采空区遗煤自然发火问题严重威胁着煤矿安全，对其隐蔽火源的精确定位是有效治理的关键。为了精准探查出自燃隐蔽火源位置，结合国内外学者最新研究成果，综述了钻孔探测法、红外探测法、光纤测温法、热电偶测温法、地质雷达探测法、同位素测氡法以及标志气体探测法等探查方法的原理和应用情况；上述方法解决了自燃危险区域的判定问题，但受限于自身技术特征不能完全适应采空区错综复杂的环境，难以实现火源位置精准探查。声学法测温技术对比传统方法具有适用性广、可靠性高、经济投入低等优势，已成功应用于储粮仓温度场监测，采空区松散煤体的多孔介质特征与仓储颗粒粮食相似，据此提出自燃隐蔽火源声学探测设想。针对主动式声波测温法在采空区自燃危险区域内探查隐蔽火源点的实现方式及研究进展进行了文献梳理与论述，着重探讨了现阶段声波探查技术研究中有关声波特征识别、声波衰减、声线弯曲、温度场重构算法优化等方面存在的不足，并进行展望。

The problem of spontaneous combustion of coal left in the goaf poses a serious threat to coal mine safety, and the accurate positioning of its hidden ignition source is the key to effective governance. In order to accurately detect the location of the hidden ignition source of spontaneous combustion, combining the latest research results of scholars at home and abroad, the principles and applications of drilling detection method, infrared detection method, optical fiber temperature measurement method, thermocouple temperature measurement method, geological radar detection method, isotopic radon measurement method, and indicator gas detection method are reviewed. The above methods solve the problem of determining the dangerous area of spontaneous combustion, but are limited by their own technical characteristics and cannot fully adapt to the complex environment of the goaf, making it difficult to accurately detect the location of the ignition source. Compared with traditional methods, acoustic temperature measurement technology has the advantages of wide applicability, high reliability, and low economic investment. It has been successfully applied to the temperature field monitoring of grain storage warehouses. The porous medium characteristics of loose coal bodies in the goaf are similar to those of grain storage particles, based on which the idea of acoustic detection of hidden ignition sources in the goaf is proposed. We review and discuss the implementation methods and research progress of active acoustic wave temperature measurement method in the detection of hidden ignition sources in the goaf spontaneous combustion hazard area, focusing on the deficiencies in the current acoustic detection technology research related to acoustic feature recognition, acoustic attenuation, acoustic bending, temperature field reconstruction algorithm optimization, etc.