Discovering State‐Parameter Mappings in Subsurface Models Using Generative Adversarial Networks
Year: 2018
Type: article
Abstract: 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.
Source: Geophysical Research Letters
DOI: 10.1029/2018gl080404
Author: Alexander Y. Sun
Institutions: Bureau of Economic Geology, The University of Texas at Austin
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: PENNSYLVANIA UNIVERSITY
DE-FE0031544 / 0000
PROJECT TITLE: INTEGRATION OF SEISMIC-PRESSURE-PETROPHYSICS INVERSION OF CONTINUOUS ACTIVE-SEISMIC MONITORING DATA FOR MONITORING AND QUANTIFYING CO2 PLUME., G2052682234, DEVELOPMENT OF A FRAMEWORK FOR DATA INTEGRATION, ASSIMILATION, AND LEARNING FOR GEOLOGICAL CARBON SEQUESTRATION
OpenAlex ID: W2896999814