Inversion-Based 3-D Seismic Denoising for Exploring Spatial Edges and Spatio-Temporal Signal Redundancy
Inversion-Based 3-D Seismic Denoising for Exploring Spatial Edges and Spatio-Temporal Signal Redundancy
复制标题
DOI：
10.1109/lgrs.2018.2854929
复制
发表时间：
2018-07
影响因子：
4.8
通讯作者：
S. Yuan;Shangxu Wang;C. Luo;Tieyi Wang
中科院分区：
文献类型：
--
作者：
S. Yuan;Shangxu Wang;C. Luo;Tieyi Wang
为了不断提高勘探程度，对地震数据的质量要求越来越高。从反演的角度来看，利用更多的信息是提高地震资料信噪比的有效途径。在这封信中，我们采用同时稀疏约束的信号沿时间方向和两个空间方向的沿着一阶差分，通过最小化柯西函数描述，作为一个组合的约束（或正则化）项施加在时域数据失配，提出了一种基于反演的三维地震去噪方法。该方法同时考虑了沿着两个空间方向的时间切片和地震剖面之间的冗余性，并能保留沿空间方向的沿着边缘。通过分析数据失配项与设计的组合正则化项（或目标函数）之和的一阶导数，推导出在一阶邻域范围内数据与期望信号样本之间的关系可以表示为一个具有7个数据相关系数的线性系统。此外，可以推断，信号差异的稀疏约束沿沿着不同的三维数据的维方向具有一些互补的功能，降噪和信号的保护。我们使用一个3-D的合成数据集，一个3-D的真实的叠后数据集，和一个3-D的真实的叠前数据集，以确定所提出的方法是一个有效的振幅保持去噪工具与可接受的计算成本。
Seismic data are increasingly required to be high quality for the continuous improvement of the degree of exploration. From the viewpoint of inversion, the utilization of more information is an effective way to improve the signal-to-noise ratio of seismic data. In this letter, we adopt simultaneous sparsity constraints of the first-order differences of signals along the time direction and two spatial directions, described by minimizing the Cauchy function, as a combined constraint (or regularization) term imposed on the time-domain data misfit to propose an inversion-based 3-D seismic denoising method. In this way, the redundancies among time slices and seismic sections along two spatial directions are simultaneously considered, and the edges along the spatial directions can be preserved. Through analyzing the first-order derivative of the sum of the data misfit term and the designed combined regularization term (or the objective function), we derive that the relationship between data and desired signal samples in the range of the first-order neighborhood can be expressed as a linear system with seven data-dependent coefficients. Furthermore, it can be inferred that the sparsity constraints of signal differences along different dimensional directions of 3-D data have some complementary functions of noise reduction and signal preservation. We use a 3-D synthetic data set, a 3-D real poststack data set, and a 3-D real prestack data set to determine that the proposed method is an effective amplitude-preservation denoising tool with an acceptable computational cost.