Seismic random noise adaptive suppression based on the Shearlet transform
TONG Siyou1,2, GAO Hang1, LIU Rui1, CHEN Xueguo3
1. Key Laboratory of Submarine Geosciences and Prospecting Techniques, Ministry of Education, Qingdao, Shandong 266100, China;
2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266061, China;
3. Shengli Oilfield Branch Co., SINOPEC, Dongying, Shandong 257100, China
Abstract:Conventional thresholding methods use a unified threshold for all the transform domain coefficients.However for Shearlet transform,signals and noises in all the scales and directions are different,so a global hard threshold has some limits.Aiming at these limits,a random noise suppression is proposed with improved adaptive threshold functions.On the basis of local threshold,the Bayesian threshold is improved to form an adaptive threshold function suitable for the Shearlet transform.A relation between the signal-to-noise ratio (SNR) and threshold functions is built.Different SNRs have different weighting coefficients to adaptively calculate different thresholds.So seismic signals are preserved and the denoising is greatly improved.Tests on model and real data show that the proposed method can effectively suppress random noise and highlight weak signals.
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