We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal posterior distribution without dimensionality reduction. The framework is called WISER, short for full-Waveform variational Inference via Subsurface Extensions with Refinements. WISER leverages the power of generative artificial intelligence to perform approximate amortized inference that is low-cost albeit showing an amortization gap. This gap is closed through non-amortized refinements that make frugal use of acoustic wave physics. Case studies illustrate that WISER is capable of full-resolution, computationally feasible, and reliable uncertainty estimates of velocity models and imaged reflectivities.

翻译：我们提出了一种半摊销变分推断框架，旨在实现二维全波形反演中计算可行的不确定性量化，以探索无需降维的多模态后验分布。该框架称为WISER，全称为"通过精细化地下扩展的全波形变分推断"。WISER利用生成式人工智能的能力执行近似摊销推断，该方法虽存在摊销间隙但计算成本较低。该间隙通过非摊销精细化过程予以消除，该过程审慎运用声波物理原理。案例研究表明，WISER能够对速度模型和成像反射率提供全分辨率、计算可行且可靠的不确定性估计。