|本期目录/Table of Contents|

[1]曹亚利,李振雷,刘旭东,等.基于卷积神经网络的冲击地压预警方法研究*[J].中国安全生产科学技术,2022,18(10):101-108.[doi:10.11731/j.issn.1673-193x.2022.10.015]
　CAO Yali,LI Zhenlei,LIU Xudong,et al.Research on early-warning method of rockburst based on convolutional neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(10):101-108.[doi:10.11731/j.issn.1673-193x.2022.10.015]
点击复制

基于卷积神经网络的冲击地压预警方法研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年10期
页码:
101-108
栏目:
职业安全卫生管理与技术
出版日期:
2022-10-31

文章信息/Info

Title:
Research on early-warning method of rockburst based on convolutional neural network
文章编号:
1673-193X(2022)-10-0101-08
作者:
曹亚利; 李振雷; 刘旭东; 何学秋; 宋大钊; 王洪磊
（1.北京科技大学 金属矿山高效开采与安全教育部重点实验室，北京 100083；
2.北京科技大学 土木与资源工程学院，北京 100083；
3.北京科技大学 大安全科学研究院，北京 100083；
4.国家能源集团新疆能源有限责任公司，新疆 乌鲁木齐 830084）
Author(s):
CAO Yali;  LI Zhenlei;  LIU Xudong;  HE Xueqiu;  SONG Dazhao;  WANG Honglei
(1.Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine,University of Science and Technology Beijing,Beijing 100083,China;
2.College of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China;
3.Research Institute of Macro-Safety Science,University of Science and Technology Beijing,Beijing 100083,China;
4.CHN Energy Xinjiang Energy Co.,Ltd.,Urumqi Xinjiang 830084,China)
关键词:
矿山安全; 冲击地压; 冲击危险监测预警; 卷积神经网络; 深度学习
Keywords:
mine safety;  rockburst;  monitoring and early-warning of rockburst hazard;  convolutional neural network;  deep learning
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.10.015
文献标志码:
A
摘要:
为探究深度学习在冲击地压预警方面的应用前景，以新疆某冲击地压矿井为研究背景，将深度学习和专家评判运用到微震数据分析中，基于卷积神经网络构建冲击地压预警模型。充分利用一维卷积神经网络对时序数据有较强特征提取能力的优势，以微震数据及其特征参数作为输入，以专家评判值作为标签，借助Python-Keras框架实现冲击地压预警模型的构建和训练。研究结果表明：模型预警效果并不随着训练迭代次数的增加而逐渐最优，存在最优迭代次数，对于所建模型当迭代次数为30时测试集的冲击危险预测结果与专家评判结果基本吻合，同时说明模型可以较好地学习专家评判经验实现冲击地压预警。研究表明所建模型对研究时段内发生的5次大能量矿震事件均进行预警，其准确度较高，具有现场实际应用价值。
Abstract:
In order to explore the application prospect of deep learning in the early-warning of rockburst,taking a rockburst mine in Xinjiang as the research background,the deep learning and expert evaluation were applied in the microseismic (MS) data analysis,and an early-warning model of rockburst was constructed based on the convolutional neural network.Taking advantage of the strong feature extraction ability of one-dimensional convolutional neural network (1DCNN) for time series data,using MS data and its characteristic parameters as the input and the expert evaluation values as the label,the construction and training of rockburst early-warning model were realized with the Python-Keras framework.The results showed that the early-warning effect of the model was not gradually optimal with the increase of the number of training iteration times,and there existed the optimal number of iteration times.For the established model,when the number of iteration times was 30,the prediction results of rockburst risk by test set were basically consistent with the expert evaluation results,which also indicated that the model could learn from the expert evaluation experience to realize the early-warning of rockburst.It showed that the established model gave early-warning to all the 5 high-energy mine earthquake events in the study period,and the early-warning accuracy was high,which has the value of field application.

