Underground mining has been plagued by gob fires triggered via self-igniting coal, the emission of toxic fumes, particulate matter, and possibly induced explosions from the gob fires make mining extremely dangerous. The spontaneous combustion of underground coal seriously affects the mining of adjacent coal seams and the overall safety production. The key to control the shallow coal seam fire is to accurately locate its the location and scope by surface detection method. Based on the non-dimensional normalization processing of different types of data and the weight analysis of analytic hierarchy process (AHP), a multi index data fusion method of temperature, gas and radon concentration was proposed by using the practical radon measurement, borehole temperature measurement and gas measurement methods, and the index of fire zone delineation (IFZD) based on multi-dimensional data fusion was obtained. According to the content total method based on fractal theory, the anomalous threshold of IFZD was determined by piecewise linear fitting in double logarithmic coordinates. Eventually, the multi-dimensional data fusion underground fire detection method was established completely, and the method was applied to the fire detection of shallow coal seam in typical integrated mine. The results showed that the weight coefficients of the detection indexes (CO, CO₂, SO₂, temperature and radon) in the fire area were 0.12, 0.03, 0.05, 0.12, 0.68, and the lower bound of the anomaly of IFZD was 0.29. According to the comprehensive isoline map, abnormal area plan and stereogram drawn through IFZD and its outlier threshold, there were eight areas with the total area of 27061 m², which developed from the abandoned well, main well location to the southwest and northeast. The isolines of abandoned air shaft and main shaft were densest and the value was the largest, indicating that coal oxidation in this area is severest, so it is necessary to strengthen the ground fracture investigation and sealing. This method solved the problems such as poor accuracy of single index circle fire zones, and difficulty in direct fusion of multi-index data with different dimension and range, and difficulty in determining the abnormal lower boundary of the fusion index after dimensionless normalization, which provided a new technical method support for the detection of underground shallow coal seam fire area.

自燃煤引发的大火引发了地下采矿的困扰，有毒烟雾，颗粒物的散发，以及可能由大火引发的爆炸使采矿极为危险。地下煤的自燃严重影响相邻煤层的开采和整体安全生产。控制浅层煤层着火的关键是通过地面探测方法准确定位其位置和范围。基于不同类型数据的无量纲归一化处理和层次分析法（AHP）的权重分析，提出了一种实用的ra测量，井孔温度测量，温度，气体和ra浓度的多指标数据融合方法。和气体测量方法，以及火区轮廓指标（获得了基于多维数据融合的IFZD）。根据基于分形理论的总含量法，通过分段线性拟合双对数坐标确定IFZD的异常阈值。最终，建立了多维数据融合地下火灾探测方法，并将该方法应用于典型综合矿井浅煤层火灾探测。结果表明，火区探测指标（CO，CO ₂，SO ₂，温度和ra）的权重系数分别为0.12、0.03、0.05、0.12、0.68，是IFZD异常的下限。是0.29。根据综合等值线图，通过IFZD绘制的异常区平面图和立体图及其离群值阈值，共有8个区域，总面积为27061 m ²从废弃的油井（主要的油井位置到西南和东北）发展而成。弃风井和主轴的等值线最密，值最大，说明该地区煤的氧化最严重，因此有必要加强对地裂缝的研究和封堵。该方法解决了单指标圆火区精度低，难以对不同维度和范围的多指标数据进行直接融合，无因次归一化后难以确定融合指标异常下边界等问题。为探测地下浅煤层火区提供了一种新的技术方法支持。