Compilation of &lt;em&gt;V&lt;/em&gt;&lt;sub&gt;&lt;em&gt;S&lt;/em&gt;30&lt;/sub&gt; Data for the United States

Yong, Alan; Thompson, Eric M.; Wald, David J.; Knudsen, Kathy L.; Odum, Jack K.; Stephenson, William J.; Haefner, Scott

doi:10.3133/ds978

Cited by 37 publications

(44 citation statements)

References 14 publications

(16 reference statements)

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“…Our average 150-m S wave velocity of 380 m/s across the entire array suggests an average Vs30 near 190 m/s, with an implied Vs30 full range from 100 to 380 m/s. To our knowledge, no Vs30 measurements have been performed within the footprint of the array, but nearby stations (NP.5044, NP.5034, and NP5230) have measurements of Vs30s from 577 to 744 m/s (Yong et al, 2016). These values are larger than our estimates would imply.…”

Section: 1029/2019jb019157contrasting

confidence: 58%

Peak Ground Velocity Spatial Variability Revealed by Dense Seismic Array in Southern California

et al. 2020

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Understanding and modeling variability of ground motion is essential for building accurate and precise ground motion prediction equations, which can net site-specific characterization and reduced hazard levels. Here, we explore the spatial variability in peak ground velocity (PGV) at Sage Brush Flats along the San Jacinto Fault in Southern California. We use data from a dense array (0.6 × 0.6 km 2 , 1,108 geophones, station spacings 10-30 m) deployed in 2014 for~1 month. These data offer an opportunity to study small-scale variability in this region. We examine 38 earthquakes (2 ≤ M L ≤ 4.2) within 200 km of the array. Fault strands and a small basin impact the ground motions, producing PGV variations up to 22% of the mean and a 40% reduction in P and S wave near-surface velocities. We find along-fault rupture directivity, source, and path effects can increase PGVs by 167%. Surface PGV measurements exceed the colocated borehole station (depth at 148 m) PGV by factors of 3-10, confirming the impact on PGV from near-surface fault structures, basins, topography, and amplifications from soft sediments. Consistently, we find high PGVs within the basin structure. A pair of colocated GaM L 2.6 events produce repeatable PGV values with similar spatial patterns. The average corner frequencies of these two events are 11-16 Hz, and viable measurements of stress drop can differ by 6.45 MPa. Within this small array, the PGV values are variable implying spatial extrapolation of PGV to regions of known faults and basins, even across a small area, should be done with caution. Plain Language Summary There are an infinite number of locations where seismic waves interact with Earth's surface, yet seismologists can only record a finite set of ground motions. This requires assumptions about how known measurements or predictions can be extrapolated to nearby locations. We test to what extent extrapolation is viable using an array of 1,108 geophones installed 10-20 m apart within a 0.36 km 2 study area for 1 month atop the San Jacinto Fault in Southern California. Measuring ground motions from 38 small local earthquakes, we observe peak ground velocity (PGV) variations up to 22% across the array. We find the basin structure consistently traps energy, amplifying the seismic velocities, and that the damaged region within the fault reduces the PGVs. Comparing surface and borehole measurements (148 m deep), we find PGVs recorded at the surface are 3-10 times greater than the measurements at depth. We attribute these differences to amplification from soft sediments within the basin. We show that by leveraging information from >1,000 measurements per earthquake the ground motion velocities can be tightly constrained. These results suggest the spatial extrapolation of PGVs in regions of known faults and basins, even across a small area, should be done with caution.

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Section: 1029/2019jb019157contrasting

confidence: 58%

Peak Ground Velocity Spatial Variability Revealed by Dense Seismic Array in Southern California

et al. 2020

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“…For example, earthquake stress drop and its variability have shown to be correlated with high‐frequency (e.g., PGA) event residuals on an event‐by‐event basis (e.g., Baltay et al, ; Trugman & Shearer, ), but application in a forward manner is tricky given that it is nearly impossible to say what the stress drop of any future event may be. Site effects are often represented by physical values such as Vs 30 (the time‐averaged shear wave velocity in the top 30 m of the Earth's crust) and κ (the slope or decay of the high‐frequency portion of a recorded spectrum; Anderson & Anderson & Hough, ; Anderson & Humphrey, ; Laurendeau et al, ; Yong et al, ). Considerations of path‐specific effects, however, have heretofore not been studied in relation to physical properties, only in a purely statistical sense.…”

Section: Methodsmentioning

confidence: 99%

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

et al. 2019

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To improve models of ground motion estimation and probabilistic seismic hazard analyses, the engineering seismology field is moving toward developing fully nonergodic ground motion models, models specific for individual source‐to‐site paths. Previous work on this topic has examined systematic variations in ground‐motion along particular paths (from either recorded or simulated earthquake data) and has not included physical properties of the path. We present here a framework to include physical path properties, by seeking correlations between ground motion amplitudes along specific paths and crustal properties, specifically seismic velocity and anelastic attenuation, along that path. Using a large data set of small‐magnitude earthquakes recorded in Southern California, we find a correlation between the gradient of seismic S wave velocity and the path term residual, after accounting for an average geometric spreading and anelastic attenuation, indicating that heterogeneity in crustal velocity primarily controls the path‐specific attenuation. Even in aseismic regions, details of path‐specific ground motion prediction equations can be developed from crustal structure and property data.

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“…We compared our results against the Vs30 model (Allen & Wald, ; Wald & Allen, ; Yong et al, ) which we obtained from https://earthquake.usgs.gov/data/vs30. Since the resolution of reported sites (30 arc‐sec) is coarse for our purpose, we interpolated S wave velocities near the station sites in order to estimate S wave velocity at each station.…”

Section: Discussionmentioning

confidence: 99%

Low‐Frequency Seismic Noise Characteristics From the Analysis of Co‐Located Seismic and Pressure Data

Tanimoto

Wang

2018

JGR Solid Earth

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We analyze the characteristics of seismic noise generated by surface pressure changes, using co‐located seismic and pressure data from the USArray Transportable Array for the entire year of 2014. We compare pressure and seismic signals at frequencies of 0.01 and 0.02 Hz and model their main characteristics. At these frequencies, vertical and horizontal seismic noise can be explained by the formulas SZ = A * Sp + NZ and SH = B * Sp + NH, where SZ and SH are the power spectral densities of vertical and horizontal ground velocities (sum of two horizontal components), Sp is the power spectral density of pressure, and NZ and NH are constant terms. We first determine A and B from the high pressure range (Sp > 10 Pa2/Hz) in which NZ and NH are small. From A and B, we then determine the near‐surface rigidity and pressure wave speed. This rigidity is modified from true rigidity by a factor (λ + μ)/(λ + 2 μ); this factor is about 0.7 for typical crustal rocks but approaches 1 near the Earth's surface. To estimate true rigidity, empirical formulas that relate density and seismic velocities are needed. Once A and B are determined, NZ and NH are determined. NZ and rigidity of the subsurface determine a threshold pressure in vertical data. NH is generally small but at some stations it changes seasonally and creates separate branches in plots of pressure versus seismic velocity. This may explain why seismic‐noise reduction approaches using co‐located pressure data often fail to work for horizontal data but usually work well for vertical data.

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Compilation of <em>V</em><sub><em>S</em>30</sub> Data for the United States

Cited by 37 publications

References 14 publications

Peak Ground Velocity Spatial Variability Revealed by Dense Seismic Array in Southern California

Peak Ground Velocity Spatial Variability Revealed by Dense Seismic Array in Southern California

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

Low‐Frequency Seismic Noise Characteristics From the Analysis of Co‐Located Seismic and Pressure Data

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