Physics-Aware Machine Learning for Large-Scale Hydro-Geodetic Inference from InSAR

Hydrological and hydro-geodetic applications increasingly require high-resolution characterization of aquifer-scale water-storage dynamics over large regions, while existing hydrology models and gravimetric observations operate at much coarser spatial resolution. InSAR provides dense surface-deformation observations sensitive to subsurface water-storage changes, but converting these measurements into reliable, vertically resolved hydrological information remains an ill-posed and computationally demanding inversion problem, especially for multi-decadal, multi-mission data sets.

We present a physics-aware machine-learning framework to enable large-scale, high-resolution hydro-geodetic inversion from long-term InSAR time series. Independent InSAR deformation time series from open SAR missions (ERS-1/2, Envisat, ALOS-1/2, Sentinel-1) are processed using reproducible workflows and harmonized across wavelengths and acquisition geometries to form spatio-temporal deformation volumes. These data are inverted using a 3D Swin Transformer U-Net constrained by elastic and poroelastic forward deformation operators and basin-scale mass conservation. Hydrological models and gravimetric observations are used as structured supervision rather than ground truth, ensuring physical consistency and stability of the inversion.

The live demonstration emphasizes scalable execution on high-performance computing platforms, reliable inversion of massive InSAR data batches, and interpretable aquifer-scale hydrological responses, including characteristic lag behaviour. The framework supports high-resolution hydrological model improvement and provides physically consistent inputs for groundwater-related geohazard assessment, such as subsidence and compaction risk.

Key words: Physics-aware machine learning; 3D Swin Transformer U-Net; InSAR time-series inversion; hydro-geodetic analysis; high-performance computing (HPC)