College of Geomatics , Xi’ an University of Science and Technology
Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Natural Resources
SHANXI Bingchang Wenjiapo Mining Limited Company

利用低空无人机航拍影像进行建模叠加可高效获取矿区地表沉陷信息，但植被覆盖和航测系统性误差往往会导致沉陷模型的噪声过大，制约了航测技术在矿区沉陷监测中的实际应用。为此，以陕北某矿采煤沉陷区为试验场地，利用低空无人机航拍影像数据构建初始沉陷模型。在选取非沉陷区样本分析植被、道路、沙地等主要地物对沉陷建模精度影响的基础上，利用可见光波段差异植被指数(VDVI)从初始沉陷模型中剔除对沉陷模型精度影响较大的植被覆盖区沉陷数据，再结合高斯卷积核与数字高程模型插值算法拟合植被区域沉陷信息，生成剔除植被影响的地表沉陷模型。进一步以非沉陷区下沉量应为零作为先验条件，通过统计非沉陷区域的误差分布特征，从中提取沉陷模型中潜在的系统误差，并以统计样区与控制点的距离作为权重，分析系统误差传播规律，对沉陷模型施加系统误差改正。通过与实测数据对比表明，经植被剔除后的沉陷模型精度得到显著改善；经系统误差改正后，沉陷模型主断面下沉曲线与实测数据更加吻合。研究结果表明：在剔除噪声并改正系统性误差影响后的无人机影像建模方法能够满足西部低植被覆盖区大范围、高强度采煤地表沉陷监测的基本要求，为无人机遥感技术用于西部矿区采煤沉陷的高效监测与精细建模提供了可行方案。

Surface subsidence information in mining areas can be obtained efficiently by low-altitude UAV aerial imagery via modelling overlay. However, vegetation cover and systematic errors in aerial survey often lead to excessive noise in the subsidence model, which limits the application of aerial survey technology in mining subsidence monitoring. To this end, taking the coal mining subsidence area of a mine in northern Shaanxi Province as the experimental region, this study constructs an initial subsidence model using low-altitude UAV aerial imagery data. On the foundation of analyzing the influence of major features such as vegetation, roads and sand on the subsidence modelling accuracy by selecting samples from non-subsidence areas, the vegetation covered area subsidence data which had great influence on the accuracy of subsidence model were removed from the initial subsidence model using the Visible Difference Vegetation Index (VDVI). So a surface subsidence model with vegetation influence removed is then generated, by combining a Gaussian convolution kernel with a digital elevation model interpolation algorithm to fit the subsidence information. According to the prior condition that the amount of subsidence in the non-subsidence area should be zero, the potential systematic errors in the subsidence model are extracted from the statistical error distribution characteristics of the non-subsidence area, and the distance between the statistical sample area and the control point is taken as the weight to analyze the propagation law of systematic errors, and the systematic error correction is then applied to the subsidence model. The comparison of the measured data shows that the accuracy of the subsidence model has been significantly improved after vegetation removal and the subsidence curve of the main section of the subsidence model is more consistent with the measured data after systematic error correction, that can meet the basic requirements of surface subsidence monitoring for large scale and high intensity coal mining in western low vegetation cover areas. The research results show that the UAV image modeling method after removing the noise and correcting the influence of systematic error can meet the basic requirements of large-scale and high-intensity coal mining surface subsidence monitoring in the western low vegetation coverage area, which provides a feasible scheme for the UAV remote sensing technology to be used for efficient monitoring and fine modeling of coal mining subsidence in the western mining area.

国家自然科学基金资助项目（52274168）

汤伏全，孙　伟，樊志刚，等. 基于无人机影像的西部矿区地表沉陷信息提取方法改进[J]. 煤炭科学技术，2023，51（S1）：334−342.

TANG Fuquan，SUN Wei，FAN Zhigang，et al. Improvement of surface subsidence information extraction method based on UAV image modeling in Western Mining Area[J]. Coal Science and Technology，2023，51（S1）：334−342.