Data-driven Characterization of Near-Surface Velocity in the San Francisco Bay Area: A Stationary and Spatially Varying Approach

TL;DR

This paper develops two new sedimentary shear-wave velocity models for the San Francisco Bay Area, using a Bayesian framework and site-specific adjustments, to improve seismic hazard assessments and near-surface representation in regional models.

Contribution

It introduces two novel velocity models, one stationary and one spatially varying, based on 200 measurements, enhancing existing models with data-driven, site-specific adjustments within a Bayesian framework.

Findings

Both models are unbiased up to 1000 m/sec.

Models outperform USGS in capturing near-surface amplification.

Incorporating depth variability improves fit and reduces over-amplification.

Abstract

This study presents the development of two new sedimentary velocity models for 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 the Bay Area community velocity model. The first velocity model is stationary and is based solely on $V_{S30}$; 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/sec. Along-depth variability models were also developed using within-profile residuals. The proposed models show higher $V_S$ in the San Jose area and Livermore Valley compared to the USGS Bay Area community velocity model by a factor of two or more in some cases. Goodness-of-fit (GOF) comparisons using one-dimensional linear site-response analysis at selected sites demonstrate that the proposed models outperform the USGS 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.