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
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
This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.I...This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.It is difficult to provide necessary and sufficient conditions to test the reliability of microseismic monitoring.Often,a few hundred meters away,the microseismic signal emitted by a hypocenter is submerged in noise,and the traditional location is invalid;Inversion for microseismic released energy distribution using data migration and stacking is in principle not unique.However,based on microseismic monitoring characteristics,forward and reverse simulations and numerous experiments,many necessary conditions can be proposed to ensure reliable monitoring with high probability.VS(Vector Scanning)ground monitoring for microseismic proposes eight necessary conditions for testing the reliability,so that VS finds the fracturing-induced effective communication seam with the characteristics of shear zones under the control of tectonic stress fields,in line with the laws of seismic and geological observations,as well as the features related to some special production data.VS uses data migration and stacking suitable for low signal-to-noise ratio and shear mechanism,and the joint inversion for correction of both traditional relocations and velocity model,can greatly improve monitoring distance and quality,complete microseismic measurement methods,and broaden applicable fields,such as:(1)VS can be a cost-effective,ground-based,routine monitoring method;(2)The BPM(Borehole Proximity Monitoring)is high cost but close to the hypocenters;It can be the best method for scientific research,but its seismic network should be improved,and the joint inversion and data stacking could be used to improve the monitoring distance and quality;(3)The early warning of mine safety can change the current monitoring of strong microseismic(or accidents have been happened)to the real microseismic level;and(4)The seismic precursor monitoring of large earthquakes can be expanded from small earthquakes to microseismic.These will establish a solid foundation and complete seismic measurements for microseismology.展开更多
In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic ...In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.展开更多
Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensi...Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.展开更多
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
Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture ...Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture growth interpretation.Waveform stacking-based location methods invert the source locations by focusing the source energy with multichannel waveforms,and these methods exhibit a high level of automation and noise-resistance.Taking the cross-correlation stacking(CCS)method as an example,this work attempts to study the influential factors of waveform stacking-based methods,and introduces a comprehensive performance evaluation scheme based on multiple parameters and indicators.The waveform data are from field monitoring of induced microseismicity in the Changning region(southern Sichuan Basin of China).Synthetic and field data tests reveal the impacts of three categories of factors on waveform stacking-based location:velocity model,monitoring array,and waveform complexity.The location performance is evaluated and further improved in terms of the source imaging resolution and location error.Denser array monitoring contributes to better constraining source depth and location reliability,but the combined impact of multiple factors,such as velocity model uncertainty and multiple seismic phases,increases the complexity of locating field microseismic events.Finally,the aspects of location uncertainty,phase detection,and artificial intelligencebased location are discussed.展开更多
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
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
Axial chain rockbursts(ACRs)repeatedly occur in deep tunnels during drilling and blasting methodology(D&B)within locked-in stress zones,severely hindering construction progress.In extremely cases,ACRs can persist ...Axial chain rockbursts(ACRs)repeatedly occur in deep tunnels during drilling and blasting methodology(D&B)within locked-in stress zones,severely hindering construction progress.In extremely cases,ACRs can persist for 7−10 d and affect areas exceeding 20 m along tunnel axis.Through integrated geological investigations and microseismic(MS)monitoring,the geological characteristics,MS activity patterns,and formation mechanisms of ACRs were analyzed.In tectonically active regions,locked-in stress zones arise from interactions between multiple structural planes.Blasting dynamic disturbances during tunnel excavation in these zones trigger early slippage along structural planes and fractures in the surrounding rock,with MS events developing ahead of the working face.High-energy MS events dominate during the development and occurrence stages of ACRs,extending 20−30 m(3−4 tunnel diameters)ahead of the working face.Following the ACRs,low-energy MS events primarily occur behind the working face.Tensile fracturing is the predominant failure mode during ACRs.Shear and mixed fractures primarily occur within the ACRs zone during the intra-ACR phase.Monitoring MS event locations ahead of the working face provides a reliable approach for prewarning potential ACR-prone zones.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
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
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
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
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
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.
文摘This is a continuation of the article“Ground Monitoring of Microseismic Based on Low Signal-to-Noise Ratio”,and a further summary and reflection after investigating the current situation of microseismic monitoring.It is difficult to provide necessary and sufficient conditions to test the reliability of microseismic monitoring.Often,a few hundred meters away,the microseismic signal emitted by a hypocenter is submerged in noise,and the traditional location is invalid;Inversion for microseismic released energy distribution using data migration and stacking is in principle not unique.However,based on microseismic monitoring characteristics,forward and reverse simulations and numerous experiments,many necessary conditions can be proposed to ensure reliable monitoring with high probability.VS(Vector Scanning)ground monitoring for microseismic proposes eight necessary conditions for testing the reliability,so that VS finds the fracturing-induced effective communication seam with the characteristics of shear zones under the control of tectonic stress fields,in line with the laws of seismic and geological observations,as well as the features related to some special production data.VS uses data migration and stacking suitable for low signal-to-noise ratio and shear mechanism,and the joint inversion for correction of both traditional relocations and velocity model,can greatly improve monitoring distance and quality,complete microseismic measurement methods,and broaden applicable fields,such as:(1)VS can be a cost-effective,ground-based,routine monitoring method;(2)The BPM(Borehole Proximity Monitoring)is high cost but close to the hypocenters;It can be the best method for scientific research,but its seismic network should be improved,and the joint inversion and data stacking could be used to improve the monitoring distance and quality;(3)The early warning of mine safety can change the current monitoring of strong microseismic(or accidents have been happened)to the real microseismic level;and(4)The seismic precursor monitoring of large earthquakes can be expanded from small earthquakes to microseismic.These will establish a solid foundation and complete seismic measurements for microseismology.
