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  1. SUMMARY

    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.

     
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  2. SUMMARY

    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.

     
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  3. We develop a new 3D ambient noise tomography (3D ANT) method for geotechnical site characterization. It requires recording ambient noise fields using a 2D surface array of geophones, from which experimental crosscorrelation functions (CCFs) are then extracted and directly inverted to obtain an S-wave velocity ([Formula: see text]) structure. The method consists of a forward simulation using 3D P-SV elastic wave equations to compute the synthetic CCF and an adjoint-state inversion to match the synthetic CCFs to the experimental CCFs for extraction of [Formula: see text]. The main advantage of the presented method, as compared with conventional passive-source seismic methods using characteristics of Green’s function (GF), is that it does not require equal energy on both sides of each receiver pair or far-field wavefields to retrieve the true GF. Instead, the source power spectrum density is inverted during the analysis and incorporated into the forward simulation of the synthetic CCFs to account for source energy distribution. After testing on synthetic data, the 3D ANT method is applied to 3 h of ambient noise recordings at the Garner Valley Downhole Array (GVDA) site in California, using a surface array of 196 geophones placed on a 14 × 14 grid with 5 m spacing. The inverted 3D [Formula: see text] model is found to be consistent with previous invasive and noninvasive geotechnical characterization efforts at the GVDA site. 
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    Free, publicly-accessible full text available July 1, 2024
  4. There is a growing need to characterize the engineering material properties of the shallow subsurface in three dimensions for advanced engineering analyses. However, imaging the near-surface in three dimensions at spatial resolutions required for such purposes remains in its infancy and requires further study before it can be adopted into practice. To enable and accelerate research in this area, we present a large subsurface imaging data set acquired using a dense network of three-component (3C) nodal stations acquired in 2019 at the Garner Valley Downhole Array (GVDA) site. Acquisition of this data set involved the deployment of 196 stations positioned on a 14 × 14 grid with a 5 m spacing. The array was used to acquire active-source data generated by a vibroseis truck and an instrumented sledgehammer, and passive-wavefield data containing ambient noise. The active-source acquisition included 66 vibroseis and 209 instrumented sledgehammer source locations. Multiple source impacts were recorded at each source location to enable stacking of the recorded signals. The active-source recordings are provided in terms of both raw, uncorrected units of counts and corrected engineering units of meters per second. For each source impact, the force output from the vibroseis or instrumented sledgehammer was recorded and is provided in both raw counts and engineering units of kilonewtons. The passive-wavefield data include 28 h of ambient noise recorded over two nighttime deployments. The data set is shown to be useful for active-source and passive-wavefield three-dimensional imaging and other subsurface characterization techniques, which include horizontal-to-vertical spectral ratios (HVSRs), multichannel analysis of surface waves (MASW), and microtremor array measurements (MAM).

     
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  5. Free, publicly-accessible full text available April 1, 2024
  6. 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. 
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  7. Abstract 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. 
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