Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location...Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.展开更多
A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the ...A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the studied area,and(ii)to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate.Despite the limited number of stations and the short-period char-acter of the seismic sensors,the empirical Green's functions were retrieved for all station pairs using two years of passive data.Both group and phase velocities were derived,the former using the widespread frequency-time analysis,the latter through the analysis of the real part of the cross-spectra.The main advantage of combining these two methods is a more accurate identification of higher modes,resulting in a reduction of ambiguity during picking and data interpretation.Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths.The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method,for this specific case.The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast.Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs.While the average model can be used for a more accurate location of the local natural seismicity,the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth.Finally,the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field.Errors as large as 4%have been found,related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources,and to the very short time widows.It was shown that using a one-year time window these errors arereduced to 0.3%.展开更多
Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for ...Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.展开更多
Monitoring and prediction of rockburst remain to be worldwide challenges in geotechnical engineering.In hydropower,transportation and other engineering fields in China,more deep,long and large tunnels have been under ...Monitoring and prediction of rockburst remain to be worldwide challenges in geotechnical engineering.In hydropower,transportation and other engineering fields in China,more deep,long and large tunnels have been under construction in recent years and underground caverns are more evidently featured by ＂long,large,deep and in group＂,which bring in many problems associated with rock mechanics problems at great depth,especially rockburst.Rockbursts lead to damages to not only underground structures and equipments but also personnel safety.It has been a major technical bottleneck in future deep underground engineering in China.In this paper,compared with earthquake prediction,the feasibility in principle of monitoring and prediction of rockbursts is discussed,considering the source zones,development cycle and scale.The authors think the feasibility of rockburst prediction can be understood in three aspects：（1） the heterogeneity of rock is the main reason for the existence of rockburst precursors;（2） deformation localization is the intrinsic cause of rockburst;and（3） the interaction between target rock mass and its surrounding rock mass is the external cause of rockburst.As an engineering practice,the application of microseismic monitoring techniques during tunnel construction of Jinping II Hydropower Station was reported.It is found that precursory microcracking exists prior to most rockbursts,which could be captured by the microseismic monitoring system.The stress concentration is evident near structural discontinuities（such as faults or joints）,which shall be the focus of rockburst monitoring.It is concluded that,by integrating the microseismic monitoring and the rock failure process simulation,the feasibility of rockburst prediction is expected to be enhanced.展开更多
Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was ...Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was usually observed near the excavation boundaries. The formation mechanism of the “11·28” rockburst, which was a typical rockburst and occurred in a drainage tunnel under a deep burial depth, high in-situ stress state and complex geological conditions, has been difficult to explain. Realistic failure process analysis(RFPA3D) software was adopted to numerically simulate the whole failure process of the surrounding rock mass around the tunnel subjected to excavation. The spatial distribution of acoustic emission derived from numerical simulation contributed to explaining the mechanical responses of the process. Analyses of the stress, safety reserve coefficient and damage degree were performed to reveal the effect of faults on the formation of rockbursts in the deep tunnel. The existence of faults results in the formation of stress anomaly areas between the tunnel and the fault. The surrounding rock mass failure propagates toward the fault from the initial failure, to different degrees. The relative positions and angles of faults play significant roles in the extent and development of surrounding rock mass failure, respectively. The increase in the lateral stress coefficient leads to the aggravation of the surrounding rock mass damage, especially in the roof and floor of the tunnel. Moreover, as the rock strength-stress ratio increases, the failure mode of the near-fault tunnel gradually changes from the stress-controlled type to the compound-controlled type. These findings were consistent with the microseismic monitoring results and field observations, which was helpful to understand the mechanical behavior of tunnel excavation affected by faults. The achievements of this study can provide some references for analysis of the failure mechanisms of similar deep tunnels.展开更多
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
For high-steep slopes in hydropower engineering, damage can be induced or accumulated due to a seriesof human or natural activities, including excavation, dam construction, earthquake, rainstorm, rapid riseor drop of ...For high-steep slopes in hydropower engineering, damage can be induced or accumulated due to a seriesof human or natural activities, including excavation, dam construction, earthquake, rainstorm, rapid riseor drop of water level in the service lifetime of slopes. According to the concept that the progressivedamage （microseismicity） of rock slope is the essence of the precursor of slope instability, a microseismicmonitoring system for high-steep rock slopes is established. Positioning accuracy of the monitoringsystem is tested by fixed-position blasting method. Based on waveform and cluster analyses of microseismicevents recorded during test, the tempo-spatial distribution of microseismic events is analyzed.The deformation zone in the deep rock masses induced by the microseismic events is preliminarilydelimited. Based on the physical information measured by in situ microseismic monitoring, an evaluationmethod for the dynamic stability of rock slopes is proposed and preliminarily implemented bycombining microseismic monitoring and numerical modeling. Based on the rock mass damage modelobtained by back analysis of microseismic information, the rock mass elements within the microseismicdamage zone are automatically searched by finite element program. Then the stiffness and strengthreductions are performed on these damaged elements accordingly. Attempts are made to establish thecorrelation between microseismic event, strength deterioration and slope dynamic instability, so as toquantitatively evaluate the dynamic stability of slope. The case studies about two practical slopes indicatethat the proposed method can reflect the factor of safety of rock slope more objectively. Numericalanalysis can help to understand the characteristics and modes of the monitored microseismic events inrock slopes. Microseismic monitoring data and simulation results can be used to mutually modify thesensitive rock parameters and calibrate the model. Combination of microseismic monitoring and numericalsimulation provides a more objective basis for the numerical model and parameters and a solidmechanical foundation for the microseismic monitoring.展开更多
Rock slide is one of the common geohazard in the Three Gorges Reservoir area, and it affects the shipping of the Yangtze River and the safety of people living on the banks. In order to investigate the internal fractur...Rock slide is one of the common geohazard in the Three Gorges Reservoir area, and it affects the shipping of the Yangtze River and the safety of people living on the banks. In order to investigate the internal fracturing mechanism of rock mass, distributed microseismic monitoring network was arranged with 15 three component geophones(3C geophones), deployed at borehole and out of the sliding mass in the unstable Dulong slope. Stein Unbiased Risk Estimation(SURE) method was used to noise suppression for the microseismic record, and decomposition parameters of the Continuous Wavelet Transform(CWT) were determined with maximum energy of correlation coefficient(MECC) method. The signal-to-noise ratio was tripled after the process, and source parameters are obtained with full waveform inversion. The rupture volume model was counted by the irregular grid statistics with the events’ density. It shows that the rock slide is of a small scale and composed of a single block. Moreover, the relationship among microseismicity, displacement and rainfall were discussed in the paper. The deformation rate was dramatically changed in the period of intensive events. There is a good consistency especially in the rainfall period. Although there is a time delay, continuous rainfall is more likely to cause the increase of microseismic events. The results show that the Dulong slope is a shallow rock slide in the state of creep deformation, and the rupture mechanism of the rock mass is left-lateral normal fault with shear failure. The research provides more key information for the early warning and prevention of rock slides and helps to reduce the risk of geohazards.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
Purpose–The microseismic monitoring technique has great advantages on identifying the location,extent and the mechanism of damage process occurring in rock mass.This study aims to analyze distribution characteristics...Purpose–The microseismic monitoring technique has great advantages on identifying the location,extent and the mechanism of damage process occurring in rock mass.This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.Design/methodology/approach–In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway.An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.Findings–Microseismic events can be divided into high density area,medium density area and low density area according to the density distribution of microseismic events.The positions where the cumulative distribution frequencies of microseismic events are 60 and 80%are identified as the boundaries between high and medium density areas and between medium and low density areas,respectively.The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock,which is affected by the grade of surrounding rock and the span of tunnel.The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters.The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock.The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed.Based on the depth of high excavation damage zone of surrounding rock,the prestressed anchor cable(rod)is designed,and the safety of anchor cable(rod)design parameters is verified by the deformation results of surrounding rock.Originality/value–The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable(rod).展开更多
The prediction study on coal and gas outbursts is carried out by monitoring some indices which are sensitive to the initiation of coal and gas outbursts. The values and changing roles of the indices are the foundation...The prediction study on coal and gas outbursts is carried out by monitoring some indices which are sensitive to the initiation of coal and gas outbursts. The values and changing roles of the indices are the foundations of coal and gas outbursts prediction. But now, only the data of ere key monitoring station is used in the coal and gas outbursts prediction practice, and the other data are ignored. In order to overcome the human factor and make full use of the monitoring information, the technique of multi-sensor target tracking is proposed to deal with the microseismic informatiion. With the results of microseismic events, the activities of geological structure, fracure-depth of roof and floor, and the location of gas channel are obtained. These studies indicate that it is considerably possible to predict the coal and gas outbursts using microseismic monitoring with its inherent ability to remotely monitor the progressive failure caused by mining.展开更多
