  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.
