Abstract
Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.
Highlights
The propagation distance of the low-frequency components in seismic data is much longer than that of the high-frequency components, and the content it carries is more abundant
Any matrix can be used as a measurement matrix for data reconstruction as long as it conforms to the restricted isometry property (RIP) properties described in Equation (4) [32,33], which theoretically guarantees that the K sparse signal d can be completely recovered from the measurement signal dobs
Conventional acquisition acquisition technology technology shows shows that that low-frequency low-frequency information information below below 10
Summary
The propagation distance of the low-frequency components in seismic data is much longer than that of the high-frequency components, and the content it carries is more abundant. Deconvolution is an indispensable part of seismic data processing It compresses the wavelets by eliminating the filtering effect of the earth to obtain the seismic reflection coefficient and improve the resolution of the data [6,7]. Pedersen-Tatalovic et al proposed a multivariate interpolation method based on logging data to improve the accuracy of lowfrequency prediction by estimating the layer velocity, layer depth, and layer thickness of seismic data [24]. This method can significantly improve the wave impedance information above 2–8 Hz. Due to considerations of the geological background, the modeling process of this method is more complicated. Sci. 2021, 11, 5028 uses the well data to constrain the sparse inversion process so as to realize low-frequency compensation for noisy seismic data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
