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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]

Distributed acoustic sensing (DAS) is a rapidly expanding tool to sense wave propagation and system deformations in many engineering applications. In terms of site characterization, DAS presents the ability to make dynamic strain measurements at scales (e.g., kilometers) and spatial resolutions (e.g., meters) that were previously unattainable with traditional measurement technologies. In this study, we use both vibroseis truck and sledgehammer sources to demonstrate that DAS can be used to make multichannel analysis of surface waves (MASW) dispersion measurements that are equivalent in quality to geophones when: (a) a tight-buffered or strain-sensing fiber-optic cable is used, (b) the cable is buried in a shallow trench to enhance coupling, and (c) short gauge lengths and small channel separations are used. We analytically demonstrate the impact gauge length has on DAS measurements in terms of phase and amplitude for off-end MASW surveys. We further show that the choice of waveform units (i.e., phase, strain, strain-rate, displacement, velocity) do not impact the dispersion results when frequency-dependent normalization is applied to the dispersion images. Finally, we show that shear wave velocity profiles recovered from the DAS data using an uncertainty-consistent, multi-mode inversion agree favorably with cone penetration tests performed at the site. This study demonstrates that DAS, when appropriate considerations are made, can be used instead of traditional sensors (i.e., geophones) for making high-resolution, multi-mode measurements of surface wave dispersion data using the MASW technique. • DAS and geophones measurements can be compared directly in terms of dispersion. • Short wavelength DAS measurements are strongly limited by the system's gauge length. • Vs inverted from DAS-derived dispersion agrees favorably with invasive results. • DAS can be used in-lieu of traditional sensors for making dispersion measurements. [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]

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]