Amplitude-versus-angle inversion with adaptive edge-preserving smoothing preconditioning

Dai, Ronghuo; Zhang, Fanchang; Chao, Yin; Xiao, Xiang

doi:10.1007/s12517-021-07601-6

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In exploration geophysics, seismic inversion can infer out the elastic parameters or petrophysical properties of the underground formation from seismic data, especially seismic reflection data 1 , 2 .…”

Section: Introductionmentioning

confidence: 99%

Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model

Dai

Yang

2022

Sci Rep

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Impedance inversion of post-stack seismic data is a key technology in reservoir prediction and characterization. Compared to the common used single-trace impedance inversion, multi-trace impedance simultaneous inversion has many advantages. For example, it can take lateral regularization constraint to improve the lateral stability and resolution. We propose to use the L2,0-norm of multi-trace impedance model as a regularization constraint in multi-trace impedance inversion in this paper. L2,0-norm is a joint-sparse measure, which can not only measure the conventional vertical sparsity with L0-norm in vertical direction, but also measure the lateral continuity with L2-norm in lateral direction. Then, we use a split Bregman iteration strategy to solve the L2,0-norm joint-sparse constrained objective function. Next, we use a 2D numerical model and a real seismic data section to test the efficacy of the proposed method. The results show that the inverted impedance from the L2,0-norm constraint inversion has higher lateral stability and resolution compared to the inverted impedance from the conventional sparse constraint impedance inversion.

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Section: Introductionmentioning

confidence: 99%

Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model

Dai

Yang

2022

Sci Rep

Self Cite

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Porosity prediction from pre-stack seismic data via a data-driven approach

Yang

Zhao³

et al. 2023

Journal of Applied Geophysics

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Amplitude-Versus-Angle (AVA) Inversion for Pre-Stack Seismic Data with L0-Norm-Gradient Regularization

Dai

Yang

2023

Mathematics

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Amplitude-versus-angle (AVA) inversion for pre-stack seismic data is a key technology in oil and gas reservoir prediction. Conventional AVA inversion contains two main stages. Stage one estimates the relative change rates of P-wave velocity, S-wave velocity and density, and stage two obtains the P-wave velocity, S-wave velocity and density based on their relative change rates through trace integration. An alternative way merges these two stages to estimate P-wave velocity, S-wave velocity and density directly. This way is less sensitive to noise in seismic data compared to conventional two-stage AVA inversion. However, the regularization for the direct AVA inversion is more complex. To regularize this merged inverse problem, the L0-norm-gradient of P-wave velocity, S-wave velocity and density was used. L0-norm-gradient regularization can provide inversion results with blocky features to make formation interfaces and geological edges precise. Then, L0-norm-gradient regularized AVA inversion was performed on the synthetic seismic traces. Next, a real seismic data line that contains three partial angle stack profiles was used to test the practice application. The inversion results from synthetic and real seismic data showed that L0-norm-gradient regularized AVA inversion is an effective way to estimate P-wave velocity, S-wave velocity and density.

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Amplitude-versus-angle inversion with adaptive edge-preserving smoothing preconditioning

Cited by 3 publications

References 21 publications

Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model

Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model

Porosity prediction from pre-stack seismic data via a data-driven approach

Amplitude-Versus-Angle (AVA) Inversion for Pre-Stack Seismic Data with L0-Norm-Gradient Regularization

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