基于时序SAR技术的采空区上方高速公路变形监测及预测方法  

Deformation monitoring and prediction methods for expressway above goaf based on time series SAR technique

作　　者：范洪冬[1,2] 邓喀中[1,2] 祝传广[1,2] 陈炳乾[1,2] 李培现[1,2] 

机构地区：[1]中国矿业大学国土环境与灾害监测国家测绘局重点实验室,江苏徐州221116 [2]中国矿业大学江苏省资源环境信息工程重点实验室,江苏徐州221116

出　　处：《煤炭学报》2012年第11期1841-1846,共6页Journal of China Coal Society

基　　金：国家自然科学基金资助项目(41071273);中央高校基本科研业务费专项资金资助项目(2010QNA21);国土环境与灾害监测国家测绘局重点实验室开放基金资助项目(LEDM2011B07)

摘　　要：为掌握采空区上方所建高速公路的变形趋势,解决老采空区上方地表变形监测数据较少,不易建立时序沉降预测模型的问题,利用D-InSAR(Differential Interferometric Synthetic Aperture Radar)技术对某高速公路进行了变形监测和分析,同时将其结果同地面实测数据相融合,并以LS-SVM(Least Squares-Support Vector Machine)为基础,建立了采空区上方高速公路变形预计模型,通过实例,验证了模型的正确性。具体过程:处理融合数据为等时间间隔,并将其趋势项去除,对余项进行平稳性、正态性及零均值处理;利用Cao方法计算嵌入维数,建立训练样本集,并进行LS-SVM学习训练;最后,采用训练好的模型对未来地表沉降进行预计。以511号监测点为研究对象,建立滚动预计方法,结果显示其最大下沉绝对误差3 mm,最大相对误差2.2%,取得了较为可靠的预计成果。In order to obtain the deformation law of expressway above goal, solve not enough monitoring data for aban- doned mine to establish the subsidence prediction models, the fused deformation values of level measure and Differenti- al Interferometric Synthetic Aperture Radar（D-InSAR） technique were used to establish the prediction models based on Least Squares-Support Vector Machine（LS-SVM）. The details are as follows：the fused data were processed to get equal-time interval time series deformation values, whose trend items should be rejected, and the residues were pro- cessed by stationary, normality and zero mean;Using Cao method to calculate embedding dimension, and establishing sample set to train LS-SVM model;Finally, using the model to predict the land subsidence in the future. The rolling prediction results of the No. 511 point show that the maximum absolute error of subsidence is 3 mm, maximum relative error is 2.2%. Therefore, the predicting results are reliability.

关 键 词：高速公路 形变监测 D-INSAR LS-SVM 预计 

分 类 号：TD325.4[矿业工程—矿井建设]