3D Localization of Coal Fires Based on Self-Potential Data: Sandbox Experiments

Location of coal fires is indispensable in firefighting planning and engineering. The self-potential method has been used for decades in the qualitative determination of coal fires. However, papers about the 3D inversion of coal fires in terms of self-potential data are scarce. In this paper, a 3D i...

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Published in:	Pure and applied geophysics Vol. 178; no. 11; pp. 4583 - 4603
Main Authors:	Shao, Zhenlu, Deng, Rong, Zhou, Tao, Cao, Fei, Sun, Huahai, Chen, Long, Yuan, Yu, Zhong, Xiaoxing
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.11.2021 Springer Nature B.V
Subjects:	Algorithms Burning Coal Current density Distribution Earth and Environmental Science Earth Sciences Experiments Fire control Fire fighting Fires Geophysics/Geodesy Localization Temperature variations source current density Coal fires self-potential 3D inversion
ISSN:	0033-4553, 1420-9136
Online Access:	Get full text
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Abstract	Location of coal fires is indispensable in firefighting planning and engineering. The self-potential method has been used for decades in the qualitative determination of coal fires. However, papers about the 3D inversion of coal fires in terms of self-potential data are scarce. In this paper, a 3D inversion algorithm is adopted to localize coal fires. This algorithm is first benchmarked on three synthetic cases to test the peculiar electrode configuration used in this study. Five sandbox experiments are performed with an electric heater buried at different depths and directions. The first experiment is designed to investigate the temperature variation during the heating process. The other four experiments (experiments #1–4) are conducted to obtain the self-potential anomaly under different conditions. The 3D inverted results show that the position of the heater is well-retrieved. A sandbox experiment with a portion of the sandbox containing burning coal is also carried out. Both resistivity and self-potential data are collected and inverted to obtain the 3D distribution of the causative source current density responsible for the observed self-potential signals. The distribution of the inverted source current density is consistent with the true position of the coal fire.
AbstractList	Location of coal fires is indispensable in firefighting planning and engineering. The self-potential method has been used for decades in the qualitative determination of coal fires. However, papers about the 3D inversion of coal fires in terms of self-potential data are scarce. In this paper, a 3D inversion algorithm is adopted to localize coal fires. This algorithm is first benchmarked on three synthetic cases to test the peculiar electrode configuration used in this study. Five sandbox experiments are performed with an electric heater buried at different depths and directions. The first experiment is designed to investigate the temperature variation during the heating process. The other four experiments (experiments #1–4) are conducted to obtain the self-potential anomaly under different conditions. The 3D inverted results show that the position of the heater is well-retrieved. A sandbox experiment with a portion of the sandbox containing burning coal is also carried out. Both resistivity and self-potential data are collected and inverted to obtain the 3D distribution of the causative source current density responsible for the observed self-potential signals. The distribution of the inverted source current density is consistent with the true position of the coal fire.
Author	Deng, Rong Yuan, Yu Zhong, Xiaoxing Zhou, Tao Cao, Fei Sun, Huahai Chen, Long Shao, Zhenlu
Author_xml	– sequence: 1 givenname: Zhenlu surname: Shao fullname: Shao, Zhenlu organization: School of Safety Engineering, China University of Mining and Technology – sequence: 2 givenname: Rong surname: Deng fullname: Deng, Rong organization: School of Safety Engineering, China University of Mining and Technology – sequence: 3 givenname: Tao surname: Zhou fullname: Zhou, Tao organization: Xinjiang Coalfield Fire-Extinguishing Engineering Bureau – sequence: 4 givenname: Fei surname: Cao fullname: Cao, Fei organization: Xinjiang Coalfield Fire-Extinguishing Engineering Bureau – sequence: 5 givenname: Huahai surname: Sun fullname: Sun, Huahai organization: Xinjiang Coalfield Fire-Extinguishing Engineering Bureau – sequence: 6 givenname: Long surname: Chen fullname: Chen, Long organization: School of Safety Engineering, China University of Mining and Technology, Xinjiang Coalfield Fire-Extinguishing Engineering Bureau – sequence: 7 givenname: Yu surname: Yuan fullname: Yuan, Yu organization: Xinjiang Coalfield Fire-Extinguishing Engineering Bureau – sequence: 8 givenname: Xiaoxing surname: Zhong fullname: Zhong, Xiaoxing email: zhxxcumt@cumt.edu.cn organization: School of Safety Engineering, China University of Mining and Technology
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CitedBy_id	crossref_primary_10_1016_j_jappgeo_2024_105448 crossref_primary_10_1038_s41598_024_55304_1 crossref_primary_10_1080_00102202_2024_2365260 crossref_primary_10_1007_s00024_023_03350_7
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Snippet	Location of coal fires is indispensable in firefighting planning and engineering. The self-potential method has been used for decades in the qualitative...
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SubjectTerms	Algorithms Burning Coal Current density Distribution Earth and Environmental Science Earth Sciences Experiments Fire control Fire fighting Fires Geophysics/Geodesy Localization Temperature variations
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Title	3D Localization of Coal Fires Based on Self-Potential Data: Sandbox Experiments
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