Development of a ground penetrating radar system for large-depth disaster detection in coal mine

被引:17
|
作者
Xu, Xianlei [1 ]
Peng, Suping [1 ]
Yang, Feng [2 ]
机构
[1] China Univ Min & Technol Beijing, State Key Lab Coal Resources & Safe Min, Beijing 100083, Peoples R China
[2] China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
GPR; Mine disaster source; Low frequency antenna; Large-depth detection; Explosion-proof; GPR;
D O I
10.1016/j.jappgeo.2018.07.006
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
With the increasing coal mining efficiency, the geological disaster sources in the mine serious threat production safety, Ground Penetrating Radar (GPR) is widely used for detecting those hidden disaster sources attributed to the advantage of high precision, but with detection distance limitation. We describe a novel GPR system for large depth disaster detection in mine, which has the large-depth detection ability and the explosion-proof capability to adapt to the mine working environment. In comparison with the existing GPR systems, this novel system is improved with the following design features: the step acquisition accuracy of the host control unit is improved to be 2 ps; the split pluggable low frequency combined antenna realizes the impedance matching and improves the detection distance; a photoelectric conversion module is developed to guarantee 6000 m transmission distance for the high-frequency synchronization signal in mine. We carry out some experiments to validate the design, and the measurement results show that: the proposed GPR system can realize the fine exploration and the accurate identification; the maximum detection distance can be up to 80 m with reflection method and 300 m with transmission method under uniform geological conditions, which break the current detection distance limitations within 30 m, simultaneously, the detection accuracy can be up to 3 m. We expect that the proposed GPR system will be used for the large-depth geological disaster sources detection and provide an advanced approach for improving mining safety. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:41 / 47
页数:7
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