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ABSTRACT
The ongoing spontaneous combustion of coal seams beneath the earth’s surface leads to the exhaustion of nonrenewable resources and poses a substantial threat to environmental integrity. Precise and efficacious monitoring of subsurface coal fire activities is an indispensable precondition for the prevention and management of coalfield conflagrations, as well as for the exploitation of geothermal energy resources. The accurate detection and localization of covert coal fires depend on the procurement and analytical assessment of distribution data for parameters that are intrinsically linked to the activities associated with coal combustion. To this end, our review work investigated the theoretical foundations, application effects, and inherent limitations of the diverse detection techniques currently available. It has been observed that the ambiguities inherent to individual detection tools can be effectively mitigated through the cross validation of findings derived from multiple detection tools. The role of detection tools can be extended to the entire process of coal fire management, yet the distinct contributions of each tool throughout the various stages of the process warrant further investigation and elucidation. In addition, the potential of emerging technologies such as machine learning algorithms and 5 G networks to promote automation and intelligence in coal fire management work was also discussed. It is our hope that the insights presented herein will serve as a valuable resource for policymakers and stakeholders in the formulation of effective strategies for the prevention and control of coalfield fires.
Nomenclature
| CSC | = | Coal spontaneous combustion |
| D-InSAR | = | Differential InSAR |
| DS-InSAR | = | Distributed Scatterer InSAR |
| EMR | = | Electromagnetic radiation |
| ERT | = | Electrical resistivity tomography |
| InSAR | = | Interferometric Synthetic Aperture Radar |
| ISCO | = | In-situ chemical oxidation |
| LST | = | Land surface temperature |
| PAHs | = | Polycyclic aromatic hydrocarbons |
| PS-InSAR | = | Persistent Scatterer InSAR |
| RS | = | Remote sensing |
| SBAS-InSAR | = | Small baseline subset InSAR |
| SP | = | Self-potential |
| TEM | = | Transient Electromagnetic |
| UAV | = | Unmanned Aerial Vehicle |
| UCF | = | Underground coal fire |
| WUSN | = | Wireless underground sensor network |
Disclosure statement
No potential conflict of interest was reported by the author(s).
Credit authorship contribution statement
Yifan Gu: Writing-original draft, Investigation. Haidong Li: Investigation. Longhui Dou: Investigation. Meng Wu: Investigation. Huina Guo: Investigation. Wenshi Huang: Investigation. Junping Gu: Investigation. Saeideh Babaee: Writing-review & editing. Liangliang Jiang: Writing-review & editing. Lele Feng: Conceptualization.