Real Data Applications of Seismic Full Waveform Inversion

Kurzmann, A.; Gassner, Laura; Shigapov, R.; Thiel, Niklas; Αθανασόπουλος, Νικόλαος; Bohlen, Thomas; Steinweg, T.

doi:10.1007/978-3-319-68394-2_28

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Reference [20] showed that taking key elements properly into account, FWI produces a reliable high-resolution near-surface model that could not be otherwise recovered through traditional methods. Although few attempts have been reported that incorporate FWI for land studies [18]; however, they were convincing in providing acceptable seismic image. Therefore, it is supposed that deriving a processing workflow modified for accurate imaging of seismic data from complex regions would be promising in resolving the problem of low SNR and strong lateral velocity changes due to complexity in wave propagation media.…”

Section: Introductionmentioning

confidence: 98%

“…Reference [9] implemented the FWI-SIMAT algorithm to investigate the capability of the acoustic FWI in the reconstruction of the Marmousi velocity model both in the time and frequency domain. Reference [18] used a developed FWI method in which a two-stage sequential approach (SFWI) was tested on the field datasets recorded in the Black Sea and in the shallow-water area of a river delta in the Atlantic Ocean to obtain detailed subsurface images containing rock formations that might be potential gas deposits. Most applications of the FWI methods on complex structures have been performed in the frequency domain or ignored seismic wave attenuation.…”

Section: Introductionmentioning

confidence: 99%

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Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

et al. 2022

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In the presented study, multi-parameter inversion in the presence of attenuation is used for the reconstruction of the P- and the S- wave velocities and the density models of a synthetic shallow subsurface structure that contains a dipping high-velocity layer near the surface with varying thicknesses. The problem of high-velocity layers also complicates selection of an appropriate initial velocity model. The forward problem is solved with the finite difference, and the inverse problem is solved with the preconditioned conjugate gradient. We used also the adjoint wavefield approach for computing the gradient of the misfit function without explicitly build the sensitivity matrix. The proposed method is capable of either minimizing the least-squares norm of the data misfit or use the Born approximation for estimating partial derivative wavefields. It depends on which characteristics of the recorded data—such as amplitude, phase, logarithm of the complex-valued data, envelope in the misfit, or the linearization procedure of the inverse problem—are used. It showed that by a pseudo-viscoelastic time-domain full-waveform inversion, structures below the high-velocity layer can be imaged. However, by inverting attenuation of P- and S- waves simultaneously with the velocities and mass density, better results would be obtained.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

et al. 2022

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“…Originally developed within seismology [ 8 , 9 ], FWI has successful applications in exploration seismic (e.g. [ 10 , 11 ]) which has attracted interest in many other areas such as applications in ultrasonic medical tomography [ 12 ], solar interior studies [ 13 ] and ground-penetrating radar [ 14 ]. The strong point of the FWI is to exploit the full information of waveforms to simulate all phenomena wave, whilst most of the imaging techniques use only the travel-time kinematics information.…”

Section: Introductionmentioning

confidence: 99%

An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning

et al. 2020

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Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution subsurface models, from seismic data. However, FWI is an ill-posed problem, which means that the solution is not unique, and therefore the expert use of the information is required to mitigate the FWI ill-posedness, especially when wide-aperture seismic acquisitions are considered. In this way, we investigate the multiscale frequency-domain FWI by using a weighting operator according to the distances between each source-receiver pair. In this work, we propose a weighting operator that acts on the data misfit as preconditioning of the objective function that depends on the source-receiver distance (offset) and the frequency used during the inversion. The proposed operator emphasizes information from long offsets, especially at low frequencies, and as a consequence improves the update of deep geological structures. To demonstrate the effectiveness of our proposal, we perform numerical simulations on 2D acoustic Marmousi2 case study, which is widely used in seismic imaging tests, considering three different scenarios. In the first two ones, we have used an acquisition geometry with a maximum offset of 4 and 8 km, respectively. In the last one, we have considered all-offsets. The results show that our proposal outperforms similar strategies, for all scenarios, providing more reliable quantitative subsurface models. In fact, our inversion result has the lowest error and the highest similarity to the true model than similar approaches.

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Full-waveform inversion based on generalized Rényi entropy using patched Green’s function techniques

et al. 2022

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The estimation of physical parameters from data analyses is a crucial process for the description and modeling of many complex systems. Based on Rényi α-Gaussian distribution and patched Green’s function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). From the assumption that the residual seismic data (the difference between the modeled and observed data) obeys the Rényi α-Gaussian probability distribution, we introduce an outlier-resistant criterion to deal with erratic measures in the FWI context, in which the classical FWI based on l2-norm is a particular case. The new misfit function arises from the probabilistic maximum-likelihood method associated with the α-Gaussian distribution. The PGF technique works on the forward modeling process by dividing the computational domain into outside target area and target area, where the wave equation is solved only once on the outside target (before FWI). During the FWI processing, Green’s functions related only to the target area are computed instead of the entire computational domain, saving computational efforts. We show the effectiveness of our proposed approach by considering two distinct realistic P-wave velocity models, in which the first one is inspired in the Kwanza Basin in Angola and the second in a region of great economic interest in the Brazilian pre-salt field. We call our proposal by the abbreviation α-PGF-FWI. The results reveal that the α-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit α → 2/3, being α the Rényi entropic index.

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Real Data Applications of Seismic Full Waveform Inversion

Cited by 3 publications

References 15 publications

Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

An objective function for full-waveform inversion based on frequency-dependent offset-preconditioning

Full-waveform inversion based on generalized Rényi entropy using patched Green’s function techniques

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