The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse m...The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.展开更多
Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monit...Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.展开更多
Excavation-induced microseismicity and rockburst occurrence in deep underground projects provide invaluable information that can be used to warn rockburst occurrence,facilitate rockburst mitigation procedures,and anal...Excavation-induced microseismicity and rockburst occurrence in deep underground projects provide invaluable information that can be used to warn rockburst occurrence,facilitate rockburst mitigation procedures,and analyze the mechanisms responsible for their occurrence.Based on the deep parallel tunnels with the maximum depth of 1890 m created as part of the Neelum–Jhelum hydropower project in Pakistan,similarities and differences on excavation-induced microseismicity and rockburst occurrence between parallel tunnels with soft and hard alternant strata are studied.Results show that a large number of microseismic(MS)events occurred in each of the parallel tunnels during excavation.Rockbursts occurred most frequently in certain local sections of the two tunnels.Significant differences are found in the excavation-induced microseismicity(spatial distribution and number of MS events,distribution of MS energy,and pattern of microseismicity variation)and rockbursts characteristics(the number and the spatial distribution)between the parallel tunnels.Attempting to predict the microseismicity and rockburst intensities likely to be encountered in subsequent tunnel based on the activity encountered when the parallel tunnel was previously excavated will not be an easy or accurate procedure in deep tunnel projects involving complex lithological conditions.展开更多
This paper selects some representative regions to obtain their G-R relation curves according to their seismicity characteristics,by using ML≥2.0 microseismicity data(1970~1993)in North China.The annual occurrence rat...This paper selects some representative regions to obtain their G-R relation curves according to their seismicity characteristics,by using ML≥2.0 microseismicity data(1970~1993)in North China.The annual occurrence rate of events of each magnitude can be inferred from the G-R relation.At the same tune,the actual annual occurrence rate of earthquakes of higher magnitudes can be calculated from historical earthquakes(1300-1993)recorded in the same region.It seems that both results are almost the same.Therefore,the rate of events of higher magnitudes can be obtained by using microseismicity data when the proper region is selected.However,two points should be noticed:(1)The method can only give the annual occurrence rate in a seismicity system and estimate the whole situation of the system.(2)When there is a very large earthquake in and near the period in which the microseismicity data are applied,the actual occurrence rate of the system,including this larger earthquake,cannot be obtained by this method.展开更多
The volume of influence of excavation at the right bank slope of Dagangshan Hydropower Station, southwest China, is essentially determined from microseismic monitoring, numerical modeling and conventional measurements...The volume of influence of excavation at the right bank slope of Dagangshan Hydropower Station, southwest China, is essentially determined from microseismic monitoring, numerical modeling and conventional measurements as well as in situ observations. Microseismic monitoring is a new application technique for investigating microcrackings in rock slopes. A micro- seismic monitoring network has been systematically used to monitor rock masses unloading relaxation due to continuous exca- vation of rock slope and stress redistribution caused by dam impoundment later on, and to identify and delineate the potential slippage regions since May, 2010. An important database of seismic source locations is available. The analysis of microseismic events showed a particular tempo-spatial distribution. Seismic events predominantly occurred around the upstream slope of 1180 m elevation, especially focusing on the hanging wall of fault XL316-1. Such phenomenon was interpreted by numerical modeling using RFPA-SRM code （realistic failure process analysis-strength reduction method）. By comparing microseismic activity and results of numerical simulation with in site observation and conventional measurements results, a strong correlation can he obtained between seismic source locations and excavation-induced stress distribution in the working areas. The volume of influence of the rock slope is thus determined. Engineering practices show microseismic monitoring can accurately diagnose magnitude, intensity and associated tempo-spatial characteristics of tectonic activities such as faults and unloading zones. The integrated technique combining seismic monitoring with numerical modeling, as well as in site observation and conventional surveying, leads to a better understanding of the internal effect and relationship between microseismic activity and stress field in the right bank slope from different perspectives.展开更多
