Physics-informed W-Net GAN for the direct stochastic inversion of fullstack seismic data into facies models.

ABSTRACT: Predicting the subsurface spatial distribution of geological facies from fullstack geophysical data is a main step in the geo-modeling workflow for energy exploration and environmental tasks and requires solving an inverse problem. Generative adversarial networks (GANs) have shown great potential for geologically accurate probabilistic inverse modeling, but existing methods require multiple sequential steps and do not account for the spatial uncertainty of facies-dependent continuous properties, linking the facies to the observed geophysical data. This can lead to biased predictions of facies distributions and inaccurate quantification of the associated uncertainty. To overcome these limitations, we propose a GAN able to learn the physics-based mapping between facies and seismic domains, w

SUBMITTER: Miele R 

PROVIDER: S-EPMC10907746 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature