Three key factors in seismic data reconstruction based on compressive sensing

被引：0
|
作者：
Wen R. [1 ]
Liu G. [1 ]
Ran Y. [2 ]
机构：
[1] State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing
[2] Daqing Logging and Testing Service Company, Daqing Oilfield Company, PetroChina, Daqing, 163000, Heilongjiang
来源：
Liu, Guochang (guochang.liu@cup.edu.cn) | 2018年 / Science Press卷 / 53期
关键词：
Compressive sensing; Iterative algorithm; Seismic data reconstruction; Sparsity transform; Threshold model;
D O I：
10.13810/j.cnki.issn.1000-7210.2018.04.004
中图分类号：
学科分类号：
摘要：
Seismic data reconstruction is significant for seismic data processing and imaging. Reconstruction methods based on compressive sensing are widely used. In these methods, the sparsity transform, the iterative algorithm, and the threshold model affect the final reconstruction performance and computation efficiency. For the sparsity transform, we analyze the influence of Fourier transform, Curvelet transform and Seislet transform in seismic data reconstruction. For the iterative algorithm, we discuss the projection onto convex sets (POCS), the iterative hard thresholding (IHT), Bregman, and the joint reconstruction by sparsity-promoting inversion (JRSI) reconstruction methods and analyze the advantages and disadvantages of these four methods. For the threshold model, we work on the linear threshold model, the exponential threshold model, and the data-driven threshold model. We analyze how these three key factors affect the reconstruction results through synthetic and real data examples. At the end we draw some conclusions and propose suggestions for practical seismic data reconstruction. © 2018, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
引用
收藏
页码：682 / 693
页数：11
相关论文
共 41 条
  • [11] Duijndam A.W., Schonewille M.A., Hindriks K., Reconstruction of band-limited signals irregularly sampled along one spatial direction, Geophysics, 64, 2, pp. 524-538, (1999)
  • [12] Trad D., Ulrych T., Sacchi M.D., Accurate interpolation with high resolution time-variant Radon transforms, Geophysics, 67, 2, pp. 644-656, (2002)
  • [13] Hargreaves N., ver West B., Wombell R., Et al., Multiple attenuation using an apex-shifted radon transform, SEG Technical Program Expanded Abstracts, 22, pp. 1929-1932, (2003)
  • [14] Thomson D., Herrmann F.J., Hennenfent G., Et al., A parallel windowed fast-discrete curvelet transform applied to seismic processing, SEG Technical Program Expanded Abstracts, 25, pp. 2767-2771, (2006)
  • [15] Liu G., Chen X., Guo Z., Et al., Missing seismic data rebuilding by interpolation based on Curvelet transform, OGP, 46, 2, pp. 237-246, (2011)
  • [16] Liu C., Li P., Liu Y., Et al., Iterative interpolation beyond aliasing using Seislet transform, Chinese Journal of Geophysics, 56, 5, pp. 1619-1627, (2013)
  • [17] Gan S.W., Wang S.D., Chen Y.K., Et al., De-aliased seismic data interpolation using seislet transform with low-frequency constraint, SEG Technical Program Expanded Abstracts, 34, pp. 3900-3904, (2015)
  • [18] Zhang L., Han L., Xu D., Et al., Seismic data reconstruction with Shearlet transform based on compressed sensing technology, OGP, 52, 2, pp. 220-225, (2017)
  • [19] Bregman L., The method of successive projection for finding a common point of convex sets (Theorems for determining common point of convex sets by method of successive projection), Soviet Math, 6, 3, pp. 487-490, (1965)
  • [20] Stark H., Oskoui P., High-resolution image recovery from image plane arrays, using convex projections, Journal of the Optical Society of America A (Optics, Image Science and Vision), 6, 11, pp. 1715-1726, (1989)
12345