Uncertainty-aware Frequency-domain Acoustic Full Waveform Inversion Using Gaussian Random Fields and Ensemble Kalman Inversion

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ID: 322886
2026
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Abstract
Summary In recent years, full waveform inversion (FWI) research has increasingly focused on providing informative uncertainty estimates alongside inversion results. Bayesian inference methods-particularly Monte Carlo-based approaches-have been widely employed to quantify uncertainty. However, these techniques often require extensive posterior sampling, resulting in high computational costs. To address this challenge and enable efficient uncertainty quantification in FWI, we introduce an uncertainty-aware FWI framework-EKI-GRFs-FWI-that integrates Gaussian random fields (GRFs) with the ensemble Kalman inversion (EKI) algorithm. This approach jointly infers subsurface velocity fields and provides reliable uncertainty estimates in a computationally efficient manner. Specifically, we leverage the highly parallelizable nature and derivative-free nature of the EKI algorithm with an effective stopping criterion, making it suitable for large-scale inverse problems. Meanwhile, we incorporate prior knowledge of the spatial correlation via GRFs, enabling the generation of physically feasible initial ensembles for EKI. Numerical results demonstrate that EKI-GRFs-FWI yields reasonably accurate velocity reconstructions while delivering informative uncertainty estimates.
Reference Key
openalex_W7171528615 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yunduo Li, Yijie Zhang, Xueyu Zhu
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag296
URL
https://doi.org/10.1093/gji/ggag296
Keywords Keywords not found

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