Summary of Geofuse: a High-efficiency Surrogate Model For Seawater Intrusion Prediction and Uncertainty Reduction, by Su Jiang et al.
GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction
by Su Jiang, Chuyang Liu, Dipankar Dwivedi
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes GeoFUSE, a deep-learning-based surrogate framework that combines the U-Net Fourier Neural Operator (U-FNO) with Principal Component Analysis (PCA) and Ensemble Smoother with Multiple Data Assimilation (ESMDA). This framework enables fast and efficient simulation of seawater intrusion into coastal aquifers while reducing uncertainty in model predictions. The authors apply GeoFUSE to a 2D cross-section of the Beaver Creek tidal stream-floodplain system, achieving a speedup of approximately 360,000 times compared to traditional numerical simulations. The framework also improves predictive accuracy by integrating measurement data from monitoring wells and reduces geological uncertainty over a 20-year period. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Seawater intrusion into coastal aquifers is a big problem because it can contaminate our water supply. Climate change makes this problem even worse because sea levels are rising. Scientists want to be able to predict where the seawater will go, but traditional computer simulations take too long and aren’t very accurate. The new framework, called GeoFUSE, uses artificial intelligence to make predictions much faster and more accurately. It works by combining different ideas from computer science and geology. The authors tested GeoFUSE on a real area in Washington State and found that it worked really well. |
Keywords
» Artificial intelligence » Deep learning » Pca » Principal component analysis
