Research on Method of D-InSAR and Level Data Fusion

doi:10.13474/j.cnki.11-2246.2018.0181

Abstract

Abstract: Ground subsidence is a global problem,which seriously affects the ecological environment and sustainable development.Therefore,it is very important to carry out careful monitoring and deformation prediction on the ground.According to the shortcomings of D-InSAR monitoring technology in the central area of the settlement basin,and the traditional level measurement can only get the finite element deformation information,this paper uses the ensemble Kalman filter to assimilate the D-InSAR data and the level measurement data,so that the assimilation results are more consistent with the settlement law of the subsidence area.The example shows that the results of D-InSAR and leveling data assimilation based on the ensemble Kalman filter have greatly improved the accuracy and reliability of the subsurface settlement monitoring.The average error after assimilation is 3.10 mm,which is much higher than that of D-InSAR value of 6.90 mm.

Key words: D-InSAR, ensemble Kalman filter, data fusion, deformation monitoring

CLC Number:

P258

YANG Fan, ZHAO Zengpeng, ZHANG Lei, ZHANG Ziwen. Research on Method of D-InSAR and Level Data Fusion[J]. 测绘通报, 2018, 0(6): 82-85.

References

[1] HU R L,YUE Z Q,WANG L C,et al.Review on Current Status and Challenging Issuse of Land Subsidence in China[J].Engineering Geology,2004,76(1):65-77.
[2] 刘国祥,张瑞,李陶,等.基于多卫星平台永久散射体雷达干涉提取三维地表形变速度场[J].地球物理学报,2012,55(8):2598-2610.
[3] 舒宁.雷达影像干涉测量原理[M].武汉:武汉大学出版社,2003.
[4] 路旭,匡绍君,贾有良.用InSAR做地面沉降监测的试验研究[J].大地测量与地球动力学,2002,22(4):66-70.
[5] ZHAO Chaoying,DING Xiaoli,ZHANG Qin,et al.Monitoring of Land Subsidence and Ground Fissures in Xi'an China 2005-2006:Mapped by SAR Interferometry[J].Environ Geol,2009,58(3):1533-1540.
[6] 刘国祥,丁晓利.沿海地区D-InSAR形变探测:精度与可应用性分析[J].测绘通报,2006(9):9-13.
[7] 陈春,吴振森,孙树计,等.集合卡尔曼滤波在电离层短期预报中的应用[J].空间科学学报,2010(2):148-153.
[8] EVENSEN G.Sequential Data Assimilation with a Non-linear Quasi-geostrophic Model Using Monte Carlo Methods to Forecast Error Statistics[J].Journal of Geophysical Research Oceans,1994,99(C5):10143-10162.
[9] HOUTEKAMER P L,MITCHELL H L.A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation[J].Monthly Weather Review,2001,129(1):123-137.
[10] GILLIJINS S,MENDOZA O B,CHANDRASEKAR J,et al.What Is the Ensemble Kalman Filter and How Well Does it Work?[J].American Control Conference,2006(6):4448-4453.
[11] 崔波,张家树,杨宇.基于集合卡尔曼滤波的非线性目标跟踪算法[J].计算机仿真,2013,30(4):317-321.
[12] 董亚宁.基于集合卡尔曼滤波全球臭氧卫星观测资料同化试验研究[D].南京:南京信息工程大学,2016.
[13] 惠雪峰.基于集合卡尔曼滤波的森林资源动态预测[D].南京:南京林业大学,2012.
[14] 米丽倩,查剑锋,王新.老采空区残余沉降的集合卡尔曼滤波预测[J].金属矿山,2012(8):138-141.
[15] 李喜佳,肖志强,王锦地,等.双集合卡尔曼滤波估算时间序列LAI[J].遥感学报,2014,18(1):27-44.