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Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

Reconstruction of 3D non-uniformly sampled seismic data along two spatial coordinates using non-equispaced curvelet transform

Hua Zhang 1 2 Su Diao 1 Haiyang Yang 1 Guangnan Huang 1 Xiao Chen 1 Lei Li 1
+ Author Affiliations
- Author Affiliations

1 Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang, Jiangxi 330013, China.

2 Corresponding author. Email: zhhua1979@163.com

Exploration Geophysics 49(6) 906-921 https://doi.org/10.1071/EG17135
Submitted: 12 October 2017  Accepted: 4 February 2018   Published: 24 April 2018

Abstract

Seismic data acquisition often faces the challenge of non-uniformly sampled data with missing traces. Only a few existing multitrace reconstruction methods can natively handle non-uniformly sampled data with missing traces. In this paper, we propose the non-equispaced fast discrete curvelet transform (NFDCT)-based reconstruction method designed for 3D seismic data that are non-uniformly sampled along two spatial coordinates. By partitioning 3D seismic datasets into time slices along source-receiver coordinates, we introduce 2D non-equispaced fast Fourier transform in the conventional fast discrete curvelet transform and formulate a regularised inversion of operator that links the uniformly sampled curvelet coefficients to non-uniformly sampled data. Numerically, the uniform curvelet coefficients are calculated by solving the L1-norm problem via the spectral projected-gradient algorithm. With the uniform curvelet coefficients, the NFDCT is formed via the conventional inverse curvelet transform and is used to reconstruct 3D non-uniformly sampled seismic data along two spatial coordinates. At the hand of reconstructed results from synthetic and field data, we demonstrate that the proposed method shows significant improvement over the conventional anti-leakage Fourier transform-based reconstruction method. The method we propose, which has a strong anti-aliasing and anti-noise ability, can be used to reconstruct the subset of observed data to a specified uniform grid along two spatial coordinates.

Key words: L1 norm, data reconstruction, non-equispaced curvelet transform, non-equispaced fast Fourier transform.


References

Candès, E. J., 1998, Ridgelets: theory and applications: Ph.D. thesis, Stanford University.

Candès, E. J., and Donoho, D. L., 2004, New tight frames of curvelets and optimal representations of objects with piecewise-C2 singularities: Communications on Pure and Applied Mathematics, 57, 219–266
New tight frames of curvelets and optimal representations of objects with piecewise-C2 singularities:Crossref | GoogleScholarGoogle Scholar |

Candès, E. J., Demanet, L., Donoho, D., and Ying, L., 2006, Fast discrete curvelet transforms: SIAM Multiscale Modeling and Simulation, 5, 861–899
Fast discrete curvelet transforms:Crossref | GoogleScholarGoogle Scholar |

Cao, J. J., and Zhao, J. T., 2016, Simultaneous seismic interpolation and denoising based on sparse inversion with a 3D low redundancy curvelet transform: Exploration Geophysics, 47, 213–220

Cao, J. J., Wang, Y. F., and Wang, B. F., 2015, Accelerating seismic interpolation with a gradient projection method based on tight frame property of curvelet: Exploration Geophysics, 46, 253–260
Accelerating seismic interpolation with a gradient projection method based on tight frame property of curvelet:Crossref | GoogleScholarGoogle Scholar |

Chen, S. S., Donoho, D. L., and Saunders, M. A., 1998, Atomic decomposition by basis pursuit: SIAM Journal on Scientific Computing, 20, 33–61
Atomic decomposition by basis pursuit:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXkvV2ltrw%3D&md5=ff8adb6945e8b608f7a3dd32c5ba2aacCAS |

Chiu, S. K., 2014, Multidimensional interpolation using a model-constrained minimum weighted norm interpolation: Geophysics, 79, V191–V199
Multidimensional interpolation using a model-constrained minimum weighted norm interpolation:Crossref | GoogleScholarGoogle Scholar |

Duijndam, A. J. W., Schonewille, M. A., and Hindriks, C. O. H., 1999, Reconstruction of band-limited signals, irregularly sampled along one spatial direction: Geophysics, 64, 524–538
Reconstruction of band-limited signals, irregularly sampled along one spatial direction:Crossref | GoogleScholarGoogle Scholar |

