Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust l...Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.展开更多
Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting fo...Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.展开更多
Aiming at evaluating the stability of a rock mass near a fault,a microseismic(MS) monitoring system was established in Hongtoushan copper mine.The distribution of displacement and log(/),the relationship between MS ac...Aiming at evaluating the stability of a rock mass near a fault,a microseismic(MS) monitoring system was established in Hongtoushan copper mine.The distribution of displacement and log(/),the relationship between MS activity and the exploitation process,and the stability of the rock mass controlled by a fault were studied.The results obtained from microseismic data showed that MS events were mainly concentrated al the footwall of the fault.When the distance to the fault exceeded 20 m,the rock mass reached a relatively stable state.MS activity is closely related to the mining process.Under the strong disturbance from blasting,the initiation and propagation of cracks is much faster.MS activity belongs in the category of aftershocks after large scale excavation.The displacement and log(C/) obtained from MS events can reflect the difference in physical and mechanical behavior of different areas within the rock mass,which is useful in judging the integrity and degradation of the rock mass.展开更多
Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powe...Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.展开更多
For the purpose of having a better understanding of failure mechanisms of rock fracturing in mines, the equivalent point source models of tensile, shear and explosive seismic events were established, and the relations...For the purpose of having a better understanding of failure mechanisms of rock fracturing in mines, the equivalent point source models of tensile, shear and explosive seismic events were established, and the relationship between far-field seismic displacements of the waves and the corresponding equivalent forces were analyzed as well. Based on the results of a microseismic monitoring carried out in the mining progress of 9202 working face under the upper remnant coal pillar in Sanhejian Mine, the waveform features of the seismic events associated with different failure modes were further analyzed. The results show that the signals corresponding to different failure mechanisms have different radiation patterns of the seismic displacements, and different characteristics in waveform features, such as dominant frequency, energy released, the ratio of S- to P-wave energy, and so on. In addition, the rock burst happened in the high stress zone is mainly the result of the strong shear fracturing in the mining process. The results of this study have significantly improved the understanding of the characteristics of the failures associated with underground mining, and will greatly benefit the prevention and control of rock burst hazards in burst-prone mines.展开更多
It is believed that the microseismicity induced by mining effect and gas gradient disturbance stress is a precursor to the essential characteristics of roadway unstability. In order to effectively identify and evaluat...It is believed that the microseismicity induced by mining effect and gas gradient disturbance stress is a precursor to the essential characteristics of roadway unstability. In order to effectively identify and evaluate the stability of coal roadways in the process of mine development and extraction, a microseismic monitoring system was deployed for the study of the stress evolution process, damage degree and distribution characteristics in the tailgate and headgate. The mine under study is the 62113 outburst working face of Xin Zhuangzi coalmine in Huainan mining area. The whole process of microfractures initiation,extension, interaction and coalescence mechanisms during the progressive failure processes of the coal rock within the delineated and typical event clusters were investigated by means of a two dimensional realistic failure process analysis code(RFPA2D-Flow). The results show that the microseismic events gradually create different-sized event clusters. The microseismicity of the tailgate is significantly higher than that of the headgate. The study indicates that the greater anomalous stress region matches the area where microfractures continuously develop and finally connect to each other and form a fissure zone.Due to the mine layout and stress concentration, the ruptured area is mainly located on the left shoulder of the tailgate roof. The potential anomalous stress region of the coal roadway obtained by numerical simulation is relatively in good agreement with the trend of spatial macro evolution of coal rock microfractures captured by the microseismic monitoring system. The research results can provide important basis for understanding instability failure mechanism of deep roadway and microseismic activity law in complex geologic conditions, and it ultimately can be used to guide the selection and optimization of reinforcement and protection scheme.展开更多
Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed ...Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.展开更多
The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster ...The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.展开更多
In order to effectively monitor the concealed fault activation process in excavation activities, based on the actual condition of a working face containing faults with high outburst danger in Xin Zhuangzi mine in Huai...In order to effectively monitor the concealed fault activation process in excavation activities, based on the actual condition of a working face containing faults with high outburst danger in Xin Zhuangzi mine in Huainan, China, we carried out all-side tracking and monitoring on the fault activation process and development trend in excavation activities by establishing a microseismic monitoring system. The results show that excavation activities have a rather great influence on the fault activation. With the working face approaching the fault, the fault activation builds up and the outburst danger increases; when the excavation activities finishes, the fault activation tends to be stable. The number of microseismic events are corresponding to the intensity of fault activation, and the distribution rules of microseismic events can effectively determine the fault occurrence in the mine. Microseismic monitoring technique is accurate in terms of detecting geologic tectonic activities, such as fault activations lying ahead during excavation activities. By utilizing this technique, we can determine outburst danger in excavation activities in time and accordingly take effective countermeasures to prevent and reduce the occurrence of outburst accidents.展开更多
