4.7 Article

Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijmst.2013.07.014

关键词

D-InSAR; 3D laser scanning; Inverse distance weighting; Subsidence monitoring; TerraSAR-X

资金

  1. Doctoral Program Foundation of Institutions of Higher Education of China [20090095110002]
  2. Priority Academic Program Development of Jiangsu Higher Education Institutions [SZBF2011-6B35]

向作者/读者索取更多资源

Large-scale deformation can not be detected by traditional D-InSAR technique because of the limit of its detectable deformation gradient, we propose a method that combines SAR data with point cloud data obtained by 3D laser scanning to improve the gradient of deformation detection. The proposed method takes advantage of high-density of 3D laser scanning point cloud data and its high precision of point positioning after 3D modeling. The specific process can be described as follows: first, large-scale deformation points in the interferogram are masked out based on interferometric coherence; second, the interferogram with holes is unwrapped to obtain a deformation map with holes, and last, the holes in the deformation map are filled with point cloud data using inverse distance weighting algorithm, which will achieve seamless connection of monitoring region. We took the embankment dam above working face of a certain mining area in Shandong province as an example to study large-scale deformation in mining area using the proposed method. The results show that the maximum absolute error is 64 mm, relative error of maximum subsidence value is 4.95%, and they are consistent with leveling data of ground observation stations, which confirms the feasibility of this method. The method we presented provides new ways and means for achieving large-scale deformation monitoring by D-InSAR in mining area. (C) 2013 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据