Research On The Seismic Signal Process Based On Wavelet Transform

The prerequisite condition of improving the resolving power is to improve the SNR of signal, which need to remove the noise from seismic data. There are two kind of noise in seismic data: coherent noises and random noises. Coherent noises are disciplinary and can be removed according to the rules. Usually, coherent noises comprise of surface wave, multiple wave, refraction and ringing. Random noises have uniform rules and they comprise of circumstance noise, measurement error and ground microseism. The paper discusses the random noise removing.Fourier transformation and short Fourier transformation are used in many seismic data noise removing. But Fourier transformation is applicable to deterministic signal and stationary and related stochastic processes. The practical signal usually is time-varied and non-stationary and it is important to comprehend its local characteristic. Short Fourier transform can't consider time resolution and frequency resolution. Recently, wavelet transform has become hotspot in seismic signal process and many fruits have been acquired. Wavelet transform has good local characteristic in time and frequency domain and can decomposes arbitrary detail of signal.The detail characteristic of signal and noise are different during wavelet transform, which can be used to remove noise of seismic data in order to distinguish effective signal and noise. The effective signal can be reserved when the noises are removed as far as possible. The dyadic discrete wavelet transform is used to analyze seismic data and remove noise.The characteristic of seismic wave scene is introduced first, then the main noise styles in seismic data are discussed. The noise removing method of coherent noise and random noise are also introduced with their characteristics. Then, the paper discusses the discipline of wavelet transform and several wavelet functions. As the different propagation characteristic of signal and noise on different scales of modules maxima, the signal can be de-noised and reconstructed. The method can remove random noise in seismic data preferably, and the signal and noise can be distinguished on different scales, which can reserve the effective high frequency information as far as when removing high frequency noise. The white noise has native singular, its amplitude denseness decrease with scales, so if the amplitude and denseness of local modules maxima for a signal decrease quickly with scales, it indicates that the singular of this point is controlled by noise and should be removed. Therefore, a modules maxima algorithm controlled by noise is adopted in the paper to remove noise and acquires prefer results.The general de-noising method based on wavelet threshold is also introduced and its performance is analyzed. Although the threshold method can remove random noise, the effect couldn't turn up trumps. So the weighted threshold combined soft and hard-threshold is adopted in the paper, the process results demonstrate that it can approach to signal preferably and restrain Gibbs oscillation.The noise removing based on modules maxima of wavelet transform has the problem such as bad stability and large calculation, so inter-scale relativity de-noising method is discussed. The wavelet coefficients are enhanced relatively by correlating the inter-scale coefficients to distinguish the signal and noise. The paper analyzes the noise removing effect such as iterative course on same scale, different style and SNR, and an improved inter-scale relativity de-noising algorithm is discussed. The results demonstrate its effectiveness.At last, the work and harvest is summarized, the deficiency and future direction are also indicated.