3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion
Jaehong Chung, Andrew Lockwood, Jef Caers
Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens…
Variability in Performance of a Machine-Learning Seismicity Catalog: Central Italy, 2016-2017
Jaehong Chung, Yifan Yu, Lauro Chiaraluce +2
Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We…
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Jaehong Chung, Michael Manga, Timothy Kneafsey +2
Microearthquakes (MEQs) generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their full spatiotemporal evolution is…
Image registration of 2D optical thin sections in a 3D porous medium: Application to a Berea sandstone digital rock image
Jaehong Chung, Wei Cai, Tapan Mukerji
This study proposes a systematic image registration approach to align 2D optical thin-section images within a 3D digital rock volume. Using template image matching with differentia…
Accelerating Multiphase Flow Simulations with Denoising Diffusion Model Driven Initializations
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +4
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of f…
Generating Multiphase Fluid Configurations in Fractures using Diffusion Models
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +3
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen…