Microseismic monitoring technology has become an important technique to assess stability of rock mass in metal mines.Due to the special characteristics of underground metal mines in China,including the high tectonic s...Microseismic monitoring technology has become an important technique to assess stability of rock mass in metal mines.Due to the special characteristics of underground metal mines in China,including the high tectonic stress,irregular shape and existence of ore body,and complex mining methods,the application of microseismic technology is more diverse in China compared to other countries,and is more challenging than in other underground structures such as tunnels,hydropower stations and coal mines.Apart from assessing rock mass stability and ground pressure hazards induced by mining process,blasting,water inrush and large scale goaf,microseismic technology is also used to monitor illegal mining,and track personnel location during rescue work.Moreover,microseismic data have been used to optimize mining parameters in some metal mines.The technology is increasingly used to investigate cracking mechanism in the design of rock mass supports.In this paper,the application,research development and related achievements of microseismic technology in underground metal mines in China are summarized.By considering underground mines from the perspective of informatization,automation and intelligentization,future studies should focus on intelligent microseismic data processing method,e.g.,signal identification of microseismic and precise location algorithm,and on the research and development of microseismic equipment.In addition,integrated monitoring and collaborative analysis for rock mass response caused by mining disturbance will have good prospects for future development.展开更多
Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was ...Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was usually observed near the excavation boundaries. The formation mechanism of the “11·28” rockburst, which was a typical rockburst and occurred in a drainage tunnel under a deep burial depth, high in-situ stress state and complex geological conditions, has been difficult to explain. Realistic failure process analysis(RFPA3D) software was adopted to numerically simulate the whole failure process of the surrounding rock mass around the tunnel subjected to excavation. The spatial distribution of acoustic emission derived from numerical simulation contributed to explaining the mechanical responses of the process. Analyses of the stress, safety reserve coefficient and damage degree were performed to reveal the effect of faults on the formation of rockbursts in the deep tunnel. The existence of faults results in the formation of stress anomaly areas between the tunnel and the fault. The surrounding rock mass failure propagates toward the fault from the initial failure, to different degrees. The relative positions and angles of faults play significant roles in the extent and development of surrounding rock mass failure, respectively. The increase in the lateral stress coefficient leads to the aggravation of the surrounding rock mass damage, especially in the roof and floor of the tunnel. Moreover, as the rock strength-stress ratio increases, the failure mode of the near-fault tunnel gradually changes from the stress-controlled type to the compound-controlled type. These findings were consistent with the microseismic monitoring results and field observations, which was helpful to understand the mechanical behavior of tunnel excavation affected by faults. The achievements of this study can provide some references for analysis of the failure mechanisms of similar deep tunnels.展开更多
Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic ev...Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.展开更多
Rock mass large deformation in underground powerhouse caverns has been a severe hazard in hydropower engineering in Southwest China.During the development of rock mass large deformation,a sequence of fractures was gen...Rock mass large deformation in underground powerhouse caverns has been a severe hazard in hydropower engineering in Southwest China.During the development of rock mass large deformation,a sequence of fractures was generated that can be monitored using microseismic(MS)monitoring techniques.Two MS monitoring systems were established in two typical underground powerhouse caverns featuring distinct geostress levels.The MS b-values associated with rock mass large deformation and their temporal variation are analysed.The results showed that the MS bvalue in course of rock mass deformation was less than 1.0 in the underground powerhouse caverns at a high stress level while larger than 1.5 at a low stress level.Prior to the rock mass deformation,the MS b-values derived from both the high-stress and low-stress underground powerhouse caverns show an incremental decrease over 10%within 10 d.The results contribute to understanding the fracturing characteristics of MS sources associated with rock mass large deformation and provide a reference for early warning of rock mass large deformation in underground powerhouse caverns.展开更多
基金Projects(51974059,52174142)supported by the National Natural Science Foundation of ChinaProject(2017YFC0602904)supported by the National Key Research and Development Program of ChinaProject(N180115010)supported by the Fundamental Research Funds for the Central Universities,China。
文摘Microseismic monitoring technology has become an important technique to assess stability of rock mass in metal mines.Due to the special characteristics of underground metal mines in China,including the high tectonic stress,irregular shape and existence of ore body,and complex mining methods,the application of microseismic technology is more diverse in China compared to other countries,and is more challenging than in other underground structures such as tunnels,hydropower stations and coal mines.Apart from assessing rock mass stability and ground pressure hazards induced by mining process,blasting,water inrush and large scale goaf,microseismic technology is also used to monitor illegal mining,and track personnel location during rescue work.Moreover,microseismic data have been used to optimize mining parameters in some metal mines.The technology is increasingly used to investigate cracking mechanism in the design of rock mass supports.In this paper,the application,research development and related achievements of microseismic technology in underground metal mines in China are summarized.By considering underground mines from the perspective of informatization,automation and intelligentization,future studies should focus on intelligent microseismic data processing method,e.g.,signal identification of microseismic and precise location algorithm,and on the research and development of microseismic equipment.In addition,integrated monitoring and collaborative analysis for rock mass response caused by mining disturbance will have good prospects for future development.
