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Multi-source wavefield reconstruction of distributed acoustic sensing data using compressive sensing and seismic

Patipan Saengduean1, Jihyun Yang1, Jeffrey Shragge1

  • 1Department of Geophysics, Colorado School of Mines, Golden, Colorado 80401, USA.

The Journal of the Acoustical Society of America
|August 23, 2024
PubMed
Summary

Compressive sensing improves seismic interferometry using distributed acoustic sensing (DAS) data. This multi-source wavefield reconstruction technique reduces data storage and enhances signal quality for subsurface imaging.

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Area of Science:

  • Geophysics
  • Seismology
  • Optical Sensing

Background:

  • Distributed Acoustic Sensing (DAS) utilizes deployed optical fibers for subsurface imaging.
  • Seismic interferometry can reduce costs in active-source DAS surveys by estimating inter-sensor wavefields.
  • Challenges include large data storage needs and unusable fiber sections due to sensitivity and noise.

Purpose of the Study:

  • To apply compressive sensing for wavefield reconstruction in DAS data.
  • To mitigate data storage and usability issues associated with long-term DAS recordings.
  • To evaluate a multi-source wavefield reconstruction technique for ambient seismic interferometry.

Main Methods:

  • Applied compressive sensing-based multi-source wavefield reconstruction to ambient DAS records.
  • Utilized Fourier and curvelet transforms for sparsifying interferometric wavefields.
  • Compared simultaneous multi-source reconstruction with conventional single-source methods.

Main Results:

  • Demonstrated the applicability of multi-source reconstruction for DAS data.
  • Fourier multi-source reconstruction improved recovered wavefields by 5-10 dB compared to single-source methods.
  • Successfully estimated correlograms of ambient DAS records from a fiber array in Perth, Australia.

Conclusions:

  • Compressive sensing is effective for wavefield reconstruction in DAS seismic interferometry.
  • The multi-source approach offers significant improvements over single-source methods for DAS data.
  • This technique enhances the efficiency and reliability of subsurface imaging and monitoring using DAS.