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

干旱区研究 ›› 2025, Vol. 42 ›› Issue (6): 1126-1137.doi: 10.13866/j.azr.2025.06.15 cstr: 32277.14.AZR.20250615

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

李帅飞¹(), 刘昌义¹(), 胡夏嵩¹, 唐彬元²^,³, 吴志杰¹, 邓太国¹, 邢光延⁴, 赵吉美⁴, 雷浩川¹

1.青海大学地质工程学院，青海西宁 810016
2.青海省地质测绘地理信息院，青海西宁 810001
3.青海省高原测绘地理信息新技术重点实验室，青海西宁 810001
4.青海大学农牧学院，青海西宁 810016

收稿日期:2024-11-14 修回日期:2025-01-24 出版日期:2025-06-15 发布日期:2025-06-11
通讯作者: 刘昌义. E-mail: liuchangyi1991@sina.com
作者简介:李帅飞（1998-），男，硕士研究生，主要从事遥感技术应用等方面的研究工作. E-mail: afei2275@163.com
基金资助:
国家自然科学基金项目(42041006);第二次青藏高原综合科学考察研究项目(2019QZKK0905);青海省自然科学基金项目(2020-ZJ-906)

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

LI Shuaifei¹(), LIU Changyi¹(), HU Xiasong¹, TANG Binyuan²^,³, WU Zhijie¹, DENG Taiguo¹, XING Guangyan⁴, ZHAO Jimei⁴, LEI Haochuan¹

1. School of Geological Engineering, Qinghai University, Xining 810016, Qinghai, China
2. Qinghai Institute of Geological Surveying and Mapping Geographic Information, Xining 810001, Qinghai, China
3. Qinghai Provincial Key Laboratory of New Geographic Information Technology for Plateau Surveying and Mapping, Xining 810001, Qinghai, China
4. College of Agriculture and Animal Husbandry, Qinghai University,Xining 810016, Qinghai, China

Received:2024-11-14 Revised:2025-01-24 Published:2025-06-15 Online:2025-06-11

摘要/Abstract

摘要：

为研究黄河上游龙羊峡至积石峡流域两岸巨型滑坡地表形变特征及形变量预测，本研究以位于黄河上游贵德地区境内的席芨滩巨型滑坡作为研究区，利用小基线干涉测量（Small Baseline Subset Interferometric Synthetic Aperture Rader，SBAS-InSAR）技术对席芨滩巨型滑坡开展地表形变监测，探讨了2019—2022年滑坡地表形变速率及其变化特征。结果表明：（1）区内滑坡体最大地表形变速率为-96 mm·a^-1，最大累计形变量为464.71 mm，滑坡体前缘与后缘存在明显形变区域，其地表形变速率为-96~16 mm·a^-1。（2）基于SBAS-InSAR技术得到区内滑坡体地表布设的特征点的累计形变量，其最大累计形变量为-140.50 mm。（3）采用长短期记忆（Long Short-Term Memory，LSTM）神经网络模型进行特征点累计形变量预测，并与支持向量机（Support Vector Machine，SVM）、BP（Back Propagation）神经网络模型进行对比，LSTM神经网络模型计算得到预测结果反映出相对较高的预测精度，其绝对误差为5 mm以内，拟合优度（R²）高于0.8，反映出采用LSTM神经网络模型应用于滑坡体地表累计形变量预测有效性。研究结果可为进一步开展黄河上游巨型滑坡地表形变监测、潜在滑坡早期识别提供数据支撑和实际指导。

关键词: 黄河上游, 龙羊峡至积石峡流域, 席芨滩巨型滑坡, LSTM神经网络, SBAS-InSAR, 地表形变量监测, 地表累计形变量预测

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

李帅飞, 刘昌义, 胡夏嵩, 唐彬元, 吴志杰, 邓太国, 邢光延, 赵吉美, 雷浩川. 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[J]. 干旱区研究, 2025, 42(6): 1126-1137.

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.

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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

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

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

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