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Abstract One of the main sources of seismic noise below 0.05 Hz is the atmospheric pressure variation, especially when surface pressure variations are large. When surface broadband seismic stations are equipped with pressure sensors, there is high coherence between pressure and seismic signals at low frequencies. The amount of ground deformation under surface pressure variations reflects the characteristics of near‐surface elastic structure and allows us to estimate near‐surface shear‐modulus structure using an inversion method. In the inversion method, we have the surface observable η(f) = Sz/Sp, where f is a frequency between 0.01 and 0.05 Hz, and Sz and Sp are the power spectral densities of vertical seismic data and of surface pressure data. We derive depth sensitivity kernels for η(f) with which we invert for elastic moduli of the shallow structure. Between 0.01 and 0.05 Hz, sensitivity kernels typically have peaks at depths within the uppermost 100 m. Based on vertically heterogeneous 1‐D structures, we estimate Vs30 at 744 USArray Transportable Array stations. Vs30 is the time‐averaged shear‐wave velocity from the surface to the 30‐m depth. We compare our results with various surficial geology maps. Although Vs30 has high horizontal variability over a short distance on the scale of hundreds of meters, we find correlations between Vs30 and large‐scale geological structures, such as mapped units and surficial materials. We find good agreement between estimated Vs30 and mapped Quaternary sediment depths, where stations with thicker underlying sediment tend to have slower Vs30.
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Intersectional Impacts of the 2021 Mw 7.2 Nippes, Haiti, Earthquake from Geotechnical and Social Perspectives
The Mw 7.2 Nippes, Haiti, earthquake occurred on 14 August 2021 in Haiti’s southwest peninsula and in the midst of significant social, economic, and political crises. A hybrid reconnaissance mission (i.e., combined remote and field investigation) was coordinated to document damage to the built environment after the event. This article evaluates two ground‐motion records available in Haiti in the context of the geology of the region and known areas with significant damage, such as Les Cayes. We also present a new map of time‐averaged shear‐wave velocity values to 30 m depth (VS30 ) for Les Cayes and Port‐au‐Prince based on the geostatistical approach of kriging and accounting for region‐specific geology proxies and field measurements of VS30 . Case studies of ground failure observations, including landslides and liquefaction triggering, are described as well as the intersection of social and engineering observations. Maps depicting this important intersection are provided to facilitate the assessment of how natural hazards and social conflicts have influenced the vulnerability of Haiti’s population to earthquakes.
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- Award ID(s):
- 2145466
- PAR ID:
- 10441201
- Editor(s):
- Eric Calais; Silvia Chacón-Barrantes; Roby Douilly; O’Leary Gonzalez; Xyoli Pérez-Campos; Richard Robertson, Elizabeth Vanacore
- Date Published:
- Journal Name:
- Bulletin of the Seismological Society of America
- Volume:
- 113
- Issue:
- 1
- ISSN:
- 0037-1106
- Page Range / eLocation ID:
- 73-98
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The shear-wave velocity time averaged over the upper 30 m (VS30) is widely used as a proxy for site effects, forms the basis of seismic site class, and underpins site-amplification factors in empirical ground-motion models. Many earthquake simulations, therefore, require VS30. This presents a challenge at regional scale, given the infeasibility of subsurface testing over vast areas. Although various models for predicting VS30 have thus been proposed, the most popular U.S. national, or “background,” model is a regression equation based on just one variable. Given the growth of community data sets, satellite remote sensing, and algorithmic learning, more advanced and accurate solutions may be possible. Toward that end, we develop national VS30 models and maps using field data from over 7000 sites and machine learning (ML), wherein up to 17 geospatial parameters are used to predict subsurface conditions (i.e., VS30). Of the two models developed, that using geologic data performs marginally better, yet such data are not always available. Both models significantly outperform existing solutions in unbiased testing and are used to create new VS30 maps at ∼220 m resolution. These maps are updated in the vicinity of field measurements using regression kriging and cover the 50 U.S. states and Puerto Rico. Ultimately, and like any model, performance cannot be known where data is sparse. In this regard, alternative maps that use other models are proposed for steep slopes. More broadly, this study demonstrates the utility of ML for inferring below-ground conditions from geospatial data, a technique that could be applied to other data and objectives.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1785/0120220118
