A Comparison of Surface-to-Coal Mine Roadway TEM and Surface TEM Responses to Water-Enriched Bodies
Open Access
- 18 November 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 167320-167328
Abstract
The surface transient electromagnetic method (TEM) is a geophysical technology normally used in detecting water-enriched zones. However, with an increase of mining depth, the accuracy and reliability of surface detection is gradually reduced, and surface TEM cannot meet the requirements of high-precision detection for coal mine production safety. In this study, surface-to-coal mine roadway TEM is proposed to detect water-enriched zones in coal mines. The resolution of this method in detecting targets, however, is still unknown. Based on a 1-D layered model, the surface-to-coal mine roadway TEM response is numerically simulated using the finite-difference time-domain method (FDTD). The results show that the surface-to-coal mine roadway TEM has higher resolution of the target above the underground receiving point and weaker resolution of the target below the receiving point. 3-D geo-electric models for typical water-enriched zones such as a water-filled mining goaf, a water-filled fault and a water-filled collapse column are established. The surface-to-coal mine roadway TEM responses are numerically simulated and compared with the surface TEM responses. The results show that for the goaf model, the surface-to-coal mine roadway TEM response is more sensitive than the surface TEM response. For fault and collapse column models, the surface-to-coal mine roadway response is more sensitive than surface response in the early delay stage but less sensitive in the late delay stage. The present study provides theoretical support for device selection and for data processing interpretation in actual work.Keywords
Funding Information
- National Basic Research Program of China (2017YFC0801405, 2018YFC0807804)
- Key Research and Development Program of Shaanxi Province (2017GY-175)
- National Natural Science Foundation of China (51574250, 41830101, 41804073, 41674079)
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