首页 >  2022, Vol. 26, Issue (7) : 1326-1341

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DOI:

10.11834/jrs.20221457

收稿日期:

2021-07-05

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长短时记忆网络TS-InSAR地表形变预测
陈毅1,2,3,何毅1,2,3,张立峰1,2,3,陈宝山1,2,3,何旭1,2,3,蒲虹宇1,2,3,曹胜鹏1,2,3,高丽雅1,2,3,杨旺1,2,3
1.兰州交通大学 测绘与地理信息学院, 兰州 730070;2.地理国情监测技术应用国家地方联合工程研究中心, 兰州 730070;3.甘肃省地理国情监测工程实验室, 兰州 730070
摘要:

预测地面沉降对于城市基础设施损害的早期预警和及时采取补救措施具有重要意义。本文提出基于时序InSAR数据采用长短时记忆网络(LSTM)模型来预测地面沉降。以香港国际机场为研究区域,基于Sentinel-1A升轨影像利用时序雷达干涉技术(TS-InSAR)获取2015年—2020年机场时序地面沉降监测InSAR结果;利用机场时序InSAR形变结果建立堆叠式LSTM预测模型,并将预测结果与InSAR真实结果进行对比分析。结果表明,2015年—2020年香港国际机场地表垂直方向的平均形变速率为-19—5 mm/a。预测值与真实值的拟合均方根误差和平均绝对误差均较低,分别为0.75 mm和0.61 mm,同时其相关系数为0.99,表明LSTM预测模型在点级尺度上具有良好的性能,能够基于时序InSAR数据较准确预测地面沉降。但预测过程中发现,LSTM模型不适合长期预测,长期预测会出现失效性。本文提出的堆叠式LSTM预测模型可以作为一种有效方法来预测地表形变,尽管LSTM模型只是适用于短期预测,但其预测结果可用于辅助决策、早期预警和减轻危害。

Surface deformation prediction based on TS-InSAR technology and long short-term memory networks
Abstract:

Urban land subsidence is a geological disaster formed by natural and human factors. Cumulative land subsidence easily causes damage to buildings, infrastructure, underground engineering, and other hazards, which seriously threaten the safety of people’s lives and property and cause national economic losses. In the face of urban land subsidence, monitoring, analyzing, and predicting spatiotemporal changes in land subsidence are necessary. The prediction of land subsidence is a crucial step for the early warning of urban infrastructure damage and establishment of a timely remedy.In this study, the Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technique was utilized to monitor the time series land subsidence at Hong Kong International Airport from 2015 to 2020 by using 152 Sentinel-1A images with an ascending orbit. The local weighted scatter smoothing (Loess) method was used to reduce and smooth the noise in the original data of surface deformation points. Given that the advantages of LSTM correspond to the results of TS-InSAR, on the basis of TS-InSAR data, a stacked LSTM neural network was used to construct a surface deformation prediction model with two LSTM layers, two dense layers, and three dropout layers. The stacked LSTM model was employed to predict the surface deformation of the airport, and its results were compared the predicted results obtained with the true InSAR findings.The average vertical deformation rate of Hong Kong International Airport’s surface for 2015—2020 was -19—5 mm/year. The surface subsidence of the airport gradually increased, and the cumulative subsidence in the vertical direction reached 116 mm in December 2020. The cross-validation of the two time-series analysis methods and the comparison of the monitoring results with the level data showed that the InSAR monitoring results in this study had high accuracy and reliability. A stacked LSTM prediction model was established based on the time-series InSAR monitoring results, and the InSAR observation results were compared with the stacked LSTM prediction results. The root-mean-square error and mean absolute error of the predicted and true values were low, namely, 0.75 and 0.61 mm, respectively, and the correlation coefficient was 0.99. The LSTM prediction model demonstrated good performance at the point level scale and could predict ground subsidence accurately on the basis of TS-InSAR data. The stacked LSTM model was employed to predict the time-series subsidence of Hong Kong International Airport in 2021 by using TS-InSAR deformation data from 2015—2020. The analysis revealed that the long-term prediction process of the stacked LSTM prediction model became invalid after six months. Therefore, the stacked LSTM prediction model is suitable for short-term predictions with a short-term prediction scale of about six months, and the maximum cumulative vertical subsidence of the airport will reach 114 mm in June 2021.In summary, the stacked LSTM prediction model proposed in this study can be used as an effective method to predict surface deformation, and although the LSTM model is only suitable for short-term predictions, its prediction results can be used to assist in decision-making, early warning, and hazard mitigation. In the future, additional data can be incorporated into the LSTM model to accurately determine if the model is suitable for long-term predictions and improve the robustness of the prediction model.

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