Coal Geology & Exploration


Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models of RF, BLR, ET and KNN, which were selected using MICE. Besides, data interpolation was performed with the optimal model selected according to ERMS, in combination with the two traditional interpolation methods (Mean and Median), resulting in a complete data set. In addition, the data were flattened into a 6×1×1 one-dimensional image data as the input layer, and the sizes of the convolutional kernel and pooling kernel were calculated to be 3×1 based on the size of the input layer. Moreover, zero-padding was applied for the feature edge processing. Batch normalization layers were added to improve the model stability and convergence speed. Thus, ReLU activation function and SGDM optimizer function were selected. Further, the CNN prediction model was trained, with accuracy rates of 100.00% for the training set and 91.67% for the validation set. Meanwhile, the RBF, SVM and PNN models were established for the comparison and verification of their test set data with that of the CNN model. Generally, the CNN model shows higher accuracy (91.67%) than the other models. By comparing the confusion matrix of the PNN model with the CNN model, it is found that the CNN model tends to overestimate the degree of rockburst compared to the actual results, indicating better safety after misjudgment. This demonstrates the feasibility of the MICE-CNN prediction model of rockburst intensity grade.


rockburst, deep rcok, intensity grade, indicator system, data interpolation, deep learning, prediction model


[1] 冯夏庭,肖亚勋,丰光亮,等. 岩爆孕育过程研究[J]. 岩石力学与工程学报,2019,38(4):649−673.

FENG Xiating,XIAO Yaxun,FENG Guangliang,et al. Study on the development process of rockbursts[J]. Chinese Journal of Rock Mechanics and Engineering,2019,38(4):649−673.

[2] 苗金丽,何满潮,李德建,等. 花岗岩应变岩爆声发射特征及微观断裂机制[J]. 岩石力学与工程学报,2009,28(8):1593−1603.

MIAO Jinli,HE Manchao,LI Dejian,et al. Acoustic emission characteristics of granite under strain rockburst test and its micro–fracture mechanism[J]. Chinese Journal of Rock Mechanics and Engineering,2009,28(8):1593−1603.

[3] 何满潮,谢和平,彭苏萍,等. 深部开采岩体力学研究[J]. 岩石力学与工程学报,2005,24(16):2803−2813.

HE Manchao,XIE Heping,PENG Suping,et al. Study on rock mechanics in deep mining engineering[J]. Chinese Journal of Rock Mechanics and Engineering,2005,24(16):2803−2813.

[4] 谢和平,PARISEAU W G. 岩爆的分形特征和机理[J]. 岩石力学与工程学报,1993,12(1):28−37.

XIE Heping,PARISEAU W G. Fractal character and mechanism of rock bursts[J]. Chinese Journal of Rock Mechanics and Engineering,1993,12(1):28−37.

[5] 冯夏庭,陈炳瑞,明华军,等. 深埋隧洞岩爆孕育规律与机制:即时型岩爆[J]. 岩石力学与工程学报,2012,31(3):433−444.

FENG Xiating,CHEN Bingrui,MING Huajun,et al. Evolution law and mechanism of rockbursts in deep tunnels:Immediate rockburst[J]. Chinese Journal of Rock Mechanics and Engineering,2012,31(3):433−444.

[6] 徐林生,王兰生,李永林. 岩爆形成机制与判据研究[J]. 岩土力学,2002,23(3):300−303.

XU Linsheng,WANG Lansheng,LI Yonglin. Study on mechanism and judgement of rockbursts[J]. Rock and Soil Mechanics,2002,23(3):300−303.

[7] 王元汉,李卧东,李启光,等. 岩爆预测的模糊数学综合评判方法[J]. 岩石力学与工程学报,1998,17(5):493−501.

WANG Yuanhan,LI Wodong,LI Qiguang,et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering,1998,17(5):493−501.

[8] 张镜剑,傅冰骏. 岩爆及其判据和防治[J]. 岩石力学与工程学报,2008,27(10):2034−2042.

