During seismic acquisition, the received seismic data typically comprise many components, such as effective reflections and various interferences. Some components, such as industrial electrical interference and traffic vibrations, manifest as the equidistant narrowband discrete spectra (ENBD-spectra) in the frequency domain. Morphological component analysis (MCA) is widely used for separating different component from complicated seismic data. Therefore, it has been successfully used to extract the narrowband components from seismic data. However, the conventional MCA method overlooks equidistant feature of ENBD-spectra component in seismic data separation. In this study, we propose an improved MCA method that uses the interval between neighboring spectrum peaks as a constraint to separating the data with ENBD-spectra component. Two types of seismic datasets are used to show the proposed MCA’s effectiveness. The first type of dataset contains industrial electrical interference, while another type of dataset contains high-speed train (HST)-induced seismic signals. Both synthetic data examples and real data examples show that the proposed method has better performance in separating the seismic data with ENBD-spectra component and keeping the fidelity of separation compared with the conventional MCA method.

中文翻译：

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基于等距谱约束形态成分分析的地震数据分离


在地震采集过程中，接收到的地震数据通常包含许多分量，例如有效反射和各种干扰。某些分量（例如工业电气干扰和交通振动）在频域中表现为等距窄带离散频谱（ENBD 频谱）。形态成分分析（MCA）广泛用于从复杂的地震数据中分离不同的成分。因此，它已成功用于从地震数据中提取窄带分量。然而，传统的MCA方法忽略了地震数据分离中ENBD谱分量的等距特征。在本研究中，我们提出了一种改进的 MCA 方法，该方法使用相邻频谱峰值之间的间隔作为约束来分离具有 ENBD 频谱分量的数据。使用两种类型的地震数据集来显示所提出的 MCA 的有效性。第一种类型的数据集包含工业电气干扰，而另一种类型的数据集包含高速列车（HST）诱发的地震信号。合成数据实例和真实数据实例均表明，与传统的MCA方法相比，该方法在分离具有ENBD频谱分量的地震数据和保持分离保真度方面具有更好的性能。