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In this paper, efforts to characterize and compare the full-scale in situ three-dimensional (3D) dynamic response of a low-plasticity silt deposit to multidirectional loading from two different sources, a vibroseis shaker named T-Rex and controlled blasting, are presented. Horizontal vibroseis shaking at a frequency, f , of 10 Hz, produced dynamic responses in the silt that ranged from linear-elastic to nonlinear-inelastic, inducing maximum equivalent direct simple shear (DSS) shear strains, γDSS,max , up to 0.15% and residual excess pore pressure ratios, ru,r , of 14.1%. Blast-induced shear waves with predominant frequencies ranging from 9.6 to 14.6 Hz excited nonlinear-elastic and nonlinear-inelastic responses in the silt deposit, with γDSS,max of 1.14% and maximum ru,r of 61%. Importantly, these responses were observed to be minimally influenced by high frequency compression waves. Multidirectional loading, and excess pore pressure, ue , migration and impedance were identified as the predominant factors for achieving the large ru,r in the silt deposit from these two in situ testing techniques. The cyclic threshold shear strain, γtp , to trigger ru,r observed from the T-Rex shaking equaled 0.007% to 0.011% and varied with the initial soil stiffness. The two testing techniques demonstrated that the in-situ shear modulus, G , reduced to 90% of the maximum shear modulus, Gmax , at γDSS,max≈γtp , whereas by γDSS,max≈1% , G further reduced to 10 to 30% of Gmax corresponding to ru,r of ∼60%. Changes in soil fabric were quantified using small-strain shear-wave velocity measurements performed before and/or upon initiation and after each stage of dynamic testing, and were linked to the observed increase and decrease in γtp for the shallower and deeper 3D elements, respectively, following T-Rex shaking. The side-by-side comparison of the dynamic responses and soil properties derived from these two distinctly different field-testing techniques validate the use of controlled blasting for quantifying in situ dynamic soil properties and responses. [ABSTRACT FROM AUTHOR]

Surface wave testing is a powerful tool for noninvasive seismic site characterization. It includes a wide variety of active-source and passive-wavefield methods [e.g., spectral analysis of surface waves (SASW), multichannel analysis of surface waves (MASW), microtremor array measurements (MAM)] that can be applied to many different field conditions. The dispersion data collected with all these methods can be inverted to produce (one-dimensional) 1D shear wave velocity (VS) profiles of the subsurface. A critical part of this inversion procedure is the need to develop several trial model parameterizations with different numbers of layers as a means for investigating epistemic uncertainty and nonuniqueness when attempting to fit the experimental dispersion data. This is especially important when a priori information either is not available or does not extend to great enough depths to constrain the inversions. These trial parameterizations are used to vary the number of potential layers present in the subsurface models and to set the acceptable search ranges for depths to boundaries and stiffness in each layer. The inversion results are highly affected by the parameterizations. When performing high-quality inversions, it is important to use different parameterization options to fully explore potential conditions at the site and characterize the epistemic uncertainty of the inversion process. While this practice allows for a robust analysis of the experimental dispersion data and its uncertainty, it also generates many potential subsurface models for the site, which can represent a challenge for engineers when deciding which VS profiles best represent the true subsurface layering for use in subsequent analysis and design (e.g., seismic site response). This paper presents an approach, called the DeltaVs method, for systematically evaluating the depths of layer boundaries across all acceptable models determined from several different inversion layering parameterizations as a means to identify distinct clusters of boundaries. Using these clusters, the number and location of meaningful layer boundaries can be determined for many sites, thereby allowing for the elimination of some parameterization options containing more, or fewer, boundaries and a reduction in epistemic uncertainty. The distributions of layer boundaries within these clusters can then be used to determine statistics for the depths of layer boundaries that are consistent with the epistemic uncertainty of the models produced by the inversion procedure. The DeltaVs method is demonstrated on 12 synthetic ground models as well as one field data set. [ABSTRACT FROM AUTHOR]

