Large-Scale InSAR Deformation Monitoring Using Realistic Simulation-Based Training of a Temporal Convolutional Network

Large-scale land surface deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) requires robust detection of changes in long-term deformation trends. However, accurate change point (CP) detection remains challenging due to complex time series characteristics, including seasonal and quasi-periodic components and noise. Classical methods and many existing deep learning approaches rely on restrictive assumptions and training data that do not fully represent real-world InSAR time series, limiting their generalization and scalability in large-scale, real-world applications. In this study, we propose an integrated, fully supervised framework for CP detection in InSAR displacement time series based on Temporal Convolutional Networks (TCNs). The proposed TCN model employs dilated convolutions with multi-scale receptive fields to capture long-term temporal dependencies and complex deformation patterns, enabling robust identification of significant trend changes under noisy conditions. To effectively train this model, we introduce a deep learning-based InSAR time series simulation framework trained on real time series. This simulation framework produces physically consistent InSAR time series that retain essential temporal characteristics while introducing predefined, credible trend changes. Finally, we integrate the trained model into a large-scale anomalous change-detection pipeline that aggregates detected CPs from individual time series into spatially coherent deformation heatmaps suitable for operational monitoring. The proposed framework is evaluated using simulated data and real InSAR time series from the Phlegraean Fields caldera (Campi Flegrei), Italy. The results show clusters of anomalous behavior in the central Campi Flegrei-Pozzuoli area and in parts of Ischia and Procida, consistent with known unrest zones, associated periods, and independent measurements.