With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stabi...With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.展开更多
The rock mass failure induced by deep mining exhibits pronounced spatial heterogeneity and diverse mechanisms,with its microseismic responses serving as effective indicators of regional failure evolution and instabili...The rock mass failure induced by deep mining exhibits pronounced spatial heterogeneity and diverse mechanisms,with its microseismic responses serving as effective indicators of regional failure evolution and instability mechanisms.Focusing on the Level VI stope sublayers in the Jinchuan#2 mining area,this study constructs a 24-parameter index system encompassing time-domain features,frequency-domain features,and multifractal characteristics.Through manifold learning,clustering analysis,and hybrid feature selection,15 key indicators were extracted to construct a classification framework for failure responses.Integrated with focal mechanism inversion and numerical simulation,the failure patterns and corresponding instability mechanisms across different structural zones were further identified.The results reveal that multiscale microseismic characteristics exhibit clear regional similarities.Based on the morphological features of radar plots derived from the 15 indicators,acoustic responses were classified into four typical types,each reflecting distinct local failure mechanisms,stress conditions,and plastic zone evolution.Moreover,considering dominant instability factors and rupture modes,four representative rock mass instability models were proposed for typical failure zones within the stope.These findings provide theoretical guidance and methodological support for hazard prediction,structural optimization,and disturbance control in deep metal mining areas.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
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
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.展开更多
Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can p...Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can provide early warnings,and the seismic source location method is an essential indicator for evaluating a microseismic monitoring system.This paper proposes a nonlinear hybrid optimal particle swarm optimisation(PSO)microseismic positioning method based on this technique.The method first improves the PSO algorithm by using the global search performance of this method to quickly find a feasible solution and provide a better initial solution for the subsequent solution of the nonlinear optimal microseismic positioning method.This approach effectively prevents the problem of the microseismic positioning method falling into a local optimum because of an over-reliance on the initial value.In addition,the nonlinear optimal microseismic positioning method further narrows the localisation error based on the PSO algorithm.A simulation test demonstrates that the new method has a good positioning effect,and engineering application examples also show that the proposed method has high accuracy and strong positioning stability.The new method is better than the separate positioning method,both overall and in three directions,making it more suitable for solving the microseismic positioning problem.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
Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,ca...Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,can lead to substantial deformation and subsequent collapse.A series of in situ experiments and discrete element numerical simulations have been conducted to gain insight into the progressive failure behavior and deformation response of rocks in relation to controlled collapse scenarios involving gently inclined faults.First,the unloading damage evolution process of the surrounding rock mass is characterized by microscopic analysis using microseismic(MS)data.Second,the moment tensor inversion method is used to elucidate the temporal distribution of MS event fracture types in the surrounding rock mass.During the development stage of the collapse,numerous tensile fracture events occur,while a few shear fractures corresponding to structural plane dislocation precede their occurrence.The use of the digital panoramic borehole camera,acoustic wave test,and numerical simulation revealed that gently inclined faults and deep cracks at a certain depth from the cavern periphery are the primary factors contributing to rock collapse.These results provide a valuable case study that can help anticipate and mitigate fault-slip collapse incidents while providing practical insights for underground cave excavation.展开更多
Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and...Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and used to locate modeled microseismic sources. The proposed method improves the precision and eliminates artifacts in location profiles. Numerical experiments based on a horizontally layered isotropic medium have shown that the method offers the following advantages： It can deal with Iow-SNR microseismic data with velocity perturbations as well as relatively sparse receivers and still maintain relatively high precision despite the errors in the velocity model. Furthermore, it is more efficient than conventional traveltime inversion methods because interferometric imaging does not require traveltime picking. Numerical results using a 2D fault model have also suggested that the weighted-elastic-wave interferometric imaging can locate multiple sources with higher location precision than the time-reverse imaging method.展开更多
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1002503)。
文摘Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.
基金This study is part of the project 2021RUAPON-REACT EU-Finanziamento PON“Ricerca e Innovazione”20142020,grant n.19-G-12543-2,funded by the Italian Ministry of University and Research(MUR)This study was developed in the frame of“The Geosciences for Sustainable Development”project(Budget Minis-tero dell'Universita e della Ricerca-Dipartimenti di Eccellenza 2023-2027,code n.C93C23002690001).
文摘A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the studied area,and(ii)to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate.Despite the limited number of stations and the short-period char-acter of the seismic sensors,the empirical Green's functions were retrieved for all station pairs using two years of passive data.Both group and phase velocities were derived,the former using the widespread frequency-time analysis,the latter through the analysis of the real part of the cross-spectra.The main advantage of combining these two methods is a more accurate identification of higher modes,resulting in a reduction of ambiguity during picking and data interpretation.Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths.The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method,for this specific case.The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast.Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs.While the average model can be used for a more accurate location of the local natural seismicity,the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth.Finally,the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field.Errors as large as 4%have been found,related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources,and to the very short time widows.It was shown that using a one-year time window these errors arereduced to 0.3%.
基金supported by the National Natural Science Foundation of China(Nos.42177173,U23A20651 and 42130719)and the Outstanding Youth Science Fund Project of Sichuan Provincial Natural Science Foundation(No.2025NSFJQ0003)。
文摘Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.
基金Supported by the State Key Program of the National Natural Science Foundation of China(40638040)the Major Program of the National Natural Science Foundation of China(50820125405)
文摘Monitoring and prediction of rockburst remain to be worldwide challenges in geotechnical engineering.In hydropower,transportation and other engineering fields in China,more deep,long and large tunnels have been under construction in recent years and underground caverns are more evidently featured by ＂long,large,deep and in group＂,which bring in many problems associated with rock mechanics problems at great depth,especially rockburst.Rockbursts lead to damages to not only underground structures and equipments but also personnel safety.It has been a major technical bottleneck in future deep underground engineering in China.In this paper,compared with earthquake prediction,the feasibility in principle of monitoring and prediction of rockbursts is discussed,considering the source zones,development cycle and scale.The authors think the feasibility of rockburst prediction can be understood in three aspects：（1） the heterogeneity of rock is the main reason for the existence of rockburst precursors;（2） deformation localization is the intrinsic cause of rockburst;and（3） the interaction between target rock mass and its surrounding rock mass is the external cause of rockburst.As an engineering practice,the application of microseismic monitoring techniques during tunnel construction of Jinping II Hydropower Station was reported.It is found that precursory microcracking exists prior to most rockbursts,which could be captured by the microseismic monitoring system.The stress concentration is evident near structural discontinuities（such as faults or joints）,which shall be the focus of rockburst monitoring.It is concluded that,by integrating the microseismic monitoring and the rock failure process simulation,the feasibility of rockburst prediction is expected to be enhanced.
