摘要
我国是受地质灾害影响最大的国家之一,研究高精度、高可靠性的滑坡形变监测与预测方法对于防灾减灾工作具有切实意义。以三峡库区藕塘特大滑坡为例,针对时序InSAR技术滑坡形变提取过程中面临大气干涉效应问题,在时序InSAR滑坡形变提取中引入GACOS进行大气校正,并通过GNSS观测数据进行验证对比;针对滑坡形变预测前较少考虑时空分析的问题,计算莫兰指数、Hurst指数分析滑坡形变时空特征;针对滑坡形变不仅受历史形变影响,还与多种影响因子密切相关的问题。本文提出将滑坡影响因子与形变量耦合用于滑坡形变预测,并采用结合变分模态分解(Variational Mode Decomposition,VMD)和麻雀搜索算法(Sparrow Search Algorithm,SSA)优化的长短期记忆网络(Long Short-Term Memory,LSTM)模型进行预测。通过VMD分解滑坡位移数据为趋势项、周期项和随机项,构建LSTM网络结构并使用SSA寻找LSTM模型最优的隐藏单元数目、最大训练周期和初始学习率,同时通过数据归一化、正则化和模型评估等方法提升LSTM模型的性能和稳定性,最后利用影响因子与分解后的位移数据训练LSTM模型并完成预测,得到滑坡形变的预测结果。结果表明:(1)2021年1月—2023年6月藕塘滑坡形变最高与最低速率分别为-72.75 mm/a、50.74 mm/a;(2)研究区形变具有正向空间自相关性、沉降区域滑坡形变具有持续趋势;(3)结合VMD和SSA优化的LSTM模型滑坡形变预测误差仅为0.37 mm,较LSTM的精度提升了11.004%。本文基于时序InSAR技术,结合时空分析结果,构建了一种耦合多个影响因子与滑坡形变的高精度预测模型,为滑坡灾害的防治提供了重要的参考依据。
China is one of the countries most severely affected by geological disasters.Researching highprecision and highly reliable methods for monitoring and predicting landslide deformation holds practical significance for disaster prevention and mitigation efforts.Using the massive Outang landslide in the Three Gorges Reservoir Area as a case study,this paper addresses the issue of the atmospheric interference in extracting landslide deformation using time-series InSAR technology.To correct for atmospheric effects,the GACOS model is introduced and validated against GNSS observation data.To address the often-overlooked temporal-spatial analysis before landslide deformation prediction,the Moran index and Hurst index are calculated to analyze the spatiotemporal characteristics of landslide deformation.Recognizing that landslide deformation is influenced not only by historical deformation but also by various external factors,this paper proposes coupling landslide influencing factors with deformation data for prediction.A Long Short-Term Memory(LSTM)model,optimized by Variational Mode Decomposition(VMD)and the Sparrow Search Algorithm(SSA),is employed for the prediction.By decomposing landslide displacement data into trend,periodic,and random components using VMD,the LSTM network structure is constructed.SSA is used to determine the optimal number of hidden units,maximum training periods,and the initial learning rate of the LSTM model.Additionally,methods such as data normalization,regularization,and model evaluation are employed to enhance the performance and stability of the LSTM model.Finally,the model is trained using the influencing factors and decomposed displacement data to predict landslide deformation.The results indicate that:(1)From January 2021 to June 2023,the maximum and minimum deformation rates of the Outang landslide were-72.75 mm/a and 50.74 mm/a,respectively;(2)The deformation in the study area exhibits positive spatial autocorrelation,with the landslide in the settlement area showing a persistent trend;(
作者
林娜
谭力兵
张迪
丁凯
李双桃
肖茂池
张精平
王小华
LIN Na;TAN Libing;ZHANG Di;DING Kai;LI Shuangtao;XIAO Maochi;ZHANG Jingping;WANG Xiaohua(School of Smart City,Chongqing Jiaotong University,Chongqing 400074,China;BGI Engineering Consultants LTD.,Beijing 100038,China;Chongqing Geographic Information and Remote Sensing Application Center,Chongqing 401120,China;PIESAT Information Technology Co.,Ltd.,Beijing 100089,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第12期2772-2787,共16页
Journal of Geo-information Science
基金
重庆市自然科学基金面上项目(CSTB2023NSCQ-MSX0781)
教育部产学合作协同育人项目(220702313111054)
重庆交通大学研究生课程思政示范项目(KCSZ2023033)
重庆交通大学研究生科研创新项目(CYS240529)
重庆市研究生导师团队建设项目(JDDSTD2022002)。
关键词
形变监测
时空特征分析
时序InSAR
高精度预测
GACOS
藕塘滑坡
remote sensing science and technology
landslide deformation monitoring
spatio-temporal characteristics analysis
temporal InSAR
Outang landslide
high precision prediction
GACOS
Outang landslide
