This paper proposes a background noise suppression technique for vibroseis data based on non-stationary signal decomposition. Taking the time-frequency correlation between sweep signals and effective signals as prior constraints, a non-stationary signal decomposition model is constructed. The orthogonal matching pursuit algorithm is adopted to realize the adaptive separation of effective signals and complex background noise, thereby breaking the limitation of traditional methods that assume stationary noise. Tests on synthetic data verify that the proposed method can effectively extract uncorrelated effective vibroseis signals amid complex noise environments. Applications to field seismic data acquired from the Gobi area of the Tarim Basin with low signal-to-noise ratio further demonstrate that this method outperforms the frequency-wavenumber predictive filtering method in background noise attenuation. The processed seismic sections feature improved event continuity and elevated overall signal-to-noise ratio.

vibroseis, high-efficiency acquisition, low signal-to-noise ratio (SNR), uncorrelated, background noise; nonstationary signal decomposition

提出一种基于非平稳信号分解的可控震源数据背景噪声压制技术。该方法以扫描信号与有效信号的时频关系为先验约束, 构建非平稳信号分解模型, 并采用正交匹配追踪算法, 实现有效信号与复杂背景噪声的自适应分离, 从而突破传统方法对噪声平稳性的假设限制。基于合成数据的测试结果表明, 该方法能在复杂噪声背景下有效地分离未相关可控震源的有效信号。塔里木盆地戈壁低信噪比实际数据的应用结果进一步表明, 相较于频率–波数域预测滤波法, 该方法对背景噪声的压制效果更佳, 最终结果中同相轴连续性更好, 信噪比更高。