Data-driven simultaneous seismic inversion of multiparameters via collaborative sparse representation

She, Bin; Wang, Y; Liang, Jiajun; Hu, Guangmin

doi:10.1093/gji/ggz116

Cited by 15 publications

(7 citation statements)

References 61 publications

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“…The traditional regularization methods including total variation [4-6, TV], Tikhonov [7,TK], and their hybridization [8][9][10] simply introduced some hypothetical or empirical attributes such as smoothness or sharpness on m, which lacks adaptation to complex geologies. Recently, [11,12] proposed a data-driven regularization based on a dictionary learning and sparse representation technique (hence the name DLSR). The DLSR method captures the vertical structure features of subsurface models by learning from well-log data.…”

Section: Motivationmentioning

confidence: 99%

“…In this paper, we extend the 1D DLSR [11,12] inversion method to a structure-guided 3D inversion approach. We adopt the basic framework of DLSR to ensure the vertical structure features of solutions being consistent with the training data, i.e., well-log data.…”

Section: Our Contributionsmentioning

confidence: 99%

“…In [11,12], the regularization term Φ(m) is established in a data-driven manner by using the DLSR technique whose objective function could be formulated as:…”

Section: Dlsr-based Seismic Inversionmentioning

confidence: 99%

“…The detail solution procedure of Eq. (3) can be found in [11,12]. Briefly, by adding a penalty term to eliminate the equality constraint, Eq.…”

Section: Dlsr-based Seismic Inversionmentioning

confidence: 99%

“…The DLSR-based inversion [11,12] solves the seismic inversion problem in a trace-by-trace implementation, that is, they obtained the solution of all traces of a 3D volume by solving each Eq. (3) individually, results in the lack of lateral continuity of solutions.…”

Section: Gradient Structure Tensors (Gst)mentioning

confidence: 99%

See 4 more Smart Citations

A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

She

Wang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Existing seismic inversion methods are usually 1D, mainly focusing on improving the vertical resolution of inversion results. A few 2D or 3D inversion techniques are either too simple and lack the consideration of stratigraphic structures, or are too complicated which need to extract dip information and solve a complex constrained optimization problem. In this work, with the help of gradient structure tensor (GST) and dictionary learning and sparse representation (DLSR) technologies, we propose a 3D inversion approach (GST-DLSR) that considers both vertical and horizontal structural constraints. In the vertical direction, we investigate the vertical structural features of subsurface models from well-log data by DLSR. In the horizontal direction, we obtain the stratigraphic structural features from a 3D seismic image by GST. We then apply the acquired structural features to constraint the entire inversion procedure. The experiments show that GST-DLSR takes good advantages of both techniques, enabling to produce inversion results with high resolution, good lateral continuity, and enhanced structural features.

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Section: Motivationmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

“…In [11,12], the regularization term Φ(m) is established in a data-driven manner by using the DLSR technique whose objective function could be formulated as:…”

Section: Dlsr-based Seismic Inversionmentioning

confidence: 99%

“…The detail solution procedure of Eq. (3) can be found in [11,12]. Briefly, by adding a penalty term to eliminate the equality constraint, Eq.…”

Section: Dlsr-based Seismic Inversionmentioning

confidence: 99%

Section: Gradient Structure Tensors (Gst)mentioning

confidence: 99%

See 3 more Smart Citations

A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

She

Wang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Pre-Stack Seismic Inversion With L1-2-Norm Regularization Via A Proximal DC Algorithm And Adaptive Strategy

Wang

Chen

2022

Surv Geophys

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Seismic inversion in geophysics is an effective way to obtain underground rock properties from seismic survey data on the Earth's surface. Especially, we can obtain much more information to characterize subsurface geological structure and lithology via pre-stack seismic inversion due to offset information added to the inversion than by post-stack seismic inversion. However, pre-stack seismic inversion is usually a nonlinear and complicated process. In this article, we adopt a 12 L  -norm as a constraint on pre-stack seismic inversion, promoting the generation of a sparse solution. We also propose a novel pre-stack seismic inversion method that reduces the complexity of the solving method by utilizing an objective function decomposition scheme. Comparison of calculation time, accuracy and sparsity of the inversion solutions indicates that the proposed algorithm has better accuracy and robustness. Moreover, considering the difficulty of regularization parameter selection, we develop an adaptive parameter selection strategy based on Generalized Stein unbiased risk estimation (G-SURE) and incorporate it into the solving algorithm. The adaptive approach finds an appropriate regularization parameter in each iteration, and obtains the optimal solution directly, which is beneficial for improving computational efficiency. A synthetic data test verifies that the adaptive method can converge to the optimal solution iteratively in the case of arbitrary initial regularization parameters. Finally, in application to real field data, we explain why the adaptive method is the better choice even though adaptive and non-adaptive methods can obtain solutions with similar accuracy. Keywords Pre-stack seismic inversion • 12 L  -norm regularization • Sparse constraint • Proximal-DC algorithm • Adaptive parameter selection Article Highlights  A novel pre-stack seismic inversion method is proposed based on a proximal differenceof-convex algorithm (pDCA).  A new adaptive regularization parameter selection strategy is proposed based on Generalized Stein unbiased risk estimation (G-SURE). It was verified that one of the regularization parameters has a limited effect in 12 L  -norm.

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A robust data-driven AVO inversion with logarithm absolute error loss function

Zhang

2020

Acta Geophys.

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Data-driven simultaneous seismic inversion of multiparameters via collaborative sparse representation

Cited by 15 publications

References 61 publications

A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

A Structure-Guided and Sparse-Representation-Based 3d Seismic Inversion Method

Pre-Stack Seismic Inversion With L1-2-Norm Regularization Via A Proximal DC Algorithm And Adaptive Strategy

A robust data-driven AVO inversion with logarithm absolute error loss function

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