.png){kind=icon}
.png){kind=icon}
Return
Radon Transform Constrained Multitrace Pre-Stack Deconvolution Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3387756.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article proposes a pre-stack deconvolution algorithm for the seismic common midpoint (CMP) gathers. Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution while maintaining the SNR. As a result, the data after deconvolution are overwhelmed by noise. In addition, the deconvolution methods in the Radon transform domain are limited by the tailing of focal points in the Radon domain. Therefore, this research employs the Radon transform as a sparse-promoting transform for deconvolution. By applying thresholds in the Radon domain, this algorithm suppresses noise and reduces the instability of deconvolution. Depending on the noise distribution, either the L-2 norm or the L-1 norm is flexibly chosen as the fitting term to enhance the algorithm's versatility. Leveraging the strong denoising capability of the Radon transform, this algorithm improves resolution on gathers with a low SNR while enhancing lateral continuity. Model and actual data tests indicate that the algorithm effectively enhances the resolution of gathers, thus facilitating pre-stack amplitude versus offset (AVO) analysis and pre-stack inversion.
Keywords:
Deconvolution
Transforms
Radon
Mathematical models
Signal to noise ratio
Wavelet transforms
Trajectory
Common midpoint (CMP) gathers
deconvolution
high-resolution
radon transform
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W