Characterizing Vs profiles by the SASW method and comparison with other seismic methods



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The shear wave velocity (VS) profile has been used as an important parameter in characterizing geotechnical sites and performing earthquake designs. The SpectralAnalysis-of-Surface-Wave (SASW) method, one of the VS profiling methods, was developed in the early 1980s. This method is a non-intrusive test which uses Rayleigh waves, one kind of surface wave, to explore the subsurface. The SASW method has been widely used in geotechnical earthquake engineering to profile soil and rock sites. All equipment required to conduct the SASW test is deployed on the ground surface and no boreholes are needed. In this study, the SASW method was used to measure shear wave velocity profiles in four different geographic regions. These four regions are: (1) Imperial Valley, CA, (2) Taiwan, (3) Hanford, WA and (4) Yucca Mountain, NV. The SASW tests performed at these locations were for different purposes. At the Imperial Valley and Taiwan sites, the SASW tests were carried out at the locations of strong motion recorders (SMR) to obtain VS profiles of the top 30 m (VS,30). At the Hanford and Yucca Mountain sites, deeper profiling (>300 m) was required to obtain VS values of the geotechnical structure around or beneath critical facilities associated with the handling, treatment and/or storage of high-level radioactive waste. The VS,30 values determined by the SASW method were used to classify the test sites based on the International Building Code (IBC-2006) provisions. Available downhole and suspension logging measurements at/near the SASW test sites were also used to determine VS,30. In addition, deeper VS profiles determined by the SASW, downhole and suspension logging methods were compared. By doing so, the consistency between the three seismic surveys methods and the reliability of the SASW method were studied. Finally, sensitivity studies of the SASW method were conducted to investigate: (1) the impact on the final VS profile of changing assumed parameters in the SASW data reduction process, and (2) the capability of the SASW method to detect relatively soft layers sandwiched between stiffer layers.