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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]

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]

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]

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]