Parametric modeling of shear wave velocity profiles for the conterminous U.S

Earthquake ground motions and the related damage can be significantly impacted by near-surface soils. Accurate predictions of seismic hazard require depth-continuous models of soil stiffness, commonly described in terms of shear-wave velocity (VS). For regional-scale studies, efforts to predict VS remotely, such as the U.S. Geological Survey’s National Crustal Model, tend to emphasize deeper lithologic velocity structures, thus simplifying important near-surface soil velocity variations, and tend to be produced at relatively coarse geospatial resolution for one geographic area. In this study, we define a functional form to describe VS-with-depth across the conterminous U.S. We calibrate the parameters of the function using a national compilation of more than 9,000 in-situ geotechnical measurements. By coupling the parametric framework with geospatial machine learning, the model can be leveraged to provide consistent, high resolution VS-depth predictions of the near-surface geotechnical layer across the U.S., complementing the National Crustal Model and supporting applications such as physics-based ground motion simulations and coseismic hazard assessments.

💡 Research Summary

The paper addresses a critical gap in seismic hazard assessment: the lack of high‑resolution, depth‑continuous shear‑wave velocity (VS) information for the near‑surface geotechnical layer across the conterminous United States. While the U.S. Geological Survey’s National Crustal Model (NCM) provides a valuable representation of deep lithospheric velocity structure, it deliberately smooths or omits the shallow‑soil variability that most strongly influences ground‑motion amplification and damage. To overcome this limitation, the authors propose a parametric functional form that captures the typical increase of VS with depth and can be calibrated locally. The chosen equation, (V_S(z)=V_{S0}+(V_{S\infty}-V_{S0})