Abstract:
Recent emerging machine/deep learning (ML/DL) methods have been applied for big data analysis and real-time forecasting of coupled subsurface processes. With traditional computational methods high dimensional forward and inverse problems for coupled subsurface processes have been challenged by a number of high-fidelity forward model simulations and computational burdens with matrix calculations. Using ML-driven fast forward models and robust non-linear projection operators for dimension reduction, a ML-based framework can be developed as a robust and fast data assimilation solution for real-time forecasting. In this talk a few examples of supervised and self-supervised ML applications for fast surrogate models will be first presented to highlight promising results of ML applications for coupled subsurface processes, and then examples of generative models such as (variational) autoencoders and generative adversarial networks will be presented. With generative models as prior data assimilation framework will be presented to update the state model parameters in latent space. Finally, a current DOE effort to develop science-based machine learning for real-time forecasting for geologic carbon storage will be highlighted through a DOE SMART project (Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications). SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
Bio:
Hongkyu Yoon obtained a Ph.D degree in Environmental Engineering in Civil Engineering from the University of Illinois at Urbana-Champaign in 2005. After working as a postdoc and research scientist at UIUC, he joined the Geomechanics Department at Sandia in 2010 and currently a principal member of technical staff. He is an expert in hydrogeology, experimental and numerical microfluidic and flow cell systems, chemo-mechanical processes of nano-porous geomaterials using multiscale imaging techniques, and high-fidelity inverse modeling, specializing in applications of coupled hydrogeological, geomechanical, geophysical, and geochemical processes. His recent research focuses on induced seismicity during subsurface energy activities, machine learning/deep learning applications for geologic carbon storage, enhanced geothermal systems, nonproliferation, coupled thermal-mechanical-hydrological-chemical processes with applications to subsurface energy technologies, and geomaterial (shale, carbonate rocks, salt, clay) characterizations with multiscale imaging and mechanical testing.
