Coal and Gas Outburst Early Warning Technology: Research Status, Key Bottlenecks and Development Trends

Abstract

Coal and gas outburst is one of the most destructive dynamic disasters in coal mining, posing a severe threat to coal mine safety production and miners' lives. This paper systematically summarizes the research status of coal and gas outburst early warning technologies. Firstly, it elaborates on the mechanism of outbursts and the theoretical basis for early warning; then, it focuses on analyzing early warning methods based on the fusion of multi-source information such as gas emission dynamics, acoustic emission/microseism, electromagnetic radiation, and geophysical methods, and comments on their principles, applications, advantages, and disadvantages; subsequently, it discusses the latest applications and challenges of new-generation information technologies such as big data, artificial intelligence, and the Internet of Things in the field of outburst early warning; finally, it looks forward to the future development trends of outburst early warning technologies, pointing out that multi-field coupled intelligent perception, deep fusion of multi-source information, intelligent early warning models and decision-making platforms, and a new early warning paradigm based on digital twins are the main future development directions. The research results provide theoretical references and technical ideas for improving the level of coal and gas outburst early warning technologies and realizing the intrinsic safety of coal mines.