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.

中文翻译：

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基于拖缆的 Contourlet 变换同步源分离


同步拖缆海洋采集的优点是减少了调查的总时间要求和成本。然而，所获得的地震记录是混合地震数据，因此此类数据的成功去混合是该方法的关键。在本文中，我们提出了一种基于轮廓波域中的整形正则化框架的具有新阈值算子的有效去混合方法。新的阈值运算符由自适应贝叶斯阈值和新的阈值函数组成。由于其多分辨率、局部性和方向性特性，轮廓波变换可以有效地捕获几何结构，这是自然图像的主要特征。为了使传统贝叶斯阈值在Contourlet域具有自适应性，我们提出了尺度调整因子、方向调整因子和衰减因子来修改阈值，并采用局部自适应椭圆窗来估计有用信号的标准差；最终，我们获得了自适应贝叶斯阈值。此外，新的阈值函数可以克服现有软阈值函数和硬阈值函数的缺点。实验结果表明我们的方法可以有效地分离混合数据。