(長沙礦山研究院，長沙410012)
摘 要: 簡要介紹了用于監(jiān)測巖體穩(wěn)定性的聲發(fā)射源定位系統SDL- 1和便攜式聲發(fā)射智能監(jiān)測儀DYF- 1，這些儀器能獲取一個聲發(fā)射事件所包含的盡量多的信息，基于這些信息開發(fā)了一種有效可靠的預測冒頂技術。該技術利用多個聲發(fā)射參數(AE事件率、AE能量和‑m值)評價聲發(fā)射活動，在這些參數的監(jiān)測數據基礎上應用灰色系統理論預測將來的聲發(fā)射，預測值通過訓練好的冒頂模式識別，由于人工神經網絡模型輸出對應的冒頂模式(較大規(guī)模的頂板塌落、小規(guī)模掉塊和穩(wěn)定)。實例研究結果表明，該方法的預測結果與實際情形具有很好的一致性。
關鍵字: 冒頂模式 聲發(fā)射 灰色理論 神經網絡 預測預報
（Changsha Institute of Mining Research, Changsha 410012）
Abstract:A mine-wide A coustic Emission(AE) Source Location System(SDL-1)and a Portable Intelligent AE M on it oring Device(DVF-1)were described . These instruments can acquire AE dat as much as those associated with an AE event . An effective and reliable technique to predict the occurrence of roof fall hazards has been devel-oped, based on the AE data measured. It has utilized several AE indicators(AE rate, AE energy and m value )to e-valuta the AE activity and applied grey system theory to the trained artifivial neural net work for adaptive roof fall modes recognition to automatically identify the roof fall modes(collapse, small blocks fall and stable).The case study showed that the result of forecase on the occurrence of roof fall modes has a good agreement with the practice.
Key words: roof fall mode acoustic emission grey theory neural network prediction