参考文献/References:

［1］窦林名,何学秋.冲击矿压防治理论与技术［M］.徐州:中国矿业大学出版社,2001.
［2］田向辉,李振雷,宋大钊,等.某冲击地压频发工作面微震冲击前兆信息特征及预警方法研究［J］.岩石力学与工程学报,2020,39(12):2471-2483. TIAN Xianghui,LI Zhenlei,SONG Dazhao,et al.Study on microseismic precursors and early warning methods of rockbursts in a working face［J］.Chinese Journal of Rock Mechanics and Engineering,2020,39(12):2471-2483.
［3］陈结,高靖宽,蒲源源,等.冲击地压预测预警的机器学习方法［J］.采矿与岩层控制工程学报,2021,3(1):57-68. CHEN Jie,GAO Jingkuan,PU Yuanyuan,et al.Machine learning method for predicting and warning of rockbursts［J］.Journal of Mining And Strata Control Engineering,2021,3(1):57-68.
［4］刘洋,邱黎明,娄全,等.岩石受载破坏过程声电信号时频特征研究［J］.工矿自动化,2020,46(6):87-91. LIU Yang,QIU Liming,LOU Quan,et al.Research on time-frequency characteristics of acoustic-electric signals in process of rock failure under load［J］.Industry and Mine Automation,2020,46(6):87-91.
［5］李振雷,何学秋,窦林名,等.煤冲击破坏过程规律及同源声电响应特征［J］.岩石力学与工程学报,2019,38(10):2057-2068. LI Zhenlei,HE Xueqiu,DOU Linming,et al.Bursting failure behavior of coal and response of acoustic and electromagnetic emissions［J］.Chinese Journal of Rock Mechanics and Engineering,2019,38(10):2057-2068.
［6］罗浩,潘一山,肖晓春,等.矿山动力灾害多参量危险性评价及分级预警［J］.中国安全科学学报,2013,23(11):85-90. LUO Hao,PAN Yishan,XIAO Xiaochun,et al.Multi-parameter risk evaluation and graded early warning of mine dynamic disaster［J］.China Safety Science Journal,2013,23(11):85-90.
［7］CAI W,DOU L M,ZHANG M,et al.A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring［J］.Tunnelling and Underground Space Technology,2018,80:232-245.
［8］殷欣,刘泉声,王心语,等.基于组合赋权和属性区间识别理论的岩爆烈度分级预测模型［J］.煤炭学报,2020,45(11):3772-3780. YIN Xin,LIU Quansheng,WANG Xinyu,et al.Prediction model of rockburst intensity classification based on combined weighting and attribute interval recognition theory［J］.Journal of China Coal Society,2020,45(11):3772-3780.
［9］刘晓斐.冲击地压电磁辐射前兆信息的时间序列数据挖掘及群体识别体系研究［D］.徐州:中国矿业大学,2008.
［10］史加荣,马媛媛.深度学习的研究进展与发展［J］.计算机工程与应用,2018,54(10)：1-10. SHI Jiarong,MA Yuanyuan.Research progress and development of deep learning［J］.Computer Engineering and Applications,2018,54(10)：1-10.
［11］荀晓玉,苏璨,李威,等.基于CNN-LSTM的煤矿瓦斯浓度预测［J］.现代信息科技,2020,4(20):149-152. XUN Xiaoyu,SU Can,LI Wei,et al.Coal mine gas concentration prediction based on CNN-LSTM［J］.Modern Information Technology,2020,4(20):149-152.
［12］董陇军,唐正,李夕兵,等.基于卷积神经网络与原始波形的微震与爆破事件辨识方法［J］.中南大学学报(英文版),2020,27(10):3078-3089. DONG Longjun,TANG Zheng,LI Xibing,et al.Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform［J］.Journal of Central South University,2020,27(10):3078-3089.
