A Deep Learning Application for Deformation Prediction from Ground-Based InSAR

被引:14
|
作者
Han, Jianfeng [1 ]
Yang, Honglei [1 ]
Liu, Youfeng [1 ]
Lu, Zhaowei [1 ]
Zeng, Kai [1 ]
Jiao, Runcheng [2 ]
机构
[1] China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China
[2] Beijing Inst Geol Hazard Prevent, Beijing 100120, Peoples R China
基金
中国国家自然科学基金;
关键词
GB-InSAR; LSTM model; deformation prediction; real-time processing;
D O I
10.3390/rs14205067
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.
引用
收藏
页数:20
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