To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelli...To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelling with a moment tensor source was proposed.The modelling was carried out based on a rotated-staggered-grid(RSG)scheme.In contrast to staggered-grids,the RSG scheme defines the velocity components and densities at the same grid,as do the stress components and elastic parameters.Therefore,the elastic moduli do not need to be interpolated.In addition,the detailed formulation and implementation of moment-tensor source loaded on the RSG was presented by equating the source to the stress increments.Meanwhile,the RSG-based 3D wave equation forward modelling was performed in parallel using compute unified device architecture(CUDA)programming on a graphics processing unit(GPU)to improve its efficiency.Numerical simulations including homogeneous and anisotropic models were carried out using the method proposed in this paper,and compared with other methods to prove the reliability of this method.Furthermore,the high efficiency of the proposed approach was evaluated.The results show that the computational efficiency of proposed method can be improved by about two orders of magnitude compared with traditional central processing unit(CPU)computing methods.It could not only help the analysis of microseismic full wavefield records,but also provide support for passive source inversion,including location and focal mechanism inversion,and velocities inversion.展开更多
In order to study the evolution laws during the development process of the coal face overburden rock mining-induced fissure,we studied the process of evolution of overburden rock mining-induced fissures and dynamicall...In order to study the evolution laws during the development process of the coal face overburden rock mining-induced fissure,we studied the process of evolution of overburden rock mining-induced fissures and dynamically quantitatively described its fractal laws,based on the high-precision microseismic monitoring method and the nonlinear Fractal Geometry Theory.The results show that:the overburden rock mining-induced fissure fractal dimension experiences two periodic change processes with the coal face advance,namely a Small→ Big→ Small process,which tends to be stable;the functional relationship between the extraction step distance and the overburden rock mining-induced fissure fractal dimension is a cubic curve.The results suggest that the fractal dimension reflects the evolution characteristics of the overburden rock mining-induced fissure,which can be used as an evaluation index of the stability of the overburden rock strata,and it provides theoretical guidance for stability analysis of the overburden rock strata,goaf roof control and the support movements in the mining face.展开更多
Microseismic monitoring provides a valuable tool for evaluating the effectiveness of hydraulic fracturing operations.However,robust detection and accurate location of microseismic events are challenging due to the low...Microseismic monitoring provides a valuable tool for evaluating the effectiveness of hydraulic fracturing operations.However,robust detection and accurate location of microseismic events are challenging due to the low signal to noise ratio(SNR)of their signals on seismograms.In a downhole monitoring setting,P-wave polarization direction measured from 3-component records is usually considered as the backazimuth of the microseismic event,i.e.,the direction of the event.The direction and arrival time difference between the P and S waves is used to locate the seismic event.When SNR is low,an accurate estimate of event backazimuth becomes very challenging with the traditional covariance matrix method.Here we propose to employ a master event and use a grid search method to find the backazimuth of a target event that maximizes the dot product of the two backazimuthal vectors of the master and target events.We compared the backazimuths measured with the proposed grid-search and the conventional covariance-matrix methods using a large synthetic dataset.We found that the grid-search method yields more accurate backazimuth estimates from low SNR records when measurements are made at single geophone level.When array data are combined,the proposed method also has some advantage over the covariance-matrix method,especially when the number of geophones is low.We also applied the method to a microseismic dataset acquired by a hydraulic fracturing project at a shale play site in southwestern China and found that the relocated microseismic events tend to align along existing faults more tightly than those in the original catalog.展开更多
The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise...The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise ratio.We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation.The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section.The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly.Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section,and the characteristic profile obtains the start point with a specific threshold value.Finally,the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions,and the filed data example also shows its reliability and adaptability.展开更多
基金the financial support provided by the National Key Research and Development Program for Young Scientists(No.2021YFC2900400)Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(No.GZB20230914)+2 种基金National Natural Science Foundation of China(No.52304123)China Postdoctoral Science Foundation(No.2023M730412)Chongqing Outstanding Youth Science Foundation Program(No.CSTB2023NSCQ-JQX0027).
文摘Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.
基金supported by Western Research Interdisciplinary Initiative R6259A03.
文摘Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.
