An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR

Abstract

Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment.

Post-mining deformations in the area affected by the former “Siersza” Hard Coal Mine in Trzebinia (southern Poland)

“…A highly ef fec tive method of de ter min ing dis con tin u ous defor ma tion in the study area was to ana lyse multi-tem po ral ALS and ULS data. The iden ti fi ca tion of sink hole ar eas us ing DTMs is well-known world wide and has been suc cess fully ap plied in re cent years for both karst sink holes (e.g., Filin et al, 2011;Zhu et al, 2014;Wu et al, 2016;Kim et al, 2019) and post-min ing sink holes (e.g., Suh and Choi 2017;Mar ian and Onica, 2021;Zheng et al, 2022;Yang et al, 2023). When the ef fects are intense, as was the case in Trzebinia, ac quir ing up-to-date im aging of the ground sur face is an im por tant el e ment.…”

A Comprehensive Review on Mining Subsidence and its Geo-environmental Impact

“…The average rate of subsidence exceeded 10 cm/year in northern Jakarta, with the most significant rate recorded in the Tambora district at 15.9 cm/year. Additionally, a negative 4D microgravity anomaly was identified in the southern part of Jakarta, which appeared to be linked to both ground-level uplift and declining groundwater levels 15 .…”

The potential of UAV-based Laser Scanning for Deformation Monitoring – Case Study on a Water Dam

“…For land deformation detection, the digital elevation model (DEM) is usually derived to detect deformations from different epochs. This is demonstrated in several examples and typically compared to methods such as TLS or total stations (Moudrý et al, 2019;Wang et al, 2020, Zheng et al, 2022.…”