Application of Seismic Data Denoising Method Based on Curvelet Transform
How to remove noise without losing effective signals is a major challenge in seismic data processing.In the experiment,a Curvelet adaptive threshold denoising method is applied to seismic data processing.The Curvelet transform coefficient is constrained by the adaptive threshold,and the estimated coefficient is mapped by the threshold function.Finally,the estimated coefficient is inverted to achieve seismic data denoising,which effectively improves the signal-to-noise ratio of seismic data.The model and actual data processing results show that the Curvelet adaptive threshold denoising method effectively suppresses the noise while protecting the effective signals,overcomes the defect of new noise generated by F-X domain filtering,and achieves the good denoising results.
Curvelet transformF-X domain filteringthresholddenoisingsignal-to-noise ratio
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