基金Project(42177143) supported by the National Natural Science Foundation of ChinaProject(2020JDJQ0011) supported by the Science Foundation for Distinguished Young Scholars of Sichuan Province,China。
文摘Rockbursts were frequently encountered in the construction of deeply buried tunnels at the Jinping-II hydropower station, Southwest China. In those cases, the existence of large structural planes, such as faults, was usually observed near the excavation boundaries. The formation mechanism of the “11·28” rockburst, which was a typical rockburst and occurred in a drainage tunnel under a deep burial depth, high in-situ stress state and complex geological conditions, has been difficult to explain. Realistic failure process analysis(RFPA3D) software was adopted to numerically simulate the whole failure process of the surrounding rock mass around the tunnel subjected to excavation. The spatial distribution of acoustic emission derived from numerical simulation contributed to explaining the mechanical responses of the process. Analyses of the stress, safety reserve coefficient and damage degree were performed to reveal the effect of faults on the formation of rockbursts in the deep tunnel. The existence of faults results in the formation of stress anomaly areas between the tunnel and the fault. The surrounding rock mass failure propagates toward the fault from the initial failure, to different degrees. The relative positions and angles of faults play significant roles in the extent and development of surrounding rock mass failure, respectively. The increase in the lateral stress coefficient leads to the aggravation of the surrounding rock mass damage, especially in the roof and floor of the tunnel. Moreover, as the rock strength-stress ratio increases, the failure mode of the near-fault tunnel gradually changes from the stress-controlled type to the compound-controlled type. These findings were consistent with the microseismic monitoring results and field observations, which was helpful to understand the mechanical behavior of tunnel excavation affected by faults. The achievements of this study can provide some references for analysis of the failure mechanisms of similar deep tunnels.
基金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 grants from the National Basic Research Program of China (Grant Nos. 2011CB013503, 2014CB047103)the National Natural Science Foundation of China (Grant Nos. 51279024, 51209127)
文摘For high-steep slopes in hydropower engineering, damage can be induced or accumulated due to a seriesof human or natural activities, including excavation, dam construction, earthquake, rainstorm, rapid riseor drop of water level in the service lifetime of slopes. According to the concept that the progressivedamage （microseismicity） of rock slope is the essence of the precursor of slope instability, a microseismicmonitoring system for high-steep rock slopes is established. Positioning accuracy of the monitoringsystem is tested by fixed-position blasting method. Based on waveform and cluster analyses of microseismicevents recorded during test, the tempo-spatial distribution of microseismic events is analyzed.The deformation zone in the deep rock masses induced by the microseismic events is preliminarilydelimited. Based on the physical information measured by in situ microseismic monitoring, an evaluationmethod for the dynamic stability of rock slopes is proposed and preliminarily implemented bycombining microseismic monitoring and numerical modeling. Based on the rock mass damage modelobtained by back analysis of microseismic information, the rock mass elements within the microseismicdamage zone are automatically searched by finite element program. Then the stiffness and strengthreductions are performed on these damaged elements accordingly. Attempts are made to establish thecorrelation between microseismic event, strength deterioration and slope dynamic instability, so as toquantitatively evaluate the dynamic stability of slope. The case studies about two practical slopes indicatethat the proposed method can reflect the factor of safety of rock slope more objectively. Numericalanalysis can help to understand the characteristics and modes of the monitored microseismic events inrock slopes. Microseismic monitoring data and simulation results can be used to mutually modify thesensitive rock parameters and calibrate the model. Combination of microseismic monitoring and numericalsimulation provides a more objective basis for the numerical model and parameters and a solidmechanical foundation for the microseismic monitoring.
