Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network
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摘要: 微震能級隨時間發生變化，高能級微震事件與沖擊地壓有良好的對應關系，為預測礦山微震能量時序變化，基于一維卷積神經網絡（Convolutional neural networks，CNN），建立微震能級時間序列預測模型；通過模型訓練，實現以前十次微震事件的能量級別作為輸入來預測下一次微震事件的能量級別。由于微震樣本數據類間不平衡問題，導致模型測試時將106能量級別的微震事件全部判斷為105能量級別的微震事件，為進一步提高模型對106能級微震事件預測的準確率，對模型進行改進并使用混合采樣方法訓練改進后的模型；利用硯北煤礦250202工作面微震能級實測部分數據，改進后模型的總體測試正確率達到98.4%，其中106能量級別的微震事件測試正確率提升到99%。將模型應用于硯北煤礦250202工作面進行微震能級時序預測，模型的預測正確率整體達到93.5%，且對高能級微震事件的預測正確率接近100%。Abstract: With the gradual transition of coal mining to deep mining, the number and intensity of rock burst events in the deep mining process are gradually increasing. Thus, it is of great significance to study the change of rock burst precursor signal for the prediction of rock burst. Microseismic signal monitoring plays an important role in rock burst prediction. The microseismic energy level changes with time, a good corresponding relationship exists between the high-energy microseismic events and rock burst. To advance the time node of rock burst prediction and provide more time guarantee for rock burst prevention and control, a time series prediction model of mine microseismic energy based on the one-dimensional convolutional neural network (CNN) was established to predict the temporal variation of mine microseismic energy. Through model training, the energy level of the previous 10 microseismic events can be used as input to predict the energy level of the next microseismic event. Due to the imbalance of the microseismic sample data, the microseismic events of the 106-energy level were all judged as 105-energy level microseismic events in the model test. To improve the prediction accuracy of the model for the 106-energy level microseismic events, a hybrid sampling method was used to train the improved model. Using the microseismic energy level data of 250202 working face in Yanbei coal mine, the overall test accuracy of the improved model reaches 98.4% and the test accuracy of the 106-energy level microseismic events increased to 99%. The improved prediction model of the microseismic energy level time series based on the one-dimensional convolution neural network was applied to 250202 working face of Yanbei coal mine to predict the microseismic energy level time series. The overall prediction accuracy of the model is 93.5%, and the prediction accuracy of high-energy microseismic events is close to 100%.
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圖 1 一維卷積神經網絡微震能級時序預測模型結構
Figure 1. Structure of the prediction model of the microseismic energy level time series based on the one-dimensional convolution neural network
圖 4 改進的基于一維卷積神經網絡的微震能級時序預測模型總體框架
Figure 4. General framework of improved prediction model of microseismic energy level time series based on the one-dimensional convolution neural network
圖 5 微震能量預測值與實測值對比
Figure 5. Comparison between the predicted and measured microseismic energy
表 1 各卷積層超參數
Table 1. Hyperparametric table of each convolution layer
Convolution layer number Convolution kernel number Convolution kernel length 1 32 3 2 64 3 3 128 3 4 64 3 5 32 2 
下載: 導出CSV
表 2 250202工作面微震能量級別測試結果
Table 2. Test results of the microseismic energy level of the 250202 working face
% Class Test accuracy 102, 103, 104 98.7 105 98.3 106 0 Total 97.9
下載: 導出CSV
表 3 250202工作面微震能量級別測試結果
Table 3. Test results of the microseismic energy level of the 250202 working face
% Class Test accuracy before improvement Test accuracy after improvement 102, 103, 104 98.7 98.7 105 98.3 93.3 106 0 99.0 Total 97.9 98.4
下載: 導出CSV
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