Improving ground deformation prediction in satellite InSAR using ICA-assisted RNN model

Geological hazards caused by both natural forces and human-induced disturbances, such as land subsidence, earthquakes, tectonic motion, mining activities, coastal erosion, volcanic activities, and permafrost alterations, cause great adverse effects to earth’s surface. The preservation of a comprehensive record detailing past, present, and future surface movements is imperative for effective disaster risk mitigation and property protection. Interferometric Synthetic Aperture Radar (InSAR) is widely recognized as a highly effective and extensively employed geodetic technique for comprehending the spatiotemporal evolution of historical ground surface deformation. However, it only reveals the past deformation evolution process and the deformation update is slowly considering the long revisit cycle of satellites. Deformation evolution in the future is also crucial for preventing and mitigating geological hazards. Unlike traditional mathematical-statistical models and physical models, machine learning methods provide a new perspective and possibility to efficiently and automatically mine the time series information over a large-scale area. In the context of InSAR time series prediction over large areas, the previous researches do not consider the spatiotemporal heterogeneity caused by various factors over a large-scale area and mainly focus on one typical deformation point.

Therefore, in this study, we present a framework designed to predict large-scale spatiotemporal InSAR time series by integrating independent component analysis (ICA) and a Long Short-Term Memory (LSTM) machine learning model. This framework is developed with a specific focus on addressing spatiotemporal heterogeneity within the dataset. The utilization of the ICA method is employed to identify and capture the displacement signals of interest within the InSAR data, enabling the characterization of independent time series signals associated with various natural or anthropogenic processes. Additionally, a K-means clustering approach is incorporated to partition the study area into spatiotemporal homogeneity subregions across a large-scale region, aiming to mitigate potential decreases in model accuracy caused by data heterogeneity. Subsequently, LSTM models are constructed for each cluster, and optimal parameters are determined. The proposed framework is rigorously tested using simulated datasets and validated against two real-world cases—land subsidence in the Willcox Basin and post-seismic deformation following the Sarpol-e Zahab earthquake. Comparative analysis demonstrates that the proposed model surpasses the original LSTM, resulting in a 34% and 17% improvement in average prediction accuracy, respectively. The spatial prediction results in 60 days over the two cases are mapped with high accuracy.

This study introduces an integrated framework that seamlessly integrates InSAR data processing with machine learning techniques such as LSTM to enhance our ability to predict deformation over large-scale geographical areas. The adaptability of the proposed model has made it an alternative to numerical or empirical models, especially when detailed on-site data is scarce or challenging to obtain. While our immediate applications have focused on scenarios on land subsidence and post-seismic deformation, the broader implications of our methodology are evident. We anticipate the proposed framework will be expanded to various application domains, including mining, infrastructure stability, and other situations involving sustained motions. The proposed framework will ultimately contribute to more informed decision-making and risk assessment in complex dynamic systems.