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

|View full text |Cite

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

Oliver Dorn

Yifan Wu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article4

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 53 publications

(125 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

mentioning

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

mentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

Salt reconstruction in full-waveform inversion using topology optimization techniques

Gonçalves

Silva

2023

Geophysical Journal International

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Contact Info

Product

Resources

About