The U.S. Atlantic and Gulf Coastal Plain (CP) sediments are kilometers‐thick, low‐velocity deposits underlain by high‐velocity bedrock that create regional patterns of amplification and attenuation of ground motions. Capturing these effects is challenging because of the limited subsurface information available, particularly the shear wave velocity (
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) structure down to bedrock (or assumed reference rock condition) and the associated uncertainties. This paper introduces a new suite of regional velocity models for the CPs, called CPVMv2.0, which builds upon previous efforts integrating geology,
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measurements, and sediment thickness in the region. CPVMv2.0 uses 750 measured
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profiles and identifies nine surficial‐geologic‐age‐based and geography‐based groups (with alternative physiographic‐province‐based subgroupings) to create surface‐based median
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profiles. This suite of new models also includes six subsurface‐based median profiles developed using geologic age boundaries from depth‐dependent stratigraphic sequences extracted from the U.S. Geological Survey National Crustal Model. In addition to quantifying aleatory variability in sediment velocity, weathered‐rock velocity, bedrock velocity, and the depth to the top of weathered rock and bedrock, CPVMv2.0 provides alternative profile models that allow for the characterization of the epistemic uncertainty. This is a unique and relevant feature of CPVMv2.0. Our suite of models was validated using measured and estimated fundamental resonant frequencies as well as other
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models in the region. CPVMv2.0 enables the incorporation of crustal velocities (and their associated variability and uncertainty) into physics‐based ground‐motion simulations in the eastern United States and into the development of new ground‐motion models aiming to characterize systematic site effects via
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, depths to
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isosurfaces, and/or sediment thickness in the region.
Cassie Gann‐Phillips, Ashly Cabas, Chunyang Ji et al.· Earthquake spectra· 0 citations
Ground-motion model (GMM) site terms developed from continuously available geospatial data and enhanced with site-specific measured geotechnical data have been demonstrated to be an effective approach for site term development in California (Roberts et al., 2025; Roberts et al., 2026). This study extends this methodology to the Basin and Range physiographic province in the western United States. Ground-motion data from 16,852 recordings at 433 stations in the Next Generation Attenuation-West3 database (Buckreis and Stewart, 2025) are used for model development. A base site term model is developed using mapped geospatial variables (e.g., sediment thickness, elevation, and surficial geologic units) to capture trends in soil stiffness and basin effects. The target site amplifications of peak ground acceleration, peak ground velocity, and pseudospectral accelerations from 0.01 to 10 s are decomposed from the residuals of the Boore et al. (2014) (BSSA14) GMM. A linear mixed-effects regression model is then developed to predict each target site amplification using geospatial variables. The resulting model is a linear geospatial site term that provides a consistent site term for all locations in the region. The geospatial site term shows a substantial reduction in site-to-site variability; on average, an 8.5% reduction is achieved compared with BSSA14. This base geospatial model is then enhanced with local geotechnical information where available. The proposed geotechnical site term adjustment models are developed using microtremor horizontal-to-vertical spectral ratio data (Anbazhagan et al., 2025) and measured VS30 data (Buckreis and Stewart, 2025). Additional reductions in the site-to-site variability are achieved when the geotechnical adjustments are applied. This study illustrates that the approach of incorporating broadly available geospatial data before site-specific geotechnical data is effective in regions outside of California and demonstrates how geospatial site amplification models with explicit uncertainty characterization can be developed where ground-motion data are sparse.
Maggie Roberts, L. Baise, J. Kaklamanos et al.· Bulletin of The Seismologica...· 0 citations
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