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Distributed acoustic sensing (DAS) is a relatively new technology for recording the propagation of seismic waves, with promising applications in both engineering and geophysics. DAS's ability to simultaneously collect high spatial resolution waveforms over long arrays suggests that it is well-suited for near-surface imaging applications such as 2-D multichannel analysis of surface waves (MASWs), which require, at a minimum, long, linear arrays of single-component receivers. The 2-D MASW method uses a large number of sensor subarrays deployed along a linear alignment to produce 1-D shear-wave velocity (VS) profiles beneath each subarray. The 1-D VS profiles are then combined to form a pseudo-2-D VS image beneath the entire linear alignment that can be used for the purpose of identifying and characterizing lateral variations in subsurface layering. Traditionally, 2-D MASW is conducted using arrays consisting of either 24 or 48 geophones. While additional receivers could easily be incorporated into the testing configuration, it is rare for researchers and practitioners to have access to greater numbers of seismographs and geophones. When a limited number of geophones are available for deployment, there is a need to pre-determine the geophone spacing and subarray length prior to field data acquisition. Studies examining how the choice of subarray geometry impacts the resulting pseudo-2-D VS cross-sections have been largely limited to synthetic data. In response, this study utilizes DAS data to examine the effects of using various subarray lengths by comparing pseudo-2-D VS cross-sections derived from active-source waveforms collected at a well-characterized field site. DAS is particularly useful for 2-D MASW applications because the subarray geometry does not need to be determined prior to field data acquisition. We organize the DAS waveforms into multiple sets of overlapping MASW subarrays of differing lengths, ranging from 11 to 47 m, along the same alignment, allowing for direct comparison of the derived pseudo-2-D VS results at the site. We show that the length of the individual MASW subarrays has a significant effect on the resulting VS cross-sections, including the resolved location of a strong impedance contrasts at our study site, and evaluate the results relative to ground truth from invasive testing. Our results suggest that the choice of subarray length is important and should be carefully chosen to meet project-specific goals. Furthermore, analysts may consider using multiple subarray geometries during the data processing stage, as is made possible by DAS, to properly evaluate the uncertainty of 2-D MASW results. This study demonstrates the potential of using DAS to collect data for 2-D MASW in a manner that is efficient and flexible, and can be easily scaled up for use with very long arrays. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) and distributed acoustic sensing (DAS) are powerful tools with potential to improve how seismic site characterization is performed. FWI is able to provide true 2D or 3D images of the subsurface by inverting stress wave recordings collected over a wide variety of scales. DAS can be used to efficiently collect high-resolution stress wave recordings from long and complex fiber optic arrays and is well-suited for large-scale site characterization projects. Due to the relative novelty of combining FWI and DAS, there is presently little published literature regarding the application of FWI to DAS data for near-surface (depths <30 m) site characterization. We perform 2D FWI on DAS data collected at a well-characterized site using four different, site-specific 1D and 2D starting models. We discuss the unique benefits and challenges associated with inverting DAS data compared to traditional geophone data. We examine the impacts of using the various starting models on the final 2D subsurface images. We demonstrate that while the inversions performed using all four starting models are able to fit the major features of the DAS waveforms with similar misfit values, the final subsurface images can be quite different from one another at depths greater than about 10 m. As such, the best representation(s) of the subsurface are evaluated based on: (1) their agreement with borehole lithology logs that were not used in the development of the starting models, and (2) consistency at shallow depths between the final inverted images derived from multiple starting models. Our results demonstrate that FWI applied to DAS data has significant potential as a tool for near-surface site characterization while also emphasizing the significant impact that starting model selection can have on FWI results. [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]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (⁠|${f_{0,site}}$|⁠). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (⁠|${f_{0,HVSR}}$|⁠). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for |${f_{0,HVSR}}$| are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the |${f_{0,HVSR}}$| values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in |${f_{0,HVSR}}$|⁠ , and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (⁠|${f_0}$|⁠). For sites with a strong impedance contrast, the HVSR peak frequency (⁠|${f_{0,\mathrm{ HVSR}}}$|⁠) has been shown to be a good estimate of |${f_0}$|⁠. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in |${f_{0,\mathrm{ HVSR}}}$| estimates. Hence, it is important to report |${f_{0,\mathrm{ HVSR}}}$| in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in |${f_{0,\mathrm{ HVSR}}}$|⁠. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe |${f_{0,\mathrm{ HVSR}}}$| rather than the more commonly used normal distribution. The use of a lognormal distribution for |${f_{0,\mathrm{ HVSR}}}$| has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