Site-specific subsurface conditions are known to significantly influence the frequency content and amplitude of seismic ground shaking. When attempting to model these effects, known as seismic site effects, most studies have focused on the importance of vertically characterizing subsurface geomaterials. However, laterally characterizing the geomaterials' variability has generally either been overlooked or oversimplified. As such, little is actually known about the spatial area that influences seismic site response. In this article, we discuss insights that have been gained regarding the spatial area that influences site response from performing two-dimensional (2D) ground response analyses (GRAs) for the Treasure Island Downhole Array (TIDA) site. In these numerical analyses, we use a site-specific, large-scale, three-dimensional (3D) shear wave velocity (Vs) model that has been developed to a depth of 150 m over the entirety of Treasure Island (an area of approximately 1.6×1.0 km2). We investigate the lateral extent influencing site response and its azimuthal variability using: (1) four cross sections with increasing lateral extent along the same azimuth; and (2) 12 cross sections with different azimuths that uniformly sample the full 3D Vs model. By comparing 2D GRA predictions to recorded earthquake ground motions at TIDA, we systematically highlight the large spatial area that influences site response, which extends to distances as much as 1 km from the TIDA sensors. Although the TIDA site is often assumed to be archetype of one-dimensional (1D) site conditions, our findings support the idea that there may not be many truly 1D sites due to the large spatial area that influences site response. While some sites can be reasonably modeled using 1D GRAs, moving the engineering profession forward requires focusing our efforts on developing site-specific subsurface models over larger areas to better understand and accurately model seismic site response. [ABSTRACT FROM AUTHOR]

Non-invasive surface wave methods are increasingly being used as the primary technique for estimating a site's small-strain shear wave velocity (Vs). Yet, in comparison to invasive methods, non-invasive surface wave methods suffer from highly variable standards of practice, with each company/group/analyst estimating surface wave dispersion data, quantifying its uncertainty (or ignoring it in many cases), and performing inversions to obtain Vs profiles in their own unique manner. In response, this work presents a well-documented, production-tested, and easy-to-adopt workflow for developing estimates of experimental surface wave dispersion data with robust measures of uncertainty. This is a key step required for propagating dispersion uncertainty forward into the estimates of Vs derived from inversion. The paper focuses on the two most common applications of surface wave testing: the first, where only active-source testing has been performed, and the second, where both active-source and passive-wavefield testing has been performed. In both cases, clear guidance is provided on the steps to transform experimentally acquired waveforms into estimates of the site's surface wave dispersion data and quantify its uncertainty. In particular, changes to surface wave data acquisition and processing are shown to affect the resulting experimental dispersion data, thereby highlighting their importance when quantifying uncertainty. In addition, this work is accompanied by an open-source Python package, swprocess, and associated Jupyter workflows to enable the reader to easily adopt the recommendations presented herein. It is hoped that these recommendations will lead to further discussions about developing standards of practice for surface wave data acquisition, processing, and inversion. [ABSTRACT FROM AUTHOR]

A significant amount of seismic site response research over the past decade has focused on our abilities to replicate recorded ground motions at borehole array sites, where both the input (rock) and output (surface) ground motions are known. When viewed in aggregate, these studies have found that approximately 50% of borehole array sites are poorly modeled using one-dimensional (1D) ground response analyses (GRAs) based on a single shear wave velocity (Vs) profile, with individual studies reporting values between approximately 30%–80%. While there is no doubt that some sites are indeed too variable to be modeled using 1D GRAs, it is possible that simple 1D analyses could still be effectively used at many sites if spatial variability is accounted for via a rational, site-specific approach. In this study, we investigate five alternative approaches that can be used to account for spatial variability in 1D GRAs: (1) Vs randomization, (2) shear wave travel time randomization, (3) utilization of Vs suites derived from surface wave testing covering a large area, (4) incorporation of a pseudo-3D Vs model derived from a horizontal-to-vertical spectral ratio geostatistical approach, and (5) damping modifications. These approaches are investigated at two US borehole array sites (the Treasure Island and Delaney Park Downhole Arrays) so that the GRA results can be compared with recorded small-strain ground motions. Spatial variability is accounted for by generating approximately 250 Vs profiles for each approach, except for damping modifications, wherein only a single Vs profile is used, but with increased damping to account for wave scattering originating from spatial discontinuities. Through qualitative and quantitative comparisons, we assess the relative and absolute effectiveness of each approach, highlight their limitations, and propose potential improvements that can help overcome these limitations in practice. [ABSTRACT FROM AUTHOR]

SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and specific actionable recommendations for surface wave inversion. In particular, the workflow encourages the use of multiple layering parametrizations to address the inversion's non-uniqueness, multiple global searches for each parametrization to address the inverse problem's non-linearity and quantification of Vs uncertainty in the resulting profiles. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented are of relevance to analysts using other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broad-band dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. To encourage adoption of the SWinvert workflow, an open-source Python package (SWprepost), for pre- and post-processing of surface wave inversion data, and an application on the DesignSafe-Cyberinfrastructure (SWbatch), for performing batch-style surface wave inversions with Dinver using high-performance computing, have been developed and released in conjunction with this work. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles. [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]

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]

