A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models
Abstract Hydrogeologic models generally require gridded subsurface properties, however these inputs are often difficult to obtain and highly uncertain. Parametrizing computationally expensive models where extensive calibration is computationally infeasible is a long standing challenge in hydrogeolog...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2024GL114285 |
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| _version_ | 1849397339824848896 |
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| author | Amanda Triplett Andrew Bennett Laura E. Condon Peter Melchior Reed M. Maxwell |
| author_facet | Amanda Triplett Andrew Bennett Laura E. Condon Peter Melchior Reed M. Maxwell |
| author_sort | Amanda Triplett |
| collection | DOAJ |
| description | Abstract Hydrogeologic models generally require gridded subsurface properties, however these inputs are often difficult to obtain and highly uncertain. Parametrizing computationally expensive models where extensive calibration is computationally infeasible is a long standing challenge in hydrogeology. Here we present a machine learning framework to address this challenge. We train an inversion model to learn the relationship between water table depth and hydraulic conductivity using a small number of physical simulations. For a 31M grid cell model of the US we demonstrate that the inversion model can produce a reliable K field using only 30 simulations for training. Furthermore, we show that the inversion model captures physically realistic relationships between variables, even for relationships that were not directly trained on. While there are still limitations for out of sample parameters, the general framework presented here provides a promising approach for parametrizing expensive models. |
| format | Article |
| id | doaj-art-148ff8d590f54e4e88bce34cd44fe4a4 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-148ff8d590f54e4e88bce34cd44fe4a42025-08-20T03:39:03ZengWileyGeophysical Research Letters0094-82761944-80072025-04-01528n/an/a10.1029/2024GL114285A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater ModelsAmanda Triplett0Andrew Bennett1Laura E. Condon2Peter Melchior3Reed M. Maxwell4Department of Hydrology and Atmospheric Sciences University of Arizona Tucson AZ USADepartment of Hydrology and Atmospheric Sciences University of Arizona Tucson AZ USADepartment of Hydrology and Atmospheric Sciences University of Arizona Tucson AZ USADepartment of Astrophysical Sciences Princeton University Princeton NJ USAIntegrated Groundwater Modeling Center Princeton University Princeton NJ USAAbstract Hydrogeologic models generally require gridded subsurface properties, however these inputs are often difficult to obtain and highly uncertain. Parametrizing computationally expensive models where extensive calibration is computationally infeasible is a long standing challenge in hydrogeology. Here we present a machine learning framework to address this challenge. We train an inversion model to learn the relationship between water table depth and hydraulic conductivity using a small number of physical simulations. For a 31M grid cell model of the US we demonstrate that the inversion model can produce a reliable K field using only 30 simulations for training. Furthermore, we show that the inversion model captures physically realistic relationships between variables, even for relationships that were not directly trained on. While there are still limitations for out of sample parameters, the general framework presented here provides a promising approach for parametrizing expensive models.https://doi.org/10.1029/2024GL114285groundwaterinversionmodelingcalibrationmachine learninghydraulic conductivity |
| spellingShingle | Amanda Triplett Andrew Bennett Laura E. Condon Peter Melchior Reed M. Maxwell A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models Geophysical Research Letters groundwater inversion modeling calibration machine learning hydraulic conductivity |
| title | A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models |
| title_full | A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models |
| title_fullStr | A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models |
| title_full_unstemmed | A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models |
| title_short | A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models |
| title_sort | deep learning based parameter inversion framework for large scale groundwater models |
| topic | groundwater inversion modeling calibration machine learning hydraulic conductivity |
| url | https://doi.org/10.1029/2024GL114285 |
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