Seismic cone penetration testing (SCPT) is a powerful geotechnical site characterization tool, allowing for simultaneous collection of routine cone penetration testing data and rapid downhole-type measurements of shear-wave velocity (VS). However, the uncertainties associated with developing VS profiles from SCPT measurements are rarely considered or communicated to the end-user. One important source of VS uncertainty is related to how the shear wave travel times are interpreted from the recorded waveforms, while another critical source of uncertainty is related to the analysis method used to transform the travel times to velocities. In this study, four common ways of obtaining travel times were considered: (i) first arrival picks, (ii) peaks and troughs picks, (iii) crossover picks, and (iv) the peak response of the cross-correlation function. Using these different travel times, a number of VS profiles were developed using four different velocity analysis methods: (i) pseudo-interval, (ii) true-interval, (iii) corrected vertical travel time slope-based, and (iv) raytracing. Through consideration of multiple wave arrival time and velocity analysis methods, a robust and meaningful quantification of the intramethod, depth-dependent epistemic uncertainty in VS obtained from several example SCPT datasets has been developed. VS uncertainty is further examined through consideration of the intermethod variability and bias between SCPT and direct-push crosshole testing. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Nonlinear site response analyses are generally preferred over equivalent linear analyses for soft soil sites subjected to high-intensity input ground motions. However, both nonlinear (NL) and equivalent linear (EQL) analyses often result in large shear strain estimates (3–10%) at soft sites, and these large strains may generate unusual characteristics in the predicted surface ground motions, such as irregular time histories and atypical spectral shapes. One source of unusual ground motion predictions may be attributed to unrealistically low shear strengths implied by commonly used modulus reduction curves. Therefore, modulus reduction and damping curves can be modified at shear strains greater than approximately 0.1% to provide a more realistic soil model for site response. However, even after these modifications, nonlinear and equivalent linear site response analyses still may generate unusual surface acceleration time histories and Fourier amplitude spectra at soft soil sites when subjected to high-intensity input ground motions. In this study, we use equivalent linear and nonlinear 1D site response analyses for the well-known Treasure Island site to demonstrate challenges associated with accurately modeling large shear strains, and subsequent surface response, at soft soil sites. [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]

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]

Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change near-surface (<30 m) site characterization by enabling the recovery of high-resolution (metre-scale) 2-D/3-D maps of subsurface elastic material properties. Yet, FWI results are quite sensitive to their starting model due to their dependence on local-search optimization techniques and inversion non-uniqueness. Starting model dependence is particularly problematic for near-surface FWI due to the complexity of the recorded seismic wavefield (e.g. dominant surface waves intermixed with body waves) and the potential for significant spatial variability over short distances. In response, convolutional neural networks (CNNs) are investigated as a potential tool for developing starting models for near-surface 2-D elastic FWI. Specifically, 100 000 subsurface models were generated to be representative of a classic near-surface geophysics problem; namely, imaging a two-layer, undulating, soil-over-bedrock interface. A CNN has been developed from these synthetic models that is capable of transforming an experimental wavefield acquired using a seismic source located at the centre of a linear array of 24 closely spaced surface sensors directly into a robust starting model for FWI. The CNN approach was able to produce 2-D starting models with seismic image misfits that were significantly less than the misfits from other common starting model approaches, and in many cases even less than the misfits obtained by FWI with inferior starting models. The ability of the CNN to generalize outside its two-layered training set was assessed using a more complex, three-layered, soil-over-bedrock formation. While the predictive ability of the CNN was slightly reduced for this more complex case, it was still able to achieve seismic image and waveform misfits that were comparable to other commonly used starting models, despite not being trained on any three-layered models. As such, CNNs show great potential as tools for rapidly developing robust, site-specific starting models for near-surface elastic FWI. [ABSTRACT FROM AUTHOR]

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies, and non-invasive site investigation using full waveform inversion (FWI). Distributed acoustic sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical transfer function, reception patterns, and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10 s of Hz), high amplitude (10 s of microstrain), and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser frequency drift is mitigated in the DAS measurements. The transfer function of DAS arrays is presented considering the gauge length, pulse shape, and cable design. The theoretical relationship between DAS-measured and pointwise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. Estimating measurement quality based on the wavelength-to-gauge length ratio for field data is demonstrated. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of a DAS system is proposed. [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]

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]

