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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
Summary
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.
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.
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