【摘要】 根据现场岩爆监测中声发射(AE)时间序列的特点,采用小波神经网络与突变理论,建立了一种新的岩爆预测模型。该模型首先针对监测到的声发射建立小波神经网络模型,对声发射时间序列进行了拟合与预测;再运用突变理论对预测的声发射建立了岩爆突变预测模型。通过实例分析表明,声发射的预测精度较高,岩爆预测结果与现场情况一致,证明了该模型工程实用性较强。更多还原

【Abstract】 Based on the features of acoustic emission(AE) time series monitored for rockburst,adopting the wavelet neural network and catastrophe theory,a new rockburst prediction model is established.Firstly,a wavelet neural network model based on the AE monitored is established to forecast the future AE.Secondly,a catastrophe prediction model for rockburst is founded based on AE forecasted.A practical example shows that the predicted AE time series has high prediction accuracy;and rockburst prediction are consistent with field situation.It is shown that the model has the advantages of high forecasting accuracy and strong practicality.更多还原