Bridging the Acoustic–Elastic Gap in Seismic Inversion via Robust Summary Statistics

Simulation-based Bayesian inference (SBI) enables uncertainty quantification in seismic inversion but typically assumes that simulated and observed data follow the same physics. In reality, this assumption does not hold. The Earth is elastic, yet large-scale simulations are often acoustic due to the prohibitive cost of elastic modeling. This acoustic–elastic mismatch introduces model misspecification, leading to biased posterior estimates and reduced interpretability. We propose a robust conditional generative framework that mitigates this mismatch by learning a shared representation between acoustic and elastic domains. The approach integrates a robust summary network within a conditional normalizing flow, trained primarily on acoustic simulations and guided by limited elastic supervision. It enables elastic-consistent posterior inference from inexpensive acoustic simulations, balancing computational efficiency with physical realism. This framework offers a scalable path toward uncertainty-aware subsurface characterization under model misspecification, bridging the gap between fast acoustic modeling and physically accurate elastic data.