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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.
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Wavefield-based evaluation of DAS instrument response and array design
SUMMARY Distributed acoustic sensing (DAS) networks promise to revolutionize observational seismology by providing cost-effective, highly dense spatial sampling of the seismic wavefield, especially by utilizing pre-deployed telecomm fibre in urban settings for which dense seismic network deployments are difficult to construct. However, each DAS channel is sensitive only to one projection of the horizontal strain tensor and therefore gives an incomplete picture of the horizontal seismic wavefield, limiting our ability to make a holistic analysis of instrument response. This analysis has therefore been largely restricted to pointwise comparisons where a fortuitious coincidence of reference three-component seismometers and colocated DAS cable allows. We evaluate DAS instrument response by comparing DAS measurements from the PoroTomo experiment with strain-rate wavefield reconstructed from the nodal seismic array deployed in the same experiment, allowing us to treat the entire DAS array in a systematic fashion irrespective of cable geometry relative to the location of nodes. We found that, while the phase differences are in general small, the amplitude differences between predicted and observed DAS strain rates average a factor of 2 across the array and correlate with near-surface geology, suggesting that careful assessment of DAS deployments is essential for applications that require reliable assessments of amplitude. We further discuss strategies for empirical gain corrections and optimal placement of point sensor deployments to generate the best combined sensitivity with an already deployed DAS cable, from a wavefield reconstruction perspective.
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- Award ID(s):
- 1848166
- PAR ID:
- 10405145
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 229
- Issue:
- 1
- ISSN:
- 0956-540X
- Page Range / eLocation ID:
- 21 to 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/gji/ggab439
