Objective‐Function Behavior in Acoustic Full‐Waveform Inversion

Dong, Lu; Chi, Benxin; Ji-Xia, Tao; Liu, Yuzhu

doi:10.1002/cjg2.20063

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…When seismic data lack this far‐offset information in the middle and deep parts of the stratum, FWI often only updates the high‐wavenumber components corresponding to the reflected waves, which is equivalent to least‐squares reverse time migration (LSRTM). Moreover, the inaccuracy of the low‐ and middle‐wavenumber components spatially misplaces the updated high‐wavenumber components, and the inversion can very easily fall into local extremes (Brossier et al., 2015; Dong et al., 2013; Zhou et al., 2015). As shown in Figure 1, FWI requires a larger angular aperture for deep inversion to ensure sufficient low‐wavenumber illumination under the same frequency and background of the velocity model.…”

Section: Introductionmentioning

confidence: 99%

Wide‐spectrum reconstruction of a velocity model based on the wave equation reflection inversion method and its application

Zhang

et al. 2023

Geophysical Prospecting

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Most seismic data used for conventional exploration lack far offset signals and effective low frequency components. This makes it difficult for conventional full waveform inversion methods to recover the low‐wavenumber components of mid‐ and deep‐layer models. To resolve the bottleneck of ray‐theoretical reflection traveltime tomography, wave equations, such as reflection inversion methods, are receiving increasing attention. We reconstructed a wide‐spectrum velocity model based on the multiscale wave‐equation reflection inversion method for model decomposition. Firstly, using the dynamic image warping method to obtain reflection traveltime residuals, the wave‐equation reflection traveltime inversion was used to recover the low‐wavenumber components of the background model, which also solved the cycle skipping problem. Secondly, the medium‐wavenumber component of the model is then supplemented by the reflection waveform inversion, while the reflectivity model is obtained relying on the least squares reverse time migration method. Based on this method, the background and perturbation models are updated alternately and iteratively. At the same time, the stratum structural tensor information was extracted using the perturbed model image to construct a preconditioned operator for the stratum structural constraint, suppress unreasonably high‐wavenumber components in the generalized gradient, and improve the geological consistency of the inversion results. Testing of the model using the Sigsbee2b model and seismic field data from the East China Sea showed that, compared with the conventional reflection travel time tomography method based on prestack depth migration, the wave‐equation reflection inversion strategy with well‐matched information from travel times to waveforms significantly improved the accuracy of the middle and deep velocity modeling, and enhanced the imaging quality of the whole body.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%

Wide‐spectrum reconstruction of a velocity model based on the wave equation reflection inversion method and its application

Zhang

et al. 2023

Geophysical Prospecting

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Joint data and model-driven simultaneous inversion of velocity and density

Chen,

Jensen

et al. 2024

Geophysical Journal International

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Summary Density is an important parameter for both geological research and geophysical exploration. However, for model-driven seismic inversion methods, high-fidelity density inversion is challenging due to seismic wave travel-time insensitivity to density, and crosstalk that density has with velocity. To circumvent the challenge of density inversion, some inversion methods treat density as a constant value or derive density from velocity through empirical equation. On the other hand, deep learning approaches are completely driven by data and have strong target-oriented characteristics, offering a new way to solve multi-parameter coupling problems. Nevertheless, the accuracy of the inversion results of data-driven algorithms is directly related to the amount and diversity of the training data, and thus, they lack the universality of model-driven algorithms. To achieve accurate density inversion, we propose a simultaneous inversion algorithm for velocity and density that combines the advantages of data- and model- driven approaches: A neural network model (U-T), combining the U-net and Transformer architectures, is proposed to construct nonlinear mappings between seismic data as inputs and the velocity and density as predictions. Next, the model-driven inversion algorithm uses the U-T prediction as the initial model to obtain the final accurate solution. In the model-driven module, envelope-based sparse constrained deconvolution is used to obtain full-band seismic data, while a variable dominant frequency full waveform inversion algorithm is employed to perform multi-scale inversion, ultimately leading to accurate inversion results of velocity and density. The performance of the algorithm on the Sigsbee2A and Marmousi models demonstrates its effectiveness.

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Seismic reflection waveform inversion based on Gauss–Newton optimization

et al. 2022

Journal of Geophysics and Engineering

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Owing to the lack of long-offset signals and effective low-frequency components, it is extremely difficult for classical full-waveform inversion (FWI) to obtain the long-wavelength components of medium and deep elastic parameter models. Considering ray theory, reflection traveltime tomography is restricted by resolution, and the reflection waveform inversion (RWI) method of the wave equation, and its practicability, has received greater attention. However, previous studies into RWI have primarily used first-order optimization methods, in which convergence and resolution still need substantial improvement. Based on the second-order optimization inversion theory, we deduced the reflection wave-sensitive kernel, objective function gradient and Hessian operator of background and perturbation model. Additionally, we revealed the de-fuzzification effect of the Hessian matrix on the gradient of inversion and the working mechanism of background velocity updating on direction optimization and inversion resolution improvement. Experiments on the SEG/EAGE overthrust model showed that an RWI with the Gauss–Newton method using an approximate Hessian matrix significantly improved convergence and wide-spectrum modeling capability compared with the conjugate gradient method. On the streamer seismic data of the East China Sea, the second-order optimization RWI method surpassed the commonly used reflection traveltime tomography based on pre-stack depth migration. In addition, the RTM of inversion model can depict the complex fault system in the Changjiang sag with a high resolution, which improves the imaging quality of the deep basement.

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Objective‐Function Behavior in Acoustic Full‐Waveform Inversion

Cited by 4 publications

References 25 publications

Wide‐spectrum reconstruction of a velocity model based on the wave equation reflection inversion method and its application

Wide‐spectrum reconstruction of a velocity model based on the wave equation reflection inversion method and its application

Joint data and model-driven simultaneous inversion of velocity and density

Seismic reflection waveform inversion based on Gauss–Newton optimization

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