Risk identification for coal and gas outburst in underground coal mines: A critical review and future directions

Coal and gas outbursts are common mining hazards encountered worldwide. As we mine deeper, the complexity of these outbursts demands smarter, more precise risk identification methods. This is not just a pressing concern but also a growing area of research. However, a noticeable gap exists between current research and the actual implementation of measures to prevent these outbursts at mining sites. This gap spans various areas, from indicator development and the choice of mathematical and machine learning tools to model creation and the use of detection, monitoring, and early-warning systems. This article seeks to review the latest research, evaluating the advantages and drawbacks of various risk identification methods. This work pays special attention to the real-world practices of China's outburst prevention strategies and the need for advanced identification techniques. By diving deep into theoretical, model-based, and technological facets, the main goal is to underline the primary challenges and suggest potential domains for future innovation.