Salt Reconstruction in Full-Waveform Inversion With a Parametric Level-Set Method

Kadu, Ajinkya; Leeuwen, Tristan van; Mulder, W.A.

doi:10.1109/tci.2016.2640761

Cited by 82 publications

(29 citation statements)

References 54 publications

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“…For ellipsoidal RBFs, the derivatives of (4.10) include: 23) and the derivative with respect to b j is similar to (8.18). Altogether, we obtain the Jacobian:…”

Section: Derivatives Of the Ellipsoidal Pals Functionmentioning

confidence: 99%

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

Eliasof¹,

Sharf²,

Treister³

2020

SIAM J. Imaging Sci.

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We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can come in diverse forms such as sparse point sets, volumetric slices, 2D photos and so on. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration-related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CT scans and XRays), and visual measurements obtained from shape silhouettes as well as point-clouds.

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“…For ellipsoidal RBFs, the derivatives of (4.10) include: 23) and the derivative with respect to b j is similar to (8.18). Altogether, we obtain the Jacobian:…”

Section: Derivatives Of the Ellipsoidal Pals Functionmentioning

confidence: 99%

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

Eliasof¹,

Sharf²,

Treister³

2020

SIAM J. Imaging Sci.

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“…In that case, the manually driven 'migrate-pick-flood' approach remains a preferable, albeit erroneous, industrial solution to build the velocity model. However, several FWI studies based on the level-set method (Lewis et al, 2012;Kadu et al, 2017), novel objective functions (Yang and Engquist, 2017;Wu and Chen, 2018), model regularization (Esser et al, 2016;Brandsberg-Dahl et al, 2017) and so on, have been proposed in recent years as an alternative to the above mentioned industrial ad-hoc practice. Towards that aim, this article extends the idea proposed in Kalita et al (2018a,b) to a 3-D regime.…”

Section: Introductionmentioning

confidence: 99%

Regularized full-waveform inversion for large 3-D salt bodies

Kalita

Ghazali

Xin

et al. 2019

SEG Technical Program Expanded Abstracts 2019

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Our objective is to invert for large 3-D salt models using fullwaveform inversion (FWI), especially in the absence of good starting models and low frequencies in the seismic data. This objective incurs a couple of critical issues. First, the presence of salt geobodies aggravates the non-linearity and illposedness of FWI. Second, a 3-D FWI is computationally very expensive, even more so when the initial model is in hindsight very far from the target one. To mitigate the ill-posedness, we propose to utilize model regularization in the FWI framework to promote a limited variation in the inverted model followed by a post-processing step of FWI to penalize sharp velocity drops in the model. Next, to reduce the computational overburden, we propose to utilize a multi-excitation assumption (MExA) of source wavefields in the FWI gradient calculation step. This assumption, due to the simplistic nature of the source wavelet, approximates the source wavefield wiggle at a gridpoint by a series of its energetic arrivals. As a result, the gradient evaluation using MExA requires us neither to store the entire source wavefield nor to include an additional extrapolation step to propagate the source wavefield from its temporary storage at the boundary. The versatility of the proposed method is demonstrated on a synthetic dataset of the modified SEG/EAGE salt model in which the lowest available frequency is 3 Hz.

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“…Based on the two-way wave equation, RTM has proven to be an important tool in imaging complex subsurface structures without dip limitation (Whitmore, 1983;Baysal et al, 1983). RTM has also got applications to ground-penetrating radar imaging (Foroozan and Asif, 2010;Liu et al, 2017a), medical imaging (Kosmas and Rappaport, 2006;Wang et al, 2016) and 20 waveform inversion (Van Leeuwen and Mulder, 2010;Kadu et al, 2017). However, in practice, the survey zone usually has the topography effect (Burgin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

An adaptive unstructured mesh based solution to topography least-squares reverse-time imaging

Liu

Zhang

2019

Preprint

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Least-squares reverse-time migration (LSRTM) attempts to invert for the broadband-wavenumber reflectivity image by minimizing the residual between observed and predicted seismograms via linearized inversion. However, rugged topography poses a challenge in front of LSRTM. To tackle this issue, we present an unstructured mesh-based solution to topography LSRTM. As to the forward/adjoint modeling operators in LSRTM, we take a so-called unstructured mesh-based "grid method".Before solving the two-way wave equation with the grid method, we prepare for it a velocity-adaptive unstructured mesh using 5 a Delaunay Triangulation plus Centroidal Voronoi Tessellation (DT-CVT) algorithm. The rugged topography acts as constraint boundaries during mesh generation. Then, by using the adjoint method, we put the observed seismograms to the receivers on the topography for backward propagation to produce the gradient through the cross-correlation imaging condition. We seek the inverted image using the conjugate gradient method during linearized inversion to linearly reduce the data misfit function.Through the 2D SEG Foothill synthetic dataset, we see that our method can handle the LSRTM from rugged topography. 10

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Salt Reconstruction in Full-Waveform Inversion With a Parametric Level-Set Method

Cited by 82 publications

References 54 publications

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

Regularized full-waveform inversion for large 3-D salt bodies

An adaptive unstructured mesh based solution to topography least-squares reverse-time imaging

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