## 1 Introduction  
Interferometric synthetic aperture radar (InSAR), which combines space geodesy with active microwave remote sensing, has become a powerful tool for monitoring surface deformation. Its advantages--including wide spatial coverage, high spatial resolution, and all-weather, day-and-night observation capabilities--make it especially well-suited for geodetic applications. InSAR has been widely adopted in geoscience research, playing a critical role in monitoring earthquakes and fault movements [1, 2, 3, 4], volcanic activity [5, 6, 7],land subsidence [8; 9], landslides [10; 11], and changes in glaciers and permafrost [12; 13; 14; 15]. In recent years, advances in synthetic aperture radar (SAR) satellite missions have significantly enhanced the availability and accessibility of SAR data, further propelling the development and application of InSAR techniques. The Sentinel-1 SAR satellites, operated by the European Space Agency (ESA), have become a cornerstone of modern InSAR-based deformation monitoring. Featuring a short revisit cycle (12 days for a single satellite and 6 days for the two-satellite constellation), a wide imaging swath (~250 km per scene), and an open-access policy, the Sentinel-1 mission generates over 10 terabytes of SAR data daily [16]. This unprecedented data volume lays a solid foundation for leveraging deep learning (DL) techniques in InSAR analysis, opening new possibilities for large-scale, data-driven deformation research. Deep learning has been widely adopted in remote sensing [17], with most research centered on tasks such as land cover classification, object detection, change detection, and semantic segmentation using optical data (including multispectral and hyperspectral imagery) and SAR data [18; 19; 20]. In contrast, research specifically focused on InSAR remains relatively limited, especially studies that leverage its geodetic measurement capability--specifically, the phase information in SAR data--for deep learning applications. As illustrated in Figure 1, current deep learning applications with InSAR primarily rely on artificial neural networks (ANNs) and address tasks such as the classification of wrapped or unwrapped interferograms [21; 22], millimeter-level ground deformation extraction from InSAR time series [23; 24], phase unwrapping [25; 26], and phase filtering [27; 28]. However, the effectiveness of these methods is often limited by the lack of high-quality labeled training data and the high cost of manual annotation. To address these challenges, many studies utilize large-scale synthetic datasets and data augmentation (DA) techniques [29]. While such strategies help alleviate issues related to limited training data and class imbalance [30], significant distributional differences between synthetic and real-world observational data may hinder the generalization capability of trained models in real-world scenarios. Currently, the absence of a large-scale, high-quality training dataset ofoseismic InSAR samples poses a major barrier to the advancement and application of deep learning in geophysical research and earthquake hazard monitoring. Owing to the complexity of acquiring and processing InSAR data, most current deep learning studies rely on small, manually curated datasets, which are insufficient to meet the data-intensive demands of  
Figure 1: Application of deep learning in InSAR [24]. modern deep learning models. InSAR observations consist of a complex superposition of signals, including not only actual ground deformation but also residual atmospheric delays, topographic artifacts, and spatiotemporal decorrelation noise introduced during data processing [31]. Although numerical models for simulating surface deformation are relatively advanced, the complex spatiotemporal variability of InSAR noise often limits the effectiveness of synthetic datasets in training deep learning models [21, 32].