Physics-Informed Spatiotemporal Disentangling for Unsupervised Anomaly Detection in Earth Observation Sensing Nodes

Abstract

Long-term health monitoring of unattended sensing nodes is essential for remote Earth observation networks (EONs); yet, it remains challenging because anomaly-induced response drifts often resemble genuine geophysical variations in both spectral and morphological characteristics. This ambiguity makes sensor degradation difficult to distinguish from valid observations, particularly when measurements are affected by regional spatiotemporal coupling and labeled fault data are unavailable. To address these issues, we propose a physics-informed and data-driven anomaly detection framework for EON sensing nodes. Multidomain complementary features describing amplitude, spectral, and phase behaviors are constructed to improve the separability between natural geophysical variability and sensor-induced distortions. Building on these features, a memory-enhanced Transformer-graph convolutional network (ME-TGCN) is developed to model spatiotemporal dependencies and disentangle node-specific abnormal responses from shared regional variations, while an external memory mechanism preserves long-term healthy operating patterns. Residual modeling errors are further compensated to improve normal-response estimation. Anomalies are then identified from the discrepancy between predicted and observed sensor responses through Mahalanobis-distance-based residual analysis with adaptive thresholding, enabling detection without labeled fault samples. Experiments on real-world EON datasets show that the proposed method outperforms representative baseline methods in both normal-state prediction and anomaly detection and can reliably track progressive sensor degradation and localized anomalies. Cross-regional transfer results further demonstrate its robustness, generalization capability, and practical value for large-scale unattended sensor network monitoring.