Shot- and angle-domain wave-equation traveltime inversion of reflection data: Synthetic and field data examples

Zhang, Sanzong; Luo, Yi; Schuster, Gerard T.

doi:10.1190/geo2014-0223.1

Cited by 16 publications

(5 citation statements)

References 16 publications

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“…Since the Hessian matrix is too large in scale and the direct inversion process is not realistic, the LSM method is always recast as a data-driven linear optimization problem. Due to the good performance on improving resolution, it has been investigated in seismic imaging for acoustic-wave single component data [7][8][9][10][11][12][13][14][15][16][17][18] and elastic-wave multiple component data [19][20][21][22]. Besides, the elastic least-squares migration is extended to obtain multiparameter images, such as P-and S-wave velocity and density, to cope with the trade-off effects [23,24].…”

Section: Introductionmentioning

confidence: 99%

Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution

Sun

Chen

et al. 2022

Applied Sciences

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The classical least-squares migration (LSM) translates seismic imaging into a data-fitting optimization problem to obtain high-resolution images. However, the classical LSM is highly dependent on the precision of seismic wavelet and velocity models, and thus it suffers from an unstable convergence and excessive computational costs. In this paper, we propose a new LSM method in the imaging domain. It selects a spatial-varying point spread function to approximate the accurate Hessian operator and uses a high-dimensional spatial deconvolution algorithm to replace the common-used iterative inversion. To keep a balance between the inversion precision and the computational efficiency, this method is implemented based on the strategy of regional division, and the point spread function is computed using only one-time demigration/migration and inverted individually in each region. Numerical experiments reveal the differences in the spatial variation of point spread functions and highlight the importance to use a space-varying deconvolution algorithm. A 3D field case in Northwest China can demonstrate the effectiveness of this method on improving spatial resolution and providing better characterizations for small-scale fracture and cave units of carbonate reservoirs.

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Section: Introductionmentioning

confidence: 99%

Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution

Sun

Chen

et al. 2022

Applied Sciences

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“…An accurate initial model building is a key component in FWI. Alkhalifah and Choi (2014) and Zhang et al (2015) propose a workflow to use traveltime tomography and multiple objective functions to obtain a low-wavenumber initial model for the subsequent FWI. However, limited by the high-frequency asymptotic approximation, this ray-based tomography encounters difficulties in a complex velocity model.…”

Section: Introductionmentioning

confidence: 99%

“…The wave-equation-based traveltime tomography can accommodate the multipathing problem by incorporating finite-frequency wave propagation (Luo and Schuster, 1991;Clément et al, 2001;Almomin and Biondi, 2012;Xu et al, 2012;Ma and Hale, 2013). Other ray-based or wave-equation-based methods (Zhang and Wang, 2009;Biondi and Almomin, 2013;Zhang et al, 2015;Luo, et al, 2016) were proposed to estimate a smooth large-scale initial velocity model. These methods emphasize the usefulness of the traveltime information more than the amplitude information of the waves.…”

Section: Introductionmentioning

confidence: 99%

Full-intensity waveform inversion

Liu

et al. 2018

GEOPHYSICS

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Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operation, which separates the original waveform spectrum into an ultra-low-frequency part and a higher frequency part, even for data that originally do not have low-frequency contents. Therefore, conducting multiscale inversions starting from ultra-low-frequency intensity data can largely avoid the cycle-skipping problem. We formulate the intensity objective function, the minimization process, and the gradient. Using numerical examples, we determine that the proposed method was very promising and could invert for the model using data lacking low-frequency information.

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“…By inverting the traveltimes instead of the full waveforms, it largely mitigates the cycle-skipping problem (Ma and Hale, 2013) and provides a robust convergence property compared to traditional FWI (Zhang et al, 2015;Van Leeuwen and Mulder, 2010). With long offset in the data acquisition, WT can reliably invert for the deeper parts of the earth's velocity model, which provides a good initial model for FWI.…”

Section: Introductionmentioning

confidence: 99%

Wave-equation traveltime inversion with multifrequency bands: Synthetic and land data examples

2018

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A wave-equation traveltime inversion method with multi-frequency bands is proposed to invert for the shallow or intermediate subsurface velocity distribution. Similar to the classical wave equation traveltime inversion, this method searches for the velocity model that minimizes the squared sum of the traveltime residuals using source wavelets with progressively higher peak frequencies. Wave-equation traveltime inversion can partially avoid the cycle skipping problem by recovering the low-wavenumber parts of the velocity model. However, we also utilize the frequency information hidden in the traveltimes for obtaining a more highly resolved tomogram. Therefore, we employ different frequency bands when calculating the Fréchet derivatives so that tomograms with better resolution can be reconstructed. Results are validated by the zero offset gathers from the raw data associated with moderate geometrical irregularities.The improved wave-equation traveltime method is robust and merely needs a rough estimate of the starting model. Numerical tests on both the synthetic and field data sets validate the above claims.

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Shot- and angle-domain wave-equation traveltime inversion of reflection data: Synthetic and field data examples

Cited by 16 publications

References 16 publications

Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution

Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution

Full-intensity waveform inversion

Wave-equation traveltime inversion with multifrequency bands: Synthetic and land data examples

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