Bayesian Inversion Beyond the Generator's Range: Reducing Non-physical Constraints from Generative Neural Networks

To address non-uniqueness in geophysical inversions, Generative Neural Networks (GNNs) are often used to parameterise geological or other prior information in a lower-dimensional space (the so-called latent space), thereby reducing the number of inversion parameters. However, the training samples th