Tracking Lightning Through 3D Thunder Source Location With Distributed Acoustic Sensing

Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.

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DASPy: A Python Toolbox for DAS Seismology

Hu,

2024

Seismological Research Letters

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Distributed acoustic sensing (DAS) has emerged as a novel technology in geophysics, owing to its high-sensing density, cost effectiveness, and adaptability to extreme environments. Nonetheless, DAS differs from traditional seismic acquisition technologies in many aspects: big data volume, equidistant sensing, measurement of axial strain (strain rate), and noise characteristics. These differences make DAS data processing challenging for new hands. To lower the bar of DAS data processing, we develop an open-source Python toolbox called DASPy, which encompasses classic seismic data processing techniques, including preprocessing, filter, spectrum analysis, and visualization, and specialized algorithms for DAS applications, including denoising, waveform decomposition, channel attribute analysis, and strain–velocity conversion. Using openly available DAS data as examples, this article makes an overview and tutorial on the eight modules in DASPy to illustrate the algorithms and practical applications. We anticipate DASPy to provide convenience for researchers unfamiliar with DAS data and help facilitate the rapid growth of DAS seismology.

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Tracking Lightning Through 3D Thunder Source Location With Distributed Acoustic Sensing

Cited by 5 publications

References 35 publications

Thunder observation using urban telecom optical fiber cable with distributed acoustic sensing

Thunder observation using urban telecom optical fiber cable with distributed acoustic sensing

Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging

DASPy: A Python Toolbox for DAS Seismology

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