Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning

岩爆 声发射 微震 信号(编程语言) 预警系统 计算机科学 人工神经网络 深度学习 地震学 地质学 人工智能 声学 工程类 煤矿开采 电信 物理 程序设计语言 废物管理
作者
Yangyang Di,Enyuan Wang,Zhonghui Li,Xiaofei Liu,Tao Huang,Jiajie Yao
出处
期刊:International Journal of Rock Mechanics and Mining Sciences [Elsevier BV]
卷期号:170: 105519-105519 被引量:31
标识
DOI:10.1016/j.ijrmms.2023.105519
摘要

Microseismic, acoustic emission, and electromagnetic radiation monitoring methods are often used to monitor rock burst disasters in coal mines. In the process of coal mining, the time series characteristics and amplitude characteristics of microseismic, acoustic emission, and electromagnetic radiation data are mainly used to identify rockburst risk, but the results of risk identification through the three monitoring methods are quite different. Consequently, the accurate and comprehensive early warning of rock burst risk is still an urgent problem to be solved. The development of deep learning provides a new means for intelligent early warning of rock burst risk. In this paper, a comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation (MS-AE-EMR) signals of rock bursts was proposed based on a deep learning algorithm. This method uses long short-term memory recurrent neural networks (LSTM-RNNs) to intelligently identify the MS-AE-EMR precursor signal of rock burst risk, predicts the MS-AE-EMR signal by a convolution neural network (CNN), analyses the MS-AE-EMR precursor signal of rock burst risk through the data analysis method and obtains the risk coefficient of rock burst. Moreover, by using the MS-AE-EMR original signal and risk coefficient, it trains the multi-input CNN and inputs the predicted signal into the trained multi-input CNN to obtain the predicted risk coefficient of rock burst. Analysing the risk coefficient completes the comprehensive early warning of the MS-AE-EMR signal of rock burst. After field verification, the RNN-based comprehensive early warning method of the MS-AE-EMR signal can respond positively to rock burst risk and capture the information in advance. Therefore, this method is of great significance for accurate monitoring and early warning of rock burst in coal mines.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
清浅完成签到,获得积分10
刚刚
王冉冉发布了新的文献求助10
1秒前
2秒前
所所应助zjz采纳,获得30
3秒前
3秒前
小吴同学发布了新的文献求助10
4秒前
量子星尘发布了新的文献求助10
4秒前
4秒前
5秒前
shaocat完成签到 ,获得积分10
5秒前
风中的眼神完成签到,获得积分10
5秒前
CAOHOU应助奋斗水香采纳,获得10
6秒前
俊逸梦蕊完成签到,获得积分10
6秒前
7秒前
典雅牛青关注了科研通微信公众号
7秒前
Xinxxx发布了新的文献求助10
7秒前
illusion完成签到,获得积分10
8秒前
wanci应助王冉冉采纳,获得30
9秒前
树小夏发布了新的文献求助10
10秒前
小吴同学完成签到,获得积分10
10秒前
赘婿应助kk_yang采纳,获得10
11秒前
成就伟祺关注了科研通微信公众号
12秒前
能干的语芙完成签到 ,获得积分10
12秒前
无欲无求傻傻完成签到,获得积分10
12秒前
12秒前
12秒前
尊敬寒松完成签到 ,获得积分10
12秒前
糊涂的麦片完成签到,获得积分10
12秒前
13秒前
13秒前
14秒前
wanci应助wangdafa采纳,获得10
14秒前
竹子co完成签到,获得积分10
14秒前
steventj完成签到,获得积分10
14秒前
yz完成签到 ,获得积分10
15秒前
朴实山兰完成签到,获得积分10
16秒前
tkkdy发布了新的文献求助10
16秒前
蓁蓁发布了新的文献求助10
16秒前
醉熏的鑫发布了新的文献求助10
17秒前
独家双层汉堡完成签到,获得积分10
17秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds第二卷 1200
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038657
求助须知:如何正确求助?哪些是违规求助? 3576306
关于积分的说明 11375198
捐赠科研通 3306108
什么是DOI,文献DOI怎么找? 1819379
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066