Hydraulic fracturing （HF） technique has been extensively used for the exploitation of unconventional oiland gas reservoirs. HF enhances the connectivity of less permeable oil and gas-bearing rock formationsby fluid ...Hydraulic fracturing （HF） technique has been extensively used for the exploitation of unconventional oiland gas reservoirs. HF enhances the connectivity of less permeable oil and gas-bearing rock formationsby fluid injection, which creates an interconnected fracture network and increases the hydrocarbonproduction. Meanwhile, microseismic （MS） monitoring is one of the most effective approaches to evaluatesuch stimulation process. In this paper, the combined finite-discrete element method （FDEM） isadopted to numerically simulate HF and associated MS. Several post-processing tools, includingfrequency-magnitude distribution （b-value）, fractal dimension （D-value）, and seismic events clustering,are utilized to interpret numerical results. A non-parametric clustering algorithm designed specificallyfor FDEM is used to reduce the mesh dependency and extract more realistic seismic information.Simulation results indicated that at the local scale, the HF process tends to propagate following the rockmass discontinuities; while at the reservoir scale, it tends to develop in the direction parallel to themaximum in-situ stress. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.展开更多
Petroleum reservoir operations such as oil and gas production, hydraulic fracturing, and water injection induce considerable stress changes that at some point result in rock failure and emanation of seismic energy. Su...Petroleum reservoir operations such as oil and gas production, hydraulic fracturing, and water injection induce considerable stress changes that at some point result in rock failure and emanation of seismic energy. Such seismic energy could be large enough to be felt in the neighborhood of the oil fields, therefore many issues are recently raised regarding its environmental impact. In this research we analyze the magnitudes of microseismicity induced by stimulation of unconventional reservoirs at various basins in the United States and Canada that monitored the microseismicity induced by hydraulic fracturing operations. In addition, the relationship between microseismic magnitude and both depth and injection parameters is examined to delineate the possible framework that controls the system. Generally, microseismicity of typical hydraulic fracturing and injection operations is relatively similar in the majority of basins under investigation and the overall associating seismic energy is not strong enough to be the important factor to jeopardize near surface groundwater resources. Furthermore, these events are less energetic compared to the moderately active tectonic zones through the world and usually do not extend over a long period at considerably deep parts. However, the huge volume of the treatment fluids and improper casing cementing operation seem to be primary sources for contaminating near surface water resources.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on th...The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overb...Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.展开更多
Evaluation of the seismicity of a region depends on records of earthquakes.This article assesses the microseismicity of Tehran region,bounded by the coordinates 34.43°-36.87°N and 49.07°-53.13°E.Th...Evaluation of the seismicity of a region depends on records of earthquakes.This article assesses the microseismicity of Tehran region,bounded by the coordinates 34.43°-36.87°N and 49.07°-53.13°E.This was investigated by the Tehran Disaster Mitigation and Management Organization(TDMMO)network from 2004 to 2010.The main purpose of this study was to substantiate the current tectonic activity of the region.We checked whether the observed faults in the region are active or not.Some active trends were found with the strongest activity in the eastern part of the region,especially associated with the North Tehran fault.In addition,records exist of strong historical earthquakes in this zone.展开更多
The Anninghe–Zemuhe Fault and the Xiaojiang Fault are critical active faults along the middle-eastern boundary of the South Chuan–Dian Block. Many researchers have identified these faults as potential strong-earthqu...The Anninghe–Zemuhe Fault and the Xiaojiang Fault are critical active faults along the middle-eastern boundary of the South Chuan–Dian Block. Many researchers have identified these faults as potential strong-earthquake risk zones. In this study, we leveraged a dense seismic array to investigate the high-resolution shallow crust shear wave velocity(Vs) structure beneath the junction of the Zemuhe Fault Zone and the Xiaojiang Fault Zone, one of the most complex parts of the eastern boundary of the South Chuan–Dian Block. We analyzed the distribution of microseismic events detected between November 2022 and February 2023 based on the fine-scale Vs model obtained. The microseismicity in the study region was clustered into three groups, all spatially related to major faults in this region. These microseismic events indicate near-vertical fault planes, consistent with the fault geometry revealed by other researchers.Moreover, these microseismic events are influenced by the impoundment of the downstream Baihetan Reservoir and the complex tectonic stress near the junction of the Zemuhe Fault Zone and the Xiaojiang Fault Zone. The depths of these microseismic events are shallower in the junction zone, whereas moving south along the Xiaojiang Fault Zone, the microseismic events become deeper.Additionally, we compared our fine-scale local Vs model with velocity models obtained by other researchers and found that our model offers greater detail in characterizing subsurface heterogeneity while demonstrating improved reliability in delineating fault systems.展开更多
Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture ...Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture growth interpretation.Waveform stacking-based location methods invert the source locations by focusing the source energy with multichannel waveforms,and these methods exhibit a high level of automation and noise-resistance.Taking the cross-correlation stacking(CCS)method as an example,this work attempts to study the influential factors of waveform stacking-based methods,and introduces a comprehensive performance evaluation scheme based on multiple parameters and indicators.The waveform data are from field monitoring of induced microseismicity in the Changning region(southern Sichuan Basin of China).Synthetic and field data tests reveal the impacts of three categories of factors on waveform stacking-based location:velocity model,monitoring array,and waveform complexity.The location performance is evaluated and further improved in terms of the source imaging resolution and location error.Denser array monitoring contributes to better constraining source depth and location reliability,but the combined impact of multiple factors,such as velocity model uncertainty and multiple seismic phases,increases the complexity of locating field microseismic events.Finally,the aspects of location uncertainty,phase detection,and artificial intelligencebased location are discussed.展开更多
Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A tot...Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A total of 349 MS events were analyzed across different fracturing sections,revealing significant heterogeneity in fracture propagation.Energy scanning results showed that cumulative energy values ranged from 240 to 1060 J across the sections,indicating notable differences.Stimulated reservoir volume(SRV)analysis demonstrated well-developed fracture networks in certain sections,with a total SRV exceeding 1540000 m^(3).The hydraulic fracture network analysis revealed that during the midfracturing stage,the density and spatial extent of MS events significantly increased,indicating rapid fracture propagation and the formation of complex networks.In the later stage,the number of secondary fractures near fracture edges decreased,and the fracture network stabilized.By comparing the branching index,fracture length,width,height,and SRV values across different fracturing sections,Sections No.1 and No.8 showed the best performance,with high MS event densities,extensive fracture networks,and significant energy release.However,Sections No.4 and No.5 exhibited sparse MS activity and poor fracture connectivity,indicating suboptimal stimulation effectiveness.展开更多
Deep coal-energy mining frequently results in microseismic(MS)events,which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure.Therefore,quantitatively predicting the time,en...Deep coal-energy mining frequently results in microseismic(MS)events,which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure.Therefore,quantitatively predicting the time,energy,and location(TEL)of future MS events is crucial for understanding and preventing potential catastrophic events.In this study,we introduced the application of spatiotemporal graph convolutional networks(STGCN)to predict the TEL of MS events induced by deep coal-energy mining.Notably,this was the first application of graph convolution networks(GCNs)in the spatiotemporal prediction of MS events.The adjacency matrices of the sensor networks were determined based on the distance between MS sensors,the sensor network graphs we constructed,and GCN was employed to extract the spatiotemporal details of the graphs.The model is simple and versatile.By testing the model with on-site MS monitoring data,our results demonstrated promising efficacy in predicting the TEL of MS events,with the cosine similarity(C)above 0.90 and the mean relative error(MRE)below 0.08.This is critical to improving the safety and operational efficiency of deep coal-energy mining.展开更多
