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简要介绍了用于监测岩体稳定性的声发射源定位系统ＳＤＬ－１和便携式声发射智能监测仪ＤＹＦ－１，这些仪器能获取一个声发射事件所包含的尽量多的信息，基于这些信息开发了一种有效可靠的预测冒顶技术。该技术利用多个声发射参数（ＡＥ事件率、ＡＥ能量和ｍ值）评价声发射活动，在这些参数的监测数据基础上应用灰色系统理论预测将来的声发射，预测值通过训练好的冒顶模式识别，由人工神经网络模型输出对应的冒顶模式（较大规模的顶板塌落、小规模掉块和稳定）。实例研究结果表明，该方法的预测结果与实际情形具有很好的一致性
A brief description of the SDL-1, an acoustic emission source localization system for monitoring the stability of rock mass, and the DYF-1, a portable acoustic emission monitoring system, are used to obtain as much information as possible for an acoustic emission event. Based on this information, An effective and reliable forecast of roof technology. The technique uses multiple AE parameters (AE event rate, AE energy and m value) to evaluate acoustic emission activity. Based on the monitoring data of these parameters, the gray system theory is used to predict the future AE. The predicted value is determined by the trained roof fall mode Identification, output by the artificial neural network model corresponding fall roof mode (larger roof collapse, small dropout and stability). The result of case study shows that the prediction result of this method is in good agreement with the actual situation