Abstract:The 3D random noise attenuation (3D-RNA) is a spatial prediction filtering, which is widely applied to poststack seismic data in the Junggar Basin.With the linearity assumption of events in a short distance, this method improves strong energy signal-to-noise ratio (SNR), but it damages weak energy signals and blurs signals which are relative to faults and cracks.Therefore, a denoising method based on 3D anisotropic Laplacian filtering is proposed to solve these problems.Tests on model and real seismic data show that the proposed method is better than 3D-RNA in random noise suppression.Not only seismic data SNR is improved, but also weak signals are preserved.Meanwhile, geological-body edge characteristics become clearer and better fault imaging is achieved.The proposed method has been successfully applied in different areas in the Junggar Basin.This research provides a solid foundation for further reservoir inversion and seismic interpretation.
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