基于压缩感知和Contourlet变换的地震数据重建方法

发布时间：2018-03-19 12:37

本文选题：压缩感知　切入点：Contourlet变换　出处：《石油物探》2017年06期 　论文类型：期刊论文

【摘要】：基于压缩感知技术的地震数据字典重建算法在训练字典时耗时较长,基于压缩感知技术的稀疏变换重建算法对稀疏基的要求较高,权衡信噪比和时间,采用目前已应用于地震数据重建的Contourlet稀疏基,提出了一种基于压缩感知技术和Contourlet变换的地震数据重建方法。首先根据设计的测量矩阵,在Contourlet域中采用快速迭代收缩阈值算法(Fast Iterative Shrinkage-Thresholding Algorithm,FISTA)重建缺失的稀疏系数,然后进行Contourlet反变换完成地震数据的缺失重建。合成数据和实际地震数据测试结果表明,基于压缩感知技术的Contourlet变换能够很好地完成地震数据的缺失重建;与压缩感知技术中常用的短时傅里叶变换和小波变换方法相比,基于压缩感知的Contourlet变换重建结果信噪比更高,并且增加的耗时有限,在可以接受的范围之内。
[Abstract]:The reconstruction algorithm of seismic data dictionary based on compression sensing technology takes a long time to train the dictionary, and the sparse transform reconstruction algorithm based on compression sensing technology requires high sparse basis, and weighs the signal-to-noise ratio (SNR) and time. In this paper, a method of seismic data reconstruction based on compression sensing and Contourlet transform is proposed, which is based on the Contourlet sparse basis, which has been applied to seismic data reconstruction. Fast iterative shrinkage threshold algorithm Fast Iterative Shrinkage-Thresholding algorithm (Fast Iterative Shrinkage-Thresholding algorithm) is used to reconstruct missing sparse coefficients in Contourlet domain, and then Contourlet inverse transformation is performed to complete the missing reconstruction of seismic data. The test results of synthetic data and actual seismic data show that, Contourlet transform based on compression sensing technology can accomplish the missing reconstruction of seismic data very well. Compared with the methods of short time Fourier transform and wavelet transform commonly used in compression sensing technology, the reconstruction result of Contourlet transform based on compression perception has higher signal-to-noise ratio (SNR). And the added time is limited and within acceptable limits.
【作者单位】：吉林大学地球探测科学与技术学院;
【基金】：国家自然科学基金项目(41304086)资助~~
【分类号】：P631.44

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