This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.I...This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.It is difficult to provide necessary and sufficient conditions to test the reliability of microseismic monitoring.Often,a few hundred meters away,the microseismic signal emitted by a hypocenter is submerged in noise,and the traditional location is invalid;Inversion for microseismic released energy distribution using data migration and stacking is in principle not unique.However,based on microseismic monitoring characteristics,forward and reverse simulations and numerous experiments,many necessary conditions can be proposed to ensure reliable monitoring with high probability.VS(Vector Scanning)ground monitoring for microseismic proposes eight necessary conditions for testing the reliability,so that VS finds the fracturing-induced effective communication seam with the characteristics of shear zones under the control of tectonic stress fields,in line with the laws of seismic and geological observations,as well as the features related to some special production data.VS uses data migration and stacking suitable for low signal-to-noise ratio and shear mechanism,and the joint inversion for correction of both traditional relocations and velocity model,can greatly improve monitoring distance and quality,complete microseismic measurement methods,and broaden applicable fields,such as:(1)VS can be a cost-effective,ground-based,routine monitoring method;(2)The BPM(Borehole Proximity Monitoring)is high cost but close to the hypocenters;It can be the best method for scientific research,but its seismic network should be improved,and the joint inversion and data stacking could be used to improve the monitoring distance and quality;(3)The early warning of mine safety can change the current monitoring of strong microseismic(or accidents have been happened)to the real microseismic level;and(4)The seismic precursor monitoring of large earthquakes can be expanded from small earthquakes to microseismic.These will establish a solid foundation and complete seismic measurements for microseismology.展开更多
In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic ...In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.展开更多
Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensi...Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.展开更多
基金Projects(52209132, 52309156) supported by the National Natural Science Foundation of ChinaProject(BK20251905) supported by the Natural Science Foundation of Jiangsu Province,China+2 种基金Project(252102320037) supported by the Henan Province Science and Technology Research,ChinaProject(CKWV20231173/KY) supported by the CRSRI Open Research Program,ChinaProject(2023KSD15) supported by the Open Research Fund of Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering,China。
文摘The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.
基金supported by National Natural Science Foundation of China Grant(No.42004040,42474092,U2239204,and 42304145)Natural Science Foundation of Jiangxi Province Grant(20242BAB25190 and 20232BAB213077).
文摘Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.
基金Projects(41972295,U1965205)supported by the National Natural Science Foundation of ChinaProject(2019ZDK034)supported by the Guangxi Key Laboratory of Disaster Prevention and Engineering Safety,China。
文摘Excavation-induced microseismicity and rockburst occurrence in deep underground projects provide invaluable information that can be used to warn rockburst occurrence,facilitate rockburst mitigation procedures,and analyze the mechanisms responsible for their occurrence.Based on the deep parallel tunnels with the maximum depth of 1890 m created as part of the Neelum–Jhelum hydropower project in Pakistan,similarities and differences on excavation-induced microseismicity and rockburst occurrence between parallel tunnels with soft and hard alternant strata are studied.Results show that a large number of microseismic(MS)events occurred in each of the parallel tunnels during excavation.Rockbursts occurred most frequently in certain local sections of the two tunnels.Significant differences are found in the excavation-induced microseismicity(spatial distribution and number of MS events,distribution of MS energy,and pattern of microseismicity variation)and rockbursts characteristics(the number and the spatial distribution)between the parallel tunnels.Attempting to predict the microseismicity and rockburst intensities likely to be encountered in subsequent tunnel based on the activity encountered when the parallel tunnel was previously excavated will not be an easy or accurate procedure in deep tunnel projects involving complex lithological conditions.
基金This project was sponsored by the National Natural Science Foundation of China under the contract of No. 49574207
文摘This paper selects some representative regions to obtain their G-R relation curves according to their seismicity characteristics,by using ML≥2.0 microseismicity data(1970~1993)in North China.The annual occurrence rate of events of each magnitude can be inferred from the G-R relation.At the same tune,the actual annual occurrence rate of earthquakes of higher magnitudes can be calculated from historical earthquakes(1300-1993)recorded in the same region.It seems that both results are almost the same.Therefore,the rate of events of higher magnitudes can be obtained by using microseismicity data when the proper region is selected.However,two points should be noticed:(1)The method can only give the annual occurrence rate in a seismicity system and estimate the whole situation of the system.(2)When there is a very large earthquake in and near the period in which the microseismicity data are applied,the actual occurrence rate of the system,including this larger earthquake,cannot be obtained by this method.
