Seismic data reconstruction method based on spatial constrained compressed sensing

Abstract

The invention belongs to the technical field of oilfield seismic big data reconstruction, and particularly relates to a seismic data reconstruction method based on spatial constrained compressed sensing. The method comprises the steps that a part of data is taken as training data, K-SVD dictionary learning is used for training an overcomplete dictionary to reconstruct original earthquake data; a joint sparse decomposition method is used for extracting shared spatial information, and a sensing matrix in a compression sensing algorithm is transformed; a sparsity self-adaptive matching tracking algorithm is improved, an initial sparsity estimation method is introduced, and the data is reconstructed by adopting a variable step size strategy. The reconstructed result not only is clear in detail, but also greatly reduces the operation time compared with IRLS and SAMP, and the horizontal transition is smoother, which indicate that the designed algorithm utilizes spatial related information and the reconstructed result is more real.

Description

technical field [0001] The invention belongs to the technical field of oilfield seismic big data reconstruction, and in particular relates to a seismic data reconstruction method based on space-constrained compressed sensing. Background technique [0002] Data reconstruction is an important part of data processing. In the signal field, the signal data collected due to factors such as environment, equipment, and human factors may not be complete. If incomplete data is used for data interpretation and analysis, there will be large deviations in the analysis results, so the data needs to be reconstructed before data interpretation and analysis. In addition, for seismic exploration, a large amount of data acquisition work, a large amount of data will generate huge costs in various links such as acquisition, storage and transportation. Therefore, on the one hand, it is hoped to reduce the collected data as much as possible, and on the other hand, it is hoped that the reconstruc...

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Problems solved by technology

[0008] In order to solve the problem that the traditional seismic data reconstruction algorithm needs to meet the limitation of Nyquist sampling theorem, it is difficult to select the sparse base of seismic data reconstruction using compressed sensing algorithm and the lack of frame continuity information, and the reconstruction algorithm needs to know the reconstruction data sparsity and low reconstruction efficiency. Problem, the present invention proposes a seismic data reconstruction method based on spatially constrained compressed sensing, including:

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