Probabilistic Inversion of Airborne Electromagnetic Data Using Conditional Variational Autoencoder: Imaging Hydrothermal Systems at Yellowstone

Airborne electromagnetic (AEM) inversion is inherently ill-posed, resulting in challenges for reliable subsurface interpretation. While Bayesian methods offer principled uncertainty quantification, their high computational cost limits scalability. We propose a conditional variational autoencoder (CVAE) framework for fast, probabilistic AEM inversion. By introducing a latent variable and conditioning on both AEM data and transceiver height, the CVAE learns the mapping relationship from latent space to posterior resistivity distributions. Synthetic tests demonstrate the CVAE’s noise robustness and its ability to reconstruct the model posterior. A comparison with McMC-based inversion is also discussed to highlight methodological and computational differences. Applied to AEM data acquired from Yellowstone region, the CVAE effectively recovers key hydrothermal features and provides quantified uncertainty estimates. This approach achieves orders-of-magnitude speed-up over traditional Bayesian methods, enabling large-scale, uncertainty-informed subsurface resistivity imaging.