.png){kind=icon}
.png){kind=icon}
Return
Conditional Denoising Diffusion Probabilistic Model for Ground-Roll Attenuation
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2024.3489716.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Ground-roll attenuation is a challenging seismic processing task in land seismic surveys. The ground-roll coherent noise with low frequency and high amplitude seriously contaminates the valuable reflection events, corrupting the quality of seismic data. The transform-based filtering methods leverage the distinct characteristics of the ground roll and seismic reflections within the transform domain to attenuate the ground-roll noise. However, the ground roll and seismic reflections often share overlaps in the transform domain, making it challenging to remove ground-roll noise without attenuating useful reflections. We propose to apply a conditional diffusion denoising probabilistic model (c-DDPM) to attenuate the ground-roll noise and recover the reflections efficiently. We prepare the training dataset using the finite-difference modeling method and the convolution modeling method. After the training process, the c-DDPM can generate the clean data given the seismic data as the condition. The ground roll obtained by subtracting the clean data from the seismic data might contain some residual reflection energy. Thus, we further improve the c-DDPM to allow for generating the clean data and ground roll simultaneously. We then demonstrate the feasibility and effectiveness of our proposed method by using the synthetic data and the field data. The methods based on the local time-frequency (LTF) transform and U-Net are also applied to these two examples for comparison with our proposed method. The test results show that the proposed method performs better in attenuating the ground-roll noise from the seismic data than the LTF and U-Net methods.
Keywords:
Noise
Reflection
Transforms
Training
Noise measurement
Attenuation
Noise reduction
Time-frequency analysis
Geoscience and remote sensing
Generative adversarial networks
Conditional denoising diffusion probabilistic model
deep generative model
ground-roll attenuation
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W