Analysis and prediction of reservoir bank landslide deformation based on time series InSAR and BiGRU

doi:10.13474/j.cnki.11-2246.2026.0109

Abstract

Abstract: Due to the influence of geological structure and reservoir storage,landslides are frequent in the Three Gorges Reservoir area.Accurate monitoring and prediction of landslide deformation is crucial to ensuring regional safety.Due to the limitations of traditional methods for monitoring large-scale,high-precision deformation,this paper uses 73 Sentinel-1A up-orbit images from January 2021 to December 2023 and the Stanford method for persistent scatterers (StaMPS) technique to analyze the Sanmendong landslide's surface deformation in the Three Gorges Reservoir area.We systematically analyze the landslide deformation characteristics by screening the highly coherent control points and introduce global navigation satellite system (GNSS) data for validation.At the same time,we combine the ARIMA model with the CNN-BiGRU-Attention model to predict the displacement of the control points in the highly deformed area.The results show that the leading edge and middle part of the Sanmendong landslide are high-deformation areas with deformation rates ranging from -108.9 to -43.9 mm/a.The combined prediction model's RMSE is 1.11 mm and its MAE is 0.97 mm.It significantly improves the prediction accuracy and provides a new technical solution for intelligent early warning of geologic hazards.

Key words: time series InSAR, StaMPS, bank landslide, deformation analysis, prediction model

CLC Number:

P237

LIU Yiliang, LI Yongyi, ZHU Qian, ZUO Qingjun, FAN Xifeng, SONG Kun, SHEN Gaowei, TANG Luosheng. Analysis and prediction of reservoir bank landslide deformation based on time series InSAR and BiGRU[J]. Bulletin of Surveying and Mapping, 2026, 0(1): 51-56.

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