Research progress and visualization of underground coal fire detection methods

Abstract

Underground coal fires are a widespread disaster prevailing in major coal-producing countries globally, posing serious threats to the ecological environment and restricting the safe exploitation of coal mines. The accuracy of underground coal fire detection directly affects the effectiveness of fire control engineering. In this study, we searched 426 articles from the Web of Science database within 2002-2022 as the data foundation and visualized the research contents of the underground coal fire field using VOSviewer and CiteSpace. The results reveal that the investigation of "underground coal fire detection techniques" is currently the focal area of research in this field. Additionally, the "underground coal fire multi-information fusion inversion detection methods" are considered to be the future research trend. Moreover, we reviewed the strengths and weaknesses of various single-indicator inversion detection methods, including the temperature method, gas and radon method, natural potential method, magnetic method, electric method, remote sensing, and geological radar method. Furthermore, we conducted an analysis of the advantages of the multi-information fusion inversion detection methods, which possesses high precision and wide applicability for detecting coal fires, while highlighting the challenges in handling diverse data sources. It is our hope that the research results presented in this paper will provide valuable insights and ideas for researchers involved in the detection and practical research of underground coal fires.

Keywords: Coal fire; Detection methods; Inversion; Multi-source information fusion; Visualization.