also developed using within-profile residuals. The proposed models show higher V S in the South Bay, East Bay, and Livermore Valley compared to the USGS SFBA velocity model by a factor of two or more in some cases. Goodness-of-fit (GOF) comparisons using one-dimensional (1D) linear site response analysis at selected sites demonstrate that the proposed models outperform the USGS SFBA velocity model in capturing near-surface amplification across a broad frequency range. Incorporating along-depth variability further improves the GOF scores by reducing over-amplification at high frequencies. These results underscore the importance of integrating data-driven models of the shallow crust, like the ones presented here, in coarser regional community velocity models to enhance regional seismic hazard assessments.