An Open-Access Database of Active-source and Passive-wavefield DAS and Nodal Station Measurements at the Newberry Florida Site

Auteurs : Aser Abbas, Brady R. Cox, Khiem T. Tran, Isabella Corey, Nishkarsha Dawadi

arXiv: 2305.15578v1 - DOI (physics.geo-ph)
33 pages, 12 figures, dataset paper
Licence : CC BY-NC-SA 4.0

Résumé : This paper documents a comprehensive subsurface imaging experiment using stress waves in Newberry, Florida, at a site known for significant spatial variability, karstic voids, and underground anomalies. The experiment utilized advanced sensing technologies, including approximately two kilometers of distributed acoustic sensing (DAS) fiber optic cable, forming a dense 2D array of 1920 channels, and a 2D array of 144 three-component nodal stations, to sense active-source and passive-wavefield stress waves. The active-source data was generated using a vibroseis shaker truck and impact sources, and it was simultaneously sensed by both the DAS and the nodal stations. The vibroseis truck was used to excite the ground in the three directions at 260 locations inside and outside the instrumented array, while the impact sources were used at 268 locations within the instrumented array. The passive-wavefield data recorded using the nodal stations comprised 48 hours of ambient noise collected over a period of four days in four twelve-hour time blocks. Meanwhile, the passive wavefield data collected using DAS consisted of four hours of ambient noise recordings. This paper aims to provide a comprehensive overview of the testing site, experiment layout, the DAS and nodal station acquisition parameters, implemented processing steps, and potential use cases of the dataset. While 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 paper is on documenting this unique dataset rather than on processing the data for detecting anomalies or generating subsurface 2D/3D imaging results. The raw and processed data, along with detailed documentation of the experiment and Python tools to aid in visualizing the DAS dataset have been archived and made publicly available on DesignSafe under project PRJ-3521.

Soumis à arXiv le 24 Mai. 2023

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