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
Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring te...Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.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
This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great...This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.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
Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characte...Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention,often resulting in suboptimal performance when dealing with complex and noisy data.In this study,we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network.Our model integrates the ad-vantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously.We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam.The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data.The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the Tran-sUNet achieves the optimal balance in its architecture and inference speed.With relatively low inference time and network complexity,it operates effectively in high-precision microseismic phase pickings.This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reser-voir monitoring applications.展开更多
The principal stresses will increase or decrease during mining,leading to variations in surrounding rock strength and subsequently an influence on the risk of rockbursts.To address this issue,this study conducted theo...The principal stresses will increase or decrease during mining,leading to variations in surrounding rock strength and subsequently an influence on the risk of rockbursts.To address this issue,this study conducted theoretical analysis,numerical simulation,and field monitoring.A rockburst risk analysis method that integrates dynamic changes in the stress and strength of surrounding rock was proposed and verified in the field.The dynamic changes in maximum(σ_(1))and minimum(σ_(3))principal stresses are represented by the σ_(1) and σ_(3) differentials,respectively.The difference in principal stress differential(DPSD),defined as the difference between σ_(1) and σ_(3),was introduced as a novel indicator for rockburst risk analysis.The findings of this study demonstrate a positive correlation between increases in DPSD and heightened risks of rockbursts,as evidenced by an increase in both the frequency of rockbursts and the occurrence of large-energy microseismic events.Conversely,a decrease in DPSD is associated with a reduction in risk.Specifically,in the W1123 panel of a coal mine susceptible to rockbursts,areas exhibiting higher DPSD values experienced more frequent and severe rockbursts.The DPSD-based analysis aligned well with the observed rockburst occurrences.Subsequent optimization of rockburst prevention measures in areas with elevated DPSD led to a reduction in DPSD.Following these adjustments,the W1123 panel predominantly experienced low-energy microseismic events,with a significant decrease in large-energy microseismic events and no further rockbursts.The DPSD analysis is a valuable tool for evaluating rockburst risk and aiding in prevention,which is of great significance for disaster prevention.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
With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stabi...With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.展开更多
A 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
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
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
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
The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster predic...The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.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
The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic techno...The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.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
This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-...This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-time acquisition and quality control of seismic data,the real-time monitoring of equipment location and health status,the synchronous transmission of collected data between the cloud and client,and the real-time issuance of operational instructions.It addresses the critical limitation of existing seismic node equipment,which is often restricted to mining and blind storage due to the absence of a wired or wireless communication link between the acquisition node device and the central control unit.This limitation necessitates local data storage and rendering real-time quality control unfeasible.Typically,quality control is conducted post-task completion,requiring the overall retrieval and downloading of data.If data issues are identifi ed,it becomes necessary to eliminate faulty tracks and determine the need for supplementary acquisition,which can lead to delays in the acquisition process.The implementation of real-time monitoring and early warning systems for equipment health status aims to mitigate the risk of poor data quality resulting from equipment anomalies.Furthermore,the real-time synchronous transmission between the cloud and server addresses the bottleneck of slow download speeds associated with the centralized retrieval of data from multiple node devices during blind acquisition and storage.A real-time microseismic data acquisition test and verifi cation were conducted at a fracturing site in an eastern oil and gas fi eld.Analysis of the test data indicates that the overall performance indicators of the system are comparable to those of existing mainstream system equipment,demonstrating stability and reliability.The performance parameters fully satisfy the technical requirements for oilfield fracturing monitoring scenarios,suggesting promising prospects for further promotion and application.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