Ely, G., Aeron, S., Ning, H., and Kilmer, M. E., 2015, 5D seismic data completion and denoising using a novel class of tensor decompositions: Geophysics, 80, V83–V95
5D seismic data completion and denoising using a novel class of tensor decompositions:Crossref | GoogleScholarGoogle Scholar |

Fomel, S., 2003, Seismic reflection data interpolation with differential offset and shot continuation: Geophysics, 68, 733–744
Seismic reflection data interpolation with differential offset and shot continuation:Crossref | GoogleScholarGoogle Scholar |

Fomel, S., and Liu, Y., 2010, Seislet transform and seislet frame: Geophysics, 75, V25–V38
Seislet transform and seislet frame:Crossref | GoogleScholarGoogle Scholar |

Gan, S., Wang, S., and Chen, Y., 2016, Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform: Journal of Applied Geophysics, 130, 194–208
Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform:Crossref | GoogleScholarGoogle Scholar |

Gao, J. J., Stanton, A., and Sacchi, M. D., 2015, Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising: Geophysics, 80, V173–V187
Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising:Crossref | GoogleScholarGoogle Scholar |

Hennenfent, G., and Herrmann, F. J., 2008, Simply denoise: wavefield reconstruction via jittered undersampling: Geophysics, 73, V19–V28
Simply denoise: wavefield reconstruction via jittered undersampling:Crossref | GoogleScholarGoogle Scholar |

Hennenfent, G., Fenelon, L., and Herrmann, F. J., 2010, Nonequispaced curvelet transform for seismic data reconstruction: a sparsity-promoting approach: Geophysics, 75, WB203–WB210
Nonequispaced curvelet transform for seismic data reconstruction: a sparsity-promoting approach:Crossref | GoogleScholarGoogle Scholar |

Herrmann, F. J., 2010, Randomized sampling and sparsity: getting more information from fewer samples: Geophysics, 75, WB173–WB187
Randomized sampling and sparsity: getting more information from fewer samples:Crossref | GoogleScholarGoogle Scholar |

Herrmann, F. J., and Hennenfent, G., 2008, Non-parametric seismic data recovery with curvelet frames: Geophysical Journal International, 173, 233–248
Non-parametric seismic data recovery with curvelet frames:Crossref | GoogleScholarGoogle Scholar |

Hindriks, C. O. H., and Duijndam, A. J. W., 2000, Reconstruction of 3D seismic signals irregularly sampled along two spatial coordinates: Geophysics, 65, 253–263
Reconstruction of 3D seismic signals irregularly sampled along two spatial coordinates:Crossref | GoogleScholarGoogle Scholar |

Jin, S., 2010, 5D seismic data regularization by a damped least-norm Fourier inversion: Geophysics, 75, WB103–WB111
5D seismic data regularization by a damped least-norm Fourier inversion:Crossref | GoogleScholarGoogle Scholar |

Kim, B., Jeong, S., and Byun, J., 2016, An efficient interpolation approach for insufficient 3D field data: Exploration Geophysics, 47, 213–220

Kreimer, N., Stanton, A., and Sacchi, M. D., 2013, Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction: Geophysics, 78, V273–V284
Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction:Crossref | GoogleScholarGoogle Scholar |

Krilov, D., Vaniarho, Y., and Basaev, D., 2016, Smart averaging interpolation algorithm comparative test: Geophysical Prospecting, 64, 642–656
Smart averaging interpolation algorithm comparative test:Crossref | GoogleScholarGoogle Scholar |

Kumar, R., Silva, C., Akalin, O., Aravkin, A., and Mansour, H., 2015, Efficient matrix completion for seismic data reconstruction: Geophysics, 80, V97–V114
Efficient matrix completion for seismic data reconstruction:Crossref | GoogleScholarGoogle Scholar |

Kunis, S., 2006, Nonequispaced FFT: generalisation and inversion: Ph.D. thesis, Lübeck University.

