摘要
微震能级随时间发生变化,高能级微震事件与冲击地压有良好的对应关系,为预测矿山微震能量时序变化,基于一维卷积神经网络(Convolutional neural networks,CNN),建立微震能级时间序列预测模型;通过模型训练,实现以前十次微震事件的能量级别作为输入来预测下一次微震事件的能量级别.由于微震样本数据类间不平衡问题,导致模型测试时将106能量级别的微震事件全部判断为105能量级别的微震事件,为进一步提高模型对106能级微震事件预测的准确率,对模型进行改进并使用混合采样方法训练改进后的模型;利用砚北煤矿250202工作面微震能级实测部分数据,改进后模型的总体测试正确率达到98.4%,其中106能量级别的微震事件测试正确率提升到99%.将模型应用于砚北煤矿250202工作面进行微震能级时序预测,模型的预测正确率整体达到93.5%,且对高能级微震事件的预测正确率接近100%.
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%.
作者
裴艳宇
杨小彬
传金平
吴学松
程虹铭
吕祥锋
PEI Yan-yu;YANG Xiao-bin;CHUAN Jin-ping;WU Xue-song;CHENG Hong-ming;LÜ Xiang-feng(School of Emergency Management and Safety Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;Yanbei Coal Mine,Huating Coal Industry Company,Huating 744105,China;School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2021年第7期1003-1009,共7页
Chinese Journal of Engineering
基金
国家自然科学基金资助项目(51774015,51774048)
中央高校基本科研业务费资助项目(2021YJSAQ03)。
关键词
微震能级时序预测
一维卷积神经网络
类间不平衡
混合采样
冲击地压
time series prediction of microseismic energy levels
one-dimensional convolution neural network
class imbalance
hybrid sampling
rock burst
