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Non-stationary multichannel spectral inversion of seismic data.

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Title: Non-stationary multichannel spectral inversion of seismic data.
Authors: Sun, Yaoguang1 (AUTHOR) sunyg_guang@126.com, Cao, Siyuan2 (AUTHOR) csy@cup.edu.cn, Su, Yuxin3 (AUTHOR), Zhou, Jie4 (AUTHOR), Ma, Zhenshuo2 (AUTHOR)
Source: Studia Geophysica & Geodaetica. Jun2025, Vol. 69 Issue 1, p41-56. 16p.
Subjects: Constraint algorithms, Quality factor, Fourier transforms, Data quality, Algorithms
Abstract: Spectral inversion, based on the odd-even decomposition principle of reflectivity, used the relationship between seismic data and wavelet amplitude spectrum to establish the inversion equation and achieve resolution-enhancement processing. Compared with deconvolution based on the L2 norm, the odd and even components of reflectivity using spectral inversion can weaken the tuning effect, identify thin layers, and obtain data with higher resolution. However, most post-stack seismic data are non-stationary, i.e., attenuation of amplitude, phase, and frequency with time exists. We derived a resolution-enhancement algorithm of non-stationary seismic data with quality factor Q based on the short-time Fourier transform. Due to the instability of the spectral inversion algorithm, the lateral continuity of the obtained result is poor. Therefore, we proposed a multichannel spectral inversion algorithm with lateral constraints. The algorithm inherits the high-resolution characteristics of spectral inversion and effectively enhances lateral continuity. Applications to model and field data sets show that the proposed L2 norm-based non-stationary multichannel spectral inversion method can be effectively applied to the resolution-improvement processing of non-stationary seismic data. [ABSTRACT FROM AUTHOR]
Copyright of Studia Geophysica & Geodaetica is the property of Springer Nature 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.)
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  Data: <searchLink fieldCode="JN" term="%22Studia+Geophysica+%26+Geodaetica%22">Studia Geophysica & Geodaetica</searchLink>. Jun2025, Vol. 69 Issue 1, p41-56. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Constraint+algorithms%22">Constraint algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+factor%22">Quality factor</searchLink><br /><searchLink fieldCode="DE" term="%22Fourier+transforms%22">Fourier transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Data: Spectral inversion, based on the odd-even decomposition principle of reflectivity, used the relationship between seismic data and wavelet amplitude spectrum to establish the inversion equation and achieve resolution-enhancement processing. Compared with deconvolution based on the L2 norm, the odd and even components of reflectivity using spectral inversion can weaken the tuning effect, identify thin layers, and obtain data with higher resolution. However, most post-stack seismic data are non-stationary, i.e., attenuation of amplitude, phase, and frequency with time exists. We derived a resolution-enhancement algorithm of non-stationary seismic data with quality factor Q based on the short-time Fourier transform. Due to the instability of the spectral inversion algorithm, the lateral continuity of the obtained result is poor. Therefore, we proposed a multichannel spectral inversion algorithm with lateral constraints. The algorithm inherits the high-resolution characteristics of spectral inversion and effectively enhances lateral continuity. Applications to model and field data sets show that the proposed L2 norm-based non-stationary multichannel spectral inversion method can be effectively applied to the resolution-improvement processing of non-stationary seismic data. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Studia Geophysica & Geodaetica is the property of Springer Nature 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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        Value: 10.1007/s11200-023-0309-3
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        Text: English
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        Type: general
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      – SubjectFull: Fourier transforms
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              Text: Jun2025
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              Y: 2025
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