The open-pit coal mine dump in the study area contains many low-concentration heavy metal pollutants, which may cause pollution to the soil interface. Firstly, statistical analysis and geostatistical spatial interpolation methods described heavy metal pollution’s spatial distribution. The mine dump heavy metal pollution distribution is strongly random due to disorderly piles, but it is closely related to slope soil erosion. Furthermore, the soil deposition area is where pollutants accumulate. For example, all heavy metal elements converge at the bottom of the dump. Usually, the pollution in the lower part is higher than that in the upper part; the pollution in the lower step is higher than the upper step; the pollution in the soil deposition locations such as flat plate and slope bottom is higher than the soil erosion locations such as slope tip and middle slope. Finally, the hyperspectral remote sensing method described heavy metals pollution’s migration characteristics, that the pollutants could affect the soil interface by at least 1 km. This study provides a basis for preventing and controlling critical parts of mine dump heavy metal pollution and pollution path control.
The granularity distribution of mine dump materials has received extensive attention as an essential research basis for dump stability and mine land reclamation. Image analysis is widely used as the fastest and most efficient method to obtain the granularity distribution of the dump materials. This article proposes a deep learning-based approach for granularity detection and identification of mine dump material, conglomerate, and clay. Firstly, a Conglomerate and Clay Dataset (CCD) is proposed to study the granularity of the mine dump. A typical study area is selected for field sampling, and the sampled conglomerate and clay is photographed and labeled. In addition, this article proposes a keypoint-based detection algorithm for the conglomerate and clay detection. The algorithm considers the scale variation of conglomerate and clay in orthophoto images and adopts center point detection to avoid the difficulty of localization. On this basis, dense convolution is introduced in feature extraction to reduce the computational redundancy to conduct detection more efficiently. Finally, the corresponding granularity distributions of conglomerate and clay are obtained by geometric calculation in the deep learning-based detection results. The proposed algorithm is validated on the proposed dataset CCD, and the experiments demonstrate the effectiveness of the proposed algorithm and its application to the granularity analysis of mine dump material.
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