Parametric modeling of shear wave velocity profiles for the conterminous U.S
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912620689227776 |
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| author | Sanger, Morgan D. Maurer, Brett W. |
| author_facet | Sanger, Morgan D. Maurer, Brett W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00372 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parametric modeling of shear wave velocity profiles for the conterminous U.S Sanger, Morgan D. Maurer, Brett W. Geophysics Machine Learning 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. |
| title | Parametric modeling of shear wave velocity profiles for the conterminous U.S |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2510.00372 |