摘要
Abstract
Random noise widely exists in seismic data,either in prestack or poststack data.The dip-scanning singu-lar value decomposition (SVD)algorithm has been proven to be very effective for eliminating seismic data noise,es-pecially for data with complex deep structures.However,limited volumes of data,especially data with strong noise, in a small window cannot completely reflect the strong correlation among traces.Therefore,the dip-scanning SVD application results are severely constrained.By examining factors that restrict the full utilization of SVD,we devel-oped a new joint denoising approach that uses empirical mode decomposition (EMD)and dip-scanning SVD to elimi-nate random noise in seismic data.First,this method uses EMD to reconstruct a signal to both reduce noise variance and enhance the correlation of effective signals among traces.Second,it automatically tracks seismic events with dip-scanning SVD to solve the singular value selection problem.Finally,it intercepts small data volumes,flattens an e-vent,and identifies noise points so that dip-scanning SVD can be used on horizontal events to effectively and effi-ciently eliminate noise.Through the development of a theoretical model and real data application,we prove that the EMD-SVD joint denoising method is a more efficient algorithm when compared with conventional dip-scanning SVD. Simulated and field data results show that the EMD-SVD method can effectively eliminate random noise and signifi-cantly increase the signal-to-noise ratio of seismic data,thereby significantly improving the quality of a stack section. For this purpose,proper noise-identifying threshold values should be set according to the features of real seismic da-ta.Moreover,the direction parameter applied by dip-scanning SVD may be modified depending on the dip angle of e-vents.Seismic data random noise can be efficiently and automatically eliminated with relatively short window lengths and fewer constrained conditions of this approach.关键词
奇异值分解/自动追踪/经验模式分解/随机噪声压制Key words
singular value decomposition (SVD)/automatic tracing/empirical mode decomposition (EMD)/ran-分类
天文与地球科学
