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The effect of data measurement uncertainty and inversion uncertainty on VS,30 estimation is studied in this article. An attempt has also been made to separate out the inversion and data measurement uncertainties, and their effect on VS,30 is quantified separately. A relaxation to the misfit value is introduced to study the consequence of inversion uncertainty, whereas data variation up to one standard deviation is considered to study the effect of data uncertainty. All possible profiles are generated within this limiting misfit value (after relaxation) in case of inversion uncertainty and within measured data uncertainty bound in case of data uncertainty. Finally, few profiles are selected from entire range of generated profiles for VS,30 estimation. For the profiles falling short of 30 m depth, shear wave velocities are extrapolated using a simplified method proposed by Boore (2004) [4]. The VS,30 values show limited variation in terms of CoV (

The application of Multichannel Analysis of Surface Wave (MASW) technique is increasing in geotechnical engineering to characterize near surface materials. The dispersion property of Rayleigh wave is utilized in MASW method. MASW often suffers from near-field effects which may result in either underestimation or overestimation of Rayleigh wave phase velocity due to the body waves contamination near to the source. In this paper, a detailed numerical study has been carried out to examine the near-field effects considering three different types of typical S-wave velocity models with four different impedance scenarios in each case. The study shows that the impedance contrast between the half-space and overlying soil layer is having a considerable effect on the underestimation of phase velocity. These near-field effects are also found to be influenced by the type of the S-wave velocity model as well as far and near offset distances. With the increase of impedance contrast, the level of underestimation seems to increase at lower normalized array centre distance due to mode jump. However, such jump can not be observed with limited far offset distances generally used in usual practice due to poor resolution in the dispersion spectra. Significant near-field effect are observed for lower far offset distances and inversely dispersive S-wave velocity models. Underestimation of Rayleigh wave phase velocity is quantified in terms of two normalized parameters. Finally, a field study is also conducted to verify our numerical findings.

The article studies the propagation of dispersion uncertainty in Vs profiles to VS,30 and linear soil amplification, and at the same time, it also attempts to infer some insights on how the inversion non-uniqueness plays a role in the extracted Vs profiles and subsequent analyses. Three synthetic and two field studies have been presented for this purpose. Target dispersion curve has been assigned with an uncertainty estimate and inversion has been performed using Neighbourhood algorithm. Profiles have been selected from different misfit ranges and combined into one in order to map dispersion misfit with VS-misfit, VS,30 and peak soil amplification. The term VS-misfit has been introduced for synthetic cases to represent the deviation of an extracted VS profile after inversion from the true solution using a single value. The results clearly depict that the range of variation of VS-misfit, VS,30 and peak amplification increases as the dispersion misfit increases. This implies that if the minimum misfit achieved during inversion is higher, the inversion non-uniqueness might play a bigger role in the subsequent analyses. As the dispersion misfit increases, more number of solutions are found to exist at very similar misfit values and the non-uniqueness of inversion manifests more noticeably in the subsequent analyses. Standard deviation is found to increase with the increase in misfit ranges. In terms of COV, VS-misfit exhibits the highest variation, whereas the variations get reduced in VS,30 and in peak amplification.

The extent of data uncertainty of surface-wave measurements may have significant consequences on 1D seismic response analysis and it may lead to inaccurate estimate of design ground motion. In this paper, an attempt has been made to quantify the data measurement uncertainty from a large repetition of the field data at two test sites and to determine bounds of data uncertainty. In the inversion with neighborhood algorithm, we generated profiles below the misfit value calculated from the uncertainty bound. Equivalent profiles have been selected covering the whole misfit value range and these profiles are subjected to equivalent linear 1D ground response analysis. The results of seismic response analysis are presented in the form of amplification spectra and response spectra which show remarkable variations. Significant variation is observed in peak frequency and peak amplification and it differs from one site to the other based on their measured data uncertainty bound. Peak Ground Acceleration (PGA) and peak spectral acceleration at both the sites reflect very high Coefficient of Variation (COV).