Seismic data reconstruction method based on improved curvelet transform

被引：0

作者：

Hou W. ^{[1
,2
]}

Jia R. ^{[1
,2
]}

Sun Y. ^{[1
,2
]}

Yu G. ^{[1
,2
]}

机构：

[1] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao

[2] Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao

来源：

Jia, Ruisheng (jrs716@163.com) | 2018年 / China Coal Society卷 / 43期

关键词：

Compressive sensing; Curvelet transform; Data reconstruction; Exponential threshold;

D O I：

10.13225/j.cnki.jccs.2017.1799

中图分类号：

学科分类号：

摘要：

As a result of the acquisition of environment and instrument performance constraints,seismic data collected are often irregular and incomplete.Thus it is necessary to reconstruct the complete seismic data before proceeding to the next step in the seismic data analysis.The authors present a modified Curvelet algorithm for image reconstruction based on seismic compression.First in the framework of compressed sensing theory,using the sparse characteristic of Curvelet,a missing data reconstruction model is built,and then using the CRSI(Curvelet Recovery by sparsity-promoting Inversion,CRSI) algorithm framework,adopting improved exponential threshold algorithm,the missing seismic data are restored and reconstructed.In this paper,the authors use four level homogeneous medium model and the seismic data simulated by Marmousi model to carry out numerical experiments of random sparse sampling and reconstruction.The result of the experiment shows that compared with the traditional recon-struction algorithm,the proposed method not only accelerates the convergence speed of the original algorithm,but also guarantees a high SNR of the reconstructed data,which verifies the feasibility and effectiveness of the proposed method. © 2018, Editorial Office of Journal of China Coal Society. All right reserved.

引用

页码：2570 / 2578

页数：8

共 23 条

[1]

Wang X., Chen J., Guo Q., Et al., Research of the CSAMT exploration mode and experiment for the coalbed enrichment region in the north Qinshui basin, Geophysics Journal, 56, 12, pp. 4310-4323, (2013)

[2]

Wang J., Wu R., Zhang P., Characteristics and applications of gas desorption with excavation disturbances in coal mining, International Journal of Coal Science & Technology, 2, 1, pp. 30-37, (2015)

[3]

Liu Y., Zhu Y., Comparison of pore characteristics in the coal and shale reservoirs of Taiyuan Formation, Qinshui Basin, China, International Journal of Coal Science & Technology, 3, 3, pp. 330-338, (2016)

[4]

Donoho D.L., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)

[5]

Herrmann F.J., Hennenfent G., Non-parametric seismic data recovery with curvelet frames, Geophysical Journal of the Royal Astronomical Society, 173, 1, pp. 233-248, (2010)

[6]

Candes E.J., Donoho D.L., New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities, Communications on Pure & Applied Mathematics, 57, 2, pp. 219-266, (2004)

[7]

Daubechies I., Defrise M., De Mol C., An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure & Applied Mathematics, 57, 11, pp. 1413-1457, (2003)

[8]

Ma J., Plonka G., The curvelet transform, Signal Processing Magazine IEEE, 27, 2, pp. 118-133, (2010)

[9]

Yin W., Analysis and generalizations of the linearized bregman method, Siam Journal on Imaging Sciences, 3, 4, pp. 856-877, (2010)

[10]

Candes E.J., Romberg J., Tao T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2004)

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