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Kong Liyun　Yu Siwei　Cheng Lin　Yang Huizhucom sh ad. 2012: Application of compressive sensing to seismic data reconstruction. Acta Seismologica Sinica, 34(5): 659-666.
Citation: Kong Liyun　Yu Siwei　Cheng Lin　Yang Huizhucom sh ad. 2012: Application of compressive sensing to seismic data reconstruction. Acta Seismologica Sinica, 34(5): 659-666.
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Application of compressive sensing to seismic data reconstruction

  • (Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China）

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  • Published Date: August 29, 2012
    Graphical Abstract
    • Abstract
  • There is a contradiction between seismic data processing and remote transmission of field seismic data, which is their different requirements about the data volume. More precisely, the former requires that the data quantity be as large as possible, while the latter requires the opposite. In order to solve this problem, a new method, compressed sensing (CS) technology, based on curvelet transform, is introduced into seismic data processing to reconstruct the actual incomplete data. The results show the advantage of curvelet transform compared with FFT in CS method. However, when it is used to actual data processing, surface wave must be removed first and the effect of the number of missing traces should be considered simultaneously. Finally, a reasonable reconstructed result is achieved, with clear texture and natural connection, illustrating the applicability and validity of the CS method.
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    • References (1)

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    Donoho D L, Huo X. 2001. Uncertainty principles and ideal atomic decomposition［J］. IEEE T Inform Theory, 47: 2845——2862.

    Donoho D L. 2006. Compressed sensing［J］. IEEE T Inform Theory, 52: 1289——1306.

    Herrmann F J, Hennenfent G. 2008. Non——parametric seismic data recovery with curvelet frames［J］. Geophys J Int, 173(1): 233——248.

    刘保童. 2009. 一种基于傅里叶变换的去假频内插方法及应用［J］. 煤田地质与勘探, 37(2): 63——67.

    唐刚. 2010. 基于压缩感知和稀疏表示的地震数据重建与去噪［D］. 北京: 清华大学: 3——24.

    仝中飞. 2009. Curvelet阈值迭代法在地震数据去噪和插值中的应用研究［D］. 长春: 吉林大学: 1——6.

    Candès E J, Romberg J K. 2005. Practical signal recovery from random projections［C］∥Proceedings of SPIE Computational Imaging Ⅲ: 76——86.

    Candès E J, Romberg J K, Tao T. 2005. Stable signal recovery from incomplete and inaccurate measurements［J］. Commun Pur Appl Math, 99: 1207——1223.

    Candès E J, Romberg J K. 2006. Quantitative robust uncertainty principles and optimally sparse decompositions［J］. Found Comput Math, 6: 227——254.

    Candès E J, Romberg J K, Tao T. 2006. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information［J］. IEEE T Inform Theory, 52: 489——509.

    Candès E J, Wakin M B. 2008. An introduction to compressive sampling［J］. IEEE Signal Proc Mag, 25(2): 21——30.

    Donoho D L, Huo X. 2001. Uncertainty principles and ideal atomic decomposition［J］. IEEE T Inform Theory, 47: 2845——2862.

    Donoho D L. 2006. Compressed sensing［J］. IEEE T Inform Theory, 52: 1289——1306.

    Herrmann F J, Hennenfent G. 2008. Non——parametric seismic data recovery with curvelet frames［J］. Geophys J Int, 173(1): 233——248.
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