基于提升算法和百分位数软阈值的小波去噪技术

在地震勘探领域,随机噪声一直是影响地震信号信噪比的主要因素之一,如何从被干扰的地震信号中有效去除随机噪声并保护有用信号具有重要的意义.针对经典小波变换在计算效率方面的缺陷,本文推荐应用提升算法实现第二代小波变换的构建,分析和对比了提升算法(Lifting Scheme)下不同小波变换方法的特性,选取更加符合小波域去噪原理的CDF 9/7双正交小波变换作为基本算法,同时应用了简单、有效的百分位数(Percentiles)软阈值进行信噪分离.通过理论模型处理,本方法可以在去噪能力和保护有用信号之间找到很好的平衡点.实际剖面的处理效果表明,此方法不仅能有效的滤除随机噪声,而且很好地保护有用信号,提高地震数据分析的精确性.

Application of wavelet transform based on lifting scheme and percentiles soft-threshold to elimination of seismic random noise

In seismic prospecting, random noise is always one of the foremost factors that affect SNR of seismic signal, so how to eliminatie random noise and protect useful information is very important. Aiming at the shortage of classical wavelet transforms that have lower computational efficiency, the paper recommends a second generation wavelet transform based on lifting scheme. By analyzing and comparing the characteristics of different wavelet transform so with the lifting scheme method, we choose the CDF 9/7 biorthogonal wavelet transform to be the basic algorithm because this method fits the denoising theory in wavelet domain better. At the same time, we apply a simple and effective percentiles soft-threshold to separate the signal from random noise. By processing the model, we find that the recommended method strikes a balance between eliminating random noise and protecting useful information. Processing field data shows that the method can perfectly filter random noise, protect useful information and improve the accuracy of seismic data analysis.