Discovering state-parameter mappings in subsurface models using generative adversarial networks
This addresses a fundamental problem in geophysical modeling for subsurface flow applications, offering a new deep-learning-based paradigm, though it appears incremental as an adaptation of GANs to this domain.
The study tackled the challenge of identifying bidirectional mappings between physical parameters and model state variables in subsurface flow modeling, using a generative adversarial network (SPID-GAN) to achieve satisfactory performance in learning these mappings.
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially when parameter dimensionality is high. Deep learning has opened a new door toward knowledge representation and complex pattern identification. In particular, the recently introduced generative adversarial networks (GANs) hold strong promises in learning cross-domain mappings for image translation. This study presents a state-parameter identification GAN (SPID-GAN) for simultaneously learning bidirectional mappings between a high-dimensional parameter space and the corresponding model state space. SPID-GAN is demonstrated using a series of representative problems from subsurface flow modeling. Results show that SPID-GAN achieves satisfactory performance in identifying the bidirectional state-parameter mappings, providing a new deep-learning-based, knowledge representation paradigm for a wide array of complex geophysical problems.