Research on classification and identification of mine microseismic signals based on deep learning method

Zhao Hongbao; Liu Rui; Liu Yihong; Zhang Yixiao; Gu Tao

doi:10.19606/j.cnki.jmst.2022.02.003

Volume 7 Issue 2

Apr. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Mining Science and Technology > 2022 > 7(2): 166-174

Zhao Hongbao, Liu Rui, Liu Yihong, Zhang Yixiao, Gu Tao. Research on classification and identification of mine microseismic signals based on deep learning method[J]. Journal of Mining Science and Technology, 2022, 7(2): 166-174. doi: 10.19606/j.cnki.jmst.2022.02.003

Citation:

PDF( 7733 KB)

Research on classification and identification of mine microseismic signals based on deep learning method

doi: 10.19606/j.cnki.jmst.2022.02.003

Zhao Hongbao^{1, 2
,},
Liu Rui^{1
,
,},
Liu Yihong¹,
Zhang Yixiao¹,
Gu Tao²

1.
School of Energy and Mining Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
Hebei IoT Monitoring Engineering Technology Research Center, Langfang Hebei 065201, China

Received Date: 2021-04-25
Rev Recd Date: 2021-09-12
Publish Date: 2022-04-20

Abstract

Abstract

In order to accurately identify mine microseismic signals, this paper proposes a VGG4-CNN deep learning network model suitable for identifying mine microseismic signals. The model is written in Python language and built based on the PyTorch deep learning network architecture framework. Based on the time-domain characteristics of the microseismic signals of 9 types of events such as rock fracture, blasting operations, and background noise in the mine production process, VGG4-CNN has realized the supervised learning training and classification recognition application of 3 835 sets of mine microseismic signal data. The research results show that the recognition accuracy of the VGG4-CNN neural network constructed in this paper is as high as 94 %. This model does not require denoising of the original waveform signal and is more robust than other models. The implementation can be performed by a medium-level GPU to meet engineering requirements.
- microseismic signal,
- identification technology,
- deep learning,
- time domain analysis

FullText(HTML)

References(19)