基金financially supported by Projects of the National Key Technology R&D Program of China(Nos.2013BAB02B01 and2013BAB02B03)the National Natural Science Foundation of China(Nos.51274055 and 51204030)+1 种基金the Fundamental Research Funds for the Central University of China(Nos.N130401006,N120801002 and N120701001)the Key Science&Technology Special Project of Third Five-Year Plan of MCC(No.0012012009)
文摘Aiming at evaluating the stability of a rock mass near a fault,a microseismic(MS) monitoring system was established in Hongtoushan copper mine.The distribution of displacement and log(/),the relationship between MS activity and the exploitation process,and the stability of the rock mass controlled by a fault were studied.The results obtained from microseismic data showed that MS events were mainly concentrated al the footwall of the fault.When the distance to the fault exceeded 20 m,the rock mass reached a relatively stable state.MS activity is closely related to the mining process.Under the strong disturbance from blasting,the initiation and propagation of cracks is much faster.MS activity belongs in the category of aftershocks after large scale excavation.The displacement and log(C/) obtained from MS events can reflect the difference in physical and mechanical behavior of different areas within the rock mass,which is useful in judging the integrity and degradation of the rock mass.
基金provided by the State Key Research Development Program of China (No.2016YFC0801403)Key Research Development Program of Jiangsu Provence (No.BE2015040)+1 种基金National Natural Science Foundation of China (Nos.51674253,51734009 and 51604270)Natural Science Foundation of Jiangsu Province (No.BK20171191)
文摘Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.
基金Projects 50474068 supported by the National Natural Science Foundation of China2005CB221504 by the National Basic Research Program of China+2 种基金2006BAK04B02 and 2006BAK04B06 by the National Eleventh Five-Year Key Science & Technology Project[2007]3020 by the State Scholarship Fund of China Scholarship Councilprovided by the National Basic Research Program of China (2005CB221501)
文摘For the purpose of having a better understanding of failure mechanisms of rock fracturing in mines, the equivalent point source models of tensile, shear and explosive seismic events were established, and the relationship between far-field seismic displacements of the waves and the corresponding equivalent forces were analyzed as well. Based on the results of a microseismic monitoring carried out in the mining progress of 9202 working face under the upper remnant coal pillar in Sanhejian Mine, the waveform features of the seismic events associated with different failure modes were further analyzed. The results show that the signals corresponding to different failure mechanisms have different radiation patterns of the seismic displacements, and different characteristics in waveform features, such as dominant frequency, energy released, the ratio of S- to P-wave energy, and so on. In addition, the rock burst happened in the high stress zone is mainly the result of the strong shear fracturing in the mining process. The results of this study have significantly improved the understanding of the characteristics of the failures associated with underground mining, and will greatly benefit the prevention and control of rock burst hazards in burst-prone mines.
基金Financial support for this work, provided by the National Natural Science Foundation of China (Nos.51674189,51304154,and 51327007)the Youth Science and technology new star of Shaanxi Province (No.2016KJXX-37)the Scientific research plan of Shaanxi Education Department (No.16JK1487)
文摘It is believed that the microseismicity induced by mining effect and gas gradient disturbance stress is a precursor to the essential characteristics of roadway unstability. In order to effectively identify and evaluate the stability of coal roadways in the process of mine development and extraction, a microseismic monitoring system was deployed for the study of the stress evolution process, damage degree and distribution characteristics in the tailgate and headgate. The mine under study is the 62113 outburst working face of Xin Zhuangzi coalmine in Huainan mining area. The whole process of microfractures initiation,extension, interaction and coalescence mechanisms during the progressive failure processes of the coal rock within the delineated and typical event clusters were investigated by means of a two dimensional realistic failure process analysis code(RFPA2D-Flow). The results show that the microseismic events gradually create different-sized event clusters. The microseismicity of the tailgate is significantly higher than that of the headgate. The study indicates that the greater anomalous stress region matches the area where microfractures continuously develop and finally connect to each other and form a fissure zone.Due to the mine layout and stress concentration, the ruptured area is mainly located on the left shoulder of the tailgate roof. The potential anomalous stress region of the coal roadway obtained by numerical simulation is relatively in good agreement with the trend of spatial macro evolution of coal rock microfractures captured by the microseismic monitoring system. The research results can provide important basis for understanding instability failure mechanism of deep roadway and microseismic activity law in complex geologic conditions, and it ultimately can be used to guide the selection and optimization of reinforcement and protection scheme.
文摘Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.
基金the National Key R&D Program of China(No.2021YFC2900500).
文摘The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.
