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A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering

Received: 20 April 2017     Published: 20 April 2017
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Abstract

The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.

Published in Science Discovery (Volume 5, Issue 2)
DOI 10.11648/j.sd.20170502.16
Page(s) 118-128
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Time-Varying Wavelet Extraction, Non-stationary Signal, Preprocessing, Mirror Extension, EMD

References
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Cite This Article
  • APA Style

    Lu Zi-hao, Dai Yong-shou, Gao Xu, Zhang Peng, Tan Yong-cheng, et al. (2017). A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Science Discovery, 5(2), 118-128. https://doi.org/10.11648/j.sd.20170502.16

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    ACS Style

    Lu Zi-hao; Dai Yong-shou; Gao Xu; Zhang Peng; Tan Yong-cheng, et al. A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Sci. Discov. 2017, 5(2), 118-128. doi: 10.11648/j.sd.20170502.16

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    AMA Style

    Lu Zi-hao, Dai Yong-shou, Gao Xu, Zhang Peng, Tan Yong-cheng, et al. A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering. Sci Discov. 2017;5(2):118-128. doi: 10.11648/j.sd.20170502.16

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  • @article{10.11648/j.sd.20170502.16,
      author = {Lu Zi-hao and Dai Yong-shou and Gao Xu and Zhang Peng and Tan Yong-cheng and Zhang Hong-qian},
      title = {A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering},
      journal = {Science Discovery},
      volume = {5},
      number = {2},
      pages = {118-128},
      doi = {10.11648/j.sd.20170502.16},
      url = {https://doi.org/10.11648/j.sd.20170502.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170502.16},
      abstract = {The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering
    AU  - Lu Zi-hao
    AU  - Dai Yong-shou
    AU  - Gao Xu
    AU  - Zhang Peng
    AU  - Tan Yong-cheng
    AU  - Zhang Hong-qian
    Y1  - 2017/04/20
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sd.20170502.16
    DO  - 10.11648/j.sd.20170502.16
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 118
    EP  - 128
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20170502.16
    AB  - The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

  • College of Information and Control Engineering, China university of Petroleum, Qingdao, China

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