Q-least-squares reverse time migration with viscoacoustic deblurring filters

Chen, Yuqing; Dutta, Gaurav; Dai, Weihui; Schuster, Gerard T.

doi:10.1190/geo2016-0585.1

Cited by 45 publications

(7 citation statements)

References 33 publications

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“…However, directly computing the inverse Hessian is extremely challenging because of its computational costs. To address this issue, the migration deconvolution method in the image domain (Aoki & Schuster, 2009; Chen et al., 2017; Dai et al., 2011) or gradient‐based optimization (commonly utilizing a preconditioned gradient method) in the data domain can be adopted to approximate the inverse Hessian in LSRTM. In this study, we will use gradient‐based optimization for LSRTM in the data domain.…”

Section: Review Of Least‐squares Reverse Time Migrationmentioning

confidence: 99%

Least‐squares reverse time migration using the most energetic source wavefields based on excitation amplitude imaging condition

Kim

Chung

2023

Geophysical Prospecting

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Least‐squares reverse time migration, a linearized inversion problem, can provide high‐quality migration image by minimizing the misfit function, which is defined by predicted and observed data. According to the theory of data‐domain least‐squares reverse time migration, a forward source wavefield that is simulated with a fixed background velocity does not change during iterations. However, storing the forward source wavefield directly into computer memory involves substantial memory consumption. Although a source wavefield reconstruction technique can be applied during least‐squares reverse time migration iterations, this approach can increase the computational cost because of the need for additional wavefield simulations. To alleviate this computational issue in storing the forward source wavefield, we propose an efficient least‐squares reverse time migration scheme based on an excitation amplitude method. Unlike conventional excitation amplitude imaging conditions, the proposed least‐squares reverse time migration scheme enables one to reconstruct the forward source wavefield by convolving a source wavelet with the excitation amplitude of Green's function at the excitation time. With this excitation amplitude method, the forward source wavefield can be efficiently stored in the computer memory because the sizes of the excitation amplitude and excitation time maps are equal to the size of one snapshot. To validate the feasibility of our least‐squares reverse time migration scheme, we perform dot‐product tests and compare forward source wavefields, demigrated data and gradient vectors obtained by conventional least‐squares reverse time migration and our proposed least‐squares reverse time migration. Using numerical tests on synthetic data, we confirm that our least‐squares reverse time migration can produce high‐quality migration results with a significant improvement in computational efficiency with respect to performance time and memory consumption.

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Section: Review Of Least‐squares Reverse Time Migrationmentioning

confidence: 99%

Least‐squares reverse time migration using the most energetic source wavefields based on excitation amplitude imaging condition

Kim

Chung

2023

Geophysical Prospecting

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“…To date, acoustic or visco‐acoustic LSRTM methods abound in the literature (Dutta and Schuster ; Zhang, Duan and Xie ; Yao and Jakubowicz ; Chen et al . ; Xu and Sacchi ).…”

Section: Introductionmentioning

confidence: 97%

Imaging of elastic seismic data by least‐squares reverse time migration with weighted L2‐norm multiplicative and modified total‐variation regularizations

Ren

2019

Geophysical Prospecting

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Least‐squares reverse time migration has the potential to yield high‐quality images of the Earth. Compared with acoustic methods, elastic least‐squares reverse time migration can effectively address mode conversion and provide velocity/impendence and density perturbation models. However, elastic least‐squares reverse time migration is an ill‐posed problem and suffers from a lack of uniqueness; further, its solution is not stable. We develop two new elastic least‐squares reverse time migration methods based on weighted L2‐norm multiplicative and modified total‐variation regularizations. In the proposed methods, the original minimization problem is divided into two subproblems, and the images and auxiliary variables are updated alternatively. The method with modified total‐variation regularization solves the two subproblems, a Tikhonov regularization problem and an L2‐total‐variation regularization problem, via an efficient inversion workflow and the split‐Bregman iterative method, respectively. The method with multiplicative regularization updates the images and auxiliary variables by the efficient inversion workflow and nonlinear conjugate gradient methods in a nested fashion. We validate the proposed methods using synthetic and field seismic data. Numerical results demonstrate that the proposed methods with regularization improve the resolution and fidelity of the migration profiles and exhibit superior anti‐noise ability compared with the conventional method. Moreover, the modified‐total‐variation‐based method has marginally higher accuracy than the multiplicative‐regularization‐based method for noisy data. The computational cost of the proposed two methods is approximately the same as that of the conventional least‐squares reverse time migration method because no additional forward computation is required in the inversion of auxiliary variables.

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“…The data-domain LSRTM method seeks to iteratively update the subsurface reflectivity by minimizing the residual between the simulated data and observed data (Dai and Schuster, 2013;Zhang et al, 2015;Wang et al, 2017). It has been extended to elastic (Feng and Schuster, 2017;Ren et al, 2017;Gu et al, 2018), viscoacoustic (Dutta and Schuster, 2014;Sun et al, 2016;Chen et al, 2017; and anisotropic cases (Qu et al, 2017;Mu et al, 2020) due to its superiority in balancing amplitude, suppressing artifacts, and improving image resolution. However, limited by large computing costs (Dai and Schuster, 2013), the sensitivity to migration velocity (Tan and Huang, 2014;Li et al, 2017), and the mismatch of amplitudes (Zhang et al, 2015), conventional LSRTM is not extensively used in large-scale field data processing.…”

Section: Introductionmentioning

confidence: 99%

Adaptive variable-grid least-squares reverse-time migration

Huang

Chen²,

Wang³

et al. 2023

Front. Earth Sci.

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Variable-grid methods have the potential to save computing costs and memory requirements in forward modeling and least-squares reverse-time migration (LSRTM). However, due to the inherent difficulty of automatic grid discretization, conventional variable-grid methods have not been widely used in industrial production. We propose a variable-grid LSRTM (VG-LSRTM) method based on an adaptive sampling strategy to improve computing efficiency and reduce memory requirements. Based on the mapping relation of two coordinate systems, we derive variable-grid acoustic wave equation and its corresponding Born forward modeling equation. On this basis, we develop a complete VG-LSRTM framework. Numerical experiments on a layered model validate the feasibility of the proposed VG-LSRTM algorithm. LSRTM tests on a modified Marmousi model demonstrate that our method can save computational costs and memory requirements with little accuracy loss.

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Q-least-squares reverse time migration with viscoacoustic deblurring filters

Cited by 45 publications

References 33 publications

Least‐squares reverse time migration using the most energetic source wavefields based on excitation amplitude imaging condition

Least‐squares reverse time migration using the most energetic source wavefields based on excitation amplitude imaging condition

Imaging of elastic seismic data by least‐squares reverse time migration with weighted L2‐norm multiplicative and modified total‐variation regularizations

Adaptive variable-grid least-squares reverse-time migration

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