基金supported by the National Natural Science Foundation of China (Nos. 50820125405, 50909013 and 50804006)the National Basic Research Program (973) of China (No. 2007CB209404)
文摘The volume of influence of excavation at the right bank slope of Dagangshan Hydropower Station, southwest China, is essentially determined from microseismic monitoring, numerical modeling and conventional measurements as well as in situ observations. Microseismic monitoring is a new application technique for investigating microcrackings in rock slopes. A micro- seismic monitoring network has been systematically used to monitor rock masses unloading relaxation due to continuous exca- vation of rock slope and stress redistribution caused by dam impoundment later on, and to identify and delineate the potential slippage regions since May, 2010. An important database of seismic source locations is available. The analysis of microseismic events showed a particular tempo-spatial distribution. Seismic events predominantly occurred around the upstream slope of 1180 m elevation, especially focusing on the hanging wall of fault XL316-1. Such phenomenon was interpreted by numerical modeling using RFPA-SRM code （realistic failure process analysis-strength reduction method）. By comparing microseismic activity and results of numerical simulation with in site observation and conventional measurements results, a strong correlation can he obtained between seismic source locations and excavation-induced stress distribution in the working areas. The volume of influence of the rock slope is thus determined. Engineering practices show microseismic monitoring can accurately diagnose magnitude, intensity and associated tempo-spatial characteristics of tectonic activities such as faults and unloading zones. The integrated technique combining seismic monitoring with numerical modeling, as well as in site observation and conventional surveying, leads to a better understanding of the internal effect and relationship between microseismic activity and stress field in the right bank slope from different perspectives.
基金supported by the Natural Sciences and Engineering Research Council of Canada through Discovery Grant 341275 (G. Grasselli) and Engage EGP 461019-13
文摘Hydraulic fracturing （HF） technique has been extensively used for the exploitation of unconventional oiland gas reservoirs. HF enhances the connectivity of less permeable oil and gas-bearing rock formationsby fluid injection, which creates an interconnected fracture network and increases the hydrocarbonproduction. Meanwhile, microseismic （MS） monitoring is one of the most effective approaches to evaluatesuch stimulation process. In this paper, the combined finite-discrete element method （FDEM） isadopted to numerically simulate HF and associated MS. Several post-processing tools, includingfrequency-magnitude distribution （b-value）, fractal dimension （D-value）, and seismic events clustering,are utilized to interpret numerical results. A non-parametric clustering algorithm designed specificallyfor FDEM is used to reduce the mesh dependency and extract more realistic seismic information.Simulation results indicated that at the local scale, the HF process tends to propagate following the rockmass discontinuities; while at the reservoir scale, it tends to develop in the direction parallel to themaximum in-situ stress. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.
文摘Petroleum reservoir operations such as oil and gas production, hydraulic fracturing, and water injection induce considerable stress changes that at some point result in rock failure and emanation of seismic energy. Such seismic energy could be large enough to be felt in the neighborhood of the oil fields, therefore many issues are recently raised regarding its environmental impact. In this research we analyze the magnitudes of microseismicity induced by stimulation of unconventional reservoirs at various basins in the United States and Canada that monitored the microseismicity induced by hydraulic fracturing operations. In addition, the relationship between microseismic magnitude and both depth and injection parameters is examined to delineate the possible framework that controls the system. Generally, microseismicity of typical hydraulic fracturing and injection operations is relatively similar in the majority of basins under investigation and the overall associating seismic energy is not strong enough to be the important factor to jeopardize near surface groundwater resources. Furthermore, these events are less energetic compared to the moderately active tectonic zones through the world and usually do not extend over a long period at considerably deep parts. However, the huge volume of the treatment fluids and improper casing cementing operation seem to be primary sources for contaminating near surface water resources.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098 and 42122052)the Liaoning XingLiao Talent Program(Grant No.XLYC2203008).
