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<p>In seismic exploration, full waveform inversion (FWI) is a crucial tool for imaging subsurface structures. However, it must often contend with the persistent challenge of “cycle-skipping,” leading to frequent convergence to local minima. This study borrows the idea of model expansion and introduces a novel approach called lift-and-relax waveform inversion (LRWI). By lifting unknown variables (wavefields and model parameters) and relaxing the wave-equation constraint, LRWI expands the search space to mitigate the local minima issue. To further accelerate this inversion method, stochastic optimization with total variation regularization is introduced, resulting in TV-stochastic LRWI (TV-SLRWI). By randomly probing the data and wavefields and incorporating TV regularization on the model parameters, the proposed method achieves a greater than 10-fold acceleration in the inversion process while preserving inversion accuracy compared to using all the original data. In addition, a parallelization scheme utilizing graphics processing unit (GPU) acceleration is also implemented. Numerical results demonstrate that the proposed method effectively overcomes the local minima problem of conventional FWI. Furthermore, the GPU-accelerated parallelization scheme provides an impressive 210-fold speedup compared to central processing unit (CPU)-based implementations.</p>

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High-Resolution Seismic Inversion with GPU-Accelerated TV-Regularized Stochastic Lift-and-Relax Waveform Inversion

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Zhilong Fang,
  • Hua Wang,
  • Xueyuan Huang

摘要

In seismic exploration, full waveform inversion (FWI) is a crucial tool for imaging subsurface structures. However, it must often contend with the persistent challenge of “cycle-skipping,” leading to frequent convergence to local minima. This study borrows the idea of model expansion and introduces a novel approach called lift-and-relax waveform inversion (LRWI). By lifting unknown variables (wavefields and model parameters) and relaxing the wave-equation constraint, LRWI expands the search space to mitigate the local minima issue. To further accelerate this inversion method, stochastic optimization with total variation regularization is introduced, resulting in TV-stochastic LRWI (TV-SLRWI). By randomly probing the data and wavefields and incorporating TV regularization on the model parameters, the proposed method achieves a greater than 10-fold acceleration in the inversion process while preserving inversion accuracy compared to using all the original data. In addition, a parallelization scheme utilizing graphics processing unit (GPU) acceleration is also implemented. Numerical results demonstrate that the proposed method effectively overcomes the local minima problem of conventional FWI. Furthermore, the GPU-accelerated parallelization scheme provides an impressive 210-fold speedup compared to central processing unit (CPU)-based implementations.