Research And Application Of Seimic Signal De-Noising Ways Based On Wavelet

To improve the resolution of seismic data is a main task of geophysical exploration, and its prerequisite condition is to improve the signal-to-noise ratio. It is necessary to remove coherent noises and random noises in seismic data processing in order to improve the signal-to-noise ratio. Research on how to eliminate random noises effectively is the main work of this paper, and how to get rid of linear coherent noises, such as surface wave, is also concerned. In this paper, other techniques are discussed, such as wavelet transform, KL transform and median filtering.This paper makes use of integrated research ways based on wavelet to complete several separation methods of signal-noise as follows. Based on conventional de-noising method by threshold filter in wavelet domain, this paper presents a new method, which is named adaptive de-noising method by improved threshold function in wavelet domain. Theoretical model and practical data processing results show this method has better de-noising effect, and can hold the main details of seismic data well. For the different features of signals and noises in wavelet transform, a method, which is named de-noising method by combining time-frequency correlation analysis with adaptive threshold filter in wavelet domain, is introduced to process seismic data including noises. This paper proposes a method, which combines wavelet package analysis, KL transform and median filter, to process seismic data. This method first uses Wavelet package transform to subtly separate different frequency signals, after significant signals and noises are decomposed by wavelet...