张良^1,,
韩立国^1,,,
方金伟²,
张盼¹,
刘争光¹
1. 吉林大学, 地球探测科学与技术学院, 长春 130026
2. 中国石油大学(北京), 油气资源与探测国家重点实验室, CNPC物探重点实验室, 北京 102249

基金项目: 国家自然科学基金项目（41674124）资助


详细信息

作者简介: 张良, 男, 1991年生, 硕士, 研究方向为地震数据重构及去噪.E-mail:175050483@qq.com

通讯作者: 韩立国, 男, 1961年生, 教授, 博士生导师, 主要从事地震数据处理解释工作.E-mail:hanliguo@jlu.edu.cn

中图分类号: P631

收稿日期:2018-03-28
修回日期:2018-09-06
上线日期:2019-07-05

Seismic data denoising via double sparsity dictionary and fast iterative shrinkage-thresholding algorithm

ZHANG Liang^1,,
HAN LiGuo^1,,,
FANG JinWei²,
ZHANG Pan¹,
LIU ZhengGuang¹
1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
2. State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, China University of Petroleum, Beijing 102249, China


More Information

Corresponding author: HAN LiGuo,E-mail:hanliguo@jlu.edu.cn

MSC: P631

--> Received Date: 28 March 2018
Revised Date: 06 September 2018
Available Online: 05 July 2019


摘要
摘要:地震数据的随机噪声去除是地震数据处理中的一项重要步骤，双稀疏字典提供了两层稀疏模型，比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示，然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm，FISTA）对初始字典系数进行更新，接着采用数据驱动紧标架（data-driven tight frame，DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数，最后通过DDTF字典和更新后的字典系数获得新的contourlet系数，并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比，该方法可以自适应地对地震数据进行稀疏表示，在地震数据较为复杂时得到更高的信噪比；与字典学习去噪方法相比，该方法不仅拥有较快的去噪速度，而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.
关键词: 随机噪声/
双稀疏字典/
contourlet变换/
数据驱动紧标架/
快速迭代收缩阈值算法

Abstract:Seismic data denoising acts as one of the important roles in seismic data processing. Double sparse dictionary can provide the two-level sparsity for model, which has higher anti-noise ability than single sparsity transform denoising. In this paper, we developed a seismic data denoising workflow based on the double sparse transform and fast iterative shrinkage-thresholding algorithm (FISTA). We firstly represent data by contourlet transform and obtain a primary coefficient dictionary via FISTA. Then we obtain the learned dictionary through the data-driven tight frame (DDTF) and update the learned dictionary via FISTA. Finally, the new contourlet coefficients are reconstructed by DDTF dictionary and updated dictionary coefficients. Moreover, the hard thresholding and inverse contourlet transform are applied in new contourlet coefficients. Consequently, it achieves denoising. The synthetic data and field data experiments illustrated that compared with fixed-base transform, the proposed method obtains sparse representation of seismic data adaptively, and it performances well in the complexity seismic data. Compared with dictionary learning, the proposed method has less computational time-consuming. What is more, the proposed method overcomes the disadvantage that dictionary learning often produces artifacts due to no prior-constraint structural information in seismic data denoising.
Key words:Random noise/
Double sparsity dictionary/
Contourlet transform/
Data-driven tight frame/
Fast iterative shrinkage-thresholding algorithm



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