Research Of Seismic Signal De-noising Ways Based On Wavelet Analysis

Seismic exploration is an important geophysical method, seismic data collected in the field includes the information about the subsurface structure and lithology, but the information is overlaid by background interference, and distorted by external factors. So field datas can not be used to do geological interpretation directly. Thereby, increasing signal to noise ratio is an important task of seismic signal processing. This paper focuses on research of seismic signal de-noising ways based on wavelet analysis.This paper firstly introduces the characteristics and causes of the noise included in the seismic signal, and then describes wavelet analysis theory in detail; then studies wavelet threshold de-noising, wavelet transform modulus maxima de-noising and wavelet packet analysis. The threshold in the wavelet threshold de-noising algorithm is selected by empirical formula. The de-noising effect is not good for seismic signal of low signal to noise ratio, because the results lead to large effective signal loss and low resolution. For these deficiencies of common threshold selection, we propose a new de-noising method based on wavelet entropy high-resolution threshold. In order to reserving the high frequency information of the effective signal and improving resolution, the high-frequency wavelet coefficient is processed by the correlation. Then the threshold is selected by the wavelet entropy. The wavelet entropy is the combination of wavelet transform entropy and information entropy. Through the de-noising of noisy ricker wavelet, synthetic seismic signal and actual seismic signal, the good effect of this algorithm has been proved. The largest scale threshold in wavelet transforms modulus maxima de-noising algorithm is selected by Exp, and it is prone to be a wrong choice during the process of searching the modulus maximum points generated by valid signal. For these deficiencies of wavelet transform modulus maxima de-noising, we propose a wavelet entropy and correlation with the modulus maxima de-noising method. The threshold on the largest scale is selected by the wavelet entropy, and the wrong choice issue during the the process of searching is solved by the correlation. The improved algorithm has a good de-noising effect during the the process of de-noising the noisy ricker wavelet, the synthetic seismic signal and the actual seismic signal. In addition, we use the method of wavelet packet layered threshold and global threshold to process the noisy ricker wavelet, the synthetic seismic signal and the actual seismic signal. The wavelet packet layered threshold also has a good de-noising effect.