门控循环神经网络的时序PS-InSAR地面沉降预测

The prediction of the time-series PS-InSAR ground subsidence based on gated recurrent neural network

为防止中国香港国际机场填海造陆引起的地面沉降对基础设施造成破坏,利用永久散射体合成孔径雷达干涉(PS-InSAR)技术,获得中国香港国际机场2016~2020年地面沉降数据,并利用小基线集雷达干涉(SBAS-InSAR)技术以及水准点数据验证;引入门控循环(GRU)神经网络构建堆叠式GRU地面沉降预测模型,对中国香港国际机场未来地面沉降进行时序预测,并与SVM和MLP神经网络进行比较。结果表明:中国香港国际机场2016~2020年地面沉降空间分布不均匀,累计沉降逐渐增加,2020年12月垂直向的累积沉降量已达106 mm。构建的堆叠式GRU神经网络地面沉降方法相比SVM和MLP更准确,2021年7月中国香港国际机场最大累积地面沉降可达111.8 mm。本文提出的地面沉降时序预测模型,可作为一种有效预测地面沉降的方法,为地面沉降早期预警提供关键技术支持。

To prevent damage to infrastructure caused by ground subsidence caused by land reclamation at Hong Kong International Airport,China,this paper used the Persistent Scatterer Interferometric Synthetic Aperture Radar(PS-InSAR)technique to obtain ground subsidence data for 2016-2020 at Hong Kong International Airport,China.We used the Small Baseline Subset Interferometric Synthetic Aperture Radar(SBAS-InSAR)technology and level data to verify the results of PS-InSAR.Moreover,Gated Recurrent Unit(GRU)neural network was introduced to construct a stacked GRU ground subsidence prediction model,and the future ground subsidence of Hong Kong International Airport in China was predicted in time-series,and compared with Support Vector Machine(SVM)and Multilayer Perceptron(MLP)neural network.The results showed that the spatial distribution of ground subsidence at Hong Kong International Airport,China,from 2016 to 2020 was uneven,the accumulated subsidence gradually increased,and the accumulated subsidence in the vertical direction in December 2020 reached 106 mm.The constructed stacked GRU neural network ground subsidence method was more accurate than SVM and MLP,and the maximum accumulated ground subsidence at Hong Kong International Airport,China,in July 2021 reached 111.8 mm.The ground subsidence time series prediction model proposed in this paper can be used as an effective method to predict ground subsidence and provide key technical support for early warning of ground subsidence.