References

[1]	李铁, 倪建明, 李忠凯. 采动岩体强矿震破裂机制反演及其防治对策[J]. 采矿与安全工程学报, 2016, 33(6): 1110-1115. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201606021.htm Li Tie, Ni Jianming, Li Zhongkai. Rupture mechanism inversion of mining-induced strong mine earthquake and its preventive methods[J]. Journal of Mining & Safety Engineering, 2016, 33(6): 1110-1115. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201606021.htm
[2]	赵洪宝, 刘一洪, 程辉, 等. 回坡底煤矿回采巷道非对称底鼓机理及防治措施[J]. 矿业科学学报, 2020, 5(6): 638-647. doi: 10.19606/j.cnki.jmst.2020.06.006 Zhao Hongbao, Liu Yihong, Cheng Hui, et al. Mechanism and prevention measures of asymmetric floor heave in mining roadway of Huipodi Coal Mine[J]. Journal of Mining Science and Technology, 2020, 5(6): 638-647. doi: 10.19606/j.cnki.jmst.2020.06.006
[3]	徐奴文, 唐春安, 沙椿, 等. 锦屏一级水电站左岸边坡微震监测系统及其工程应用[J]. 岩石力学与工程学报, 2010, 29(5): 915-925. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201005010.htm Xu Nuwen, Tang Chun'an, Sha Chun, et al. Microseismic monitoring system establishment and its engineering applications to left bank slope of Jinping I hydropower station[J]. Chinese Journal of Rock Mechanics and Engineering, 2010, 29(5): 915-925. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201005010.htm
[4]	程辉, 赵洪宝, 徐建峰, 等. 基于滑移线场理论的巷道底鼓机理与防治技术研究[J]. 矿业科学学报, 2021, 6(3): 314-322. doi: 10.19606/j.cnki.jmst.2021.03.008 Cheng Hui, Zhao Hongbao, Xu Jianfeng, et al. Study on floor heave mechanism and control technology of roadway based on slip line field theory[J]. Journal of Mining Science and Technology, 2021, 6(3): 314-322. doi: 10.19606/j.cnki.jmst.2021.03.008
[5]	陈宇龙, 曹鹏. 混凝土时间相关断裂模型研究综述[J]. 矿业科学学报, 2021, 6(3): 290-295. doi: 10.19606/j.cnki.jmst.2021.03.005 Chen Yulong, Cao Peng. Review of time-dependent fracture model of concrete[J]. Journal of Mining Science and Technology, 2021, 6(3): 290-295. doi: 10.19606/j.cnki.jmst.2021.03.005
[6]	朱梦博, 王李管, 刘晓明, 等. 基于波形参数的微震P波到时拾取值质量控制方法[J]. 岩土力学, 2019, 40(2): 767-776. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201902040.htm Zhu Mengbo, Wang Liguan, Liu Xiaoming, et al. A quality control method for microseismic P-wave phase pickup value based on waveform parameters[J]. Rock and Soil Mechanics, 2019, 40(2): 767-776. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201902040.htm
[7]	雷艺繁, 徐奴文, 董林鹭, 等. 开挖卸荷下双江口水电站地下厂房上游拱肩破坏机制研究[J]. 岩土力学, 2020, 41(S2): 1-11. Lei Yifan, Xü Nuwen, Dong Linlu, et al. Failure mechanism of spandrel under the excavation and unloading condition, at the upstream of underground powerhouse, Shuangjiangkou hydropower station[J]. Rock and Soil Mechanics, 2020, 41(S2): 1-11.
[8]	Jeffrey F T, Henry C B, Robert R S. Stewart classification of microseismic events from bitumen production at Cold Lake, Alberta[J]. CREWES Research Report, 2007, 19(1): 1-24.
[9]	Le C Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
[10]	赵国彦, 邓青林, 李夕兵, 等. 基于EMD和形态分形维数的微震波形识别[J]. 中南大学学报: 自然科学版, 2017, 48(1): 162-167. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201701023.htm Zhao Guoyan, Deng Qinglin, Li Xibing, et al. Recognition of microseismic waveforms based on EMD and morphological fractal dimension[J]. Journal of Central South University: Science and Technology, 2017, 48(1): 162-167. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201701023.htm
[11]	赵国彦, 邓青林, 马举. 基于FSWT时频分析的矿山微震信号分析与识别[J]. 岩土工程学报, 2015, 37(2): 306-312. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201502018.htm Zhao Guoyan, Deng Qinglin, Ma Ju. Recognition of mine microseismic signals based on FSWT time-frequency analysis[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(2): 306-312. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201502018.htm
[12]	尚雪义, 李夕兵, 彭康, 等. 基于EMD-SVD的矿山微震与爆破信号特征提取及分类方法[J]. 岩土工程学报, 2016, 38(10): 1849-1858. doi: 10.11779/CJGE201610014 Shang Xueyi, Li Xibing, Peng Kang, et al. Feature extraction and classification of mine microseism and blast based on EMD-SVD[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(10): 1849-1858. doi: 10.11779/CJGE201610014
[13]	董陇军, 孙道元, 李夕兵, 等. 微震与爆破事件统计识别方法及工程应用[J]. 岩石力学与工程学报, 2016, 35(7): 1423-1433. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201607013.htm Dong Longjun, Sun Daoyuan, Li Xibing, et al. A statistical method to identify blasts and microseismic events and its engineering application[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(7): 1423-1433. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201607013.htm
[14]	Zhang Xingli, Jia Ruisheng, Lu Xinming, et al. Identifi cation of blasting vibration and coal-rock fracturing microseismic signals[J]. Applied Geophysics, 2018, 15(2): 280-289, 364. doi: 10.1007/s11770-018-0682-9
[15]	朱权洁, 李青松, 李绍泉, 等. 煤与瓦斯突出试验的微震动态响应与特征分析[J]. 岩石力学与工程学报, 2015, 34(S2): 3813-3821. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2015S2023.htm Zhu Quanjie, Li Qingsong, Li Shaoquan, et al. Microseismic dynamic response and characteristic analysis of coal and gas outburst experiment[J]. Chinese Journal of Rock Mechanics and Engineering, 2015, 34(S2): 3813-3821. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2015S2023.htm
[16]	Bi L, Xie W, Zhao J J. Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM[J]. Computers & Geosciences, 2019, 123: 111-120.
[17]	王国法, 徐亚军, 孟祥军, 等. 智能化采煤工作面分类、分级评价指标体系[J]. 煤炭学报, 2020, 45(9): 3033-3044. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202009001.htm Wang Guofa, Xu Yajun, Meng Xiangjun, et al. Specification, classification and grading evaluation index for smart longwall mining face[J]. Journal of China Coal Society, 2020, 45(9): 3033-3044. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202009001.htm
[18]	李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9): 2508-2515, 2565. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201609029.htm Li Yandong, Hao Zongbo, Lei Hang. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2515, 2565. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201609029.htm
[19]	Bottou L, Curtis F E, Nocedal J. Optimization methods for large-scale machine learning[J]. SIAM Review, 2018, 60(2): 223-311. doi: 10.1137/16M1080173

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(6) / Tables(5)

Get Citation

PDF

XML

Article Metrics

Article views (717) PDF downloads(124)

Research on classification and identification of mine microseismic signals based on deep learning method

doi: 10.19606/j.cnki.jmst.2022.02.003

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Export File

Citation

Format

Content