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Seismic full-waveform inversion based on a physics-driven generative adversarial network

Xinyi Zhang, Caiyun Liu, Jie Xiong, Qingfeng Yu

Abstract

Objectives: Full-waveform inversion (FWI) is a high-resolution geophysical imaging technique that reconstructs subsurface velocity models by iteratively minimizing the misfit between predicted and observed seismic data. However, under complex geological conditions, conventional FWI suffers from strong dependence on the initial model and tends to produce unstable results when the data are sparse or contaminated by noise. Methods: To address these limitations, this paper proposes a physics-driven generative adversarial network-based full-waveform inversion method. The proposed approach integrates the data-driven capability of deep neural networks with the physical constraints imposed by the seismic wave equation, and employs adversarial training through a discriminator to enhance the stability and robustness of the inversion results. Results: Experimental results on two representative benchmark geological models demonstrate that the proposed method can effectively recover complex velocity structures and achieves superior performance in terms of structural similarity (SSIM) and signal-to-noise ratio (SNR). Conclusions: This method provides a promising solution for alleviating the initial-model dependence in full-waveform inversion and shows strong potential for practical applications.

Seismic full-waveform inversion based on a physics-driven generative adversarial network

Abstract

Objectives: Full-waveform inversion (FWI) is a high-resolution geophysical imaging technique that reconstructs subsurface velocity models by iteratively minimizing the misfit between predicted and observed seismic data. However, under complex geological conditions, conventional FWI suffers from strong dependence on the initial model and tends to produce unstable results when the data are sparse or contaminated by noise. Methods: To address these limitations, this paper proposes a physics-driven generative adversarial network-based full-waveform inversion method. The proposed approach integrates the data-driven capability of deep neural networks with the physical constraints imposed by the seismic wave equation, and employs adversarial training through a discriminator to enhance the stability and robustness of the inversion results. Results: Experimental results on two representative benchmark geological models demonstrate that the proposed method can effectively recover complex velocity structures and achieves superior performance in terms of structural similarity (SSIM) and signal-to-noise ratio (SNR). Conclusions: This method provides a promising solution for alleviating the initial-model dependence in full-waveform inversion and shows strong potential for practical applications.
Paper Structure (15 sections, 7 equations, 6 figures, 3 tables)

This paper contains 15 sections, 7 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Basic Principles
  3. Traditional FWI
  4. GAN Architecture
  5. Model Design
  6. Overall Workflow
  7. Physics-Driven Generator
  8. Discriminator
  9. Experiments
  10. Experimental Setup
  11. Evaluation Metrics
  12. Marmousi Model Experiments
  13. Overthrust Model Experiments
  14. Inversion Experiments with a Linear Initial Velocity Model
  15. Conclusions

Figures (6)

  • Figure 1: Overall workflow
  • Figure 2: Architecture of the physics-driven generator
  • Figure 3: Architecture of the discriminator: (a) Discriminator structure; (b) Convolutional block structure.
  • Figure 4: Inversion results of the Marmousi model: (a) True velocity model; (b) Gaussian-smoothed initial velocity model; (c) Inversion result by Yang et al. using noise-free seismic data; (d) Inversion result by the proposed method using noise-free seismic data; (e) Inversion result by Yang et al. using noisy seismic data; (f) Inversion result by the proposed method using noisy seismic data.
  • Figure 5: Inversion results of the Overthrust model: (a) True velocity model; (b) Gaussian-smoothed initial velocity model; (c) Inversion result by Yang et al. using noise-free seismic data; (d) Inversion result by the proposed method using noise-free seismic data; (e) Inversion result by Yang et al. using noisy seismic data; (f) Inversion result by the proposed method using noisy seismic data.
  • ...and 1 more figures