Seismic Site Characterization: Measure, Mark and Cut of VS Profiles and VS30 Using a Flexible Multi-method Approach

Alan Yong

USGS, Earthquake Science Center, Pasadena

Date & Time
Building 3, Rambo Auditorium

The main considerations for modeling seismic ground motions typically involve a trichotomy of effects that are partitioned into source, path, and site. Site conditions—often representing no more than 1% of the path from the seismic source—can strongly influence site response, thus dominate the intensity of shaking. To account for this phenomenon, the engineering community has traditionally used the index of VS30, the time-averaged shear-wave velocity (VS) from the surface to a depth of 30 meters. I present a review of the state-of-practice for estimating VS30, as well as select developments for advancing measured VS30 methods. VS30 values were traditionally derived from direct borehole recordings of seismic travel-times. As a result of cost and/or environmental factors that restrict the drilling of boreholes, less-expensive noninvasive methods are now frequently used to estimate VS30 values. Noninvasive methods commonly involve the recording of active- or passive-source, surface-wave or body-wave energy at the ground surface using multi-sensor arrays. Because of the indirect nature of surface-based methods, their VS30 estimates can have substantial uncertainties. Moreover, surface-wave analyses rely on the dispersive nature of Love or Rayleigh waves to derive their frequency-dependent phase velocities, which are used as input to inversion techniques that inherently yield non-unique solutions of VS profiles. Despite limited data, conclusions from a number of recent studies suggest that there is a trend towards supporting the reliability of surface-wave based VS profiles and VS30 values for use in site response analyses. We analyze surface-wave dispersion data of more than two dozen sites where individual surface-wave methods and combinations of different surface-wave methods produced adequate wavelengths to model VS profiles to at least the requisite 30-meter depth. We find the inter- and intra-method variability of the VS30 estimates generally remain insignificant (arithmetic mean of 5% difference) despite substantial variability observed in the equivalent VS profiles. At sites where the minimum recorded wavelengths from each method did not satisfy the 30-meter depth criteria, VS30 could not (by definition) be estimated by the individual method, suggesting that reliable VS profiles for estimating VS30 values are best developed using a combination of complimentary methods. Additionally, we propose the use of the Rayleigh-wave phase velocity at a wavelength of 40 m (VR40) as derived from the site fundamental mode dispersion data to expedite estimations of VS30 values. We find VR40-based VS30 values correlate well with those derived from surface wave methods (r2 = 0.99). Moreover, VR40 values can be readily derived using a single-source two-receiver spacing configuration, thus facilitating rapid data collection. It is also beneficial to use VR40 values as a means to rapidly prescreen the subsurface for lateral velocity variability or to supplement data where there are insufficient records. For microzonation purposes, VR40 values can also be used to densify sparse distributions of profile-based VS30 point values. We, however, do not advocate circumventing the development of VS profiles, as site-specific response analyses require VS profiles. Direct reliance on VR40 values as the standalone approach is also not suggested because a complete dispersion curve is necessary to confirm that the VR40 parameter was indeed derived from the fundamental mode. In general, we recommend the use of complementary methods to generate composite dispersion data because we find the reliability of the resultant VS30 estimate of a site is consistently dependent on two key factors: the flexible use of complementary methods that adequately record wavelengths to resolve subsurface details to below the 30-meter depth and the quality of the goodness of fit of the theoretical dispersion data to the experimentally observed data for each method or combination of methods.

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