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- Title
Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network.
- Authors
Kreyenberg, Philipp J.; Bauser, Hannes H.; Roth, Kurt
- Abstract
Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities of closing the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density‐driven solute transport. Using high‐resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both variable and uniform concentration boundary conditions showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density‐driven solute transport in a Hele‐Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible. Key Points: A convolutional neural network is trained on density‐driven solute transport simulations to incorporate the physical process representationMissing velocity fields on experimental data are estimated utilizing the physical process representation learned on purely synthetic dataLight transmission measurements using a Hele‐Shaw cell are conducted to infer spatially resolved solute concentration distributions
- Subjects
VELOCITY; OPEN learning; DEEP learning; MISSING data (Statistics); MACHINE learning
- Publication
Water Resources Research, 2019, Vol 55, Issue 8, p7275
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2019WR024833