Investigating hidden disaster factors in coal mines using UAV: case study of Tongxin Coal Mine, Shanxi Province, China

Abstract

Traditional investigations into hidden disaster-causing factors in coal mines are severely constrained by low efficiency, long detection cycles, and terrain limitations, which hinder timely safety management in mining operations. To address these challenges, this study employed unmanned aerial vehicle (UAV)-borne visible light and infrared thermal remote sensing technologies to conduct rapid, high-precision scanning and identification of hidden hazards at Tongxin Coal Mine, a representative mine in the Datong Mining Area of Shanxi Province, China. By optimizing UAV flight parameters (altitude, azimuth, and shooting width), key geometric parameters of ground fissures (extension length, strike direction, and development scale) were quantitatively extracted and calculated with a relative error of less than 8%. Visible light imagery achieved full-coverage detection of surface water bodies (rivers, ponds, springs, etc.), while the fusion of infrared and visible light data enabled the capture and dynamic tracking of ground high-temperature anomalies, with a hidden fire zone identification accuracy of 92%. This integrated technology also identified surface bedrock outcrops, refined the boundaries of weathered rock areas (coincidence rate > 88%), and delineated the spatial location, scale, and potential hazard range of waste rock piles. Additionally, UAV remote sensing effectively detected illegal mining shafts and geomorphic features prone to landslides, which are difficult to identify via traditional methods. The results demonstrate that UAV-based remote sensing overcomes the shortcomings of conventional ground surveys and satellite remote sensing, providing a low-cost, high-efficiency, and high-safety technical approach for the detection of hidden coal mine disasters. This research lays a technical foundation for the construction of smart mines and the precise management of mining hazards.