Seismic data reconstruction via weighted nuclear-norm minimization
Seismic data reconstruction via weighted nuclear-norm minimization
复制标题
通过加权核范数最小化地震数据重建
DOI：
10.1080/17415977.2014.890616
复制
发表时间：
2015-02
影响因子：
1.3
通讯作者：
Cheng, Yuanfeng
中科院分区：
文献类型：
--
作者：
Wang, Jing;Ma, Jianwei;Han, Bo;Cheng, Yuanfeng
基于核范数最小化的低秩矩阵完备化理论在实际应用中日益普及，并已被应用于地震数据的缺失道重建。本文研究了通过设计纹理片预变换获得的低秩地震纹理矩阵的加权核模最小化模型。与以往的核范数模型对地震数据矩阵的所有奇异值一视同仁不同，加权核范数模型基于迭代重加权的思想，更接近于“计数秩函数”，并能提升低秩矩阵。我们采用加权谱软阈值迭代方案和加权迭代支持检测方法来求解加权模型。对合成和真实的地震数据的实验研究表明，加权低秩方法可以获得高质量的重建结果。
The low-rank matrix completion theory based on nuclear-norm minimization is becoming increasingly popular in practice, and has been applied to seismic data reconstruction of missing traces. In this paper, we investigate the weighted nuclear-norm minimization model on low-rank seismic texture matrix which is obtained with a designed texture-patch pre-transformation. Unlike the previous nuclear-norm model that treats all singular values of the seismic data matrix equally, the weighted nuclear-norm model, based on the idea of iterative reweighting, makes a closer approximation to the ‘counting rank function’ and can promote the low-rank matrices. We apply weighted spectral soft-thresholding iterative scheme and weighted iterative supported detection method to solve the weighted model. Experimental study on synthetic and real seismic data shows that high-quality reconstruction can be obtained by the weighted low-rank methods.
登录
查看更多内容
影响因子：
3.3
作者：
M. Naghizadeh;K. Innanen
通讯作者：
M. Naghizadeh;K. Innanen
影响因子：
2.6
作者：
R. Shahidi;Gang Tang;Jianwei Ma;F. Herrmann
通讯作者：
R. Shahidi;Gang Tang;Jianwei Ma;F. Herrmann
影响因子：
22.7
作者：
Candes, Emmanuel;Recht, Benjamin
通讯作者：
Recht, Benjamin
DOI：
--
发表时间：
2011-07
期刊：
ArXiv
影响因子：
--
作者：
Stéphane Gaïffas;Guillaume Lecué
通讯作者：
Stéphane Gaïffas;Guillaume Lecué
影响因子：
3.3
作者：
Oropeza, Vicente;Sacchi, Mauricio
通讯作者：
Sacchi, Mauricio