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Towed Streamer-Based Simultaneous Source Separation by Contourlet Transform
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AI
DOI：10.1109/TGRS.2022.3181598.png)
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摘要
En 中文
Simultaneous towed-streamer marine acquisition has the advantages of reducing the total time requirements and costs of surveys. However, the seismic records obtained are blended seismic data, so the successful deblending of such data is the key to this method. In this article, we propose an effective deblending method with a new thresholding operator based on the shaping regularization framework in the contourlet domain. The new thresholding operator consists of an adaptive Bayesian threshold and a new thresholding function. Because of its multiresolution, locality, and directionality properties, the contourlet transform can effectively capture geometrical structures, which are the main features in natural images. To make the traditional Bayesian threshold adaptive in the contourlet domain, we propose a scale adjustment factor, a direction adjustment factor, and an attenuation factor to modify the threshold, and we also adopt local adaptive elliptic windows to estimate the standard deviations of useful signals; eventually, we obtain an adaptive Bayesian threshold. Furthermore, the new thresholding function can overcome the shortcomings of the existing soft and hard thresholding functions. Experimental results demonstrate that our method can effectively separate blended data.
Keyword:
Transforms
Bayes methods
Wavelet transforms
Thresholding (Imaging)
Filter banks
Estimation
Imaging
Adaptive Bayesian threshold
contourlet
new thresholding function
simultaneous sources
towed streamer
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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