Robust data-driven AVO inversion algorithm based on generalized nonconvex dictionary learning

Du, Siyuan; Zhang, Jiashu; Hu, Guangmin

doi:10.1016/j.petrol.2022.110536

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Hu et al 21 derived the gradient of the target generalized function on the model parameters from the Love wave fluctuation equation combined with the PRP conjugate gradient algorithm, and model tests verified that the method can improve the computational efficiency. Du et al 22 used log-absolute error functions for AVO inversion and used a new spectral PRP conjugate gradient method in iterations to solve large-scale optimization problems, and then they combined a smooth nonconvex regularization method with adaptive individual weight gain and used a PRP conjugate gradient method to minimize the objective function 23 , recently they used a smooth L1 parametrization as the loss function and used a new spectral PRP conjugate gradient algorithm to optimize the inversion, and proposed a robust AVO inversion algorithm based on generalized nonconvex dictionary learning 24 .…”

Section: Introductionmentioning

confidence: 99%

Least squares reverse time migration imaging with illumination preconditioned based on improved PRP conjugate gradients

Zhang,

Li,

Cui

et al. 2023

Sci Rep

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Least squares reverse time migration (LSRTM) imaging is the one of the most accurate methods for migration imaging at present, and Polak–Ribiere–Polyak conjugate gradient (PRPCG) for LSRTM has the good numerical performance but weak convergence, so we construct an optimization factor to improve the iteration direction of the gradient, which can automatically generate a sufficient descent direction. The improved PRPCG (IPRPCG) can reduce the data residual values and speed up the iteration. And the illumination preconditioned (IP) operator is employed to IPRPCG-LSRTM which solves the problem of low resolution due to the insufficient iterative gradient information. In this paper, the experiments show that the imaging results of the proposed method (IPRPCG-IP-LSRTM) is improved greatly in detail characterization and events continuity, the iterative curve converged faster significantly, and the normalized data residual was reduced by 6.55% on average, which improved the accuracy of migration imaging effectively.

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Section: Introductionmentioning

confidence: 99%

Least squares reverse time migration imaging with illumination preconditioned based on improved PRP conjugate gradients

Zhang,

Li,

Cui

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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Prestack sparse envelope seismic inversion method adopting the L0−L2-norm regularization

Yang,

Wu,

Shan

et al. 2024

Interpretation

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The essence of pre-stack inversion is the model inversion, but challenges hinder its accuracy in obtaining precise initial models, particularly in marine environments or regions with limited well-log data. To enhance stability and accuracy in pre-stack seismic inversion in these areas, we propose an elastic parameter estimation approach utilizing sparse envelope inversion with L0- L2 norm regularization. Our method combines signal sparse representation and modulation theories to derive a new formula for sparse envelope extraction at lower frequencies. By applying L2 norm regularization to the sparse envelope, we obtain parameter inversion results with smoothed trends, augmenting low wave-number information for improved model constraints. Additionally, taking the envelope inversion results obtained by the L2 norm as the model constraint, the sparsest inversion results with obvious block-like characteristics are obtained by regularizing the inversion equation with the L0 norm. Notably, our method effectively suppresses wavelet side-lobes, resulting in stable and accurate inversions without the need for initial low-frequency models based on well-logs, as required in traditional methods. We present a synthetic example to illustrate the feasibility and stability of our proposed approach, and further demonstrate its practicality in reservoir parameter estimation through a field case study of gas exploration.

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Robust data-driven AVO inversion algorithm based on generalized nonconvex dictionary learning

Cited by 2 publications

References 25 publications

Least squares reverse time migration imaging with illumination preconditioned based on improved PRP conjugate gradients

Least squares reverse time migration imaging with illumination preconditioned based on improved PRP conjugate gradients

Prestack sparse envelope seismic inversion method adopting the L0−L2-norm regularization

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