文摘The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金support from the Joint Funds of the National Natural Science Foundation of China(Grant No.42177143)the National Natural Science Foundation of China(Grant No.U23A2060).
文摘Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.
文摘Evaluation of the seismicity of a region depends on records of earthquakes.This article assesses the microseismicity of Tehran region,bounded by the coordinates 34.43°-36.87°N and 49.07°-53.13°E.This was investigated by the Tehran Disaster Mitigation and Management Organization(TDMMO)network from 2004 to 2010.The main purpose of this study was to substantiate the current tectonic activity of the region.We checked whether the observed faults in the region are active or not.Some active trends were found with the strongest activity in the eastern part of the region,especially associated with the North Tehran fault.In addition,records exist of strong historical earthquakes in this zone.
基金funded by the National Key R&D Program of China (Grant No. 2021YFC3000704)the National Natural Science Foundation of China (Grant No. 42125401)the Central Public-interest Scientific Institution Basal Research Fund (Grant No. CEAIEF20240401)。
文摘The Anninghe–Zemuhe Fault and the Xiaojiang Fault are critical active faults along the middle-eastern boundary of the South Chuan–Dian Block. Many researchers have identified these faults as potential strong-earthquake risk zones. In this study, we leveraged a dense seismic array to investigate the high-resolution shallow crust shear wave velocity(Vs) structure beneath the junction of the Zemuhe Fault Zone and the Xiaojiang Fault Zone, one of the most complex parts of the eastern boundary of the South Chuan–Dian Block. We analyzed the distribution of microseismic events detected between November 2022 and February 2023 based on the fine-scale Vs model obtained. The microseismicity in the study region was clustered into three groups, all spatially related to major faults in this region. These microseismic events indicate near-vertical fault planes, consistent with the fault geometry revealed by other researchers.Moreover, these microseismic events are influenced by the impoundment of the downstream Baihetan Reservoir and the complex tectonic stress near the junction of the Zemuhe Fault Zone and the Xiaojiang Fault Zone. The depths of these microseismic events are shallower in the junction zone, whereas moving south along the Xiaojiang Fault Zone, the microseismic events become deeper.Additionally, we compared our fine-scale local Vs model with velocity models obtained by other researchers and found that our model offers greater detail in characterizing subsurface heterogeneity while demonstrating improved reliability in delineating fault systems.
基金supported by National Natural Science Foundation of China(Nos.42374076,42174128 and 42004115)Natural Science Foundation for Excellent Young Scholars of Hunan Province,China(No.2022JJ 20057)+1 种基金Central South University Innovation-Driven Research Programme(No.2023CXQD063)the Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology(No.2022B1212010002).
文摘Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture growth interpretation.Waveform stacking-based location methods invert the source locations by focusing the source energy with multichannel waveforms,and these methods exhibit a high level of automation and noise-resistance.Taking the cross-correlation stacking(CCS)method as an example,this work attempts to study the influential factors of waveform stacking-based methods,and introduces a comprehensive performance evaluation scheme based on multiple parameters and indicators.The waveform data are from field monitoring of induced microseismicity in the Changning region(southern Sichuan Basin of China).Synthetic and field data tests reveal the impacts of three categories of factors on waveform stacking-based location:velocity model,monitoring array,and waveform complexity.The location performance is evaluated and further improved in terms of the source imaging resolution and location error.Denser array monitoring contributes to better constraining source depth and location reliability,but the combined impact of multiple factors,such as velocity model uncertainty and multiple seismic phases,increases the complexity of locating field microseismic events.Finally,the aspects of location uncertainty,phase detection,and artificial intelligencebased location are discussed.
基金supported by Yunlong Lake Laboratory of Deep Underground Science and Engineering Project(No.104024008)the National Natural Science Foundation of China(Nos.52274241 and 52474261)the Natural Science Foundation of Jiangsu Province(No.BK20240207).
