D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
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D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.