Extraction Of Subsidence Information In Yushin Mining Area Based On UAV Images

Yushin mining area is one of the main coal production bases in China.The surface subsidence caused by high intensity underground mining has seriously affected the safety production of coal mines and the ecological environment of the mining area.In order to obtain the surface subsidence deformation information quickly and efficiently,in addition to the conventional observation stations to carry out deformation monitoring,InSAR,laser scanning,photogrammetry and other technologies to carry out mining subsidence monitoring have also achieved some achievements in recent years,but there are certain limitations.Among them,the low-altitude uav aerial technology has been widely used in 3D modeling of real by superimposing multiphase image build fine terrain model for surface subsidence information,on technology is simple and feasible,but when the terrain modeling error caused by vegetation such as noise impact is too large,often restricted the practical application of this technique in coal mining subsidence monitoring.Aiming at the characteristics of yushen mining area with low surface vegetation coverage but large amount of subsidence,this paper takes the mining subsidence area of fully mechanized mining face of Xiaobaodang Coal Mine as the research object,and uses low-altitude UAV multi-period aerial image data to construct a subsidence model to realize efficient extraction of subsidence information of the whole basin.The main research contents and results are as follows:(1)Combined with the characteristics of surface subsidence of fully mechanized mining face in Yushen Mining area,the technical scheme of data acquisition and processing of aerial photography by UAV was designed.Based on the requirement of image spatial resolution,the corresponding field flight parameters and image control point layout scheme are determined.Based on the optimization of data acquisition and processing technology process,the actual accuracy of the obtained image data in plane position and elevation is compared and analyzed by using checkpoints,which indicates that the modeling of low-altitude UAV aerial image can meet the basic requirements of fine modeling of the mining subsidence basin with large mining height under the condition of gentle terrain in Yushen Mining area.(2)A variety of filtering and interpolation algorithms were used for point cloud data processing of the images acquired in the experimental area,and the point cloud filtering and interpolation algorithm suitable for the geomorphic environment of Yushen mining area was determined through experimental comparison.The algorithm of triangulation network progressive encryption filtering,mathematical morphology filtering,moving window filtering and terrain slope filtering were used to remove ground point cloud respectively.It was found that the effect of triangulation network progressive encryption filtering algorithm was the best,and the corresponding optimal iteration parameters were determined.By comparing and analyzing the interpolation precision of different algorithms in four experimental areas,it is shown that the DEM precision constructed by local polynomial interpolation algorithm is superior to other algorithms.(3)Use machine learning classification algorithm to classify the images in the study area and post process the vegetation point cloud to further improve the point cloud denoising effect.Artificial neural network method(ANN),maximum likelihood method(MLC),support vector machine(SVM)and random forest method(RFC)were used for classification experiments.The results show that SVM method has better classification accuracy than the other three methods.After the secondary removal of vegetation points,the proportion of retained ground point cloud is significantly increased,and the mean and median errors of point cloud are significantly reduced,which effectively removes the influence of non-ground information and improves the actual accuracy of DEM fine modeling in subsidence area.(4)Based on the DEM superimposed by multi-period UAV images in the experimental area,a fine 3D model of the surface subsidence basin is constructed.The actual accuracy of the subsidence model is verified by the measured data,and the mathematical modeling of the subsidence basin is realized.The subsidence model is compared with the 3d laser scanning monitoring results and the conventional observation line subsidence curve respectively to verify the accuracy of the constructed subsidence model.Furthermore,the probability integration method is used to fit the subsidence prediction model of mining basin,and the corresponding model parameters are determined,and the mathematical expression of aerial survey subsidence model is realized.The research results of this paper improve the actual accuracy of the model by improving the technical process of constructing the mining subsidence model based on UAV image data,which is conducive to promoting the application of this technology method in the monitoring of large-scale and high-intensity mining surface subsidence in Yushen Mining area.