联合InSAR与神经网络的范家坪滑坡形变监测及预测研究

徐文正; 卢书强; 林振; 周王敏

doi:10.16030/j.cnki.issn.1000-3665.202308028

联合InSAR与神经网络的范家坪滑坡形变监测及预测研究

1.
湖北长江三峡滑坡国家野外科学观测研究站，湖北宜昌　443002

2.
三峡库区地质灾害教育部重点实验室，湖北宜昌　443002

基金项目: 国家自然科学基金项目(42077234)

详细信息

作者简介: 徐文正（1997—），男，硕士研究生，主要从事地质灾害监测预警、InSAR原理及应用的研究工作。E-mail：1546145012@qq.com

通讯作者: 卢书强（1973—），男，教授，硕士生导师，主要从事地质灾害监测预警的研究工作。E-mail：lsq2197@163.com

中图分类号: P237；P642

收稿日期: 2023-08-12

修回日期: 2023-12-30

录用日期: 2024-01-12

刊出日期: 2025-03-15

Combination of InSAR and neural networks for the deformation monitoring and prediction of Fanjiaping landslide

1.
National Field Observation and Research Station of Landslides in Three Gorges Reservoir Area of Yangtze River, Yichang, Hubei　443002, China

2.
Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, Yichang, Hubei　443002, China

Fund Project: Supported by the National Natural Science Foundation of China(Grant No. 42077234)

More Information

Corresponding author: LU Shuqiang, lsq2197@163.com

Received Date: 12 August 2023

Revised Date: 30 December 2023

Accepted Date: 12 January 2024

Publish Date: 15 March 2025

摘要

摘要:
传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点，且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对象，利用差分干涉测量短基线集时序分析技术（small baseline subset InSAR，SBAS-InSAR），结合地表GPS监测数据进行滑坡形变监测，基于SBAS-InSAR时间序列数据及长短时记忆网络（long short term memory，LSTM）开展滑坡形变预测研究。结果表明：研究时段内，范家坪滑坡SBAS-InSAR形变监测结果与地表GPS监测数据所反映出的形变区域及形变量级基本保持一致，与现场调查情况相吻合；范家坪滑坡的位移变形与坡体的高程分布及库水位条件密切相关，当库水位高于160 m时，滑坡前缘阻滑段主要受“浮托减重”效应影响，当库水位低于160 m时，渗流压力占主导作用，水位下降阶段的位移变形总体明显大于水位上升阶段，库水位下降速率对范家坪滑坡的位移变形产生重要影响，且木鱼包滑坡区相较于谭家河滑坡区对库水位下降速率的变形响应更为强烈；将LSTM神经网络模型与传统神经网络模型的预测结果进行效果对比、置信区间估计及相关性检验，结果显示，LSTM神经网络模型的预测结果始终保持较高的预测精度，验证了InSAR与神经网络结合的滑坡监测与预测方法能够为三峡库区地质灾害防治提供重要的数据参考和信息支撑。
- 差分干涉测量短基线集时序分析技术 /
- 长短时记忆网络 /
- 深度学习 /
- 范家坪滑坡 /
- 三峡库区
Abstract:
Traditional methods for monitoring surface deformation of landslides have significant limitations, including small monitoring coverage, difficulty in acquiring information in complex terrains, and high economic costs. Furthermore, the nonlinear and uncertain characteristics of deformation time series for large and complex landslides remain a critical challenge in landslide deformation monitoring and prediction research. Taking the Fanjiaping landslide in the Three Gorges Reservoir Area as the study area, small baseline subset InSAR (SBAS-InSAR) combined with surface GPS monitoring data was used for landslide deformation monitoring. Based on the SBAS-InSAR time series data and long short term memory (LSTM), a study on landslide deformation prediction was conducted. The results show that during the study period, the SBAS-InSAR deformation monitoring results of the Fanjiaping landslide were largely consistent with the deformation areas and magnitude levels indicated by surface GPS monitoring data, aligning with on-site investigation findings. The displacement deformation of the Fanjiaping landslide was found to be closely related to the elevation distribution of the slope and reservoir water level conditions. When the reservoir water level exceeds 160 m, the influence of seepage pressure on slope displacement deformation is minimal. However, when the reservoir water level falls below 160 m, seepage pressure becomes the dominant factor, with displacement deformation during the water level decline phase significantly exceeding that during the water level rise phase. Additionally, the rate of reservoir water level decline has a substantial impact on the displacement deformation of the Fanjiaping landslide, with the Muyubao landslide area showing a more pronounced deformation response to the rate of reservoir water level decline compared to the Tanjiahe landslide area. By comparing the predictive performance, confidence interval estimation, and correlation tests of the LSTM model and traditional neural network models, the results indicate that the LSTM model consistently maintains high predictive accuracy. This validates that the combination of InSAR and neural networks for landslide monitoring and prediction can provide critical data references and information support for geological disaster prevention and mitigation in the Three Gorges Reservoir Area.
- small baseline subset InSAR /
- long short term memory /
- deep learning /
- Fanjiaping landslide /
- the Three Gorges Reservoir area

HTML全文

图 1 范家坪滑坡工程地质平面图

Figure 1.

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图 2 范家坪滑坡剖面图

Figure 2.

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图 3 范家坪滑坡年形变速率

Figure 3.

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图 4 范家坪滑坡部分形变图

Figure 4.

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图 5 范家坪滑坡现场调查照片

Figure 5.

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图 6 范家坪滑坡库水升降速率与InSAR位移曲线

Figure 6.

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图 7 范家坪滑坡GPS累计位移曲线

Figure 7.

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图 8 GPS实测值与InSAR时间序列相关性

Figure 8.

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图 9 特征点LOS向时序变形曲线

Figure 9.

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图 10 LSTM模型单元结构

Figure 10.

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图 11 特征点1、特征点2 模型预测结果

Figure 11.

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图 12 置信区间估计结果

Figure 12.

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图 13 不同模型相关性检验结果

Figure 13.

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表 1 Sentinel-1卫星影像数据信息

Table 1. Sentinel-1 satellite image information

信息类别	信息值
起始日期	2021-07-01
终止日期	2022-08-25
卫星影像数量/帧	28
卫星重访周期/d	12
飞行方向	升轨
入射角/（°）	33.96
方位角/（°）	347.3

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表 2 相关系数与相关性

Table 2. Correlation coefficient and correlation

相关系数	相关性强弱
\|r\|≥0.8	高度相关
0.5≤\|r\|<0.8	显著相关
0.3≤\|r\|<0.5	低度相关
\|r\|<0.3	极弱相关或无相关

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表 3 LSTM模型预测结果误差表

Table 3. Errors of LSTM model prediction

指标	RMSE/mm		MAE/mm		MBE/mm		R²
指标	训练集	测试集	训练集	测试集	训练集	测试集	训练集	测试集
特征点1	0.87	2.13	0.68	2.7	−0.18	2.68	0.99	−0.01
特征点2	0.70	1.99	0.55	2.27	0.12	2.25	0.94	-0.6

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