基于SBAS-InSAR技术及LSTM神经网络的席芨滩巨型滑坡形变监测及预测

doi:10.13866/j.azr.2025.06.15

Abstract

Abstract:

This study examines the surface deformation characteristics and deformation rate prediction of large-scale landslides in the upper regions of the Yellow River between the Longyang and Jishi Gorge riverbanks. The study area was the Xijitan giant landslide within the Guide region of the upper Yellow River. The Small Baseline Subset Interferometric Synthetic Aperture Rader（SBAS-InSAR）technology was employed to monitor the surface deformation of the Xijitan giant landslide and analyze, its deformation rates and variation characteristics for the period 2019-2022. The results show that the following. (1) The maximum surface deformation rate of the landslide body was -96 mm·a^-1, with a maximum cumulative deformation of 464.71 mm. Distinct deformation zones were observed along the front and rear edges of the landslide body, with surface deformation rates ranging across -96-16 mm·a^-1. (2) The cumulative deformation of characteristic points on a landslide body, determined using SBAS-InSAR technology, exhibited a maximum cumulative deformation of -140.50 mm. (3) The long short-term memory (LSTM) neural network model was used to predict the cumulative deformation of these points, and the results were compared with those obtained using Support Vector Machine（SVM） and Back Propagation（BP） neural network models. The LSTM model demonstrated high prediction accuracy, with an absolute error within 5 mm and a goodness-of-fit (R²) greater than 0.8. This confirmed the effectiveness of the LSTM model in predicting the cumulative surface deformation of landslides. Thus, the findings of this study provide data support and practical guidance for the enhanced monitoring of giant landslide deformation in the upper Yellow River region and the early detection of potential landslides.

Key words: upper Yellow River, Longyang Gorge to Jishi Gorge Basin, Xijitan giant landslide, LSTM neural network, SBAS-InSAR, surface deformation monitoring, prediction of surface cumulative deformation

LI Shuaifei, LIU Changyi, HU Xiasong, TANG Binyuan, WU Zhijie, DENG Taiguo, XING Guangyan, ZHAO Jimei, LEI Haochuan. Deformation monitoring and prediction of Xijitan giant landslide based on SBAS-InSAR technology and long short-term memory neural network[J].Arid Zone Research, 2025, 42(6): 1126-1137.

Figures/Tables 15

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References 30

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Sentinel-1A卫星影像数据参数	影像数据参数对应值
起始时间	2019-01
终止时间	2022-12
卫星影像数量/幅	119
卫星重访周期/d	12
飞行方向	升轨
入射角/（°）	34.85°
方位角/（°）	13.15°
条带扫描模式	IW
极化方式	VV+VH

特征点	预测值与真实值最小绝对误差/mm	预测值与真实值最大绝对误差/mm	平均绝对误差（MAE）/mm	均方根误差（RMSE）/mm	拟合优度（R²）
P1	0.05	4.50	1.82	2.20	0.83
P2	0.02	3.06	0.25	1.73	0.84
P4	0.06	1.60	0.48	0.69	0.96

	LSTM神经网络模型			支持向量机（SVM）模型			BP神经网络模型
	MAE/mm	RMSE/mm	R²	MAE/mm	RMSE/mm	R²	MAE/mm	RMSE/mm	R²
特征点P1	1.82	2.20	0.83	1.93	2.32	0.81	1.78	2.31	0.81
特征点P2	0.25	1.73	0.84	1.66	1.99	0.79	1.44	1.81	0.82
特征点P4	0.48	0.69	0.96	1.90	2.29	0.64	0.89	1.00	0.91

Deformation monitoring and prediction of Xijitan giant landslide based on SBAS-InSAR technology and long short-term memory neural network

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