【摘要】 压制随机噪声是地震数据处理过程中的一个重要环节,目前大多数去噪技术都不同程度存在去噪效果差、易损伤有效信号等问题。利用经验模态分解可将信号自适应地分解为不同特征尺度固有模态函数的优点,及小波变换模极大值滤波方法对噪声的依赖性较小且适合于低信噪比信号去噪的优势,构造了一种经验模态分解与小波变换模极大值相结合的新的去噪算法,该算法很好地实现了地震有效信号与随机噪声的分离,有效提高了地震数据信噪比。将该算法应用于仿真实验和实际地震数据处理,结果都表明该方法明显优于常规经验模态分解去噪效果。更多还原

【Abstract】 Random noise attenuation is an important step in seismic data processing;however,most random noise attenuation methods have some problems such as poor de-noising effect and damaging effective signals to a certain degree.Empirical Mode Decomposition(EMD)can self-adaptively decompose the signal into multi-scale Intrinsic Mode Function(IFM)and wavelet transform modulus maxima de-noising method is available for low S/N signals.We construct a new random noise attenuation algorithm by combining EMD with wavelet transform modulus maxima de-noising method,which can effectively separate signals form random noise and well improve signal to noise ratio(S/N)of seismic data.The algorithm is applied on numerical simulation and field data for random noise attenuation.The results reveals that the de-noising effect of new algorithm is obviously better than that of conventional EMD.更多还原