The implementation of an improved NPML absorbing boundary condition in elastic wave modeling

Qin, Zhihong; Lu, Minghui; Zheng, Xiaodong; Yao, Yao; Zhang, Cai; Song, Jinghong

doi:10.1007/s11770-009-0012-3

Cited by 27 publications

(3 citation statements)

References 24 publications

(27 reference statements)

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“…Both used 20th-order FD on spatial derivatives. The boundary condition is implemented by the perfectly matched layer (PML) boundary condition [37].…”

Section: Homogeneous Modelmentioning

confidence: 99%

Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN

Yan

Huang

et al. 2023

Remote Sensing

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The finite-difference (FD) method is one of the most commonly used numerical methods for elastic wave modeling. However, due to the difference approximation of the derivative, the time dispersion phenomenon cannot be avoided. This paper proposes the use of pix2pix algorithm based on a conditional generative adversarial network (cGAN) for removing time dispersion from elastic FD modeling. Firstly, we analyze the time dispersion of elastic wave FD modeling. Then, we discuss the pix2pix algorithm based on cGAN, improve the loss function of the pix2pix algorithm by introducing a Sobel operator, and analyze the parameter selection of the network model for the pix2pix algorithm. Finally, we verify the feasibility and effectiveness of the pix2pix algorithm in removing time dispersion from elastic wave FD modeling through testing some model simulation data.

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“…Both used 20th-order FD on spatial derivatives. The boundary condition is implemented by the perfectly matched layer (PML) boundary condition [37].…”

Section: Homogeneous Modelmentioning

confidence: 99%

Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN

Yan

Huang

et al. 2023

Remote Sensing

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“…We use regular cubic grids in which the source function is located at one single point in the grid. To simulate an unbounded half-space (nonreflecting boundaries), we use a 3D implementation of the perfectly matched layer absorbing boundary conditions (Berenger, 1994;Zhen et al, 2009). The reflecting sea surface at the top of the model is approximated using the method described in Mittet (2002).…”

Section: Modelingmentioning

confidence: 99%

A numerical study of 3D elastic time-lapse full-waveform inversion using multicomponent seismic data

Raknes

Arntsen

2015

GEOPHYSICS

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A common assumption in wave-propagation problems is that the subsurface is approximately an acoustic medium. Under this assumption, important wave phenomena such as S-waves are not included. Due to the increase in computational power in recent years, the acoustic assumption may be left behind and replaced by the more physically correct elastic assumption. Time-lapse seismic data contain information about changes in the subsurface due to the production of hydrocarbons or injection of CO 2. Full-waveform inversion (FWI) is an inverse method that can be used to quantify these time-lapse changes in the subsurface. Using a 3D isotropic elastic implementation of the FWI method, we studied two strategies for performing time-lapse FWI. We used synthetic ocean-bottom multicomponent seismic time-lapse data to estimate changes in the P-and S-wave velocity models. A sensitivity analysis in which the sensitivities with respect to the magnitude and physical size of the time-lapse anomalies and the noise level in the data was performed. The strategy focusing on explaining the data differences between the baseline and monitor data sets provided fewer artifacts in the inverted elastic models than the strategy that tried to explain the full monitor data set, and it was therefore preferable. The data-difference strategy depends on good repeatability in the time-lapse data sets and sufficient convergence of the inversion of the baseline data set.

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“…The numerical grid is regular with grid size 10 m, and is the same in the modeling and inversion. To simulate the nonreflecting boundaries, we use perfectly matching layer absorbing-boundary conditions (Berenger, 1994;Zhen et al, 2009). The reflecting free surface at the top of the model is implemented using the method described in Mittet (2002).…”

Section: Synthetic Examplementioning

confidence: 99%

Time-lapse full-waveform inversion of limited-offset seismic data using a local migration regularization

Raknes

Arntsen

2014

GEOPHYSICS

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Conventional methods for quantifying time-lapse seismic effects rely on a linear assumption that is easily violated. Therefore, more sophisticated methods are necessary. The full-waveform inversion (FWI) method is an inverse method that is able to reveal time-lapse changes in the image domain, in which the conventional methods break down. We investigated the behavior of FWI using different approaches for applying FWI on limited-offset time-lapse data. We compared acoustic and elastic inversion schemes. We introduced a method for constraining the model update for the monitor model to remove time-lapse artifacts. This method was based on migration of the residuals in the time-lapse data, which, in combination with a local contrast estimation algorithm, formed the update constraint. We found that for limited-offset data, elastic theory was necessary for the success of FWI and that FWI was able to quantify the time-lapse changes in the parameter models. The local migration regularization approach was able to remove time-lapse artifacts.

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The implementation of an improved NPML absorbing boundary condition in elastic wave modeling

Cited by 27 publications

References 24 publications

Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN

Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN

A numerical study of 3D elastic time-lapse full-waveform inversion using multicomponent seismic data

Time-lapse full-waveform inversion of limited-offset seismic data using a local migration regularization

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