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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. 

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. 

This is a comprehensive subsurface imaging experiment in Newberry, Florida using stress waves. The site is spatially variable and contains karstic surface and underground voids and anomalies. The sensing technologies used comprised a dense 2D array of 1920 DAS channels and a 12 x 12 grid of 144 SmartSolo 3C nodal stations, which covered an area of 155 m x 75 m and were used to record both active-source and passive-wavefield data. The active-source data was generated by a variety of vibrational and impact sources, namely: a powerful three-dimensional vibroseis shaker truck, a 40-kg propelled energy generator (PEG-40kg), and an 8-lb sledgehammer. The vibroseis shaker truck was used to vibrate the ground in the three directions at 260 locations inside and outside the instrumented area, while the impact sources were used at 268 locations inside the instrumented area. In addition to active source data, four hours of ambient noise were recorded using the DAS, while the nodal stations recorded 48 hours of ambient noise in four 12-hour increments over a period of four days. The waveforms obtained from the 1920 DAS channels for every active-source shot or passive-wavefield time block were extracted, processed, and stored in H5 files. These files can be easily visualized using a Python script incorporated with the open-access dataset. Additionally, the three-component data gathered from each SmartSolo nodal station were consolidated into a single miniSEED file, and the data from all 144 nodal stations obtained during each active-source shot or passive-wavefield time block were extracted and saved into a separate folder. We anticipate that this dataset will be a valuable resource for researchers developing techniques for void and anomaly detection using noninvasive, stress wave-based subsurface imaging techniques.