3D Multisource Full‐Waveform Inversion using Dynamic Random Phase Encoding

Boonyasiriwat, Chaiwoot; Schuster, Gerard T.

doi:10.1190/1.3513025

Cited by 31 publications

(21 citation statements)

References 19 publications

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“…The simultaneous-shot technique with source encoding has been shown to be an efficient strategy for reducing the computational burden of prestack depth migration and full waveform inversion (FWI) (Capdeville et al, 2005;Morton and Ober, 1998;Jing et al, 2000;Romero et al, 2000;Ben-Hadj-Ali et al, 2009b;Krebs et al, 2009;Boonyasiriwat and Schuster, 2010;Gao et al, 2010). The simultaneous-shot technique takes advantage of the linear relationship between a seismic wavefield and its source, by summing the individual sources into supershots to reduce the number of wave simulations during imaging.…”

Section: Introductionmentioning

confidence: 99%

An efficient frequency-domain full waveform inversion method using simultaneous encoded sources

2011

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International audienceThree-dimensional full waveform inversion (FWI) still suffers from prohibitively high computational costs that arise because of the seismic modeling for multiple sources that is performed at each nonlinear iteration of FWI. Building supershots by assembling several sources allows mitigation of the number of simulations per FWI iteration, although it adds crosstalk artifacts because of interference between the individual sources of the supershots. These artifacts themselves can be reduced by encoding each individual source with a random phase shift during assembling of the sources. The source encoding method is applied to an efficient frequency-domain FWI, in which a limited number of discrete frequencies or coarsely sampled frequency groups are inverted successively following a multiscale approach. Random codes can be regenerated at each FWI iteration or for each frequency of a group during each FWI iteration, to favor the destructive summation of crosstalk artifacts over FWI iterations. Either a limited number of sources (partial assembling) or the total number of sources (full assembling) can be combined into supershots. Wide-aperture acquisition geometries such as land or marine node acquisitions are considered, to allow one to stack a large number of shots in the full computational domain and to test different partial assembling strategies involving sources that are close to or distant from each other. Two-dimensional case studies show that partial-source assembling of distant shots has a limited sensitivity to noise, for a computational saving that is roughly proportional to the number of shots assembled into the supershots. On the other hand, full assembling is more sensitive to noise, and it requires successive inversions of finely sampled frequency groups with a large number of FWI iterations. In contrast, refining the shot interval to improve the fold degrades the models when full assembling is applied to noisy data. Preliminary 3D application of the method leads to the same conclusions that 2D case studies do, with regard to the footprint of crosstalk noise in the imaging. ©2011 Society of Exploration Geophysicist

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Section: Introductionmentioning

confidence: 99%

An efficient frequency-domain full waveform inversion method using simultaneous encoded sources

2011

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“…In earlier developments in seismic acquisition and imaging, several authors have proposed reducing the computational cost of FWI by randomly combining sources Krebs et al (2009); Moghaddam and Herrmann (2010); Boonyasiriwat and Schuster (2010); Li and Herrmann (2010); Haber et al (2010). We follow the same approach, but focus the exposition on the Gauss-Newton subproblem, setting the stage for further modifications.…”

Section: Main Contribution and Relation To Existing Workmentioning

confidence: 99%

A modified, sparsity-promoting, Gauss-Newton algorithm for seismic waveform inversion

Herrmann

Aravkin

et al. 2011

SPIE Proceedings

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Images obtained from seismic data are used by the oil and gas industry for geophysical exploration. Cutting-edge methods for transforming the data into interpretable images are moving away from linear approximations and high-frequency asymptotics towards Full Waveform Inversion (FWI), a nonlinear data-fitting procedure based on full data modeling using the wave-equation. The size of the problem, the nonlinearity of the forward model, and ill-posedness of the formulation all contribute to a pressing need for fast algorithms and novel regularization techniques to speed up and improve inversion results. In this paper, we design a modified Gauss-Newton algorithm to solve the PDEconstrained optimization problem using ideas from stochastic optimization and compressive sensing. More specifically, we replace the Gauss-Newton subproblems by randomly subsampled, 1 regularized subproblems. This allows us us significantly reduce the computational cost of calculating the updates and exploit the compressibility of wavefields in Curvelets. We explain the relationships and connections between the new method and stochastic optimization and compressive sensing (CS), and demonstrate the efficacy of the new method on a large-scale synthetic seismic example.

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“…and (2) Which is the best optimization method for the solution of the problem? The first question has received wide attention (Boonyasiriwat and Schuster, 2010;Gao et al, 2010;Habashy et al, 2010;Symes, 2010;Wang and Goo, 2010). The second question has received much less attention, despite its importance .…”

Section: Introductionmentioning

confidence: 99%

“…Because the gradient of the FWI cost function is randomized, the step toward the solution is randomized as well. However, most authors (Krebs et al, 2009;Boonyasiriwat and Schuster, 2010;Symes, 2010) still use deterministic optimization solutions such as steepest descents or quasi-Newton approaches for the randomized FWI problem and do not consider the fact that the search direction in each iteration of the solver is random.…”

Section: Introductionmentioning

confidence: 99%

A new optimization approach for source-encoding full-waveform inversion

et al. 2013

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Waveform inversion is the method of choice for determining a highly heterogeneous subsurface structure. However, conventional waveform inversion requires that the wavefield for each source is computed separately. This makes it very expensive for realistic 3D seismic surveys. Source-encoding waveform inversion, in which the sources are modeled simultaneously, is considerably faster than conventional waveform inversion but suffers from artifacts. These artifacts can partly be removed by assigning random weights to the source wavefields. We found that the misfit function, and therefore also its gradient, for source-encoding waveform inversion is an unbiased random estimation of the misfit function used in conventional waveform inversion. We found a new method of source-encoding waveform inversion that takes into account the random nature of the gradients used in the optimization. In this new method, the gradient at each iteration is a weighted average of past gradients such that the most recent gradients have the largest weights with exponential decay. This way we damped the random fluctuations of the gradient by incorporating information from the previous iterations. We compared this new method with existing source-encoding waveform inversion methods as well as conventional waveform inversion and found that the model misfit reduction is faster and smoother than those of existing source-encoding waveform inversion methods, and it approaches the model misfit reduction obtained in conventional waveform inversion.

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3D Multisource Full‐Waveform Inversion using Dynamic Random Phase Encoding

Cited by 31 publications

References 19 publications

An efficient frequency-domain full waveform inversion method using simultaneous encoded sources

An efficient frequency-domain full waveform inversion method using simultaneous encoded sources

A modified, sparsity-promoting, Gauss-Newton algorithm for seismic waveform inversion

A new optimization approach for source-encoding full-waveform inversion

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