该方法利用信号与噪声具有不同循环频率的特性实现了信噪分离，能够比较容易地从复杂背景中提取出微弱的特征信息。
Then one-dimensional and two-dimensional cyclostationary features of faults are put forward and they can be used to separate useful signals from noise and detect weak faults easily.
高密度数据的无假频特征更加适合于常规二维信噪分离方法。
Seismic data of dense spatial sample is more suitable to conventional 2-D methods for separation of signal and noise.
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