1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. 81st EAGE Conference and Exhibition 2019
  5. Conference paper

Abstract

Summary

Sedimentary cycle division is significant in the sequence stratigraphy. Time-frequency analysis is a widely used method for the division of the sedimentary cycles. However, traditional time-frequency analysis methods can not characterize the sedimentary cycles accurately because of the huge difference between the time-frequency basic atoms and the waveforms of different seismic wavelets. In this paper, the precise recognition of the sedimentary cycles is realized by introducing the synchrosqueezing three-parameter wavelet transform (STPWT), in which the time-frequency basis atom (e.g. the three parameter wavelet) can match well with different seismic wavelets. Hence, the STPWT achieves good time-frequency localization characteristics and distinguishes the spectrum units accurately corresponding to different sedimentary cycle units in the time direction. In addition, the frequency direction resolution is improved by synchrosqueezed operation. The time-frequency analysis results of STPWT can clearly depict the time-frequency characteristics of the main frequency component corresponding to the positive cycle unit from high to low or the reverse cycle unit from low to high. Experiments on synthetic and field data demonstrates its validity and effectiveness.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901164
2019-06-03
2024-04-24

  • From This Site

    /content/papers/10.3997/2214-4609.201901164
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Daubechies, I.
    , [1992] Ten lectures on wavelets: SIAM.
     [Google Scholar]
  2. Daubechies, I., Lu, J. , and Wu, H.
    , [2011] Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30(2), 243–261.
     [Google Scholar]
  3. Gao, J., T.Wan, W.Chen, and J.Mao., et al.
    [2006] Three parameter wavelet and its applications to seismic data processing. Chinese Journal of Geophysics, 49(6), 337–347.
     [Google Scholar]
  4. Liu, N., Gao, J., and Zhang, Z. , et al.
    [2017] High-resolution characterization of geologic structures using the synchrosqueezing transform. Interpretation, 5(1), T75–T85.
     [Google Scholar]
  5. Liu, N., Gao, J., and Zhang, B. , et al.
    [2018] Time–frequency analysis of seismic data using a three parameters s transform. IEEE Geoscience and Remote Sensing Letters, 15(1), 142–146.
     [Google Scholar]
  6. Li, X., ChenS., and WangJ., et al.
    [2013] Forward modeling studies on the time-frequency characteristics of thin layers. Chinese Journal of Geophysics, 56(3), 1033–1042.
     [Google Scholar]
  7. Tian, Y., LiX., and ChengY., et al.
    [2018] Comparison result of several time-frequency analysis methods on identifying sedimentary cycle. Mathematics in Practice & Theory.
     [Google Scholar]
  8. Zhan, J., Wang, Y., and Yang, G., et al.
    [2003] Concept and application of seismic cycle characteristics. Oil Geophysical Prospecting, 38(3), 281–284.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901164
Loading
Characterizing Sedimentary Cycle Using Synchrosqueezing Three-parameter Wavelet Transform
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201901164
/content/papers/10.3997/2214-4609.201901164
/content/papers/10.3997/2214-4609.201901164
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error