The importance of including density in elastic least-squares reverse time migration: multiparameter crosstalk and convergence

Chen, Ke; Sacchi, Mauricio D.

doi:10.1093/gji/ggy368

Cited by 12 publications

(1 citation statement)

References 78 publications

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“…In 2017, Qu et al [38] proposed an ELSRTM method considering the density variations by solving separated velocity−stress elastic wave equations to mitigate the crosstalk due to wave modes coupling, and derived the linearized demigration operator and gradient with respect to P-and S-wave velocities and densities. Chen and Sacchi [39] inverted density, and P-and S-wave velocity perturbation images using the conjugate gradient (CG) method to solve the LSM problem.…”

Section: Introductionmentioning

confidence: 99%

Elastic Correlative Least-Squares Reverse Time Migration Based on Wave Mode Decomposition

Zheng

Liu

et al. 2022

Processes

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The conventional elastic least-squares reverse time migration (LSRTM) generally inverts the parameter perturbation of the model rather than the reflectivity of reflected P- and S-modes, which leads to difficulty in directly interpreting the physical properties of the subsurface media. However, an accurate velocity model that is needed by the separation of seismic records of conventional LSRTM is usually unavailable in real data, which limits its application. In this study, we introduce a new practical correlative LSRTM (CLSRTM) scheme based on wave mode decomposition without amplitude and phase distortion, which frees from separation of seismic records. In this study, we deduced the migration and the de-migration operators using the decoupled P- and S-wave equations in heterogeneous media, which needs no extra wavefield decomposition in simulated data. To accelerate the convergence and improve the efficiency of the inversion, we adopted an analytical step-length formula that can be incidentally computed during the necessary de-migration process and the L-BFGS algorithm. Two numerical examples demonstrate that the proposed method can compensate the energy of deep structures, and generate clear images with balanced amplitudes and enhanced resolution even for the fault structures beneath the salt dome.

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Section: Introductionmentioning

confidence: 99%

Elastic Correlative Least-Squares Reverse Time Migration Based on Wave Mode Decomposition

Zheng

Liu

et al. 2022

Processes

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Model parameterizations in the time-domain multi-parameter acoustic least-squares reverse time migration

Zhang

Gao

2021

Acta Geophys.

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Time-domain elastic Gauss–Newton full-waveform inversion: a matrix-free approach

Chen

Sacchi

2020

Geophysical Journal International

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Summary We present a time-domain matrix-free elastic Gauss-Newton full-waveform inversion (FWI) algorithm. Our algorithm consists of a Gauss-Newton update with a search direction calculated via elastic least-squares reverse time migration (LSRTM). The conjugate gradient least-squares (CGLS) method solves the LSRTM problem with forward and adjoint operators derived via the elastic Born approximation. The Hessian of the Gauss-Newton method is never explicitly formed or saved in memory. In other words, the CGLS algorithm solves for the Gauss-Newton direction via the application of implicit-form forward and adjoint operators which are equivalent to elastic Born modelling and elastic reverse time migration, respectively. We provide numerical examples to test the proposed algorithm where we invert for P- and S-wave velocities simultaneously. The proposed algorithm performs positively on mid-size problems where we report solutions of slight improvement than those computed using the conventional nonlinear conjugate gradient method. In spite of the aforementioned limited gain, the theory developed in this paper contributes to a better understanding of time-domain elastic Gauss-Newton FWI.

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The importance of including density in elastic least-squares reverse time migration: multiparameter crosstalk and convergence

Cited by 12 publications

References 78 publications

Elastic Correlative Least-Squares Reverse Time Migration Based on Wave Mode Decomposition

Elastic Correlative Least-Squares Reverse Time Migration Based on Wave Mode Decomposition

Model parameterizations in the time-domain multi-parameter acoustic least-squares reverse time migration

Time-domain elastic Gauss–Newton full-waveform inversion: a matrix-free approach

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