中文摘要：

实际地震信号通常可表示为具有波形特征差异的多种基本波形信号的线性组合,如叠前道集中的工频干扰噪声与有效波信号、面波噪声与体波信号等.选择单一数学变换方法,往往不易实现地震信号的稀疏表示.近年来发展的形态成分分析理论,通过联合多种数学变换,可实现对复杂信号的稀疏表示.本文根据单道地震记录中面波与体波信号波形结构特征的差异性,提出一种基于形态成分分析的面波噪声衰减方法.针对面波的低频、窄带以及频散特性选择一维平稳小波变换作为其稀疏表示字典,而针对体波波形的局部相关特性选择局部离散余弦变换作为其稀疏表示字典,建立基于双波形字典的形态成分分析模型,通过求解该稀疏优化问题获得最终的信噪分离结果.理论模型和实际地震资料处理证实该方法不仅能够衰减单炮地震记录中的强面波干扰噪声,同时能够更好地保护有效信号的波形特征与频谱带宽,为地震资料的后续处理和分析提供良好的数据基础.

英文摘要：

Real seismic signals can usually be represented as a linear combination of multiple basic waveforms of different morphological characteristics, such as powerline single frequency interference noise and effective seismic signal, ground roll and body wave signals. By selecting a single mathematical transformation method, it will hard to achieve sparse representations of seismic signals. Morphological component analysis （MCA） theory has been developed in recent years, which can sparsely decompose complex signals through the augmented dictionary of basic mathematical transformations. According to the waveform divergence of ground-roll and bodywave signals, this paper proposes a ground-roll attenuation method based on the morphological component analysis theory. To match with the requirements of the MCA, the 1D stationary wavelet transform （SWT） is chosen as the Sparse representation dictionary of ground-roll due to its low frequency and narrow spectral bandwidth nature. Meanwhile, the local discrete cosine transform （LDCT） is chosen as the sparse representation dictionary of body waves due to its local interdependency characteristics. The optimization model on basis of the MCA is then built on the two amalgamated dictionaries and properly solved to obtain the final signal-noise separation results. The theoretical and real data processing results confirm that the method can not only attenuate strong ground-roll noise in seismic records but also preserves the waveform characteristics and spectral bandwidth of effective signal well. The method can provide high quality data for subsequent processing and analysis.