The horizontal-to-vertical spectral ratio (HVSR) has seen widespread use as a tool for measuring a site's resonant behavior. However, the processing of HVSR with traditional methods can be tedious and time-consuming when the HVSR is complex and exhibits multiple resonances. This work proposes the AutoHVSR algorithm that allows for fully-automated processing of HVSR measurements, including those with zero, one, or multiple clear resonances. The AutoHVSR algorithm accepts microtremor or earthquake recordings and user-defined HVSR processing parameters as input and returns the computed HVSR from each time window or earthquake record, the mean/median HVSR curve, and statistics on each automatically identified HVSR resonance in terms of frequency and amplitude. The AutoHVSR algorithm integrates robust signal processing and computational methods with state-of-the-art machine-learning models trained using a diverse dataset of 1108 HVSR measurements. The AutoHVSR algorithm demonstrates excellent performance by correctly determining the number of HVSR resonances for 1098 of the 1108 HVSR measurements (>99%) and predicting the mean resonant frequency of the correctly identified resonances with a root mean square error (RMSE) of 0.05 Hz. Furthermore, the AutoHVSR algorithm was able to produce these predictions in 13 min (including HVSR processing time) compared to the 30 h required for traditional processing (a speed up of 138). The AutoHVSR algorithm is further demonstrated on a challenging dataset from Canterbury, New Zealand that included many HVSR curves with multiple and/or ambiguous resonances. Despite the challenging nature of the Canterbury dataset, the AutoHVSR algorithm was capable of correctly determining the number of HVSR resonances for 113 of the 129 HVSR measurements (>87%) and predicting the mean resonant frequency of the correctly identified resonances with a RMSE of 0.10 Hz. The AutoHVSR algorithm produced these predictions under 2 min (including HVSR processing time) compared to the 4 h required for traditional processing (a speed up of 120). Finally, while the AutoHVSR algorithm was developed using microtremor measurements where the horizontal components were combined using the geometric mean, it is shown to extend without modification to microtremor HVSR measurements where the two horizontal components are rotated azimuthally and to HVSR measurements from earthquake recordings. The AutoHVSR algorithm has been made publicly available in v0.3.0 of HVSRweb, it can be accessed at https://hvsrweb.designsafe-ci.org/. • The AutoHVSR algorithm is proposed for fully-automating HVSR processing. • AutoHVSR can process HVSR with zero, one, or multiple clear resonances. • The predictions of AutoHVSR are fast, reliable, and reproducible. • AutoHVSR can be applied broadly including azimuthally-rotated and earthquake HVSR. • AutoHVSR has been made publicly accessible through HVSRweb. [ABSTRACT FROM AUTHOR]

The objective of the study presented herein is to assess three commonly used CPT-based liquefaction evaluation procedures and three liquefaction severity index frameworks using data from the 2010–2011 Canterbury earthquake sequence. Specifically, post-event field observations, ground motion recordings, and results from a recently completed extensive geotechnical site investigation programme at selected strong motion stations (SMSs) in the city of Christchurch and surrounding towns are used herein. Unlike similar studies that used data from free-field sites, accelerogram characteristics at the SMS locations can be used to assess the performance of liquefaction evaluation procedures prior to their use in the computation of surficial manifestation severity indices. Results from this study indicate that for cases with evidence of liquefaction triggering in the accelerograms, the majority of liquefaction evaluation procedures yielded correct predictions, regardless of whether surficial manifestation of liquefaction was evident or not. For cases with no evidence of liquefaction in the accelerograms (and no observed surficial evidence of liquefaction triggering), the majority of liquefaction evaluation procedures predicted liquefaction was triggered. When all cases are used to assess the performance of liquefaction severity index frameworks, a poor correlation is shown between the observed severity of liquefaction surface manifestation and the calculated severity indices. However, only using those cases where the liquefaction evaluation procedures yielded correct predictions, there is an improvement in the correlation, with the Liquefaction Severity Number (LSN) being the best performing of the frameworks investigated herein. However scatter in the relationship between the observed and calculated surficial manifestation still remains for all liquefaction severity index frameworks. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the soil profile characteristics at strong motion station (SMS) locations in the Christchurch Central Business District (CBD) based on recently completed geotechnical site investigations. Given the variability of Christchurch soils, detailed investigations were needed in close vicinity to each SMS. In this regard, CPT, SPT and borehole data, and shear wave velocity (Vs) profiles from surface wave dispersion data in close vicinity to the SMSs have been used to develop detailed representative soil profiles at each site and to determine site classes according to the New Zealand standard NZS1170.5. A disparity between the NZS1170.5 site classes based on Vs and SPT N60 investigation techniques is highlighted, and additional studies are needed to harmonize site classification based on these techniques. The short period mode of vibration of soft deposits above gravels, which are found throughout Christchurch, are compared to the long period mode of vibration of the entire soil profile to bedrock. These two distinct modes of vibration require further investigation to determine their impact on the site response. According to current American and European approaches to seismic site classification, all SMSs were classified as problematic soil sites due to the presence of liquefiable strata, soils which are not directly accounted for by the NZS1170.5 approach. [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]

A dynamic full scale testing program was performed to quantitatively assess the effectiveness of prefabricated vertical drains as a liquefaction countermeasure. The testing program involved a new in situ liquefaction testing technique, which uses a large hydraulic vibrator to generate waves propagating through an embedded instrumentation area to measure the coupled soil-pore water response. The effectiveness of prefabricated vertical drains is assessed experimentally by comparing the pore pressure generation, pore pressure dissipation, and settlement from two reconstituted soil specimens; one without a drain in place and the other with a single drain installed. Because the prefabricated drain was installed during the specimen preparation process, no accompanying densification during installation occurred. Therefore, the effect of drainage alone was evaluated. The testing results show that the drainage provided by prefabricated drains can significantly reduce pore pressure generation, accelerate post-shaking pore pressure dissipation, and limit associated settlement. The outcome also shows that the new developed in situ liquefaction testing technique can be an alternative to quantitatively evaluate the effects of various liquefaction remediation techniques. [Copyright &y& Elsevier]

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]