［13］裴艳宇,杨小彬,传金平,等.一维卷积神经网络特征提取下微震能级时序预测［J］.工程科学学报,2021,43(7):1003-1009. PEI Yanyu,YANG Xiaobin,CHUAN Jinping,et al.Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network［J］.Chinese Journal of Engineering,2021,43(7):1003-1009.
［14］刘帅师,程曦,郭文燕,等.深度学习方法研究新进展［J］.智能系统学报,2016,11(5):567-577. LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning［J］.CAAI Transactions on Intelligent Systems,2016,11(5):567-577.
［15］吴雨茜,王俊丽,杨丽,等.代价敏感深度学习方法研究综述［J］.计算机科学,2019,46(5):1-12. WU Yuqian,WANG Junli,YANG Li,et al.Survey on cost-sensitive deep learning methods［J］.Computer Science,2019,46(5):1-12.
［16］薛妍,沈宁,窦东阳.基于一维卷积神经网络的滚动轴承故障程度诊断［J］.轴承,2021(4):48-54. XUE Yan,SHEN Ning,DOU Dongyang.Fault degree diagnosis of rolling bearings based on one-dimensional convolutional neural network［J］.Bearing,2021(4):48-54.
［17］叶壮,余建波.基于多通道一维卷积神经网络特征学习的齿轮箱故障诊断方法［J］.振动与冲击,2020,39(20):55-66. YE Zhuang,YU Jianbo.Gearbox fault diagnosis based on feature learning of multi-channel one-dimensional convolutional neural network［J］.Journal of Vibration and Shock,2020,39(20):55-66.
［18］赵明,陈石,DAVE Yuen.基于深度学习卷积神经网络的地震波形自动分类与识别［J］.地球物理学报,2019,62(1):374-382. ZHAO Ming,CHEN Shi,DAVE Yuen.Waveform classification and seismic recognition by convolution neural network［J］.Chinese Journal of Geophysics,2019,62(1):374-382.
［19］曲建岭,余路,袁涛,等.基于一维卷积神经网络的滚动轴承自适应故障诊断算法［J］.仪器仪表学报,2018,39(7):134-143. QU Jianling,YU Lu,YUAN Tao,et al.Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network［J］.Chinese Journal of Scientific Instrument,2018,39(7):134-143.
［20］谭文侃,叶义成,胡南燕,等.LOF与改进SMOTE算法组合的强烈岩爆预测［J］.岩石力学与工程学报,2021,40(6):1186-1194. TAN Wenkan,YE Yicheng,HU Nanyan,et al.Strong rock burst prediction based on LOF and improved SMOTE algorithm［J］.Chinese Journal of Rock Mechanics and Engineering,2021,40(6):1186-1194.
［21］武雪玲,杨经宇,牛瑞卿.一种结合SMOTE和卷积神经网络的滑坡易发性评价方法［J］.武汉大学学报(信息科学版),2020,45(8):1223-1232. WU Xueling,YANG Jingyu,NIU Ruiqing.A landslide susceptibility evaluation method combining SMOTE and convolution neural network［J］.Journal of Wuhan University (Information Science Edition),2020,45(8):1223-1232.
［22］吕进国,潘立.微震预警冲击地压的时间序列方法［J］.煤炭学报,2010,35(12):2002-2005. LYU Jinguo,PAN Li.Microseismic predicting coal bump by time series method［J］.Journal of China Coal Society,2010,35(12):2002-2005.
［23］王安义,周孝铭.基于改进卷积神经网络的矿井巷道场强预测［J］.工矿自动化,2021,47(10):49-53,84. WANG Anyi,ZHOU Xiaoming.Prediction of mine roadway strength based on improved convolution neural network［J］.Industry and Mine Automation,2021,47(10):49-53,84.
［24］周志华.机器学习［M］.北京:清华大学出版社,2016.
［25］HE S Q,SONG D Z,MITRI H,et al.Integrated rockburst early warning model based on fuzzy comprehensive evaluation method［J］.International Journal of Rock Mechanics and Mining Sciences,2021,142(4):1047-1067.

相似文献/References:

[1]张黔生,谢贤平,吴劭星,等.矿山企业安全管理交互式模糊综合评价决策支持系统设计[J].中国安全生产科学技术,2010,6(1):125.
　ZHANG Qian-sheng,Xie Xian-ping,WU Shao-xing,et al.Design of an interactive fuzzy comprehensive evaluation decision support system for safety management of mine enterprises[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2010,6(10):125.
[2]杨超,李学龙,朱亚飞.基于电磁辐射的矿井冲击地压区域监测预报研究[J].中国安全生产科学技术,2013,9(7):100.[doi:10.11731/j.issn.1673-193x.2013.07.017]
　YANG Chao,LI Xue long,ZHU Ya fei,et al.Research on regional monitoring and prediction for rock burst in mine based on electromagnetic radiation[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(10):100.[doi:10.11731/j.issn.1673-193x.2013.07.017]
[3]曾晟,杜雪明,孙冰.基于GLM模糊评价在煤矿安全中的应用[J].中国安全生产科学技术,2013,9(11):91.[doi:10.11731/j.issn.1673-193x.2013.11.015]
　ZENG Sheng,DU Xue ming,SUN Bing.Application of fuzzy evaluation based on GLM in coal mine safety[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(10):91.[doi:10.11731/j.issn.1673-193x.2013.11.015]
[4]潘红洁,许开立.基于矿山安全专家系统安全信息采集及特征研究[J].中国安全生产科学技术,2011,7(3):109.
　PAN Hong-jie,XU Kai-li.Study on the information collection and safety characteristics used in mine safety Expert system[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(10):109.
[5]朱志洁,张宏伟.基于GA-ELM的冲击地压危险性预测研究[J].中国安全生产科学技术,2014,10(8):46.[doi:10.11731/j.issn.1673-193x.2014.08.008]
　ZHU Zhi-jie,ZHANG Hong-wei.Study on hazard prediction of rock burst based on GA-ELM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):46.[doi:10.11731/j.issn.1673-193x.2014.08.008]
[6]刘成敏,刘红芳,王海宁,等.某矿井通风系统优化及其可视化模型的构建[J].中国安全生产科学技术,2014,10(9):89.[doi:10.11731/j.issn.1673-193x.2014.09.015]
　LIU Cheng-min,LIU Hong-fang,WANG Hai-ning,et al.Optimization of mine ventilation system and construction of visual model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):89.[doi:10.11731/j.issn.1673-193x.2014.09.015]
[7]马海涛,刘宁武,王云海,等.金属矿山采空区灾害防治技术研究综述[J].中国安全生产科学技术,2014,10(10):75.[doi:10.11731/j.issn.1673-193x.2014.10.012]
　MA Hai-tao,LIU Ning-wu,WANG Yun-hai,et al.Review on research status of controlling techniques for goaf disaster in metal mine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):75.[doi:10.11731/j.issn.1673-193x.2014.10.012]
[8]杨明,张帆.煤层倾角对采空区流场及瓦斯分布的影响研究[J].中国安全生产科学技术,2014,10(11):34.[doi:10.11731/j.issn.1673-193x.2014.11.006]
　YANG Ming,ZHANG Fan.Study on influence of coal seam dip angle to the air flow field and gas distribution in goaf[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):34.[doi:10.11731/j.issn.1673-193x.2014.11.006]
[9]梁冰,汪北方,李刚,等.忻州窑煤矿5935巷道底板卸压槽防冲效果研[J].中国安全生产科学技术,2015,11(2):48.[doi:10.11731/j.issn.1673-193x.2015.02.008]
　LIANG Bing,WANG Bei-fang,LI Gang,et al.Study on rockburst prevention effect of floor pressure-relief slot in 5935 roadway of Xinzhouyao coal mine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(10):48.[doi:10.11731/j.issn.1673-193x.2015.02.008]
[10]谢苗,刘治翔,解敏,等.巷道超前支护装备的防冲击控制策略研究[J].中国安全生产科学技术,2015,11(3):59.[doi:10.11731/j.issn.1673-193x.2015.03.010]
　XIE Miao,LIU Zhi-xiang,XIE Min,et al.Research on anti-shock control strategy for advanced supporting equipment of roadway[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(10):59.[doi:10.11731/j.issn.1673-193x.2015.03.010]

备注/Memo

备注/Memo:
收稿日期： 2022-02-12
* 基金项目： 国家自然科学基金项目（52011530037，51904019）
作者简介： 曹亚利，硕士研究生，主要研究方向为冲击地压预警与防治。
通信作者： 李振雷，博士，副教授，主要研究方向为冲击地压监测预警与防治。
更新日期/Last Update: 2022-11-13