Spatiotemporal Prediction and Pattern Analysis of Complex Ground Deformation Fields from Multi-Temporal InSAR.
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| Title: | Spatiotemporal Prediction and Pattern Analysis of Complex Ground Deformation Fields from Multi-Temporal InSAR. |
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| Authors: | Fu, Yuanzhao1,2 (AUTHOR), Wang, Jili1,2 (AUTHOR) wangjl01@aircas.ac.cn, Zhang, Yi1 (AUTHOR), Zhang, Heng1,2 (AUTHOR), Wu, Yulun1 (AUTHOR), Kang, Litao1,2 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p925. 32p. |
| Subjects: | Deep learning, Independent component analysis, K-means clustering, Environmental risk assessment, Forecasting, Groundwater management |
| Abstract: | Highlights: What are the main findings? A spatiotemporal synchronous prediction framework is proposed for large-scale complex InSAR ground deformation fields. A combined ICA and K-means approach is proposed to identify dominant evolution patterns of the deformation field and their spatial distributions. What are the implications of the main findings? The proposed framework improves the prediction capability for complex multimodal ground deformation processes. The identified interaction patterns between ground deformation and groundwater provide insights for urban groundwater management and geohazard assessment. Ground deformation is a major geohazard in many urban areas, requiring reliable monitoring and forecasting for hazard mitigation. Although Multi-Temporal InSAR enables high-resolution deformation monitoring, most prediction approaches rely on single-point modeling and fail to exploit spatial dependencies within deformation fields. This study proposes a spatiotemporally synchronous prediction framework for large-scale InSAR deformation fields, integrating sequence preprocessing, spatiotemporal modeling, and deformation pattern analysis. First-order differencing reduces sequence non-stationarity, while a patch-based encoder-decoder structure preserves spatial topology during dimensionality reduction. The core prediction model, built on PredRNNv2, captures the long-term spatiotemporal evolution of InSAR deformation sequences. In addition, independent component analysis (ICA) combined with K-means clustering identifies dominant deformation patterns and their geological associations. The framework is evaluated using synthetic datasets simulating multiple deformation mechanisms and Sentinel-1 InSAR time-series data over the Beijing Plain from 2015 to 2025. Results show that the model accurately captures deformation evolution and identifies transitions associated with groundwater regulation. These findings demonstrate the potential of deep spatiotemporal learning for large-scale InSAR deformation prediction and geohazard mechanism interpretation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A spatiotemporal synchronous prediction framework is proposed for large-scale complex InSAR ground deformation fields. A combined ICA and K-means approach is proposed to identify dominant evolution patterns of the deformation field and their spatial distributions. What are the implications of the main findings? The proposed framework improves the prediction capability for complex multimodal ground deformation processes. The identified interaction patterns between ground deformation and groundwater provide insights for urban groundwater management and geohazard assessment. Ground deformation is a major geohazard in many urban areas, requiring reliable monitoring and forecasting for hazard mitigation. Although Multi-Temporal InSAR enables high-resolution deformation monitoring, most prediction approaches rely on single-point modeling and fail to exploit spatial dependencies within deformation fields. This study proposes a spatiotemporally synchronous prediction framework for large-scale InSAR deformation fields, integrating sequence preprocessing, spatiotemporal modeling, and deformation pattern analysis. First-order differencing reduces sequence non-stationarity, while a patch-based encoder-decoder structure preserves spatial topology during dimensionality reduction. The core prediction model, built on PredRNNv2, captures the long-term spatiotemporal evolution of InSAR deformation sequences. In addition, independent component analysis (ICA) combined with K-means clustering identifies dominant deformation patterns and their geological associations. The framework is evaluated using synthetic datasets simulating multiple deformation mechanisms and Sentinel-1 InSAR time-series data over the Beijing Plain from 2015 to 2025. Results show that the model accurately captures deformation evolution and identifies transitions associated with groundwater regulation. These findings demonstrate the potential of deep spatiotemporal learning for large-scale InSAR deformation prediction and geohazard mechanism interpretation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060925 |