基金supported by the Chongqing Administration of Science and Technology(Grants No.cstc2021jxjl20008,cstc2020jcyj-msxm X1068)the Chongqing Administration of Planning and Natural Resources(Grant No.KJ-2019018)。
文摘Rock slide is one of the common geohazard in the Three Gorges Reservoir area, and it affects the shipping of the Yangtze River and the safety of people living on the banks. In order to investigate the internal fracturing mechanism of rock mass, distributed microseismic monitoring network was arranged with 15 three component geophones(3C geophones), deployed at borehole and out of the sliding mass in the unstable Dulong slope. Stein Unbiased Risk Estimation(SURE) method was used to noise suppression for the microseismic record, and decomposition parameters of the Continuous Wavelet Transform(CWT) were determined with maximum energy of correlation coefficient(MECC) method. The signal-to-noise ratio was tripled after the process, and source parameters are obtained with full waveform inversion. The rupture volume model was counted by the irregular grid statistics with the events’ density. It shows that the rock slide is of a small scale and composed of a single block. Moreover, the relationship among microseismicity, displacement and rainfall were discussed in the paper. The deformation rate was dramatically changed in the period of intensive events. There is a good consistency especially in the rainfall period. Although there is a time delay, continuous rainfall is more likely to cause the increase of microseismic events. The results show that the Dulong slope is a shallow rock slide in the state of creep deformation, and the rupture mechanism of the rock mass is left-lateral normal fault with shear failure. The research provides more key information for the early warning and prevention of rock slides and helps to reduce the risk of geohazards.
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 Fundamental Research Funds for Chinese National Natural Science Foundation under Grant 51678035National Key Research and Development Programs of China under Grant 2017YFC0805401China Railway Corporation Research and Development Program of Science and Technology under Grant 2014004-C.
文摘Purpose–The microseismic monitoring technique has great advantages on identifying the location,extent and the mechanism of damage process occurring in rock mass.This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.Design/methodology/approach–In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway.An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.Findings–Microseismic events can be divided into high density area,medium density area and low density area according to the density distribution of microseismic events.The positions where the cumulative distribution frequencies of microseismic events are 60 and 80%are identified as the boundaries between high and medium density areas and between medium and low density areas,respectively.The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock,which is affected by the grade of surrounding rock and the span of tunnel.The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters.The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock.The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed.Based on the depth of high excavation damage zone of surrounding rock,the prestressed anchor cable(rod)is designed,and the safety of anchor cable(rod)design parameters is verified by the deformation results of surrounding rock.Originality/value–The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable(rod).
基金supported by National Basic Research Programof China(973Program,2010CB226805)Shandong Province Natural Science Fund(Z2008F01)Key Laboratory of Mine Disaster Prevention and Control of Education Ministry(MDPC0809,MDPC0811)
文摘The prediction study on coal and gas outbursts is carried out by monitoring some indices which are sensitive to the initiation of coal and gas outbursts. The values and changing roles of the indices are the foundations of coal and gas outbursts prediction. But now, only the data of ere key monitoring station is used in the coal and gas outbursts prediction practice, and the other data are ignored. In order to overcome the human factor and make full use of the monitoring information, the technique of multi-sensor target tracking is proposed to deal with the microseismic informatiion. With the results of microseismic events, the activities of geological structure, fracure-depth of roof and floor, and the location of gas channel are obtained. These studies indicate that it is considerably possible to predict the coal and gas outbursts using microseismic monitoring with its inherent ability to remotely monitor the progressive failure caused by mining.
基金Project(2022YFC2905100)supported by the National Key Research and Development Program of ChinaProject(52174098)supported by the National Natural Science Foundation of China。
文摘With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.
