When a coal mine fire occurs, the rescue of trapped mine workers is difficult, because some efficient fire-fighting measures (such as nitrogen injection) cannot be implemented. The development and severity of a fire is difficult to determine, and the integrity of the auxiliary ventilation system may be unknown. These situations pose additional challenges to the trapped mine workers and increase the difficulty level for the rescue team. A novel method using the tracer gas technique is developed to remotely gather information to determine the location and severity of the fire. Laboratory experiments were conducted to understand the tracer gas distribution characteristics when the fire causes damage to the ventilation duct. A mathematical model was developed that uses the tracer gas concentration curve to determine the location and severity of the fire. The model combines the Expectation Maximization (EM) algorithm with the Gaussian Mixture (GM) model. Validated using experimental data, it is demonstrated that the method can determine the fire location with low error. The information collected using this method reflects basic living environmental conditions of the trapped mine workers and provides important input for quickly formulating an effective fire rescue plan that saves lives.

发生煤矿火灾时，由于无法实施某些有效的灭火措施（例如注氮），因此难以营救被困的煤矿工人。火灾的发生和严重程度难以确定，辅助通风系统的完整性可能未知。这些情况给被困的地雷工人带来了额外的挑战，并增加了救援队的难度。开发了一种使用示踪气体技术的新颖方法，以远程收集信息以确定火灾的位置和严重性。进行了实验室实验，以了解大火引起通风管道损坏时的示踪气体分布特征。建立了一个数学模型，该模型使用示踪气体浓度曲线确定火灾的位置和严重性。该模型将期望最大化（EM）算法与高斯混合（GM）模型结合在一起。通过实验数据验证，证明了该方法可以确定火场位置，误差小。使用此方法收集的信息反映了被困地雷工人的基本生活环境状况，并为快速制定可挽救生命的有效消防救援计划提供了重要信息。