Whole basin modeling and parameter inversion of mining subsidence based on UAV photogrammetry technology

Abstract

Taking Tangjiahui Mining Area in Inner Mongolia as the research object, the UAV photographic image data of August 2020 and March 2021 in this area were obtained, and DEM was produced. The subsidence basin in this area was obtained by subtracting the DEM data, and the denoising effects of different denoising methods were compared with MATLAB software. Based on the subsidence data of the whole basin, the subsidence coefficient and the main influence tangent of the subsidence basin are obtained by using the probability integral parameter inversion with method of simulated annealing(SA). Using this parameter to simulate the subsidence basin, it is calculated that the measurement error is 589 mm, accounting for 8.1% of the maximum subsidence value. Finally, the robust analysis of the parameters is made, and when the error in the measurement accounts for (1% to 10%) the maximum subsidence value, the result of parameter calculation is reliable. The results show that the BP neural network algorithm can effectively remove the noise in the basin and improve the accuracy of the subsidence basin. Based on SA and the data of the whole basin in the mining area, the probability integral parameters can be obtained effectively, which can compensate for the influence of the low accuracy of UAV photogrammetry technology.

Keywords

• uav photogrammetry technology
• point cloud denoise
• simulated annealing algorithm
• parameter inversion of the whole basin
• robust analysis