English(EN) Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

扩散模型增强贝叶斯FWI以进行地下结构重建

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员通过将学习到的扩散模型与退火Stein变分梯度下降（ASVGD）相结合，开发了一种新颖的全波形反演（FWI）方法。该方法通过使用扩散模型作为学习到的地质投影算子，将粒子引导至合理的声速模型，从而增强了地下模型重建。扩散正则化的ASVGD-FWI框架提高了反演的稳定性和不确定性估计的可解释性，尤其是在复杂地质区域。 AI

影响这项研究可能带来更精确的地质成像和更好的地质勘测不确定性量化。

排序理由该项目是一篇学术论文，详细介绍了一种用于科学应用的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Chao Li, Mrinal K. Sen, Sergey Fomel, Yangkang Chen · 2026-10-15 04:00

通过扩散正则化 Stein 变分梯度下降实现结构感知贝叶斯全波形反演

arXiv:2610.19187v1 Announce Type: cross Abstract: Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, especially when the initial model is inaccurate or the acquired data are noisy an…

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通过扩散正则化 Stein 变分梯度下降实现结构感知贝叶斯全波形反演

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