Prediction Modeling And Analysis Of Urban Land Subsidence Based On SBAS-InSAR

Monitoring of urban surface deformation is one of the most important tasks to ensure the safe operation of a city.Surface subsidence is slow,complicated,difficult to reverse and harmful.The time-series InSAR technique is one of the research focuses on INSAR technique at present.It can obtain the time-series land subsidence sequence to overcome the incoherent influence in time and space.The monitoring results of large area settlement are obtained with high monitoring accuracy.As the representative of many cities in China,Beijing is the center of economy,politics and culture in China.The study area of this experiment takes Beijing as an example,in order to meet the need of deformation monitoring with high spatial density,the small baseline SAR interferometry technique(SBAS-InSAR)of time-series InSAR was used to monitor the subsidence of 25-view COSMO-SkyMed images in Beijing from January 2009 to December 2010,the monitoring results show that the subsidence of Beijing occurred mainly in the western part of Chaoyang District and Tongzhou,which is adjacent to Chaoyang and connected to South-East District,Changping District,and the subsidence center is Chaoyang and Tongzhou.The largest subsidence occurred in Chaoyang District,with a cumulative subsidence of 248 mm and an average rate of 124mm/yr.The deformation of most plain areas in Beijing is relatively gentle,and the areas with a subsidence rate of less than 10mm/yr accounts for about 68% of all coherent points.After obtaining the settlement monitoring results,this paper uses the level data of Beijing area at the same time period to verify the monitoring results of this experiment.The results show that the small baseline radar interferometry is feasible and effective for large-scale settlement monitoring.In order to be able to predict the occurrence of land subsidence,three prediction models are used in this paper,which are grey model,neural network model and grey neural network model,finally,it is concluded that the prediction effect of the model is better when the data period and time are near,and the grey neural network model is better than the other two models,the mean error of the points is smaller,and the settlement trend is basically the same as the distribution trend of the leveling verification points,but the precision of the prediction results is not particularly ideal,and it is difficult to meet the needs of production and life.It can only supplement the geodetic work,it fails to provide reference for the arrangement of future measurement work.Based on the monitoring results of time-series InSAR,this paper analyzes the reasons of the subsidence by combining the time-series subsidence evolution of the verification points with the geological structure,the ground load,the precipitation statistics,the population flow and the change of groundwater level.The results show that the buried depth of bedrock and the distribution of fault zones are the intrinsic factors of surface subsidence,which are less affected by regional geological structures with larger subsidence,and the load on the ground,the change of ground water level and the influence of meteorological hydrology and population flow are external factors,and it is found that the surface load is large in the regions where the subsidence is more serious,and the change of ground water level is the main factor affecting the land subsidence,it is found that the weather conditions and population flow also have an effect on the land subsidence.