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[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]
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基于卷积神经网络的冲击地压预警方法研究*
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《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.

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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