2 papers
cs.CV2026
Diffusion models for multivariate subsurface generation and efficient probabilistic inversion
Roberto Miele, Niklas Linde
Diffusion models offer stable training and state-of-the-art performance for deep generative modeling tasks. Here, we consider their use in the context of multivariate subsurface mo…
physics.geo-ph2025
Leveraging generative adversarial networks with spatially adaptive denormalization for multivariate stochastic seismic data inversion
Roberto Miele, Leonardo Azevedo
Probabilistic seismic inverse modeling often requires the prediction of both spatially correlated geological heterogeneities (e.g., facies) and continuous parameters (e.g., rock an…