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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. 

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. 

The in-situ small-strain shear modulus of soil and rock materials is a parameter of paramount importance in geotechnical modeling. It can be derived from non-invasive geophysical surveys, which provide the possibility of testing the subsurface in its natural and undisturbed condition by inferring the velocity of propagation of shear waves. In addition, for soil dynamics and earthquake engineering applications, the small-strain damping ratio plays a relevant role, yet its estimation is still challenging, lacking consolidated approaches for its in-situ evaluation. Recent advancements in instrumentation, such as distributed acoustic sensing (DAS), combined with advanced analysis methodologies for the interpretation of seismic wave propagation (e.g., machine learning and full waveform inversion), open new frontiers in site characterization. This paper presents and compares some advanced applications of measuring 1D and 2D variations in shear wave velocity and attenuation in-situ with reference to a specific case history. 

SUMMARY 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 This article documents a comprehensive subsurface imaging experiment using seismic waves in a well-studied outdoor laboratory at Newberry, Florida, which is known for significant spatial variability, karstic voids, and underground anomalies. The experiment used approximately two kilometers of distributed acoustic sensing (DAS) fiber-optic cable, forming a dense 2D array of 1920 horizontal-component channels, and a 2D array of 144 SmartSolo three-component nodal seismometers, to sense active-source and passive-wavefield seismic waves. The active-source data were generated using a powerful, triaxial vibroseis shaker truck (T-Rex) and impact sources (accelerated weight drop and an eight-pound sledgehammer) that were simultaneously recorded by both the DAS and nodal seismometers. The vibroseis truck was used to excite the ground in three directions (two horizontal and one vertical) at 260 locations inside and outside the instrumented array, whereas the impact sources were used at 268 locations within the instrumented array. The passive-wavefield data recorded using the nodal seismometers comprised 48 hr of ambient noise collected over a period of four days in four 12-hour time blocks, whereas the passive wavefield data collected using DAS consisted of four hours of ambient noise recordings. This article aims to provide a comprehensive overview of the testing site, experiment layout, the DAS and nodal seismometer acquisition parameters, and implemented raw data processing steps. Although potential use cases, such as surface-wave testing, full-waveform inversion, and ambient noise tomography, are discussed relative to example data, the focus of this article is on documenting this unique data set and presenting its initial data quality rather than on generating subsurface imaging results. The raw and processed data, along with detailed documentation of the experiment and Python tools to aid in visualizing the DAS data set, have been made publicly available. 

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. 

Active-source data acquisition included 66 vibroseis and 209 instrumented sledge hammer source locations. Multiple source impacts were recorded at each source location to enable stacking of the recorded signal. The source impacts at each source location have been aligned using cross-correlation, but to provide the most flexibility are provided unstacked (i.e., the signals from each source impact are provided separately). 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 sledge hammer was recorded and is provided in both raw counts and engineering units of kilonewtons. The passive-wavefield data includes 28 hours of ambient noise recorded over two night-time deployments. The passive-wavefield data is provided in raw counts, however, the instrument response files are provided should instrument correction be required in the future. The dataset can be used for active-source and passive-wavefield three-dimensional imaging, as well as other subsurface characterization techniques which include: horizontal-to-vertical spectral ratios, multichannel analysis of surface waves, and microtremor array measurements.