Shape reconstruction in seismic full waveform inversion using a level set approach and time reversal

Dorn, Oliver; Wu, Yifan

doi:10.1016/j.jcp.2020.110059

Journal of Computational Physics

2021

DOI: 10.1016/j.jcp.2020.110059

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Shape reconstruction in seismic full waveform inversion using a level set approach and time reversal

Oliver Dorn

Yifan Wu

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Cited by 4 publications

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References 53 publications

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“…Accurate and efficient numerical solutions are continuously being sought (Moseley et al, 2020;Dorn and Wu, 2021;Sandhu et al, 2021). Conventional numerical methods have matured over the years, but the progress has been relatively slow.…”

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confidence: 99%

Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks

Sandhu,

Waheed,

Song

et al. 2023

Front. Earth Sci.

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Incorporating anisotropy is crucial for accurately modeling seismic wave propagation. However, numerical solutions are susceptible to dispersion artifacts, and they often require considerable computational resources. Moreover, their accuracy is dependent on the size of discretization, which is a function of the operating frequency. Physics informed neural networks (PINNs) have demonstrated the potential to tackle long-standing challenges in seismic modeling and inversion, addressing the associated computational bottleneck and numerical dispersion artifacts. Despite progress, PINNs exhibit spectral bias, resulting in a stronger capability to learn low-frequency features over high-frequency ones. This paper proposes the use of a simple fully-connected PINN model, and evaluates its potential to interpolate and extrapolate scattered wavefields that correspond to the acoustic VTI wave equation across multiple frequencies. The issue of spectral bias is tackled by incorporating the Kronecker neural network architecture with composite activation function formed using the inverse tangent (atan), exponential linear unit (elu), locally adaptive sine (l-sin), and locally adaptive cosine (l-cos) activation functions. This allows the construction of an effectively wider neural network with a minimal increase in the number of trainable parameters. The proposed scheme keeps the network size fixed for multiple frequencies and does not require repeated training at each frequency. Numerical results demonstrate the efficacy of the proposed approach in fast and accurate, anisotropic multi-frequency wavefield modeling.

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confidence: 99%

Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks

Sandhu,

Waheed,

Song

et al. 2023

Front. Earth Sci.

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Salt reconstruction in full-waveform inversion using topology optimization techniques

Gonçalves

Silva

2023

Geophysical Journal International

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Summary With the increasing advances in the oil and gas industry, seismic imaging near or under salt structures has become an important point in deep-water exploration. Detailed velocity models of these areas are particularly interesting not only to characterize hydrocarbon reservoirs but also to identify potential sites for hydrogen and carbon dioxide storage in offshore salt caverns. Thus, we study the Full Waveform Inversion (FWI) for the salt reconstruction in acoustic media with constant density considering the time-harmonic wave propagation in a Finite Element formulation using the Topology Optimization (TO) method. This problem is challenging due to the strong velocity contrast between salt bodies and the sedimentary background, in addition to the lack of low-frequency data and the inherent ill-posedness of the inverse problem. In this context, we incorporate techniques from the TO field, usually used in design applications, to overcome or reduce these known problems. We initially defined the squared slowness as a combination of two fields, one related to the salt shape and the other to the background. An interpolation rule based on the Solid Isotropic Material with Penalization (SIMP) method, combined with filtering and projection schemes, is used to find the shape of the salt bodies with increased sharpness interfaces. A Helmholtz-type filter is applied to modify the gradient aimed to regularize the problem and provide a more stable way for the salt shape to evolve during the inversion process. In particular, we demonstrate that the proposed approach may be relevant for reconstructing media with salt bodies when a suitable starting model is unavailable, and sharp interfaces are required. In addition, we present inversion results from synthetic data generated by a variable density model to demonstrate the approach capability when subjected to a reconstruction application.

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Elastic Full Waveform Inversion Based on Full-Band Seismic Data Reconstructed by Dual Deconvolution

Chen

Yang

Wang

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Shape reconstruction in seismic full waveform inversion using a level set approach and time reversal

Cited by 4 publications

References 53 publications

Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks

Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks

Salt reconstruction in full-waveform inversion using topology optimization techniques

Elastic Full Waveform Inversion Based on Full-Band Seismic Data Reconstructed by Dual Deconvolution

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