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A robust, in situ estimate of shear-wave velocity VS and the small-strain damping ratio DS (or equivalently, the quality factor QS) is crucial for the design of buildings and geotechnical systems subjected to vibrations or earthquake ground shaking. A promising technique for simultaneously obtaining both VS and DS relies on the Multichannel Analysis of Surface Waves (MASW) method. MASW can be used to extract the Rayleigh wave phase velocity and phase attenuation data from active-source seismic traces recorded along linear arrays. Then, these data can be inverted to obtain VS and DS profiles. This paper introduces two novel methodologies for extracting the phase velocity and attenuation data. These new approaches are based on an extension of the beamforming technique which can be combined with a modal filter to isolate different Rayleigh propagation modes. Thus, the techniques return reliable phase velocity and attenuation estimates even in the presence of a multimode wavefield, which is typical of complex stratigraphic conditions. The reliability and effectiveness of the proposed approaches are assessed on a suite of synthetic wavefields and on experimental data collected at the Garner Valley Downhole Array and Mirandola sites. The results reveal that, under proper modelling of wavefield conditions, accurate estimates of Rayleigh wave phase velocity and attenuation can be extracted from active-source MASW wavefields over a broad frequency range. Eventually, the estimation of soil mechanical parameters also requires a robust inversion procedure to map the experimental Rayleigh wave parameters into soil models describing VS and DS with depth. The simultaneous inversion of phase velocity and attenuation data is discussed in detail in the companion paper. [ABSTRACT FROM AUTHOR]

This paper deals with in situ characterization of the small-strain shear-wave velocity VS and damping ratio DS from an advanced interpretation of Multi-channel Analysis of Surface Waves (MASW) surveys. A new approach based on extracting Rayleigh wave data using the CFDBFa method has been discussed in the companion paper. This paper focuses on mapping the experimental Rayleigh wave phase velocity and attenuation into profiles of VS and DS versus depth, which is achieved through a joint inversion procedure. The joint inversion of phase velocity and attenuation data utilizes a newly developed Monte Carlo global search algorithm, which implements a smart sampling procedure. This scheme exploits the scaling properties of the solution of the Rayleigh eigenvalue problem to modify the trial earth models and improve the matching with the experimental data. Thus, a reliable result can be achieved with a limited number of trial ground models. The proposed algorithm is applied to the inversion of synthetic data and of experimental data collected at the Garner Valley Downhole Array site, as described in the companion paper. In general, inverted soil models exhibit well-defined VS profiles, whereas DS profiles are affected by larger uncertainties. Greater uncertainty in the inverted DS profiles is a direct result of higher variability in the experimental attenuation data, the limited wavelength range at which reliable values of attenuation parameters can be retrieved, and the sensitivity of attenuation data to both DS and VS. Nonetheless, the resulting inverted earth models agree well with alternative in situ estimates and geological data. The results stress the feasibility of retrieving both stiffness and attenuation parameters from active-source MASW testing and the effectiveness of extracting in situ damping ratio estimates from surface wave data. [ABSTRACT FROM AUTHOR]

Abstract This paper compares measured and predicted site response at the Garner Valley Downhole Array (GVDA) using a wide range of shear wave velocity (Vs) profiles developed from both borehole methods and inversion of surface wave data. Only low amplitude ground motions, resulting in approximately linear-viscoelastic site response between the downhole accelerometer and the surface accelerometers, were considered in this study. Thus, uncertainties associated with the small-strain Vs profiles used for site response predictions play a considerable role in attempting to match the recorded site response and its associated variability. Prior to our study, two borehole Vs profiles extending into rock were available for the site. However, their predicted/theoretical transfer functions (TTFs) were quite different and in poor agreement with the measured/empirical transfer functions (ETFs). These differences provided motivation to collect and interpret an extensive set of active-source and passive-wavefield surface wave measurements in an attempt to develop deep Vs profiles for the site that might be used to more accurately model the measured site response and its associated variability. Suites of non-unique Vs profiles developed from inversion of the surface wave data visually exhibited considerable differences, yet their predicted TTFs matched the measured ETFs very well. These results provide further evidence that surface wave dispersion data and horizontal-to-vertical spectral ratio curves represent an experimental “site signature” that can be used as a quantitative means of assessing whether candidate Vs profiles are appropriate for use in site response analyses. Highlights • Empirical transfer functions (ETF) computed at Garner Valley Dowhnole Array (GVDA) site. • Theoretical transfer functions (TTFs) calculated for various Vs profiles. • TTFs for Vs profiles from borehole testing and randomization poorly match ETF. • TTFs for Vs profiles from surface wave inversion are in good agreement with ETF. • Experimental dispersion and H/V data can be considered as a “site signature”, which can aid in randomization. [ABSTRACT FROM AUTHOR]