Relationships between seismic site classification and surficial geology for the city of Fairbanks, Alaska, for both frozen (winter) and unfrozen (summer) surface conditions are investigated in this article. The work is based on 59 shear wave velocity () profiles collected using the spectral analysis of surface waves (SASW) method. Forty-three profiles were collected during the summer when the ground surface was fully thawed, and 16 profiles were collected by reoccupying some of the test locations during fully frozen winter ground surface conditions. Relationships between mean over the top 30 m of the subsurface () and surficial geologic units in Fairbanks were developed, permitting estimates of seismic site classification throughout the city for both frozen and unfrozen conditions. Much of Fairbanks was determined to have been founded on NEHRP Site Class D material when unfrozen. When frozen, the of the near-surface material increased significantly (by 700% on average). However, this drastic increase in stiffness was typically limited to the top 1 to 2 m of the subsurface and only resulted in an increase in site class from NEHRP D to NEHRP C at three of the 16 sites retested during the winter. [ABSTRACT FROM AUTHOR]

The refraction microtremor (ReMi) method uses a linear array of receivers to record passive noise stress waves, thereby negating the need for a strong active source such as those typically employed in other linear-array surface-wave methods. The simplicity of such an approach is very appealing; however, there is a potential for significant uncertainty in shear-wave velocity (Vs) estimates obtained in this manner because much is unknown about the seismic wavefield that is being sampled. This article examines intramethod variability in ReMi dispersion measurements and Vs estimates at shallow bedrock sites. The focus is not on evaluating whether ReMi measurements yielded the 'true' Vs profiles for the study sites, but rather on evaluating how the dispersion curves and Vs profiles at the sites were affected by variations in the seismic wavefield sampled by a linear array of receivers. It was found that changes in the sampled seismic wavefield, obtained through employing various array orientations at a site and inclusion of actively generated noise, resulted in significant variations in the ReMi-obtained dispersion curves and Vs profiles at the study sites, thus yielding a high level of intramethod variability for a given test location. [ABSTRACT FROM AUTHOR]

Non-invasive surface wave methods have become a popular alternative to traditional invasive forms of site-characterization for inferring a site's subsurface shear wave velocity (Vs) structure. The advantage of surface wave methods over traditional forms of site characterization is that measurements made solely at the ground surface can be used routinely and economically to infer the subsurface structure of a site to depths of engineering interest (20–50 m), and much greater depths (>1 km) in some special cases. However, the quantification and propagation of uncertainties from surface wave measurements into the Vs profiles used in subsequent engineering analyses remains challenging. While this has been the focus of much work in recent years, and while considerable progress has been made, no approach for doing so has been widely accepted, leading analysts to either address the propagation of uncertainties in their own specialized manner or, worse, to ignore these uncertainties entirely. In response, this paper presents a new, effective, and verifiable method for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data. We begin by examining four approaches presented in the literature for developing suites of Vs profiles meant to account for uncertainty present in the measured dispersion data. These methods are shown to be deficient in three specific ways. First, all approaches are shown to be highly sensitive to their many user-defined inversion input parameters, making it difficult/impossible for them to be performed repeatedly by different analysts. Second, the suites of inverted Vs profiles, when viewed in terms of their implied theoretical dispersion curves, are shown to significantly underestimate the uncertainty present in the experimental dispersion data, though some may appear satisfactory when viewed purely qualitatively. Third, if the uncertainties in the implied theoretical dispersion curves were to be examined quantitatively, which has not been done previously, there is no obvious remedy available for the analyst to resolve any inconsistency between the measured and inverted dispersion uncertainty. Therefore, a new approach is proposed that seeks to remedy these shortcomings. First, beyond appropriate considerations that must be given to all inversions, the new approach is governed by only one user-defined input parameter, to which it is not overly sensitive. Second, the new approach is shown to produce suites of Vs profiles whose theoretical dispersion curves quantitatively reproduce the uncertainties in the experimental dispersion data. Third, the final step of the procedure requires the analyst to compare the measured and inverted dispersion uncertainties quantitatively, and should the analyst find the results of the new approach to be lacking, clear guidance is provided on the additional actions necessary to produce Vs profiles whose theoretical dispersion curves better account for the experimental uncertainty. Using two synthetic tests and a real-world example, the procedure is shown to produce suites of Vs profiles that accurately capture the site's Vs structure, while rigorously propagating the dispersion data's uncertainty through the inversion process. • A procedure is developed to quantify Vs uncertainty from surface wave inversion. • The procedure results in dispersion uncertainty-consistent Vs profiles. • The procedure is new, effective, and verifiable. • The procedure propagates experimental dispersion uncertainties into the inverted Vs profiles. • The procedure is demonstrated using synthetic and real-world examples. [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]