Seismic random noise suppression based on scale-adaptive 3D-Shearlet transform
CHENG Hao1, WANG Deli2, WANG Ende1, FU Jianfei1, HOU Zhenlong1
1. Key Laboratory of Safe Mining of Deep Metal Mines, Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China; 2. College of Geo-Exploration Sciences and Technology, Jilin University, Changchun, Jilin 130026, China
Abstract：We use a scale-adaptive 3D-Shearlet transform to suppress random noise of multi-source seismic data.First multi-source seismic data is transformed into the 3D-Shearlet domain.In this domain,seismic data can be represented more sparsely.Since seismic signals distribute in a low scale and random noise distributes in the all scales,scale-adaptive factors suppress random noise based on hard thresholds.With the inverse 3D-Shearlet transform,the de-noising seismic data can be obtained.According to numerical and seismic data tests,the proposed approach can better suppress random noise than the 2D-Shearelet transform and the 3D-Shearlet transform;however it needs a larger computer memory,and may damage weak signals.
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