JOURNAL ARTICLE

Analysis of Near-Surface Shear Wave Velocity Data for Site Response Applications in the Sacramento--San Joaquin Delta Region of California.

  • Published In: Environmental & Engineering Geoscience Journal, 2025, v. 31, n. 3. P. 177 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: BUCKREIS, TRISTAN E.; PENGFEI WANG; STEWART, JONATHAN P.; BRANDENBERG, SCOTT J.; DRILLER, MICHAEL M. 3 of 3

Abstract

The article focuses on the analysis of near-surface shear wave velocity (VS) data for site response applications in California's Sacramento–San Joaquin Delta region, characterized by thick, soft peaty organic soils with low VS values. Using a compiled dataset of 175 VS profiles, the authors developed two region-specific models: one predicting the time-averaged shear wave velocity in the upper 30 m (VS30) conditioned on peat thickness, which reduces bias and uncertainty compared to existing models; and another for extrapolating shallow VS profiles to deeper, firmer reference conditions needed for ground response analyses. The VS30 model accounts for peat thickness variability and provides unbiased estimates with lower uncertainty, while the extrapolation model uses a two-layer power-law approach with a transition depth near 40 m, reflecting geologic changes. Verification through ground response analyses indicates the extrapolation model reasonably captures site amplification effects despite some limitations in representing detailed velocity variations at depth.

Additional Information

  • Source:Environmental & Engineering Geoscience Journal. 2025/08, Vol. 31, Issue 3, p177
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2025
  • ISSN:1078-7275
  • DOI:10.21663/eeg-d-24-00032
  • Accession Number:187053924
  • Copyright Statement:Copyright of Environmental & Engineering Geoscience Journal is the property of Association of Environmental & Engineering Geologists and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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