摘要
由于采煤沉陷过程复杂和地表地形影响,机载LiDAR在采煤区沉陷监测中不可避免地存在噪声数据,高密度LiDAR点云中存在的噪声容易导致提取的沉陷等值线出现锯齿、毛边和多边形碎屑等问题。将数学形态学算法格网初始值的判定方式进行改进,传统数学形态学算法选择格网内最低点高程作为格网值,改进算法对格网内所有高程值进行平面拟合,将拟合值作为格网初始值。在采煤沉陷信息提取过程,增加对地面点云的改进数学形态学算法处理,降低噪声数据对地面DEM的影响,提高沉陷DEM精度和沉陷等值线完整度,试验对比分析确定算法最优参数(格网大小为3.5 m,结构元素尺寸为3 m)。最后,采用该方法对研究区数据进行处理,获取研究区沉陷DEM,并进行数据分析挖掘,获取地表下沉范围、下沉等值线、下沉面积等。结果表明:改进算法既保证原始的地形特征和精度,又可消除沉陷等值线中出现的噪声问题,为开采沉陷预计及采后环境评估提供支撑。
Due to the complexity of coal mining subsidence process and the impact of surface topography, airborne LiDAR inevitably has noise data in coal mining subsidence monitoring.The noise in LiDAR high-density point cloud was easy to trigger the problems such as serrations, burr and polygonal debris in the extracted subsidence isoline.In this paper, the method of determining the initial value of grid in mathematical morphology algorithm is improved.The traditional mathematical morphology algorithm selected the lowest point elevation in the grid as the grid value, and the improved algorithm performs plane fitting for all the elevation values in the grid, and takes the fitting value as the initial value of the grid.In the process of mining subsidence information extraction, the processing of ground point cloud by the improved mathematical morphology algorithm was added, thus reducing the impact of noise data on DEM,and promoting the precision of subsidence DEM as well as the integrity of subsidence isoline.The optimal parameters of the algorithm was determined by the experimental analysis and comparison(the grid size is 3.5 m, and the size of structural elements is 3 m).Finally, the data of the study area were processed by the algorithm improved in this paper, and the subsidence DEM in the study area was obtained.The subsidence DEM was examined and excavated to obtain the surface subsidence range, subsidence contour, subsidence area, etc.The results show that the improved algorithm can not only ensure the original terrain characteristics and accuracy, but also eliminate the noise in the subsidence isoline, providing support for mining subsidence prediction and post mining environment assessment.
作者
甘斌
郑俊良
姚顽强
白凌霄
GAN Bin;ZHENG Junliang;YAO Wanqiang;BAI Lingxiao(Xi’an Surveying and Mapping Institute,Xi’an 710054,China;College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,China)
出处
《西安科技大学学报》
CAS
北大核心
2023年第1期175-182,共8页
Journal of Xi’an University of Science and Technology
基金
国家自然科学基金项目(42201484)。
关键词
LIDAR
点云
数学形态学
沉陷监测
沉陷DEM
LiDAR
point cloud
mathematical morphology
subsidence monitoring
subsidence DEM