基金provided by the National Natural Science Foundation of China(No.51674189,51304154,51327007)the Youth Science and technique new star of Shaanxi Province(No.2016KJXX-37)the Scientific research plan of Shaanxi Education Department(No.16JK1487),are gratefully acknowledged
文摘In order to effectively monitor the concealed fault activation process in excavation activities, based on the actual condition of a working face containing faults with high outburst danger in Xin Zhuangzi mine in Huainan, China, we carried out all-side tracking and monitoring on the fault activation process and development trend in excavation activities by establishing a microseismic monitoring system. The results show that excavation activities have a rather great influence on the fault activation. With the working face approaching the fault, the fault activation builds up and the outburst danger increases; when the excavation activities finishes, the fault activation tends to be stable. The number of microseismic events are corresponding to the intensity of fault activation, and the distribution rules of microseismic events can effectively determine the fault occurrence in the mine. Microseismic monitoring technique is accurate in terms of detecting geologic tectonic activities, such as fault activations lying ahead during excavation activities. By utilizing this technique, we can determine outburst danger in excavation activities in time and accordingly take effective countermeasures to prevent and reduce the occurrence of outburst accidents.
基金financially supported by the National Natural Science Foundation of China(No.42272204)the National Key Research and Development Program of China(No.2018YFB0605503)the Fundamental Research Funds for the Central Universities(No.2021JCCXDC02)。
文摘To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelling with a moment tensor source was proposed.The modelling was carried out based on a rotated-staggered-grid(RSG)scheme.In contrast to staggered-grids,the RSG scheme defines the velocity components and densities at the same grid,as do the stress components and elastic parameters.Therefore,the elastic moduli do not need to be interpolated.In addition,the detailed formulation and implementation of moment-tensor source loaded on the RSG was presented by equating the source to the stress increments.Meanwhile,the RSG-based 3D wave equation forward modelling was performed in parallel using compute unified device architecture(CUDA)programming on a graphics processing unit(GPU)to improve its efficiency.Numerical simulations including homogeneous and anisotropic models were carried out using the method proposed in this paper,and compared with other methods to prove the reliability of this method.Furthermore,the high efficiency of the proposed approach was evaluated.The results show that the computational efficiency of proposed method can be improved by about two orders of magnitude compared with traditional central processing unit(CPU)computing methods.It could not only help the analysis of microseismic full wavefield records,but also provide support for passive source inversion,including location and focal mechanism inversion,and velocities inversion.
基金Financial support for this work,provided by the National Natural Science Foundation of China(No.51304154)the Natural Science Foundation Anhui Province(No.1408085MKL92)
文摘In order to study the evolution laws during the development process of the coal face overburden rock mining-induced fissure,we studied the process of evolution of overburden rock mining-induced fissures and dynamically quantitatively described its fractal laws,based on the high-precision microseismic monitoring method and the nonlinear Fractal Geometry Theory.The results show that:the overburden rock mining-induced fissure fractal dimension experiences two periodic change processes with the coal face advance,namely a Small→ Big→ Small process,which tends to be stable;the functional relationship between the extraction step distance and the overburden rock mining-induced fissure fractal dimension is a cubic curve.The results suggest that the fractal dimension reflects the evolution characteristics of the overburden rock mining-induced fissure,which can be used as an evaluation index of the stability of the overburden rock strata,and it provides theoretical guidance for stability analysis of the overburden rock strata,goaf roof control and the support movements in the mining face.
基金supported by the National Natural Science Foundation of China(Grants No.41630209 and 41974045)
文摘Microseismic monitoring provides a valuable tool for evaluating the effectiveness of hydraulic fracturing operations.However,robust detection and accurate location of microseismic events are challenging due to the low signal to noise ratio(SNR)of their signals on seismograms.In a downhole monitoring setting,P-wave polarization direction measured from 3-component records is usually considered as the backazimuth of the microseismic event,i.e.,the direction of the event.The direction and arrival time difference between the P and S waves is used to locate the seismic event.When SNR is low,an accurate estimate of event backazimuth becomes very challenging with the traditional covariance matrix method.Here we propose to employ a master event and use a grid search method to find the backazimuth of a target event that maximizes the dot product of the two backazimuthal vectors of the master and target events.We compared the backazimuths measured with the proposed grid-search and the conventional covariance-matrix methods using a large synthetic dataset.We found that the grid-search method yields more accurate backazimuth estimates from low SNR records when measurements are made at single geophone level.When array data are combined,the proposed method also has some advantage over the covariance-matrix method,especially when the number of geophones is low.We also applied the method to a microseismic dataset acquired by a hydraulic fracturing project at a shale play site in southwestern China and found that the relocated microseismic events tend to align along existing faults more tightly than those in the original catalog.
基金supported in part by the National Natural Science Foundation of China under Grant 41904098Fundamental Research Funds for the Central Universities,under Grant 2462018YJRC020 and Grant 2462020YXZZ006。
文摘The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise ratio.We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation.The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section.The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly.Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section,and the characteristic profile obtains the start point with a specific threshold value.Finally,the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions,and the filed data example also shows its reliability and adaptability.
