2 papers
cs.LG2025
Towards geological inference with process-based and deep generative modeling, part 2: inversion of fluvial deposits and latent-space disentanglement
Guillaume Rongier, Luk Peeters
High costs and uncertainties make subsurface decision-making challenging, as acquiring new data is rarely scalable. Embedding geological knowledge directly into predictive models o…
cs.LG2025
Towards geological inference with process-based and deep generative modeling, part 1: training on fluvial deposits
Guillaume Rongier, Luk Peeters
The distribution of resources in the subsurface is deeply linked to the variations of its physical properties. Generative modeling has long been used to predict those physical prop…