摘要
为提高地震数据压缩感知重构的信噪比和保真度,提出一种基于曲波变换的地震数据压缩感知重构算法。建立了地震数据压缩感知重构模型,分析了基于曲波变换稀疏表示的地震数据各尺度之间能量与熵的分布特性,结合分块压缩感知技术降低随机观测的计算复杂度,利用曲波变换稀疏表示高频区域各尺度之间的相关性,设计了随信息熵变化的自适应双变量收缩阈值迭代重构的方法。实验结果表明,在相同的采样率下,该算法重构的地震数据峰值信噪比提高了1.5 d B以上,并且具有良好的细节信息保持能力。
In order to improve the signal to noise ratio and fidelity of seismic data by the compressed sensing reconstruction method,an algorithm of compressed sensing reconstruction of seismic data based on curvelet transform is proposed. A seismic data reconstruction model is built, the energy and entropy distribution characteristics of multi-scales seismic data based on sparse representation of curvelet are analyzed, the computational complexity of the random observation is reduced with the block compressed sensing technology,with the change of information entropy,an adapt bivariate shrinkage threshold iterative reconstruction method is designed based on the correlation between the high frequency region of multi-scales in curvelet domain.Experimental results show that the proposed algorithm gains above 1. 5 d B,and has better ability to maintain the detail information than the other algorithms mentioned,under the same sampling rate.
出处
《吉林大学学报（信息科学版）》
CAS
2015年第5期570-577,共8页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(60736043)
中国石油科技创新基金资助项目(2012D-5006-0609)
中国石油科技创新基金资助项目(2013D-5006-0203)
黑龙江省教育厅科学技术基金资助项目(12521050)
黑龙江省教育厅科学技术基金资助项目(12541087)
关键词
曲波变换
压缩感知
地震数据重构
稀疏表示
自适应收缩阈值迭代
curvelet transform
compressed sensing
seismic data reconstruction
sparse representation
adaptive iterative threshold shrinkage
