ARRA-funded V&lt;sub&gt;S30&lt;/sub&gt; measurements using multi-technique approach at strong-motion stations in California and central-eastern United States

Yong, Alan; Martin, Antony; Stokoe, Kenneth H.; Diehl, John

doi:10.3133/ofr20131102

Cited by 48 publications

(38 citation statements)

References 42 publications

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“…We use V S30 measurements from the Pacific Engineering and Analysis database (Walt Silva, written comm., 2011) and Yong et al (2013). To avoid misclassified sites that arise from the limited spatial precision of the WC06 map or the location of the measurements, we removed V S30 measurements from the database if the geologic classification was questionable, that is, if the V S30 values indicative of soil units were located on rock according to the geology map, or vice versa.…”

Section: Datamentioning

confidence: 99%

“…We use V S30 measurements from the Pacific Engineering and Analysis database (Walt Silva, written comm., 2011) and Yong et al (2013). Following Wald and Allen (2007), the gradient (i.e., maximum slope) was computed from 30 arcsec Shuttle Radar Tomography Mission data (Farr and Kobrick, 2000) using the Generic Mapping Tools command "grdgradient" (Wessel et al, 2013).…”

Section: Data and Resourcesmentioning

confidence: 99%

“…Here, we apply a hybrid approach for California that takes advantage of both surface geology and topographic gradient. We use the regression kriging (RK) approach for mapping V S30 (Thompson et al, 2010;Thompson et al, 2011) to ensure that the resulting map of California is locally refined to reflect the rapidly expanding database of V S30 measurements (e.g., Yong et al, 2013). The RK method for mapping V S30 is analogous to the Worden et al (2010) method of mapping ground-motion intensities used in ShakeMap, in which the spatial proxy (event-corrected GMPEs) is locally refined to reflect the instrumental and macroseismic intensities.…”

Section: Introductionmentioning

confidence: 99%

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A VS30 Map for California with Geologic and Topographic Constraints

Thompson¹,

Wald²,

Worden³

2014

Bulletin of the Seismological Society of America

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For many earthquake engineering applications, site response is estimated through empirical correlations with the time-averaged shear-wave velocity to 30 m depth (V S30 ). These applications therefore depend on the availability of either sitespecific V S30 measurements or V S30 maps at local, regional, and global scales. Because V S30 measurements are sparse, a proxy frequently is needed to estimate V S30 at unsampled locations. We present a new V S30 map for California, which accounts for observational constraints from multiple sources and spatial scales, such as geology, topography, and site-specific V S30 measurements. We apply the geostatistical approach of regression kriging (RK) to combine these constraints for predicting V S30 . For the V S30 trend, we start with geology-based V S30 values and identify two distinct trends between topographic gradient and the residuals from the geology V S30 model. One trend applies to deep and fine Quaternary alluvium, whereas the second trend is slightly stronger and applies to Pleistocene sedimentary units. The RK framework ensures that the resulting map of California is locally refined to reflect the rapidly expanding database of V S30 measurements throughout California. We compare the accuracy of the new mapping method to a previously developed map of V S30 for California. We also illustrate the sensitivity of ground motions to the new V S30 map by comparing real and scenario ShakeMaps with V S30 values from our new map to those for existing V S30 maps.

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Section: Datamentioning

confidence: 99%

Section: Data and Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A VS30 Map for California with Geologic and Topographic Constraints

Thompson¹,

Wald²,

Worden³

2014

Bulletin of the Seismological Society of America

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“…The Reader is referred to textbooks (e.g. Okada 2003;Foti et al 2014) and to the vast literature on the topic (for an overview, see Bard et al 2010;Socco et al 2010;Foti et al 2011;Schramm et al 2012;Yong et al 2013) for achieving the necessary knowledge on surface wave methods and for the theoretical details.…”

Section: Introductionmentioning

confidence: 99%

Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project

Foti

Hollender

Garofalo

et al. 2017

Bull Earthquake Eng

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398

202

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Surface wave methods gained in the past decades a primary role in many seismic projects. Specifically, they are often used to retrieve a 1D shear wave velocity model or to estimate the V S,30 at a site. The complexity of the interpretation process and the variety of possible approaches to surface wave analysis make it very hard to set a fixed standard to assure quality and reliability of the results. The present guidelines provide practical Electronic supplementary material The online version of this article

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“…Nevertheless, indirect approaches can also often lead to V S30 estimates of high uncertainty. To tackle these issues, important efforts have been made in recent years to perform in situ site characterization of accelerometric stations: e.g., in Italy (Foti et al 2011;Pileggi et al 2011), Switzerland (Michel et al 2014), and the USA (Yong et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Characterization of site conditions (soil class, VS30, velocity profiles) for 33 stations from the French permanent accelerometric network (RAP) using surface-wave methods

Hollender

Cornou

Dechamp

et al. 2017

Bull Earthquake Eng

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Data provided by accelerometric networks are important for seismic hazard assessment. The correct use of accelerometric signals is conditioned by the station site metadata quality (i.e., soil class, V S30 , velocity profiles, and other relevant information that can help to quantify site effects). In France, the permanent accelerometric network consists of about 150 stations. Thirty-three of these stations in the southern half of France have been characterized, using surface-wave-based methods that allow derivation of velocity profiles from dispersion curves of surface waves. The computation of dispersion curves and their subsequent inversion in terms of shear-wave velocity profiles has allowed estimation of V S30 values and designation of soil classes, which include the corresponding uncertainties. From a methodological point of view, this survey leads to the following recommendations: (1)

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ARRA-funded V<sub>S30</sub> measurements using multi-technique approach at strong-motion stations in California and central-eastern United States

Cited by 48 publications

References 42 publications

A VS30 Map for California with Geologic and Topographic Constraints

A VS30 Map for California with Geologic and Topographic Constraints

Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project

Characterization of site conditions (soil class, VS30, velocity profiles) for 33 stations from the French permanent accelerometric network (RAP) using surface-wave methods

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