Background EM noise characterization in deep underground mining tunnels

As shallow located mineral resources are exhausting, existing mines face difficulties to maintain productions. One relatively cost-efficient solution is to find ore bodies near the mining properties. These ore bodies are often out of the detection capability of conventional ground prospecting methods due to the size of the ore bodies or their depth. The TEM method in underground mining tunnels is a potential way to help the mining industry on its deep extension. One of the challenges to carry out TEM measurements in tunnels is the limited space, which forces to use small transmitter loops. Consequently, signals are more vulnerable to noises. Characterizing EM noise sources in tunnels will certainly help facilitate data processing and improve interpretation of EM data accurately.

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Published
2024-08-23
DOI
https://doi.org/10.1190/gem2024-036.1
Primary Topic
Geophysical Methods and Applications
Type
article
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article

Background EM noise characterization in deep underground mining tunnels

Weibiao Xie, Xueping Dai, Jun Niu, Lizhen Cheng et al.
Geophysical Methods and Applications
article

Background EM noise characterization in deep underground mining tunnels

Weibiao Xie, Xueping Dai, Jun Niu, Lizhen Cheng, Jirigalatu Jirigalatu
article en

Abstract

As shallow located mineral resources are exhausting, existing mines face difficulties to maintain productions. One relatively cost-efficient solution is to find ore bodies near the mining properties. These ore bodies are often out of the detection capability of conventional ground prospecting methods due to the size of the ore bodies or their depth. The TEM method in underground mining tunnels is a potential way to help the mining industry on its deep extension. One of the challenges to carry out TEM measurements in tunnels is the limited space, which forces to use small transmitter loops. Consequently, signals are more vulnerable to noises. Characterizing EM noise sources in tunnels will certainly help facilitate data processing and improve interpretation of EM data accurately.

China University of Petroleum, Beijing (CN), Université du Québec (CA)
Sustainable cities and communities
Openalex Percentile: Top 83%
Geophysical Methods and Applications
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