With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stabi...With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.展开更多
A series of true-triaxial compression tests were performed on red sandstone cubic specimens with a circular hole to investigate the influence of depth on induced spalling in tunnels.The failure process of the hole sid...A series of true-triaxial compression tests were performed on red sandstone cubic specimens with a circular hole to investigate the influence of depth on induced spalling in tunnels.The failure process of the hole sidewalls was monitored and recorded in real-time by a micro-video monitoring equipment.The general failure evolution processes of the hole sidewall at different initial depths(500 m,1000 m and 1500 m)during the adjustment of vertical stress were obtained.The results show that the hole sidewall all formed spalling before resulting in strain rockburst,and ultimately forming a V-shaped notch.The far-field principal stress for the initial failure of the tunnel shows a good positive linear correlation with the depth.As the depth increases,the stress required for the initial failure of the tunnels clearly increased,the spalling became more intense;the size and mass of the rock fragments and depth and width of the V-shaped notches increased,and the range of the failure zone extends along the hole sidewall from the local area to the entire area.Therefore,as the depth increases,the support area around the tunnel should be increased accordingly to prevent spalling.展开更多
The interaction of surrounding rock with a support system in deep underground tunnels has attracted extensive interest from researchers.However,the effect of high axial stress on tunnel stability has not been fully co...The interaction of surrounding rock with a support system in deep underground tunnels has attracted extensive interest from researchers.However,the effect of high axial stress on tunnel stability has not been fully considered.In this study,compression tests with and without confining pressure were conducted on solid specimens and hollow cylinder specimens filled with aluminium,lead,and polymethyl methacrylate(PMMA)to investigate the strength,deformation and failure characteristics of circular roadways subjected to high axial stress.The influence of the three-dimensional stress on the surrounding rock supported with different stiffness was studied.The results indicate that the strength and peak strain of hollow cylinders filled with PMMA are higher than those of hollow cylinders filled with aluminium or lead,indicating that flexible retaining is beneficial for roadway stability.The results obtained in this paper can contribute to better understanding the support failure of a buried roadway subjected to high axial stress and thus to analyzing and evaluating roadway stability.展开更多
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.展开更多
Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was ...Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.展开更多
基金Project(2022YFC2905100)supported by the National Key Research and Development Program of ChinaProject(52174098)supported by the National Natural Science Foundation of China。
文摘With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.
基金Projects(41877272,41472269)supported by the National Natural Science Foundation of ChinaProject(2017zzts167)supported by the Fundamental Research Funds for the Central Universities,China。
文摘A series of true-triaxial compression tests were performed on red sandstone cubic specimens with a circular hole to investigate the influence of depth on induced spalling in tunnels.The failure process of the hole sidewalls was monitored and recorded in real-time by a micro-video monitoring equipment.The general failure evolution processes of the hole sidewall at different initial depths(500 m,1000 m and 1500 m)during the adjustment of vertical stress were obtained.The results show that the hole sidewall all formed spalling before resulting in strain rockburst,and ultimately forming a V-shaped notch.The far-field principal stress for the initial failure of the tunnel shows a good positive linear correlation with the depth.As the depth increases,the stress required for the initial failure of the tunnels clearly increased,the spalling became more intense;the size and mass of the rock fragments and depth and width of the V-shaped notches increased,and the range of the failure zone extends along the hole sidewall from the local area to the entire area.Therefore,as the depth increases,the support area around the tunnel should be increased accordingly to prevent spalling.
基金Projects(11772357,51474103,51504092)supported by the National Natural Science Foundation of ChinaProject(2016YFC0600706)supported by the National Key Research and Development Program of China
文摘The interaction of surrounding rock with a support system in deep underground tunnels has attracted extensive interest from researchers.However,the effect of high axial stress on tunnel stability has not been fully considered.In this study,compression tests with and without confining pressure were conducted on solid specimens and hollow cylinder specimens filled with aluminium,lead,and polymethyl methacrylate(PMMA)to investigate the strength,deformation and failure characteristics of circular roadways subjected to high axial stress.The influence of the three-dimensional stress on the surrounding rock supported with different stiffness was studied.The results indicate that the strength and peak strain of hollow cylinders filled with PMMA are higher than those of hollow cylinders filled with aluminium or lead,indicating that flexible retaining is beneficial for roadway stability.The results obtained in this paper can contribute to better understanding the support failure of a buried roadway subjected to high axial stress and thus to analyzing and evaluating roadway stability.
基金Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
基金Projects(41807259,51604109)supported by the National Natural Science Foundation of ChinaProject(2020CX040)supported by the Innovation-Driven Project of Central South University,ChinaProject(2018JJ3693)supported by the Natural Science Foundation of Hunan Province,China。
文摘Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.
