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，无需降维即可探索多模态后验分布。该框架称为WISER（全波形反演基于子波扩展与精化的变分推断）。WISER利用生成式人工智能的强大能力，执行近似摊销推断——该推断成本低廉，但存在摊销差距。这一差距通过非摊销精化过程得以弥合，该过程节俭地利用了声波物理规律。案例研究表明，WISER能够实现全分辨率、计算可行且可靠的速度模型与成像反射率不确定性估计。