Kazakhstan is currently drafting new construction regulations that comply with the major provisions of the Eurocodes.Such regulations are created on the basis of seismic zoning maps of various degrees of detail,develo...Kazakhstan is currently drafting new construction regulations that comply with the major provisions of the Eurocodes.Such regulations are created on the basis of seismic zoning maps of various degrees of detail,developed by our Institute of Seismology using a new methodological approach for Kazakhstan.The article is about creating the first normative map of the Detailed Seismic Zoning on a probabilistic foundation for the Republic of Kazakhstan’s East Kazakhstan region.We carried out the probabilistic assessment of seismic hazard using a methodology consistent with the main provisions of Eurocode 8and updated compared with that used in developing maps of Kazakhstan’s General Seismic Zoning and seismic microzoning of Almaty.The most thorough and current data accessible for the area under consideration were combined with contemporary analytical techniques.Updates have been done to not only the databases being used but also the way seismic sources were shown,including active faults now.On a scale of 1:1000000,precise seismic zoning maps of the East Kazakhstan region were created for two probabilities of exceedance:10%and 2%in 50 years in terms of peak ground accelerations and macroseismic intensities.The obtained seismic hazard distribution is generally consistent with the General Seismic Zoning of Kazakhstan’s previous findings.However,because active faults were included and a thoroughly revised catalog was used,there are more pronounced zones of increased danger along the fault in the western part of the region.In the west of the territory,acceleration values also increased due to a more accurate consideration of seismotectonic conditions.Zoning maps are the basis for developing new state building regulations of the Republic of Kazakhstan.展开更多
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supported by the National Natural Science Founda-tion of China under Grant Nos.42472351,42177140,52404127,and 42207235the Natural Science Foundation of Hubei Province under Grant No.2024AFD359+1 种基金the Young Elite Scientist Sponsorship Program by CAST under Grant No.YESS20230742the China Postdoctoral Science Foundation Program under Grant No.2024T170684.
文摘Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.
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 dation Foun-of China(Grant Number 42272204)Key Laboratory of Coal sources Re-Exploration and Comprehensive Utilization,Ministry of Natural Resources,Canada(SMDZ-KF2024-4)+1 种基金the Fundamental Research Funds for the Central Universities,China(Grant No.2024JCCXDC06)supported in part by open fund project of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Research(SKLCRSM23KFA04)。
文摘This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.
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 a National Natural Science Foundation of China(Grant number 41974150 and 42174158)Natural Science Basic Research Program of Shaanxi(2023-JC-YB-220).
文摘Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas pro-duction.However,traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention,often resulting in suboptimal performance when dealing with complex and noisy data.In this study,we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network.Our model integrates the ad-vantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously.We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam.The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data.The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the Tran-sUNet achieves the optimal balance in its architecture and inference speed.With relatively low inference time and network complexity,it operates effectively in high-precision microseismic phase pickings.This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reser-voir monitoring applications.
基金support from the National Natural Science Foundation of China(Grant Nos.52374180 and 52327804).
文摘The principal stresses will increase or decrease during mining,leading to variations in surrounding rock strength and subsequently an influence on the risk of rockbursts.To address this issue,this study conducted theoretical analysis,numerical simulation,and field monitoring.A rockburst risk analysis method that integrates dynamic changes in the stress and strength of surrounding rock was proposed and verified in the field.The dynamic changes in maximum(σ_(1))and minimum(σ_(3))principal stresses are represented by the σ_(1) and σ_(3) differentials,respectively.The difference in principal stress differential(DPSD),defined as the difference between σ_(1) and σ_(3),was introduced as a novel indicator for rockburst risk analysis.The findings of this study demonstrate a positive correlation between increases in DPSD and heightened risks of rockbursts,as evidenced by an increase in both the frequency of rockbursts and the occurrence of large-energy microseismic events.Conversely,a decrease in DPSD is associated with a reduction in risk.Specifically,in the W1123 panel of a coal mine susceptible to rockbursts,areas exhibiting higher DPSD values experienced more frequent and severe rockbursts.The DPSD-based analysis aligned well with the observed rockburst occurrences.Subsequent optimization of rockburst prevention measures in areas with elevated DPSD led to a reduction in DPSD.Following these adjustments,the W1123 panel predominantly experienced low-energy microseismic events,with a significant decrease in large-energy microseismic events and no further rockbursts.The DPSD analysis is a valuable tool for evaluating rockburst risk and aiding in prevention,which is of great significance for disaster prevention.
