联合传统测量与InSAR技术的锰矿地表形变预测研究
A Study on Surface Deformation Prediction of Manganese Ore Using Combined with Traditional Measurement and InSAR Technology
马驷骏 1王静 1王鹏1
作者信息
- 1. 中冶一局城市安全与地下空间研究院有限公司,河北廊坊 065201
- 折叠
摘要
锰矿资源的持续开采虽然促进了经济的发展,但各类地质灾害问题也随之产生.通过联合传统测量与合成孔径雷达干涉技术,设计了分布式目标的时序合成孔径雷达干涉技术,使用HTCI检验和KS检验识别同质像元.在同质像元集合中,利用最大似然法估计获取协相干矩阵和方差矩阵,引入Goldstein滤波对干涉图像的功率谱进行滤波处理,从而去噪.测试结果显示,使用传统斯坦福永久散射体的技术选点密度约为5.4％,改进的分布式目标的时序合成孔径雷达干涉技术选点密度约为27.9％,是传统斯坦福永久散射体方法的5.16倍.实验平均误差为21.6 mm,最大误差为36.5 mm,均方根误差为12.8 mm.由此可知,此次研究设计的技术检测误差较小,显著提高了锰矿区的监测点密度,为锰矿区域地表形变预测和矿区综合治理提供了数据参考.
Abstract
Although the continuous exploitation of manganese mineral resources has promoted the development of economy,various geological disasters is also arising.In this study,the traditional measurement and synthetic aperture radar interference technology are designed in the temporal synthetic aperture radar interference technology for distributed target.It is used in HTCI test and KS test to identify the homogeneous image elements.In the homogeneous image set,the cocoherence matrix and variance matrix are estimated by the maximum likelihood method.Goldstein filter is introduced to filter the power spectrum of the interference image,thus to denoise.The test results show that the density of the traditional Stanford permanent scatterer technology is about 5.4％and the density of the timing synthetic aperture radar interferometer technology for the improved distributed target is about 27.9％,which is 5.16 times that of the method of the traditional Stanford permanent scattering.The experimental mean error is 21.6 mm,maximum error 36.5 mm and root mean square error 12.8 mm.Therefore,the technical detection error designed in this study is small,which significantly improves the density of the monitoring points in the manganese ore area.It provides data reference for the surface deformation prediction of the manganese ore area and the comprehensive treatment of the mining area.
关键词
地表形测/InSAR/DS-InSAR/锰矿/沉降监测Key words
Surface shape survey/InSAR/DS-InSAR/Manganese ore/Settlement monitoring引用本文复制引用
出版年
2024
NETL
NSTL
万方数据