ZHANG Jingjian,FU Bingjun. Rockburst and its criteria and control[J]. Chinese Journal of Rock Mechanics and Engineering,2008,27(10):2034−2042.

[9] 徐林生,王兰生. 二郎山公路隧道岩爆发生规律与岩爆预测研究[J]. 岩土工程学报,1999,21(5):569−572.

XU Linsheng,WANG Lansheng. Study on the laws of rockburst and its forecasting in the tunnel of Erlang Mountain road[J]. Chinese Journal of Geotechnical Engineering,1999,21(5):569−572.

[10] 孙飞跃,刘希亮,郭佳奇,等. 岩爆预测评估方法的动力数值分析[J]. 应用力学学报,2022,39(1):26−34.

SUN Feiyue,LIU Xiliang,GUO Jiaqi,et al. Dynamic numerical calculation analysis of rockburst prediction assessment methods[J]. Chinese Journal of Applied Mechanics,2022,39(1):26−34.

[11] KIDYBINSKI A. Bursting liability indices of coal[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,1981,18(4):295−304.

[12] SINGH S P. Burst energy release index[J]. Rock Mechanics and Rock Engineering,1988,21(2):149−155.

[13] MA C S,CHEN W Z,TAN X J,et al. Novel rockburst criterion based on the TBM tunnel construction of the Neelum−Jhelum (NJ) hydroelectric project in Pakistan[J]. Tunnelling and Underground Space Technology,2018,81:391−402.

[14] AUBERTIN M,GILL D E,SIMON R. On the use of the brittleness index modified (BIM) to estimate the post–peak behavior of rocks[J]. Aqua Fennica,1994(23):24−25.

[15] WANG J A,PARK H D. Comprehensive prediction of rock burst based on analysis of strain energy in rocks[J]. Tunnelling and Underground Space Technology,2001,16(1):49−57.

[16] 王学滨,陶帅,潘一山,等. 基于非线性屈服准则及主应力判据的圆形巷道围岩岩爆过程的数值模拟[J]. 防灾减灾工程学报,2012,32(2):131−137.

WANG Xuebin,TAO Shuai,PAN Yishan,et al. Numerical simulation of rockburst process of surrounding rock in circular tunnel based on nonlinear yielding and principal stress criteria[J]. Journal of Disaster Prevention and Mitigation Engineering,2012,32(2):131−137.

[17] SOUSA L R,MIRANDA T,SOUSA R L,et al. The use of data mining techniques in rockburst risk assessment[J]. Engineering,2017,3(4):552−558.

[18] 王迎超,尚岳全,孙红月,等. 基于功效系数法的岩爆烈度分级预测研究[J]. 岩土力学,2010,31(2):529−534.

WANG Yingchao,SHANG Yuequan,SUN Hongyue,et al. Study of prediction of rockburst intensity based on efficacy coefficient method[J]. Rock and Soil Mechanics,2010,31(2):529−534.

[19] 赵洪波. 岩爆分类的支持向量机方法[J]. 岩土力学,2005,26(4):642−644.

ZHAO Hongbo. Classification of rockburst using support vector machine[J]. Rock and Soil Mechanics,2005,26(4):642−644.

[20] DONG Longjun,LI Xibing,PENG Kang. Prediction of rockburst classification using random forest[J]. Transactions of Nonferrous Metals Society of China,2013,23(2):472−477.

[21] 兰明,刘志祥,冯凡. 在线极限学习机在岩爆预测中的应用[J]. 安全与环境学报,2014,14(2):90−93.

LAN Ming,LIU Zhixiang,FENG Fan. Attempt to study the applicability of the online sequential extreme learning machine to the rock burst forecast[J]. Journal of Safety and Environment,2014,14(2):90−93.

[22] 靳春玲,党丹丹,贡力,等. IPP–PNN模型在川藏铁路深埋长大隧道岩爆预测中的应用[J]. 铁道科学与工程学报,2023,20(3):986−995.

JIN Chunling,DANG Dandan,GONG Li,et al. Application of IPP–PNN model in rockburst prediction occurring deep–buried long tunnel of Sichuan–Tibet Railway[J]. Journal of Railway Science and Engineering,2023,20(3):986−995.

[23] 吴顺川,张晨曦,成子桥. 基于PCA–PNN原理的岩爆烈度分级预测方法[J]. 煤炭学报,2019,44(9):2767−2776.

WU Shunchuan,ZHANG Chenxi,CHENG Ziqiao. Prediction of intensity classification of rockburst based on PCA–PNN principle[J]. Journal of China Coal Society,2019,44(9):2767−2776.

[24] 张凯,张科,李昆. 主元分析–神经网络岩爆等级预测模型[J]. 中国安全科学学报,2021,31(3):96−104.

ZHANG Kai,ZHANG Ke,LI Kun. Prediction model of rockburst grade based on PCA–neural network[J]. China Safety Science Journal,2021,31(3):96−104.

[25] 赵兵,王增平,纪维佳,等. 基于注意力机制的CNN–GRU短期电力负荷预测方法[J]. 电网技术,2019,43(12):4370−4376.

ZHAO Bing,WANG Zengping,JI Weijia,et al. A short–term power load forecasting method based on attention mechanism of CNN−GRU[J]. Power System Technology,2019,43(12):4370−4376.

[26] 姚程文,杨苹,刘泽健. 基于CNN–GRU混合神经网络的负荷预测方法[J]. 电网技术,2020,44(9):3416−3423.

YAO Chengwen,YANG Ping,LIU Zejian. Load forecasting method based on CNN–GRU hybrid neural network[J]. Power System Technology,2020,44(9):3416−3423.

[27] 马永杰,程时升,马芸婷,等. 卷积神经网络及其在智能交通系统中的应用综述[J]. 交通运输工程学报,2021,21(4):48−71.

MA Yongjie,CHENG Shisheng,MA Yunting,et al. Review of convolutional neural network and its application in intelligent transportation system[J]. Journal of Traffic and Transportation Engineering,2021,21(4):48−71.

[28] 罗文慧,董宝田,王泽胜. 基于CNN–SVR混合深度学习模型的短时交通流预测[J]. 交通运输系统工程与信息,2017,17(5):68−74.

LUO Wenhui,DONG Baotian,WANG Zesheng. Short−term traffic flow prediction based on CNN–SVR hybrid deep learning model[J]. Journal of Transportation Systems Engineering and Information Technology,2017,17(5):68−74.

[29] 彭道刚,朱琪,车权,等. 基于CNN–LSTM神经网络的电网调度火电厂短期存煤预测[J]. 电力自动化设备,2021,41(6):127−132.

PENG Daogang,ZHU Qi,CHE Quan,et al. Short−term coal storage forecasting of thermal power plant for power grid dispatching based on CNN−LSTM neural network[J]. Electric Power Automation Equipment,2021,41(6):127−132.

[30] 陈建平,王春雷,王雪冬. 基于CNN神经网络的煤层底板突水预测[J]. 中国地质灾害与防治学报,2021,32(1):50−57.

CHEN Jianping,WANG Chunlei,WANG Xuedong. Coal mine floor water inrush prediction based on CNN neural network[J]. The Chinese Journal of Geological Hazard and Control,2021,32(1):50−57.

[31] 钱超,陈建勋,罗彦斌,等. 基于随机森林的公路隧道运营缺失数据插补方法[J]. 交通运输系统工程与信息,2016,16(3):81−87.

QIAN Chao,CHEN Jianxun,LUO Yanbin,et al. Random forest based operational missing data imputation for highway tunnel[J]. Journal of Transportation Systems Engineering and Information Technology,2016,16(3):81−87.

[32] LAQUEUR H S,SHEV A B,KAGAWA R M C. SuperMICE:An ensemble machine learning approach to multiple imputation by chained equations[J]. American Journal of Epidemiology,2021,191(3):516−525.

