Improving post-stack seismic data resolution based on Shearlet transform
GUO Aihua1,2, LU Pengfei1,3, YU Bo4, LU Chenghui4, WANG Bin5, WAN Lingna5
1. School of Information Engineering, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang, Jiangxi 330013, China; 3. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang, Jiangxi 330013, China; 4. Baikouquan Oil Production Plant of Petrochina Xinjiang Oilfield Branch, Karamay, Xinjiang 834000, China; 5. School of Software, East China University of Technology, Nanchang, Jiangxi 330013, China
Abstract：Deconvolution, Q-compensation, spectral whitening, wavelet transform, and other methods often enlarge noise and reduce the signal-to-noise ratio of seismic data while improving the resolution of seismic data. Since seismic random noise obeys Gaussian distribution and has no directivity, effective signals and random noise can be separated in the Shearlet domain. The seismic signal is transformed into the Shearlet domain by Shearlet transform. The coefficients in the Shearlet domain are compensated reasonably, and then the inverse Shearlet transform is carried out, which can improve the resolution of seismic data. Combined with the two characteristics of Shearlet transform, firstly, the random noise coefficients in the Shearlet domain are discarded, and at the same time, only the coefficients in the Shearlet domain within the dominant frequency band are compensated, and the frequency is raised. This not only improves the resolution of seismic data but also maintains the signal-to-noise ratio of seismic data. The proces-sing results of synthetic seismic data and post-stack real data show that this method can improve the resolution of post-stack seismic data.
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