Forecasting the surface deformation of unstable slopes based on InSAR and GRU neural network

doi:10.13474/j.cnki.11-2246.2023.0068

Abstract

Abstract: The prediction of surface deformation of unstable slope is crucial to landslide disaster prevention and early warning. The existing monitoring methods have small coverage and high cost. The relevant prediction methods are limited to single point prediction and have high requirements for the amount of historical data. To solve the above problems, this paper uses Small Base line Subset InSAR (SBAS-InSAR) technology to monitor the surface deformation of unstable slope, and designs a method for predicting the surface deformation of unstable slope based on the inversion results of InSAR and Gated Recurrent Unit (GRU) neural network. Firstly, the SBAS-InSAR technology is used to monitor the surface deformation in the study area, then the obtained time series deformation inversion results are used to establish the GRU model to study the deformation law, and finally carry out the surface deformation prediction of unstable slope.The experimental results show that the mean absolute error of this method for predicting the surface deformation of unstable slope is 0.678 mm,and the mean absolute error percentage is 2.7%.Compared with the traditional support vector regression (SVR) model,the prediction effect is improved by more than 30%, and the engineering application potential is great.

Key words: unstable slope, deformation, forecasting, SBAS-InSAR, GRU

CLC Number:

P237

PAN Jianping, CAI Zhuoyan, ZHAO Ruiqi, FU Zhanbao, YUAN Yuxin. Forecasting the surface deformation of unstable slopes based on InSAR and GRU neural network[J]. Bulletin of Surveying and Mapping, 2023, 0(3): 33-38.

References

[1] 丁继新, 尚彦军, 杨志法, 等.降雨型滑坡预报新方法[J].岩石力学与工程学报, 2004, 23(21):3738-3743.
[2] 李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用:挑战与对策[J]. 武汉大学学报(信息科学版), 2019, 44(7):967-979.
[3] 廖明生, 董杰, 李梦华, 等. 雷达遥感滑坡隐患识别与形变监测[J]. 遥感学报, 2021, 25(1):332-341.
[4] 李晓恩, 周亮, 苏奋振, 等. InSAR技术在滑坡灾害中的应用研究进展[J]. 遥感学报, 2021, 25(2):614-629.
[5] DAI Keren, XU Qiang, LI Zhenhong, et al. Post-disaster assessment of 2017 catastrophic Xinmo landslide (China) by spaceborne SAR interferometry[J]. Landslides, 2019, 16(6):1189-1199.
[6] 朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10):1717-1733.
[7] 张路, 廖明生, 董杰, 等. 基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别——以四川丹巴为例[J]. 武汉大学学报(信息科学版), 2018, 43(12):2039-2049.
[8] TOFANI V, SEGONI S, AGOSTINI A, et al. Technical note:use of remote sensing for landslide studies in Europe[J]. Natural Hazards and Earth System Sciences, 2013, 13(2):299-309.
[9] CAI Zhenglong, XU Weiya, MENG Yongdong, et al. Prediction of landslide displacement based on GA-LSSVM with multiple factors[J]. Bulletin of Engineering Geology and the Environment, 2016, 75(2):637-646.
[10] 杨背背, 殷坤龙, 杜娟. 基于时间序列与长短时记忆网络的滑坡位移动态预测模型[J]. 岩石力学与工程学报, 2018, 37(10):2334-2343.
[11] 冯非凡, 武雪玲, 牛瑞卿, 等. 一种V/S和LSTM结合的滑坡变形分析方法[J]. 武汉大学学报(信息科学版), 2019, 44(5):784-790.
[12] 张俊, 殷坤龙, 王佳佳, 等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报, 2015, 34(2):382-391.
[13] 蒋平. 基于回归神经网络的滑坡位移时间序列预测研究[D]. 武汉:华中科技大学, 2017.
[14] 刘青豪, 张永红, 邓敏, 等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报, 2021, 50(3):396-404.
[15] 岳振华, 沈涛, 毛曦, 等. 循环神经网络的地面沉降预测方法[J]. 测绘科学, 2020, 45(12):145-152.
[16] BERARDINO P, FORNARO G, LANARI R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11):2375-2383.
[17] 杨丽, 吴雨茜, 王俊丽, 等. 循环神经网络研究综述[J]. 计算机应用, 2018, 38(S2):1-6.
[18] 王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(5):53-58.
[19] 史绪国, 徐金虎, 蒋厚军, 等. 时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新[J]. 地球科学, 2019, 44(12):4284-4292.