Statistics-Guided Residual Dictionary Learning for Footprint Noise Removal
Statistics-Guided Residual Dictionary Learning for Footprint Noise Removal
复制标题
用于消除足迹噪声的统计引导残差字典学习
DOI：
10.1109/tgrs.2021.3070903
复制
发表时间：
2021-04
影响因子：
8.2
通讯作者：
Yangkang Chen
中科院分区：
文献类型：
--
作者：
Wei Chen;Omar M. Saad;Hang Wang;Yangkang Chen
足迹噪声是由采集几何结构引起的一种相干噪声。足迹噪声在3D地震体中常见，并且极大地影响地震勘探工作流程中基于振幅的处理和解释步骤。因此，去除足迹噪声对于确保地震数据处理的可靠解释输出是必要的。然而，由于足迹噪声通常是弱的并且空间相干的，因此在其去除步骤期间不可避免地对有用信号造成损害。在这里，我们提出了一种基于字典学习（DL）的方法来有效地去除足迹噪声。我们设计了一个算法框架来有效地学习信号波形的字典原子，并从学习的原子中分离出足迹噪声的特征。考虑到字典原子中足迹噪声的特殊性，我们提出了一种基于几何学的方法将字典原子分为足迹影响的原子和无足迹的原子。然后，通过2-D中值滤波步骤处理足迹影响的原子。未接触的无足迹原子和过滤的足迹影响的原子之间的组合导致信号波形和足迹原子的更好的字典。我们使用残差DL通过信号原子和足迹原子的线性组合来编码输入数据。去除足迹原子及其相应的稀疏系数导致成功的足迹去除。我们使用3-D合成和现场数据的例子来证明所提出的方法的有效性。
The footprint noise is a type of coherent noise that arises from the acquisition geometry. The footprint noise is commonly seen in 3-D seismic volumes and greatly affects the amplitude-based processing and interpretation steps in the seismic exploration workflow. Thus, removal of the footprint noise is necessary for warranting a reliable interpretation output of seismic data processing. However, because footprint noise is usually weak and also spatially coherent, it is inevitable to cause damages to useful signals during its removal steps. Here, we propose a dictionary learning (DL)-based method to effectively remove the footprint noise. We design an algorithm framework to effectively learn the dictionary atoms of the signal waveforms and separate the features of the footprint noise from the learned atoms. Considering the special features of the footprint noise in the dictionary atoms, we propose a statistics-guided way to separate the dictionary atoms into footprint-affected and footprint-free atoms. Then, the footprint-affected atoms are processed via a 2-D median filtering step. The combination between the untouched footprint-free atoms and filtered footprint-affected atoms result in a better dictionary of the signal waveforms and the footprint atoms. We use residual DL to encode the input data by a linear combination of signal atoms and footprint atoms. Removal of footprint atoms and their corresponding sparse coefficients leads to a successful footprint removal. We use both 3-D synthetic and field data examples to demonstrate the effectiveness of the proposed method.
登录
查看更多内容
DOI：
10.1190/1.3627379
发表时间：
2011
期刊：
Seg Technical Program Expanded Abstracts
影响因子：
--
作者：
O. Davogustto;K. Marfurt
通讯作者：
O. Davogustto;K. Marfurt
影响因子：
8.2
作者：
Yatong Zhou;Jian Yang;Hang Wang;Guangtan Huang;Yangkang Zhang
通讯作者：
Yatong Zhou;Jian Yang;Hang Wang;Guangtan Huang;Yangkang Zhang
影响因子：
2.8
作者：
Yangkang Zhang;S. Zu;Wei Chen-;Mi Zhang;Zhe Guan
通讯作者：
Yangkang Zhang;S. Zu;Wei Chen-;Mi Zhang;Zhe Guan
影响因子：
3.3
作者：
Chen, Yangkang;Fomel, Sergey
通讯作者：
Fomel, Sergey
影响因子：
3.3
作者：
G. Schuster;Zhaojun Liu
通讯作者：
G. Schuster;Zhaojun Liu