Compressed sensing data reconstruction technology in joint FK and Shearlet domain
YAN Haiyang1,2,3,4, ZHOU Hui1,2,3, LIU Haibo4, XU Zhaohong4, SUN Zandong4, LIU Zhao4
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China; 2. CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum (Beijing), Beijing 102249, China; 3. College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China; 4. BGP Offshore, CNPC, Tianjin 300457, China
Abstract:The irregularity of seismic data caused by sparse source-receiver acquisition or field acquisition affects the imaging quality of seismic data. The reconstruction method based on compressed sensing theory can effectively reconstruct seismic data under the condition of limited sampling. Since the spatial random absence of seismic traces is shown as spatial aliasing in the wavenumber domain, we transform the reconstruction of seismic traces in the spatiotemporal domain into random noise suppression in the frequency-wavenumber (FK) domain. Specifically, the multi-scale and multi-directional Shearlet transform is performed on FK-domain data, and by iterative inversion to eliminate spatial aliasing in the FK domain, the spatial reconstruction of seismic traces is realized. The method in this paper performs the Shearlet transform after the FK transform, which can be viewed as a new sparse basis transform. Since the spectrum of the global random sampling factor is characterized by white noise, and the spectrum of the piecewise random sampling factor is characte-rized by blue spectra, the interference of the effective signal and aliasing of the piecewise sampling data is relatively reduced, which is more conducive to data reconstruction. The reconstruction experiment indicates that the reconstruction accuracy in the FK + Shearlet domain for piecewise random sampling is higher than that in the Shearlet domain for global or piecewise random sampling as well as that in the FK + Shearlet domain for global random sampling.
闫海洋, 周辉, 刘海波, 徐朝红, 孙赞东, 刘昭. FK和Shearlet域联合压缩感知数据重构技术[J]. 石油地球物理勘探, 2022, 57(3): 557-569.
YAN Haiyang, ZHOU Hui, LIU Haibo, XU Zhaohong, SUN Zandong, LIU Zhao. Compressed sensing data reconstruction technology in joint FK and Shearlet domain. Oil Geophysical Prospecting, 2022, 57(3): 557-569.
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