文摘Through a case analysis,this study examines the spatiotemporal evolution of microseismic(MS)events,energy characteristics,volumetric features,and fracture network development in surface well hydraulic fracturing.A total of 349 MS events were analyzed across different fracturing sections,revealing significant heterogeneity in fracture propagation.Energy scanning results showed that cumulative energy values ranged from 240 to 1060 J across the sections,indicating notable differences.Stimulated reservoir volume(SRV)analysis demonstrated well-developed fracture networks in certain sections,with a total SRV exceeding 1540000 m^(3).The hydraulic fracture network analysis revealed that during the midfracturing stage,the density and spatial extent of MS events significantly increased,indicating rapid fracture propagation and the formation of complex networks.In the later stage,the number of secondary fractures near fracture edges decreased,and the fracture network stabilized.By comparing the branching index,fracture length,width,height,and SRV values across different fracturing sections,Sections No.1 and No.8 showed the best performance,with high MS event densities,extensive fracture networks,and significant energy release.However,Sections No.4 and No.5 exhibited sparse MS activity and poor fracture connectivity,indicating suboptimal stimulation effectiveness.
基金the financial support of the Key Technologies Research and Development Program(Grant No.2022YFC3003302)the National Natural Science Foundation of China(Grant Nos.51934007 and 52104230).
文摘Deep coal-energy mining frequently results in microseismic(MS)events,which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure.Therefore,quantitatively predicting the time,energy,and location(TEL)of future MS events is crucial for understanding and preventing potential catastrophic events.In this study,we introduced the application of spatiotemporal graph convolutional networks(STGCN)to predict the TEL of MS events induced by deep coal-energy mining.Notably,this was the first application of graph convolution networks(GCNs)in the spatiotemporal prediction of MS events.The adjacency matrices of the sensor networks were determined based on the distance between MS sensors,the sensor network graphs we constructed,and GCN was employed to extract the spatiotemporal details of the graphs.The model is simple and versatile.By testing the model with on-site MS monitoring data,our results demonstrated promising efficacy in predicting the TEL of MS events,with the cosine similarity(C)above 0.90 and the mean relative error(MRE)below 0.08.This is critical to improving the safety and operational efficiency of deep coal-energy mining.
文摘This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.It is difficult to provide necessary and sufficient conditions to test the reliability of microseismic monitoring.Often,a few hundred meters away,the microseismic signal emitted by a hypocenter is submerged in noise,and the traditional location is invalid;Inversion for microseismic released energy distribution using data migration and stacking is in principle not unique.However,based on microseismic monitoring characteristics,forward and reverse simulations and numerous experiments,many necessary conditions can be proposed to ensure reliable monitoring with high probability.VS(Vector Scanning)ground monitoring for microseismic proposes eight necessary conditions for testing the reliability,so that VS finds the fracturing-induced effective communication seam with the characteristics of shear zones under the control of tectonic stress fields,in line with the laws of seismic and geological observations,as well as the features related to some special production data.VS uses data migration and stacking suitable for low signal-to-noise ratio and shear mechanism,and the joint inversion for correction of both traditional relocations and velocity model,can greatly improve monitoring distance and quality,complete microseismic measurement methods,and broaden applicable fields,such as:(1)VS can be a cost-effective,ground-based,routine monitoring method;(2)The BPM(Borehole Proximity Monitoring)is high cost but close to the hypocenters;It can be the best method for scientific research,but its seismic network should be improved,and the joint inversion and data stacking could be used to improve the monitoring distance and quality;(3)The early warning of mine safety can change the current monitoring of strong microseismic(or accidents have been happened)to the real microseismic level;and(4)The seismic precursor monitoring of large earthquakes can be expanded from small earthquakes to microseismic.These will establish a solid foundation and complete seismic measurements for microseismology.
基金supported by the National Natural Science Foundation of China(Grant Nos.42472351 and 42177140)the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(Grant No.YESS20230742).
文摘In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.
基金supported by the Key Research and Development Plan of Anhui Province(202104a05020059)the Excellent Scientific Research and Innovation Team of Anhui Province(2022AH010003)support from Hefei Comprehensive National Science Center is highly appreciated.
文摘Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.
