A Seismic Data Reconstruction Method Based on Spatial Constraint Compressed Sensing

A seismic data and compressed sensing technology, applied in seismology, seismic signal processing, geophysical measurement, etc., can solve the problems of low reconstruction data sparsity and reconstruction efficiency, lack of frame continuity information, and difficult selection of seismic data sparse bases.

Active Publication Date: 2020-07-10
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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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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  • A Seismic Data Reconstruction Method Based on Spatial Constraint Compressed Sensing
  • A Seismic Data Reconstruction Method Based on Spatial Constraint Compressed Sensing
  • A Seismic Data Reconstruction Method Based on Spatial Constraint Compressed Sensing

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Embodiment Construction

[0058] The embodiments are described in detail below in conjunction with the drawings.

[0059] A. Compressed sensing algorithm with spatial correlation

[0060] The reconstruction of seismic data based on compressed sensing can be expressed as:

[0061] y=Φf (1)

[0062] Where: y∈R M For the collected incomplete seismic data, f∈R N It is the original complete seismic data (M M×N Is the observation matrix. Use an over-complete dictionary Sparse representation of the complete seismic data f can be expressed as:

[0063]

[0064] In the formula, the number of non-zero K in the sparse solution x is much smaller than N, and then the collected incomplete seismic data y is obtained through the observation matrix Φ, expressed as

[0065] y=θx (3)

[0066] In the formula, the sensor matrix Φ and Irrelevant, and finally reconstruct the seismic data, namely

[0067]

[0068] Is an estimate of x. Finally, the original seismic data is reconstructed by the following formula

[0069]

[0070] To ...

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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 spatial 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 environmental, equipment, and human factors are not necessarily complete. If incomplete data is used for data interpretation and analysis, the analysis results will have large deviations, so the data needs to be reconstructed before data interpretation and analysis. In addition, for seismic exploration, which involves a large amount of data acquisition, a large amount of data will incur huge costs in all aspects of acquisition, storage, and transportation. Therefore, on the one hand, we hope to reduce the collected data as much as possible, and on the other hand, we hope that the reconstructed data is as accurat...

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G01V1/30
CPCG01V1/307
Inventor石敏朱震东朱登明
OwnerNORTH CHINA ELECTRIC POWER UNIV (BAODING)