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Noise reduction for underwater acoustic signals has attracted considerable attention over the last few decades. Among the numerous techniques, wavelet soft-thresholding (STH) has been considered as one of the most effective noise reduction approaches, as it achieves near complete success in minimizing the mean-squared-error (MSE) and eliminating oscillations caused by noise. However, a limitation...
A main task of geophysical exploration is to remove random noises in seismic data processing to improve the signal-to-noise ratio. Recently wavelet theory is applied widely to remove random noises in seismic data processing. But conventional wavelet threshold de-noising method does not utilize the correlations of seismic data to remove random noises. So a new de-noising method is proposed in this...
Signal denoising can be considered as a function regression problem. LS-SVR (least squares-support vector regression) based on Ricker wavelet kernel function is applied to the practical seismic prospecting data denoising in this paper. To adapt LS-SVR well to the practical seismic data, the parameters including Ricker wavelet kernel parameter f and regularization parameter ? are selected automatically...
Discrete wavelet transform is an effective tool to disintegrate the time variant seismic data in time-frequency manner. This work incorporates the wavelet transform in the blind deconvolution technique to deal with the inherent non-stationarity present in seismic data and to improve the SNR of seismic data. Time varying nature of seismic data is the result of a depth varying character of seismic source...
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