3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform

Seismic data contain random noise interference and are affected by irregular subsampling. Presently, most of the data reconstruction methods are carried out separately from noise suppression. Moreover, most data reconstruction methods are not ideal for noisy data. In this paper, we choose the multis...

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Bibliographic Details
Published in:Applied geophysics 2017-03, Vol.14 (1), p.87-95
Main Authors: Zhang, Hua, Chen, Xiao-Hong, Zhang, Luo-Yi
Format: Article
Language:English
Subjects:
Algorithms
Data acquisition
Earth and Environmental Science
Earth Sciences
Electric noise
Fourier transforms
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Noise
Noise control
Reconstruction
Robustness
Seismic studies
Seismology
Strategy
Thresholds
Transformations (mathematics)
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Summary:Seismic data contain random noise interference and are affected by irregular subsampling. Presently, most of the data reconstruction methods are carried out separately from noise suppression. Moreover, most data reconstruction methods are not ideal for noisy data. In this paper, we choose the multiscale and multidirectional 2D curvelet transform to perform simultaneous data reconstruction and noise suppression of 3D seismic data. We introduce the POCS algorithm, the exponentially decreasing square root threshold, and soft threshold operator to interpolate the data at each time slice. A weighing strategy was introduced to reduce the reconstructed data noise. A 3D simultaneous data reconstruction and noise suppression method based on the curvelet transform was proposed. When compared with data reconstruction followed by denoizing and the Fourier transform, the proposed method is more robust and effective. The proposed method has important implications for data acquisition in complex areas and reconstructing missing traces.
ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-017-0607-z