[33] ROYSTON P,WHITE I R. Multiple imputation by chained equations (MICE):Implementation in stata[J]. Journal of Statistical Software,2011,45(4):1−20.

[34] 何满潮,苗金丽,李德建,等. 深部花岗岩试样岩爆过程实验研究[J]. 岩石力学与工程学报,2007,26(5):865−876.

HE Manchao,MIAO Jinli,LI Dejian,et al. Experimental study on rockburst processes of granite specimen at great depth[J]. Chinese Journal of Rock Mechanics and Engineering,2007,26(5):865−876.

[35] 齐庆新,陈尚本,王怀新,等. 冲击地压、岩爆、矿震的关系及其数值模拟研究[J]. 岩石力学与工程学报,2003,22(11):1852−1858.

QI Qingxin,CHEN Shangben,WANG Huaixin,et al. Study on the relations among coal bump,rockburst and mining tremor with numerical simulation[J]. Chinese Journal of Rock Mechanics and Engineering,2003,22(11):1852−1858.

[36] 唐礼忠,杨承祥,潘长良. 大规模深井开采微震监测系统站网布置优化[J]. 岩石力学与工程学报,2006,25(10):2036−2042.

TANG Lizhong,YANG Chengxiang,PAN Changliang. Optimization of microseismic monitoring network for large–scale deep well mining[J]. Chinese Journal of Rock Mechanics and Engineering,2006,25(10):2036−2042.

[37] KEVIN R,JEFF L,MICHAEL S. Crystal structure prediction via deep learning[J]. Journal of the American Chemical Society,2018,140(32):10158−10168.

[38] 张彪,戴兴国. 基于指标距离与不确定度量的岩爆云模型预测研究[J]. 岩土力学,2017,38(增刊2):257−265.

ZHANG Biao,DAI Xingguo. A cloud model for predicting rockburst intensity grade based on index distance and uncertainty measure[J]. Rock and Soil Mechanics,2017,38(Sup.2):257−265.

[39] 周飞燕,金林鹏,董军. 卷积神经网络研究综述[J]. 计算机学报,2017,40(6):1229−1251.

ZHOU Feiyan,JIN Linpeng,DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers,2017,40(6):1229−1251.

[40] 刘建伟,赵会丹,罗雄麟,等. 深度学习批归一化及其相关算法研究进展[J]. 自动化学报,2020,46(6):1090−1120.

LIU Jianwei,ZHAO Huidan,LUO Xionglin,et al. Research progress on batch normalization of deep learning and its related algorithms[J]. Acta Automatica Sinica,2020,46(6):1090−1120.

[41] 李校林,钮海涛. 基于VGG–NET的特征融合面部表情识别[J]. 计算机工程与科学,2020,42(3):500−509.

LI Xiaolin,NIU Haitao. Facial expression recognition using feature fusion based on VGG–NET[J]. Computer Engineering and Science,2020,42(3):500−509.

[42] 常亮,邓小明,周明全,等. 图像理解中的卷积神经网络[J]. 自动化学报,2016,42(9):1300−1312.

CHANG Liang,DENG Xiaoming,ZHOU Mingquan,et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica,2016,42(9):1300−1312.

[43] 徐冰冰,岑科廷,黄俊杰,等. 图卷积神经网络综述[J]. 计算机学报,2020,43(5):755−780.

XU Bingbing,CEN Keting,HUANG Junjie,et al. A survey on graph convolutional neural network[J]. Chinese Journal of Computers,2020,43(5):755−780.

[44] 张瑞程,王新颖,胡磊磊,等. 基于一维卷积神经网络的燃气管道泄漏声发射信号识别[J]. 中国安全生产科学技术,2021,17(2):104−109.

ZHANG Ruicheng,WANG Xinying,HU Leilei,et al. Acoustic emission signal identification of gas pipeline leakage based on one−dimensional convolution neural network[J]. Journal of Safety Science and Technology,2021,17(2):104−109.



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