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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. 

SUMMARY Full-waveform inversion (FWI) methods rely on accurate numerical simulation of wave propagation in the analysed medium. Acoustic or elastic wave equations are often used to model seismic wave propagation. These types of simulations do not account for intrinsic attenuation effects due to material anelasticity, and thus correction techniques have been utilized in practice to partially compensate the anelasticity. These techniques often only consider the waveform amplitude correction based on averaging of overall amplitude response over the entire data set, and ignore the phase correction. Viscoelastic wave equations account for the anelastic response in both waveform amplitude and phase, and are therefore a more suitable alternative. In this study, we present a novel 3-D Gauss–Newton viscoelastic FWI (3-D GN-VFWI) method. To address the main challenge of the Gauss–Newton optimization, we develop formulas to compute the Jacobian efficiently by the convolution of virtual sources and backward wavefields. The virtual sources are obtained by directly differentiating the viscoelastic wave equations with respect to model parameters. In order to resolve complex 3-D structures with reasonable computational effort, a homogeneous attenuation (Q factor) is used throughout the analysis to model the anelastic effects. Synthetic and field experiments are performed to demonstrate the utility of the method. The synthetic results clearly demonstrate the ability of the method in characterizing a challenging velocity profile, including voids and reverse velocity layers. The field experimental results show that method successfully characterizes the complex substructure with two voids and undulating limestone bedrock, which are confirmed by invasive tests. Compared to 3-D elastic FWI results, the presented viscoelastic method produces more accurate results regarding depths of the voids and bedrock. This study suggests that the improvement of imaging accuracy would warrant the widespread use of viscoelastic wave equations in FWI problems. To our best knowledge, this is the first reported study on 3-D GN-VFWI at any scale. This study provides the new theory and formulation for the use of Gauss–Newton optimization on the 3-D viscoelastic problem. 

Sinkhole collapse may result in significant property damage and even loss of life. Early detection of sinkhole attributes (buried voids, raveling zones) is critical to limit the cost of remediation. One of the most promising ways to obtain subsurface imaging is 3D seismic full-waveform inversion. For demonstration, a recently developed 3D Gauss-Newton full-waveform inversion (3D GN-FWI) method is used to detect buried voids, raveling soils, and characterize variable subsurface soil/rock layering. It is based on a finite-difference solution of 3D elastic wave equations and Gauss-Newton optimization. The method is tested first on a data set constructed from the numerical simulation of a challenging synthetic model and subsequently on field data collected from two separate test sites in Florida. For the field tests, receivers and sources are placed in uniform 2D surface grids to acquire the seismic wavefields, which then are inverted to extract the 3D subsurface velocity structures. The inverted synthetic results suggest that the approach is viable for detecting voids and characterizing layering. The field seismic results reveal that the 3D waveform analysis identified a known manmade void (plastic culvert), unknown natural voids, raveling, as well as laterally variable soil/rock layering including rock pinnacles. The results are confirmed later by standard penetration tests, including depth to bedrock, two buried voids, and a raveling soil zone. Our study provides insight into the application of the 3D seismic FWI technique as a powerful tool in detecting shallow voids and other localized subsurface features.