1887

Abstract

Summary

With the vector reflectivity-based acoustic wave equation, we can simultaneously obtain a high-resolution velocity model and the corresponding image using Full waveform inversion (FWI) imaging. However, the inversion process is quite non-linear and the field data is often blurred by noise, which makes the inversion process challenging. Thus, we propose an implicit joint imaging inversion (IJII) workflow, where the velocity and reflectivity models are implicitly represented by the weights of a neural network and can be resampled from the neural network at desired high resolution or even on irregular grids. The synthetic and field data (will be shown at the conference) examples show that the proposed method can recover high-resolution velocity models and migration images as an effective way to perform FWI imaging.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202639076
2026-03-09
2026-02-15
Loading full text...

Full text loading...

References

  1. He, Y., Xing, H., Huang, Y. and Wang, B. [2021] Inversion-based Imaging: FWI beyond Velocity. In: 82nd EAGE Annual Conference & Exhibition, 2021. European Association of Geoscientists & Engineers, 1–5.
    [Google Scholar]
  2. Müller, T., Evans, A., Schied, C. and Keller, A. [2022] Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4).
    [Google Scholar]
  3. Operto, S., Gholami, Y., Prieux, V., Ribodetti, A., Brossier, R., Metivier, L. and Virieux, J. [2013] A guided tour of multiparameter full-waveform inversion with multicomponent data: From theory to practice. The Leading Edge, 32(9), 1040–1054.
    [Google Scholar]
  4. Sun, J., Innanen, K., Zhang, T. and Trad, D. [2023] Implicit Seismic Full Waveform Inversion With Deep Neural Representation. Journal of Geophysical Research: Solid Earth, 128(3), e2022JB025964.
    [Google Scholar]
  5. Wang, B., He, Y., Mao, J., Liu, F., Hao, F., Huang, Y., Perz, M. and Michell, S. [2021] Inversion-based imaging: from LSRTM to FWI imaging. First Break, 39(12), 85–93.
    [Google Scholar]
  6. Wang, S. and Alkhalifah, T. [2025] Implicit full waveform inversion with energy-weighted gradient. In: 86th EAGE Annual Conference & Exhibition, 1. European Association of Geoscientists and Engineers, 1–5.
    [Google Scholar]
  7. Wang, S., Matteo, R. and Alkhalifah, T. [2025] Multiresolution hash encoding for high resolution implicit full waveform inversion. In: 86th EAGE Annual Conference & Exhibition, 1. European Association of Geoscientists and Engineers, 1–5.
    [Google Scholar]
  8. Whitmore, N., Ramos-Martinez, J., Yang, Y and Valenciano, A. [2020] Full Wavefield Modeling with Vector Reflectivity. In: European Association of Geoscientists and Engineers, 2020. European Association of Geoscientists and Engineers, 1–5.
    [Google Scholar]
  9. Wu, H., Zhang, S., Dong, X., Zhu, H. and Lu, S. [2024] Joint Migration Inversion Based on a Full-Wavefield Acoustic Wave Equation With Vector Reflectivity. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–11.
    [Google Scholar]
  10. Yang, Y., Ramos-Martinez, J., Whitmore, D., Huang, G. and Chemingui, N. [2021] Simultaneous velocity and reflectivity inversion: FWI + LSRTM. 2021(1), 1–5.
    [Google Scholar]
  11. Zhang, T., Sun, J., Trad, D. and Innanen, K. [2023] Multilayer Perceptron and Bayesian Neural Network-Based Elastic Implicit Full Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–16.
    [Google Scholar]
  12. Zhang, Z., Wu, Z., Wei, Z., Mei, J., Huang, R. and Wang, P. [2020] FWI Imaging: Full-wavefield imaging through full-waveform inversion. 656–660.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202639076
Loading
/content/papers/10.3997/2214-4609.202639076
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error