f-x域经验模式分解与多道奇异谱分析相结合去除随机噪声

近年来,经验模式分解法(EMD)因其处理非稳态地震信号的能力和易于实现而备受关注。总结了EMD在地震去噪中的应用情况,提出了一种基于f-x域EMD和多道奇异谱分析(MSSA)相结合的去噪新方法。该方法不同于f-x域EMD分别与f-x域预测滤波、小波阈值、曲波变换等相结合的各种去噪方法,它可以得到比f-x域MSSA更高的信噪比并能预测f-x域EMD中损失掉的线性能量。该方法的实现过程为:首先,对地震剖面应用f-x域EMD,保留所有相对水平的同相轴,这样在噪声剖面中留下很少的倾斜信号和随机噪声,然后在差异剖面中应用f-x域MSSA恢复倾斜信号,最后将水平信号和倾斜信号相加得到去噪剖面。理论测试和实际数据的处理结果验证了该方法的优越性。

 In recent years,empirical mode decomposition (EMD) has gotten a lot of public attention due to its capability in processing non-stationary seismic signal and its convenience to implement.In this paper,we summarized the existing applications of EMD in seismic data denoising,and proposed a new approach combining EMD in f-x domain and multi-channel singular spectrum analysis (MSSA) to attenuate random noise.Comparing those approaches combining f-x EMD with other methods such as prediction filtering in f-x domain,wavelet threshold and curvelet transformation,the new approach can obtain higher SNR compared to f-x MSSA and can predict the lost linear energy due to f-x EMD.The workflow of the method is as follows:implementing f-x EMD to the given seismic data at first,all the relative horizontal events can be retained,leaving part of useful dipping signals and some random noises in the difference profile;then f-x MSSA can be used in the difference profile to restore dipping signals;finally resultful profile can be gotlen by horizontal signals plus dipping signals.Two synthetic and a real data examples demonstrate the superiority of the proposed approach.