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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]

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]

A number of strategies exist to account for the epistemic uncertainty and aleatory variability in shear-wave velocity (Vs) profiles used in site response analyses. Epistemic uncertainty may be accounted for by using median and bounding-type profiles (e.g., ±20%), while aleatory variability may be accounted for by using Vs randomization procedures. A robust, quantitative method to help judge how well these statistically derived Vs profiles represent actual subsurface stiffness or layering conditions is currently not available. This paper presents a surface-wave dispersion approach for evaluating statistical models meant to account for Vs uncertainty in site response. Specifically, surfacewave dispersion data from two geologically disparate sites were used to generate 1,000 Vs profiles and layered earth models whose theoretical dispersion curves fit within the uncertainty bounds of the experimental data collected at each site. Additionally, theoretical dispersion curves generated from statistical Vs profiles such as the median, bounding-type, and randomly generated Vs profiles for each site were compared with the experimentally measured dispersion data. It was found that the theoretical dispersion curve from the median Vs profile provided a satisfactory fit to the experimental data, but the theoretical dispersion curves from bounding-type Vs profiles did not. Furthermore, randomly generated Vs profiles resulted in some theoretical dispersion curves that fit the experimental data and many that did not. The authors recommend comparing theoretical dispersion curves for candidate Vs profiles and layered earth models with the measured dispersion data at a site prior to using the candidate profiles to account for uncertainty in site response analyses as a quantitative way to judge if the Vs profiles represent realistic site conditions. [ABSTRACT FROM AUTHOR]

Uncertainty in site response analyses can be attributed to a number of parameters, including analysis methods, input ground motions, nonlinear dynamic soil properties, and shear-wave velocity profiles. In this paper, several approaches commonly used to account for shear-wave velocity (Vs) uncertainty in site response are investigated. Specifically, the Vs profiles considered are categorized into three groups: (1) Vs profiles determined directly from surface-wave inversion, (2) simple statistical Vs profiles derived indirectly from the surface-wave Vs profiles (including bounding-type, median, and other percentile Vs profiles), and (3) statistically based, randomly generated Vs profiles. A companion paper discusses the development of these Vs profiles for two international blind-study sites. In this paper, the effects of using each approach to account for Vs uncertainty in site response are investigated by linking the dispersion misfit values for each Vs profile to variability in equivalent linear site response estimates. Clear trends exist between variability in site response estimates and dispersion misfit values at both sites. Thus, the experimental dispersion data can be used to help select suites of Vs profiles, generated either directly from inversion or through a randomization model, that account for uncertainty in a meaningful way without including unrealistic statistical profiles that result in too much site response variability. [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]