This paper discusses variability and accuracy of site response predictions performed using shear wave velocity (Vs) profiles derived from non-unique surface wave inversions and other commonly used statistical methods of accounting for epistemic uncertainty and aleatory variability in Vs. Specifically, linear and equivalent linear site response analyses were performed on the following three classes of Vs profiles: (1) 350 Vs profiles developed by performing multiple surface wave inversions, each with a unique set of layering parameters, on a common dispersion dataset, (2) two upper/lower range base-case Vs profiles developed by systematically increasing or decreasing the solution Vs profile by 20%, and (3) 100 Vs profiles developed using the Vs randomization procedure proposed by Toro (1995) [26]. Vs profiles derived from surface wave inversions generally yielded accurate site response estimates with minimal variability, so long as their theoretical dispersion data fit the experimental dispersion data well. On the other hand, the upper/lower range and randomized Vs profiles generally produced inaccurate and highly variable site response predictions, although the inclusion of site-specific parameters in the randomization model improved the results. At real sites where substantial aleatory variability is anticipated and/or the epistemic uncertainty is quite high, the site response estimates associated with the randomized and/or upper/lower range Vs profiles may be deemed acceptable. However, if the experimental dispersion data and horizontal-to-vertical spectral ratios are shown to be consistent over the footprint of a site, it may be possible to significantly reduce the uncertainty associated with the input Vs profile and the resulting uncertainty in the site response. [ABSTRACT FROM AUTHOR]

Surface wave methods provide a cost effective means of developing shear wave velocity (Vs) profiles for applications such as dynamic site characterization and seismic site response analyses. However, the inverse problem involved in obtaining a realistic layered earth model from surface wave dispersion data is inherently ill-posed, non-linear and mix-determined, without a unique solution. When available, a priori information such as geotechnical boreholes or geologic well logs should be used to aid in constraining site-specific inversion parameters. Unfortunately, a priori information is often unavailable, particularly at significant depths, and a 'blind analysis' must be performed. In these situations, the analyst must decide on an appropriate number of layers and ranges for their corresponding inversion parameters (i.e. trial number of layers and ranges in their respective thicknesses, shear wave velocities, compression wave velocities and mass densities). Selection of these parameters has been shown to significantly impact the results of an inversion. This paper presents a method for conducting multiple inversions utilizing systematically varied inversion layering parametrizations in order to identify and encompass the most reasonable layered earth models for a site. Each parametrization is defined by a unique layering ratio, which represents a multiplier that systemically increases the potential thickness of each layer in the inversion parametrization based on the potential thickness of the layer directly above it. The layering ratio method is demonstrated at two sites associated with the InterPacific Project, wherein it is shown to significantly aid in selecting reasonable Vs profiles that are close representations of the subsurface. While the goal of the layering ratio inversion methodology is not necessarily to find the 'optimal' or 'best' Vs profile for a site, it may be successful at doing so for certain sites/datasets. However, the primary reason for using the layering ratio method is to find Vs profiles that realistically represent the uncertainty in Vs resulting from surface wave inversion, and to avoid selection of Vs profiles that are unrealistic and adversely influenced by the choice of inversion parametrization. [ABSTRACT FROM AUTHOR]

