Abstract
Multiple wave is one of the important factors affecting the signal-to-noise ratio of marine seismic data. The model-driven-method (MDM) can effectively predict and suppress water-related multiple waves, while the quality of the multiple wave contribution gathers (MCG) can affect the prediction accuracy of multiple waves. Based on the compressed sensing framework, this study used the sparse constraint under L0 norm to optimize MCG, which can not only reduce the false in the prediction and improve the image accuracy, but also saves computing time. At the same time, the MDM-type method for multiple wave suppression can be improved. The unified prediction of multiple types of water-related multiple waves weakens the dependence of conventional MDM on the adaptive subtraction process in suppressing water-related multiple waves, improves the stability of the method, and simultaneously, reduces the computational load. Finally, both theoretical model and practical data prove the effectiveness of the present method.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (41702168, 41604047, 51408222) and the High-level Talents Start-up Project of North China University of Water Resources and Electric power (No. 40438). We thank all reviewers and editor for their valuable comments.
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This work was supported by the National Natural Science Foundation of China (No. 41504102) and the High-level Talents Initiation Project of North China University of Water Resources and Electric Power (No. 40438).
Lv Xiao-Chun Ph.D. graduated from China University of Geology (Wuhan) in 2014 with a major in Earth Exploration and Information Technology, is now a lecturer in the college of Geosciences and Engineering, North China University of Water and Electric Power and is mainly engaged in the study of seismic data processing. forward modeling of seismic wave equation and seismic observation system design methods.
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Lv, XC., Zou, MJ., Sun, CX. et al. Multiple wave prediction and suppression based on L0-norm sparsity constraint. Appl. Geophys. 16, 483–490 (2019). https://doi.org/10.1007/s11770-019-0773-2
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DOI: https://doi.org/10.1007/s11770-019-0773-2