Abstract
Self-potential (SP) monitoring is increasingly used for subsurface flow characterization due to its sensitivity to hydrogeological and geochemical processes. However, SP inversion remains challenging due to its ill-posed nature, sparse data coverage, and strong transient noise. This study proposes a hybrid framework to image hyporheic exchange using a time-lapse SP data set monitored from a streamflow site in Oak Ridge, Tennessee. Dipole moment tomography grids generated from the physics-informed numerical inversion is first used to train a Vision Transformer (ViT) model that maps surface SP sequences to 2D source distributions. While the numerical method is more responsive to transient signals, the ViT model better captures persistent spatial structures. Their complementary outputs are jointly analyzed in the spatiotemporal domain to isolate dynamic hyporheic exchange zones and distinguish transient from steady state subsurface flow features. This approach integrates physical inversion and deep learning to enhance interpretability, generalization, and temporal awareness in SP analysis.
| Original language | English |
|---|---|
| Article number | e2025GL118772 |
| Journal | Geophysical Research Letters |
| Volume | 52 |
| Issue number | 21 |
| DOIs | |
| State | Published - Nov 16 2025 |
Funding
This work was supported by Department of Energy Minority Serving Institution Partnership Program (MSIPP) managed by the Savannah River National Laboratory under BSRA Contract TOA 800002114. Additional support was provided by the U.S. Department of Energy, Office of Science, Biological and Environmental Research - Research and Development Partnership Pilots (DE-SC0023132) and is a product of the Watershed Dynamics and Evolution Science Focus Area at Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. The authors wish to thank Gladisol Smith-Vega, Aubrey Elwes, Yusen Yuan, and Ahsan Jamil. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Writings prepared by U.S. Government employees as part of their official duties, including this paper, cannot be copyrighted and are in the public domain. This work was supported by Department of Energy Minority Serving Institution Partnership Program (MSIPP) managed by the Savannah River National Laboratory under BSRA Contract TOA 800002114. Additional support was provided by the U.S. Department of Energy, Office of Science, Biological and Environmental Research ‐ Research and Development Partnership Pilots (DE‐SC0023132) and is a product of the Watershed Dynamics and Evolution Science Focus Area at Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT‐Battelle, LLC, for the U.S. Department of Energy under Contract DE‐AC05‐00OR22725. The authors wish to thank Gladisol Smith‐Vega, Aubrey Elwes, Yusen Yuan, and Ahsan Jamil. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Writings prepared by U.S. Government employees as part of their official duties, including this paper, cannot be copyrighted and are in the public domain.
Keywords
- Vision Transformer
- deep learning
- geophysical inversion
- hyporheic
- machine learning
- self-potential