A novel seismic random noise suppression method based on wavelet threshold and Lipschitz

The seismic random noise suppression is very important in the processing and interpretation of seismic data. Wavelet transform is an effective tool to remove random noise. Aiming at the problems of signal propagation point omission and misselection, insignificant denoising effect and poor stability of the traditional wavelet maximum mode (WMM) method, this paper proposes an improved wavelet mode maximum value (IMWMM) denoising method. The Lipschitz of wavelet transform with signal and noise has different features used to distinguish signal and noise. Comparing the propagation points of adjacent scales and removing the points with reduced mode values achieve the separation of seismic signal and noise. The synthetic data, Stanford Earthquake Dataset (STEAD) data and real-field seismic signal experimental results prove the validity of the IMWMM method for both suppressing random noise and preserving seismic signal. The real-field seismic data came from Wenchuan experimental results present that the proposed IMWMM method has effectiveness of random noise suppression with SNR above 28.4527 dB, that has improvement for quality of seismic data.