Simultaneous reconstruction and denoising of seismic data using multi-channel singular spectrum analysis based on hierarchical clustering
CAO Jingjie1,2, XU Changhao1,3, ZHU Yuefei1,2,4
1. Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang, Hebei 050031, China; 2. College of Mathematics and Physics, Hebei GEO University, Shijiazhuang, Hebei 050031, China; 3. Hebei Key Laboratory of Strategic Critical Mineral Resources, Shijiazhuang, Hebei 050031, China; 4. College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Abstract：High signal-to-noise ratio, high fidelity, and high-resolution seismic data are prerequisites for clear imaging of subsurface structures. During seismic data acquisition, factors such as unfavorable topography and bad channels lead to the phenomenon that seismic data cannot satisfy the sampling theorem, and reconstruction is required to obtain complete seismic data. The multi-channel singular spectrum analysis method is a common seismic data reconstruction and denoising method, and its key is to determine the number of effective singular values for each divided data block. This parameter needs to be determined based on different data characteristics, but current manual selection methods require a lot of labor and computational resources. For each data block, based on the framework of multi-channel singular spectrum analysis in the frequency domain, the discrete point curves and spectral patterns of singular values are analyzed, and a hierarchical clustering method is proposed to automatically identify the number of singular values corresponding to the effective signals, which improves the denoising effect and reconstruction quality of seismic data. Then, the hierarchical clustering method is adopted to cluster the singular value sequence obtained by singular value decomposition of the block Hankel matrix and acquire the number of effective singular values of each frequency. Additionally, the number of singular values taken within the effective frequency is maximized to obtain the effective singular values for data blocks. In the framework of simultaneous reconstruction and denoising by damped multi-channel singular spectrum analysis, an improved multi-channel singular spectrum analysis (MSSA) method is put forward to realize simultaneous reconstruction and denoising of seismic data. Tests conducted on simulated and actual seismic data show that the MSSA method based on hierarchical clustering is superior in simultaneous reconstruction and denoising, and the accuracy of the obtained singular values is verified. The method can avoid the manual selection of the effective number of singular values and reduce the workload of seismic data processing, which is of practical significance for the reconstruction and denoising of large-scale seismic data.
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