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(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.
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 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supported by the National Research and Development Program(2022YFC3004603)the Jiangsu Province International Collaboration Program-Key National Industrial Technology Research and Development Cooperation Projects(BZ2023050)+1 种基金the Natural Science Foundation of Jiangsu Province(BK20221109)the National Natural Science Foundation of China(52274098).
文摘The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.
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 in part by the National Science Fund for Distinguished Young Scholars under Grant (42025403)the National Key Research and Development Plan of China (2021YFA0716800)the National Key Research and Development Plan of China (2022YFC2903804)。
文摘The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.
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funded by the National Natural Science Foundation of China (42074127)the Key Program of National Natural Science Foundation of China (41930425)Research on Key Technologies for the Production, Exploration, and Development of Continental Shale Oil (2023ZZ15YJ02)。
文摘This article introduces a cable-free real-time telemetry seismic acquisition system(hereinafter referred to as the cable-free real-time telemetry system)that utilizes 4G/5G technology.This system facilitates the real-time acquisition and quality control of seismic data,the real-time monitoring of equipment location and health status,the synchronous transmission of collected data between the cloud and client,and the real-time issuance of operational instructions.It addresses the critical limitation of existing seismic node equipment,which is often restricted to mining and blind storage due to the absence of a wired or wireless communication link between the acquisition node device and the central control unit.This limitation necessitates local data storage and rendering real-time quality control unfeasible.Typically,quality control is conducted post-task completion,requiring the overall retrieval and downloading of data.If data issues are identifi ed,it becomes necessary to eliminate faulty tracks and determine the need for supplementary acquisition,which can lead to delays in the acquisition process.The implementation of real-time monitoring and early warning systems for equipment health status aims to mitigate the risk of poor data quality resulting from equipment anomalies.Furthermore,the real-time synchronous transmission between the cloud and server addresses the bottleneck of slow download speeds associated with the centralized retrieval of data from multiple node devices during blind acquisition and storage.A real-time microseismic data acquisition test and verifi cation were conducted at a fracturing site in an eastern oil and gas fi eld.Analysis of the test data indicates that the overall performance indicators of the system are comparable to those of existing mainstream system equipment,demonstrating stability and reliability.The performance parameters fully satisfy the technical requirements for oilfield fracturing monitoring scenarios,suggesting promising prospects for further promotion and application.
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the“Seismic hazard assessment of the territories of regions and cities of Kazakhstan on a modern scientific and methodological basis”,program code F.0980,IRN OR11465449The funding source is the Ministry of Education and Science of the Republic of Kazakhstan。
文摘Kazakhstan is currently drafting new construction regulations that comply with the major provisions of the Eurocodes.Such regulations are created on the basis of seismic zoning maps of various degrees of detail,developed by our Institute of Seismology using a new methodological approach for Kazakhstan.The article is about creating the first normative map of the Detailed Seismic Zoning on a probabilistic foundation for the Republic of Kazakhstan’s East Kazakhstan region.We carried out the probabilistic assessment of seismic hazard using a methodology consistent with the main provisions of Eurocode 8and updated compared with that used in developing maps of Kazakhstan’s General Seismic Zoning and seismic microzoning of Almaty.The most thorough and current data accessible for the area under consideration were combined with contemporary analytical techniques.Updates have been done to not only the databases being used but also the way seismic sources were shown,including active faults now.On a scale of 1:1000000,precise seismic zoning maps of the East Kazakhstan region were created for two probabilities of exceedance:10%and 2%in 50 years in terms of peak ground accelerations and macroseismic intensities.The obtained seismic hazard distribution is generally consistent with the General Seismic Zoning of Kazakhstan’s previous findings.However,because active faults were included and a thoroughly revised catalog was used,there are more pronounced zones of increased danger along the fault in the western part of the region.In the west of the territory,acceleration values also increased due to a more accurate consideration of seismotectonic conditions.Zoning maps are the basis for developing new state building regulations of the Republic of Kazakhstan.
