Research on Denoising Methods by Combining VMD and Shearlet Transform
The noise signal in seismic data greatly reduces the signal-to-noise ratio of seismic data,which makes it difficult for the subsequent processing and interpretation,so it is of great significance to study a denoising method.This paper proposes a new denoising method based on variational mode decomposition(VMD)and Shearlet transform.Firstly,VMD is used to decompose seismic data into a series of intrinsic mode functions(IMF)with different center frequencies.Then,Shearlet transform is used to denoise the IMF components with noise and reconstruct the processed components.This method introduces a seismic data de-noising method based on sparse representation,taking into account the advantages of VMD and Shearlet transform,and can effectively remove noise.The test results of synthetic sig-nal,model and actual data show that the signal-to-noise ratio(SNR)of the proposed method is 1.69 and 1.87,higher than that of VMD and wavelet methods respectively,and the mean square error(MSE)value is reduced nearly half.The test results of analog signal and real data show that the proposed algorithm can better preserve the seismic data features while re-moving noise.
seismic signal denoisingVMDShearlet transformseismic prospecting
NETL
NSTL
万方数据
维普
