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A time-varying wavelet extraction using local similarity.

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Title: A time-varying wavelet extraction using local similarity.
Authors: Dai Yong-Shou1 daiys@upc.edu.cn, Wang Rong-Rong1 913290023@qq.com, Li Chuang2 chli0409@126.com, Zhang Peng1 upczhangpeng@163.com, Tan Yong-Cheng1 tanyongcheng1229@126.com
Source: Geophysics. Jan/Feb2016, Vol. 81 Issue 1, pV55-V68. 14p.
Subjects: Time-varying systems, Wavelets (Mathematics), High resolution spectroscopy, Time-frequency analysis, Image segmentation
Abstract: Seismic wavelet extraction is an integral part of high-resolution seismic analysis. However, most extraction methods ignore the time-varying characteristic of wavelets introduced by attenuation, scattering, and other physical processes during propagation. We have developed a time-varying wavelet extraction method based on local similarity. This method estimates the amplitude spectra by spectral modeling in the time-frequency domain. We estimated the phase of each spectrum in two steps: First, the phase range was estimated by the bispectrum of the high-order cumulants, and then the phase spectrum at every point was extracted with additional local similarity optimization. The extracted nonstationary wavelet improved the resolution of the wavelet estimation in the adjacent layers. We have determined the practicability and reliability of the proposed method using a numerical simulation, and we have compared the results of this method with those of the adaptive segmentation method. [ABSTRACT FROM AUTHOR]
Copyright of Geophysics is the property of Society of Exploration Geophysicists and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 113222516
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A time-varying wavelet extraction using local similarity.
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  Data: <searchLink fieldCode="AR" term="%22Dai+Yong-Shou%22">Dai Yong-Shou</searchLink><relatesTo>1</relatesTo><i> daiys@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang+Rong-Rong%22">Wang Rong-Rong</searchLink><relatesTo>1</relatesTo><i> 913290023@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li+Chuang%22">Li Chuang</searchLink><relatesTo>2</relatesTo><i> chli0409@126.com</i><br /><searchLink fieldCode="AR" term="%22Zhang+Peng%22">Zhang Peng</searchLink><relatesTo>1</relatesTo><i> upczhangpeng@163.com</i><br /><searchLink fieldCode="AR" term="%22Tan+Yong-Cheng%22">Tan Yong-Cheng</searchLink><relatesTo>1</relatesTo><i> tanyongcheng1229@126.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Geophysics%22">Geophysics</searchLink>. Jan/Feb2016, Vol. 81 Issue 1, pV55-V68. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+spectroscopy%22">High resolution spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Seismic wavelet extraction is an integral part of high-resolution seismic analysis. However, most extraction methods ignore the time-varying characteristic of wavelets introduced by attenuation, scattering, and other physical processes during propagation. We have developed a time-varying wavelet extraction method based on local similarity. This method estimates the amplitude spectra by spectral modeling in the time-frequency domain. We estimated the phase of each spectrum in two steps: First, the phase range was estimated by the bispectrum of the high-order cumulants, and then the phase spectrum at every point was extracted with additional local similarity optimization. The extracted nonstationary wavelet improved the resolution of the wavelet estimation in the adjacent layers. We have determined the practicability and reliability of the proposed method using a numerical simulation, and we have compared the results of this method with those of the adaptive segmentation method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Geophysics is the property of Society of Exploration Geophysicists and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1190/geo2015-0317.1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: V55
    Subjects:
      – SubjectFull: Time-varying systems
        Type: general
      – SubjectFull: Wavelets (Mathematics)
        Type: general
      – SubjectFull: High resolution spectroscopy
        Type: general
      – SubjectFull: Time-frequency analysis
        Type: general
      – SubjectFull: Image segmentation
        Type: general
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      – TitleFull: A time-varying wavelet extraction using local similarity.
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            NameFull: Dai Yong-Shou
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            NameFull: Wang Rong-Rong
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            NameFull: Li Chuang
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            NameFull: Zhang Peng
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            NameFull: Tan Yong-Cheng
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            – D: 01
              M: 01
              Text: Jan/Feb2016
              Type: published
              Y: 2016
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              Value: 81
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