Abstract:Random noise widely exists in seismic data, either in prestack or poststack data. The dip-scanning singular value decomposition (SVD) algorithm has been proven to be very effective for eliminating seismic data noise, especially 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 developed a new joint denoising approach that uses empirical mode decomposition (EMD) and dip-scanning SVD to eliminate 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 event, and identifies noise points so that dip-scanning SVD can be used on horizontal events to effectively and efficiently 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 significantly 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 data. Moreover, the direction parameter applied by dip-scanning SVD may be modified depending on the dip angle of events. Seismic data random noise can be efficiently and automatically eliminated with relatively short window lengths and fewer constrained conditions of this approach.