Regional Land Subsidence
Theme Keywords： remote sensingLSTMground subsidencespatio-temporal predictionsimulation and predictionland subsidence
- The Paper
- Abstract：The simulation and prediction model of land subsidence based on traditional numerical methods requires a large amount of hydrogeological and measured data, and predicting the deformation in areas with complex geological conditions is difficult. In this study, on the basis of land subsidence information obtained by permanent scatterers–interferometry synthetic aperture radar (PS-InSAR) technology in the east of the Beijing plain and in consideration of the influence of groundwater level in different layers on subsidence, the long-term and short-term memory network (AM-LSTM) based on an attention mechanism is used to simulate the land subsidence at typical locations in different subsidence areas. Results show the following points. (1) The spatial difference of land subsidence in the study area is obvious. From October 2010 to August 2016, the maximum subsidence rate is about 153 mm/a, and the cumulative subsidence is 1063 mm. The area is located near Sanjianfang Township in Chaoyang District. (2) The simulation accuracy of the AM-LSTM model is better than that of the traditional LSTM model, and the accuracy of this simulation reaches 22%. (3) The attention weight of the AM-LSTM model indicates that the water level of the second confined aquifer contributes the most to land subsidence. These research findings can provide a reliable model for the prevention and control of land subsidence.Keywords：remote sensing;land subsidence;AM-LSTM;simulation and prediction;groundwater level of different layers;attention weight1989|2130|11
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2022-08-08 - Abstract：The rapid and uneven ground subsidence has threatened human production activities, and high-precision subsidence prediction results are of great significance for the precise prevention and control of geological disasters. In order to grasp the evolution law of ground subsidence, a number of prediction studies have been carried out using field observation data or InSAR data. However, due to the existence of spatial heterogeneity, accurate prediction of large-scale ground subsidence is still a challenge.In this study, a spatio-temporal prediction method considering spatial heterogeneity for large-scale ground subsidence STLSTM (Spatio-temporal Long Short-Term Memory) is proposed from a data-driven perspective. First, clustering is used to identify homogenous subregions in geographic space; then, in each subregion, a special Long Short-Term Memory (LSTM) networks are used to capture the nonlinearity features of local locations; Finally, the pre-trained network is used to quantitatively predict the ground subsidence at the future time.In the experimental part, the sentinel-1 image data was used to compare the performance of STLSTM with the other 8 benchmark methods, and the effectiveness of STLSTM was analyzed using spatial statistical indicators. The results show that STLSTM achieves the highest prediction accuracy (71.4%) within 152 secs, and can effectively weaken the effect of spatial heterogeneity on large-scale subsidence prediction tasks.In conclusion, this paper integrates the spatial heterogeneity processing strategy into the deep learning model, and large-scale subsidence prediction is realized with high precision and time efficiency.Keywords：remote sensing;ground subsidence;spatio-temporal prediction;heterogeneity;LSTM;InSAR1532|3463|9<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2022-08-08
- 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.Keywords：remote sensing;Land deformation;TS-InSAR;Surface deformation prediction;deep learning;LSTM1867|2481|14<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2022-08-08
- Abstract：As a mega city in central China, Zhengzhou is in a period of large-scale metro construction. The problem of ground subsidence along metro lines occurs during metro construction and operation. Thus, monitoring and analysis of ground subsidence along metro lines are important to ensure the safety of metro operation. However, the researches on long-time-series ground subsidence in the Zhengzhou metro network are lacking. Permanent scatterers–interferometric synthetic aperture radar (PS-InSAR) technology overcomes many shortcomings of traditional ground subsidence monitoring methods, such as high cost, limited monitoring range and points, and difficulties in long-term monitoring. Therefore, PS-InSAR technology is used to monitor the subsidence of the Zhengzhou metro network in this study.35 Envisat ASAR images and 44 Sentinel-1 images are employed to obtain the surface deformation information of Zhengzhou City from February 2005 to October 2010 and from July 2015 to May 2019 via PS-InSAR technology. By extracting PS points in a certain range on both sides of the metro lines, the temporal and spatial characteristics of ground subsidence along Zhengzhou metro are subjected to statistical, profile, and overlay analyses. To address the unequal time interval of ground subsidence time series data caused by SAR image discontinuity, an equidistant processing method based on inverse distance interpolation is proposed, and the subsidence of a typical metro station is predicted and analyzed using the Long Short-Term Memory (LSTM) model.Results show that the subsidence sections are mainly concentrated in the east of Lines 1 and 5, the maximum subsidence rate is more than 20 mm/a, and the maximum cumulative subsidence is about 80 mm. The overall deformation trend of Line 1 is similar to a parabola, and the uneven deformation is prominent. The changes in PS points in the time series differ in various regions. The subsidence trough near the Henan Orthopedic Hospital Station of Line 5 is basically symmetrical in space, and the subsidence at the center is expanding yearly. Experiments show that the LSTM model has high prediction accuracy, and the prediction results reveal that the north of the New Archives of Henan Province located in the south of Zhengzhou Sports Center Station will continue to settle at a rate of about 0.5 mm/month in the next two years. Hence, the station and its vicinity must be continuously monitored. This study confirms that PS-InSAR technology can meet the application needs of large-scale urban ground subsidence monitoring, and the results provide a scientific basis for the continuous dynamic monitoring of ground subsidence along Zhengzhou metro network and metro maintenance.Keywords：Zhengzhou metro;ground subsidence;PS-InSAR;LSTM;prediction analysis2492|5152|11<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2022-08-08
