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Diffusion Imaging in the Rat Cervical Spinal Cord
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Diffusion Model for DAS-VSP Data Denoising.

Donglin Zhu1, Lei Fu1, Vladimir Kazei1

  • 1Aramco Americas-Houston Research Center, Houston, TX 77084, USA.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces diffusion models for denoising distributed acoustic sensing (DAS) vertical seismic profile (VSP) data. The new method effectively removes noise from seismic data while preserving signal integrity.

Keywords:
denoisingdiffusion modeldistributed acoustic sensing (DAS)vertical seismic profiling (VSP)

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Area of Science:

  • Geophysics
  • Seismic Data Acquisition
  • Signal Processing

Background:

  • Distributed Acoustic Sensing (DAS) is a key technology for seismic data acquisition.
  • Noise in DAS data hinders accurate seismic analysis, requiring advanced denoising methods.

Purpose of the Study:

  • To pioneer the application of diffusion models for denoising DAS vertical seismic profile (VSP) data.
  • To evaluate the effectiveness of diffusion models in suppressing noise in both synthetic and field DAS-VSP datasets.

Main Methods:

  • A diffusion model was trained on a synthetic dataset with varied acquisition parameters.
  • The trained diffusion network was applied to denoise synthetic and field DAS-VSP data.

Main Results:

  • The diffusion model effectively suppressed various noise types in DAS-VSP data.
  • The method demonstrated minimal signal leakage and outperformed conventional denoising techniques.

Conclusions:

  • Diffusion models show significant potential for enhancing DAS data processing.
  • This approach offers a promising solution for improving the quality of seismic data acquired using DAS.