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Author

Shuang Wang

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Aug 2026

Uncertainty quantification in Bayesian full waveform inversion based on underdamped Langevin Markov Chain Monte Carlo

Full waveform inversion (FWI) is a powerful technique for building high-resolution subsurface models, but it is fundamentally ill-posed. Traditional FWI cannot quantify uncertainties arising from noise, modeling errors, the intrinsic high nonlinearity, and other sources. In Bayesian inference, Markov chain Monte Carl...

Shuang Wang, Xiang-Bo Gong, Qiao Cheng et al. · 0 citations
Open access Aug 2026

Bayesian full waveform inversion using the shifted ordinary differential equation method with underdamped Langevin Markov chain Monte Carlo

Full waveform inversion (FWI) is a powerful tool for constructing high-resolution subsurface models but remains fundamentally ill-posed due to sparse and noisy data, modeling errors, and the severe nonlinearity of the forward modeling. Because this inherent nonlinearity creates complex uncertainty structures that tra...

Shuang Wang, Xiang-Bo Gong, Qiao Cheng et al. · 0 citations

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