基金Project(42177143) supported by the National Natural Science Foundation of ChinaProject(2020JDJQ0011) supported by the Science Foundation for Distinguished Young Scholars of Sichuan Province,China。
文摘Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was usually observed near the excavation boundaries. The formation mechanism of the “11·28” rockburst, which was a typical rockburst and occurred in a drainage tunnel under a deep burial depth, high in-situ stress state and complex geological conditions, has been difficult to explain. Realistic failure process analysis(RFPA3D) software was adopted to numerically simulate the whole failure process of the surrounding rock mass around the tunnel subjected to excavation. The spatial distribution of acoustic emission derived from numerical simulation contributed to explaining the mechanical responses of the process. Analyses of the stress, safety reserve coefficient and damage degree were performed to reveal the effect of faults on the formation of rockbursts in the deep tunnel. The existence of faults results in the formation of stress anomaly areas between the tunnel and the fault. The surrounding rock mass failure propagates toward the fault from the initial failure, to different degrees. The relative positions and angles of faults play significant roles in the extent and development of surrounding rock mass failure, respectively. The increase in the lateral stress coefficient leads to the aggravation of the surrounding rock mass damage, especially in the roof and floor of the tunnel. Moreover, as the rock strength-stress ratio increases, the failure mode of the near-fault tunnel gradually changes from the stress-controlled type to the compound-controlled type. These findings were consistent with the microseismic monitoring results and field observations, which was helpful to understand the mechanical behavior of tunnel excavation affected by faults. The achievements of this study can provide some references for analysis of the failure mechanisms of similar deep tunnels.
基金Projects(51822407,51774327,51664016)supported by the National Natural Science Foundation of China。
文摘Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.
基金Projects(51809221,51679158)supported by the National Natural Science Foundation of ChinaProject(KFJJ20-06M)supported by the State Key Laboratory of Explosion Science and Technology(Beijing Institute of Technology),China。
文摘Rock mass large deformation in underground powerhouse caverns has been a severe hazard in hydropower engineering in Southwest China.During the development of rock mass large deformation,a sequence of fractures was generated that can be monitored using microseismic(MS)monitoring techniques.Two MS monitoring systems were established in two typical underground powerhouse caverns featuring distinct geostress levels.The MS b-values associated with rock mass large deformation and their temporal variation are analysed.The results showed that the MS bvalue in course of rock mass deformation was less than 1.0 in the underground powerhouse caverns at a high stress level while larger than 1.5 at a low stress level.Prior to the rock mass deformation,the MS b-values derived from both the high-stress and low-stress underground powerhouse caverns show an incremental decrease over 10%within 10 d.The results contribute to understanding the fracturing characteristics of MS sources associated with rock mass large deformation and provide a reference for early warning of rock mass large deformation in underground powerhouse caverns.
