【摘要】 小波分析是20世纪80年代中后期逐渐发展起来的一个新的数学分支,它具有多分辨率分析的优良特性,在许多方面获得了广泛应用。信号中混入噪声之后,会引起信号的奇异性变化。随机噪声和有效信号本身在奇异点的奇异指数大小不同,从而它们的小波变换的模极大值在不同尺度下的传播行为也不一样。依据此区别,对于一个含噪地震道记录信号进行小波分解(即多分辨率分解)之后,在模极大值图上去除那些幅度随尺度增加而减小的极值点(对应噪声的极值点),而保留幅度随尺度增加而增大的点(对应信号突变点位置),这样就可以在模极大值图上达到去除噪声的目的。小波分析技术在地震信号噪声处理中,去噪效果明显优于传统的傅立叶变换方法。更多还原

【Abstract】 Wavelet analysis is a new math branch that has been developing since the late 1980’sWavelet analysis has excellent characteristics of multiresolution analysis(MRA) and has been widely used in many fieldsThe signal mixed with noise will occur oddity changesBecause the Oddity Index of random noise is different from that of effective signals on the degree,their spread behaviors of module max are different each other under various measuresAccording to this difference,an earthquake signal including noise is divided into different frequency bands by waveletanalysisWe can wipe off the noise’s maximum dot,whose scope decreases with the increase of measure,and retain the signal’s extremum dot,whose scope increases with the increase of measureSo,we can rebuild the original signal through getting rid of noise from the module max chatIn the denoise of earthquake signal,Wavelet analysis has the obvious advantage over conventional Fourier transform in denoise effect更多还原