Research And Application Of InSAR Atmospheric Delay Correction Based On Spatial Interpolation Algorithm

With the continuous innovation of science and technology and the continuous expansion of application fields,the requirements for the monitoring accuracy of InS AR technology are also increasing.Due to the instability of atmospheric water vapor in space and time,the removal of monitoring errors caused by atmospheric phase delays is faced with great difficulties.Although on the road to continuous development of this technology,many scholars have proposed ways to improve the accuracy of InSAR monitoring in terms of atmospheric delay,such as the use of GPS data,MERIS/MODIS near-infrared vapor data,and some surface weather monitoring data to remove atmospheric influences,or Some correction models are used for delay phase estimation and other methods to remove the atmospheric water vapor delay error,but there is still no unified correction method to remove the influence of atmospheric delay phase.The method of spatial interpolation used in this paper to remove the influence of the atmospheric turbulence layer and improve the monitoring accuracy of InSAR technology is of great significance for the monitoring of surface subsidence in coal mining areas.This article focuses on the correction of the atmospheric turbulence in InSAR with respect to the correction of atmospheric water vapor,and uses the two spatial interpolation methods of inverse distance weighting and ordinary Kriging interpolation to correct the influence of the atmospheric turbulence layer.In addition,the surface settlement of area was taken as the research object.The main research points of this paper are as follows:Firstly,the influence of atmospheric water vapor delay phase in InSAR is corrected by the method of IDW spatial interpolation,and the improved results are verified by using the third-class observation data.The results show that the corrected data of surface deformation monitoring after correction is better than the correction.The former surface deformation monitoring data is more consistent with the leveling monitoring data,and the average improvement in the slow surface deformation of InSAR technology monitoring has been greatly improved.Secondly,when the atmospheric phase function of the turbulent layer is selected,three variogram models of the exponential model,the spherical model and the Gaussian model are used respectively.Finally,a Gauss variogram model is selected by comparing the results.In the monitoring area,the sample points with relatively high coherence coefficients are selected,and the sample points are interpolated into the entire interferogram.The Kriging interpolation algorithm based on the Gauss variogram model is used to interpolate the turbulent layer atmospheric delay phase to the entire interference.Then remove the delayed phase of the turbulent atmosphere.The contrastive analysis of the correction effect of the two methods of inverse distance weighting and Kriging interpolation and the comparison and 'analysis of leveling data and reliability verification shows that the correction effect of the former in this monitoring area is better than that of the former.The latter's correction effect.Finally,using D-InSAR with ArcGIS technology to analyze the surface deformation of radar remote sensing data of Yuandian Mine,mainly analyzes the settlement changes of the surface of the same mining area over time.Subsequently,the 7225 mining face of coal seam mining at Yuandian Mine was selected during this period,and the surface settlement rule during mining was refined.Then,the accuracy of monitoring of InSAR before and after atmospheric correction was verified by using observational data.It can be seen that the monitoring results before and after atmospheric delay correction are not significantly changed in the larger area of the deformation,while the correction accuracy of the monitoring results before and after the atmospheric delay correction has been significantly improved for the long-term micro-slow deformation area.