Abstract The efficacy of rapid seismic response is fundamentally constrained by the sequential, multi‐step nature of conventional InSAR processing, where error propagation and reliance on auxiliary data hinder automation. Here, we present a holistic framework using Physics‐Aware Generative Adversarial Networks (GANs) to directly retrieve absolute coseismic displacement fields from single, noisy interferograms. By synthesizing the distinct spectral signatures of tectonic deformation against stratified and turbulent atmosphere, orbital ramps, and topographic residuals, our model achieves end‐to‐end signal extraction. This approach effectively bypasses adaptive filtering, external error corrections, and the fragile phase unwrapping step. Validation against 18 real‐world earthquakes confirms the robust removal of segmentation artifacts. Crucially, comparison with GPS data from the 2016 Amatrice earthquake demonstrates high physical fidelity (>69 within 1σ) without post‐processing. This self‐contained paradigm eliminates manual intervention, establishing a new standard for instantaneous, automated, post‐event situational awareness.
Zhu et al. (Fri,) studied this question.