Accounting for uncertainty in shear wave velocity (Vs) profiles has been recognized to be critical to seismic ground response analysis (GRA) and probabilistic seismic hazard analysis (PSHA). Vs randomization models are among the most widely used approaches to account for this uncertainty, often referred to as aleatory variability within the context of PSHA. Researchers have investigated alternate ways of more realistically accounting for Vs uncertainty, such as randomizing the cumulative shear wave travel time (tts) instead of Vs directly. While some preliminary assessments suggest that tts randomization allows for better management of Vs uncertainty, more validations are needed to guide its use in practice. This study aims to fill a knowledge gap by performing a thorough review of the tts randomization framework and suggesting changes that will lead to improved implementation. Specifically, through the use of real experimental data and Monte Carlo simulations, we identify several issues which, if left unimproved, could lead to bias and unrealistic results. These issues particularly pertain to: (1) methods used for bounding simulated layers, (2) approaches for merging column and bedrock models, and (3) appropriate values of the lognormal standard deviation on tts (σ lntts) and inter-layer correlation (ρ) for randomizing tts. For each issue, we devise recommendations and demonstrate that they provide a clear improvement on the current tts randomization framework. The revised steps put forward in this article do not require any additional model parameters, but rather, simply improve upon the existing methodology. The findings of this study enhance our understanding of the tts randomization framework, and the recommendations we provide will enable researchers and engineers to adopt it more robustly and confidently in engineering practice. ● A procedure for incorporating Vs uncertainty in seismic ground response is revisited and improved ● The procedure is based on randomizing the cumulative shear wave travel time (tts), rather than absolute Vs ● Real experimental data and Monte Carlo simulations are used to identify several issues with the current procedure which, if left unimproved, could lead to bias and unrealistic results ● For each issue, we devise recommendations and demonstrate that they provide a clear improvement on the current tts randomization framework ● The recommendations we provide will enable researchers and engineers to adopt tts randomization more robustly and confidently in engineering practice [ABSTRACT FROM AUTHOR]

The Christchurch-specific empirical correlation between shear wave velocity (Vs) and cone penetration test (CPT) data developed by McGann et al. [1-3] for the non-gravel soils of the Christchurch and Springston Formations is evaluated through comparison to Vs profiles obtained using surface wave analysis techniques at twelve Christchurch strong motion stations. These comparisons highlight the similarities and differences between the Vs profiles obtained from each approach, and allow for an assessment of the relative strengths and weaknesses of each. It is shown that, with known differences, the results of the surface wave analysis and CPT correlation compare well in terms of their independently obtained Vs magnitudes. The sources of the differences between the results of each method are identified and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (V S) imaging of near-surface geo-materials. Operating in the frequency-velocity domain allows for significant flexibility in the linear-array, active-source experimental testing configurations used for generating the CNN input, which are normalized dispersion images. While normalized dispersion images retain the most important aspects of near-surface wavefields, they are relatively insensitive to the exact experimental testing configuration used to generate and record the wavefields, accommodating various source types, source offsets, numbers of receivers, and receiver spacings. We demonstrate the effectiveness of the frequency-velocity CNN by applying it to a common near-surface geophysics problem, namely, imaging a two-layer, undulating, soil-over-bedrock interface. The frequency-velocity CNN was trained and tested using 100,000 synthetic near-surface models with variable soil-over-bedrock conditions. Then, the ability of the frequency-velocity CNN to generalize across various acquisition configurations was rigorously tested using thousands of synthetic near-surface models with different acquisition configurations from that of the training set. Lastly, it was applied to experimental field data collected at the Hornsby Bend site in Austin, Texas, USA and found to produce a subsurface 2D image that was in great agreement with ground truth from invasive site characterization data. [ABSTRACT FROM AUTHOR]

This paper presents the development of depth- and V s 30 -dependent parametric velocity and spatial correlation models to characterise shear-wave velocities within the geologic layers of the Canterbury New Zealand sedimentary basin. The models utilise data from 22 shear-wave velocity profiles of up to 2.5 k m depth, derived from surface wave analysis, juxtaposed with models which detail the three-dimensional structure of the geologic formations in the Canterbury sedimentary basin. Parametric velocity equations are presented for Fine Grained Sediments, Gravels, and Tertiary layer groupings. Spatial correlations were developed and applied to generate three-dimensional stochastic velocity perturbations. Dispersion curves for the stochastic models and observed velocity profiles show good agreement over a wide frequency range with the dispersion data underlying the velocity profiles used as input data, indicating that the parametric perturbed velocity profiles replicate the dispersion characteristics of the observed velocity profiles. Collectively, these models enable seismic velocities to be realistically represented for applications such as 3D ground motion and site response simulations. • Parametric functions to prescribe velocities in Canterbury sedimentary deposits. • Geostatistics are used to develop spatial correlations to add velocity perturbations to parametric models. • Velocity modelling for use in ground motion and site response simulations. [ABSTRACT FROM AUTHOR]