基金financial support from the Distinguished Youth Funds of the National Natural Science Foundation of China(No.52425403)the Hunan Province Graduate Research Innovation Project of China(No.CX20230168)。
文摘The rock mass failure induced by deep mining exhibits pronounced spatial heterogeneity and diverse mechanisms,with its microseismic responses serving as effective indicators of regional failure evolution and instability mechanisms.Focusing on the Level VI stope sublayers in the Jinchuan#2 mining area,this study constructs a 24-parameter index system encompassing time-domain features,frequency-domain features,and multifractal characteristics.Through manifold learning,clustering analysis,and hybrid feature selection,15 key indicators were extracted to construct a classification framework for failure responses.Integrated with focal mechanism inversion and numerical simulation,the failure patterns and corresponding instability mechanisms across different structural zones were further identified.The results reveal that multiscale microseismic characteristics exhibit clear regional similarities.Based on the morphological features of radar plots derived from the 15 indicators,acoustic responses were classified into four typical types,each reflecting distinct local failure mechanisms,stress conditions,and plastic zone evolution.Moreover,considering dominant instability factors and rupture modes,four representative rock mass instability models were proposed for typical failure zones within the stope.These findings provide theoretical guidance and methodological support for hazard prediction,structural optimization,and disturbance control in deep metal mining areas.
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Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
基金supported by the Natural Science Foundation of Henan Province,China.(No,222300420596).
文摘Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can provide early warnings,and the seismic source location method is an essential indicator for evaluating a microseismic monitoring system.This paper proposes a nonlinear hybrid optimal particle swarm optimisation(PSO)microseismic positioning method based on this technique.The method first improves the PSO algorithm by using the global search performance of this method to quickly find a feasible solution and provide a better initial solution for the subsequent solution of the nonlinear optimal microseismic positioning method.This approach effectively prevents the problem of the microseismic positioning method falling into a local optimum because of an over-reliance on the initial value.In addition,the nonlinear optimal microseismic positioning method further narrows the localisation error based on the PSO algorithm.A simulation test demonstrates that the new method has a good positioning effect,and engineering application examples also show that the proposed method has high accuracy and strong positioning stability.The new method is better than the separate positioning method,both overall and in three directions,making it more suitable for solving the microseismic positioning problem.
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funding support from the National Natural Science Foundation of China(Grant Nos.U23A2060,42177143,and 42277461).
文摘Ensuring the stability of the surrounding rock mass is of great importance during the construction of a large underground powerhouse.The presence of unfavorable structural planes within the rock mass,such as faults,can lead to substantial deformation and subsequent collapse.A series of in situ experiments and discrete element numerical simulations have been conducted to gain insight into the progressive failure behavior and deformation response of rocks in relation to controlled collapse scenarios involving gently inclined faults.First,the unloading damage evolution process of the surrounding rock mass is characterized by microscopic analysis using microseismic(MS)data.Second,the moment tensor inversion method is used to elucidate the temporal distribution of MS event fracture types in the surrounding rock mass.During the development stage of the collapse,numerous tensile fracture events occur,while a few shear fractures corresponding to structural plane dislocation precede their occurrence.The use of the digital panoramic borehole camera,acoustic wave test,and numerical simulation revealed that gently inclined faults and deep cracks at a certain depth from the cavern periphery are the primary factors contributing to rock collapse.These results provide a valuable case study that can help anticipate and mitigate fault-slip collapse incidents while providing practical insights for underground cave excavation.
基金supported by the R&D of Key Instruments and Technologies for Deep Resources Prospecting(No.ZDYZ2012-1)National Natural Science Foundation of China(No.11374322)
文摘Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and used to locate modeled microseismic sources. The proposed method improves the precision and eliminates artifacts in location profiles. Numerical experiments based on a horizontally layered isotropic medium have shown that the method offers the following advantages： It can deal with Iow-SNR microseismic data with velocity perturbations as well as relatively sparse receivers and still maintain relatively high precision despite the errors in the velocity model. Furthermore, it is more efficient than conventional traveltime inversion methods because interferometric imaging does not require traveltime picking. Numerical results using a 2D fault model have also suggested that the weighted-elastic-wave interferometric imaging can locate multiple sources with higher location precision than the time-reverse imaging method.