Ma, J., 2013, Three-dimensional irregular seismic data reconstruction via low-rank matrix completion: Geophysics, 78, V181–V192
Three-dimensional irregular seismic data reconstruction via low-rank matrix completion:Crossref | GoogleScholarGoogle Scholar |

Naghizadeh, M., and Sacchi, M. D., 2007, Multistep autoregressive reconstruction of seismic records: Geophysics, 72, V111–V118
Multistep autoregressive reconstruction of seismic records:Crossref | GoogleScholarGoogle Scholar |

Naghizadeh, M., and Sacchi, M. D., 2010, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data: Geophysics, 75, WB189–WB202
Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data:Crossref | GoogleScholarGoogle Scholar |

Oropeza, V., and Sacchi, M. D., 2011, Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis: Geophysics, 76, V25–V32
Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis:Crossref | GoogleScholarGoogle Scholar |

Ronen, J., 1987, Wave equation trace interpolation: Geophysics, 52, 973–984
Wave equation trace interpolation:Crossref | GoogleScholarGoogle Scholar |

Spitz, S., 1991, Seismic trace interpolation in the f-x domain: Geophysics, 56, 785–794
Seismic trace interpolation in the f-x domain:Crossref | GoogleScholarGoogle Scholar |

Trad, D., 2009, Five-dimensional interpolation: recovering from acquisition constraints: Geophysics, 74, V123–V132
Five-dimensional interpolation: recovering from acquisition constraints:Crossref | GoogleScholarGoogle Scholar |

Turquais, P., Asgedom, E., and Soellner, W., 2017, Structured dictionary learning for interpolation of aliased seismic data: SEG Technical Program Expanded Abstracts, 4257–4261.

van den Berg, E., and Friedlander, M. P., 2009, Probing the Pareto frontier for basis pursuit solutions: SIAM Journal on Scientific Computing, 31, 890–912
Probing the Pareto frontier for basis pursuit solutions:Crossref | GoogleScholarGoogle Scholar |

Whiteside, W., Guo, M., Sun, J., and Wang, B., 2014, 5D data regularization using enhanced antileakage Fourier transform: SEG Technical Program Expanded Abstracts, 3616–3620.

Xu, S., Zhang, Y., Pham, D., and Lambaré, G., 2005, Antileakage Fourier transform for seismic data regularization: Geophysics, 70, V87–V95
Antileakage Fourier transform for seismic data regularization:Crossref | GoogleScholarGoogle Scholar |

Xu, S., Zhang, Y., Pham, D., and Lambaré, G., 2010, Antileakage Fourier transform for seismic data regularization in higher dimensions: Geophysics, 75, WB113–WB120
Antileakage Fourier transform for seismic data regularization in higher dimensions:Crossref | GoogleScholarGoogle Scholar |

Xue, Y., Ma, J., and Chen, X., 2014, High-order sparse Radon transform for AVO-preserving data reconstruction: Geophysics, 79, V13–V22
High-order sparse Radon transform for AVO-preserving data reconstruction:Crossref | GoogleScholarGoogle Scholar |

Yang, H., Li, J., and Ma, S., 2015, A fast Fourier inversion strategy for 5D seismic data regularization: SEG Technical Program Expanded Abstracts, 3910–3914.

Yu, S. W., Ma, J. W., and Osher, S., 2016, Monte Carlo data-driven tight frame for seismic data recovery: Geophysics, 81, V327–V340
Monte Carlo data-driven tight frame for seismic data recovery:Crossref | GoogleScholarGoogle Scholar |

Zhang, H., and Chen, X. H., 2013, Seismic data reconstruction based on jittered sampling and curvelet transform: Chinese Journal of Geophysics, 56, 1637–1649

Zhang, H., Chen, X. H., and Li, H. X., 2015, 3D seismic data reconstruction based on complex-valued curvelet transform in frequency domain: Journal of Applied Geophysics, 113, 64–73
3D seismic data reconstruction based on complex-valued curvelet transform in frequency domain:Crossref | GoogleScholarGoogle Scholar |

Zhang, H., Chen, X. H., and Zhang, L. Y., 2017, 3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform: Applied Geophysics, 14, 87–95
3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform:Crossref | GoogleScholarGoogle Scholar |

Zwartjes, P. M., 2005, Fourier reconstruction with sparse inversion: Ph.D. thesis, Delft University of Technology.