基金supported by the National Natural Science Foundation of China(Grant Nos.42472351 and 42177140)the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(Grant No.YESS20230742).
文摘In recent years,tunnel boring machines(TBMs)have been widely used in tunnel construction.Rockbursts,as a dynamic geological disaster,pose a serious threat to the safety and efficienttunneling of TBMs.The microseismic monitoring technique provides an effective solution for rockburst warning.However,due to the complexity and variability of the TBM excavation environment,microseismic events induced by rock fracture are often accompanied by interference events,such as electrical noise,TBM vibration,and mechanical knock.This study proposes a multi-channel intelligent classification approach for microseismic events in TBM excavation scenarios,based on double-layer stacking learning,to identify rock fractures.In this approach,decision tree is used as the base classifieron each microseismic channel,while extreme learning machine is employed as the meta-classifierto aggregate all base classifiers.Additionally,mind evolutionary computation is integrated to optimize the built-in hyperparameters of various classifiers.Meanwhile,a comprehensive preprocessing and augmentation flowfor microseismic data has been developed,encompassing feature extraction,dimensionality reduction,outlier detection,and outlier substitution.The results reveal that the multi-channel stacking model,which combines classificationand regression tree and extreme learning machine,achieves optimal global and local generalization performance compared to other multi-channel stacking models and traditional single-channel models.The accuracy,Hamming loss,and Cohen’s kappa are 96.75%,0.0325,and 0.9148,respectively,and the F_(1)-score and AUC on rock fracture events are 0.9366 and 0.9818,respectively.Finally,a generative artificialintelligence-based scheme is invented to enhance the robustness of the model for signal-mixing events.
基金supported by the Key Research and Development Plan of Anhui Province(202104a05020059)the Excellent Scientific Research and Innovation Team of Anhui Province(2022AH010003)support from Hefei Comprehensive National Science Center is highly appreciated.
文摘Microseismic monitoring technology is widely used in tunnel and coal mine safety production.For signals generated by ultra-weak microseismic events,traditional sensors encounter limitations in terms of detection sensitivity.Given the complex engineering environment,automatic multi-classification of microseismic data is highly required.In this study,we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure,termed CNN_BAM,for automatic classification and identification of microseismic events.We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model.Results show that the CNN_BAM model exhibits good feature extraction ability,achieving a recognition accuracy of 99.29%,surpassing all its counterparts.The stability and accuracy of the classification algorithm improve remarkably.In addition,through fine-tuning and migration to the Pan Ⅱ Mine Project,the network demonstrates reliable generalization performance.This outcome reflects its adaptability across different projects and promising application prospects.
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supported by National Natural Science Foundation of China(Nos.42374076,42174128 and 42004115)Natural Science Foundation for Excellent Young Scholars of Hunan Province,China(No.2022JJ 20057)+1 种基金Central South University Innovation-Driven Research Programme(No.2023CXQD063)the Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology(No.2022B1212010002).
文摘Seismic source locations can characterize the spatial and temporal distributions of seismic sources,and can provide important basic data for earthquake disaster monitoring,fault activity characterization,and fracture growth interpretation.Waveform stacking-based location methods invert the source locations by focusing the source energy with multichannel waveforms,and these methods exhibit a high level of automation and noise-resistance.Taking the cross-correlation stacking(CCS)method as an example,this work attempts to study the influential factors of waveform stacking-based methods,and introduces a comprehensive performance evaluation scheme based on multiple parameters and indicators.The waveform data are from field monitoring of induced microseismicity in the Changning region(southern Sichuan Basin of China).Synthetic and field data tests reveal the impacts of three categories of factors on waveform stacking-based location:velocity model,monitoring array,and waveform complexity.The location performance is evaluated and further improved in terms of the source imaging resolution and location error.Denser array monitoring contributes to better constraining source depth and location reliability,but the combined impact of multiple factors,such as velocity model uncertainty and multiple seismic phases,increases the complexity of locating field microseismic events.Finally,the aspects of location uncertainty,phase detection,and artificial intelligencebased location are discussed.
基金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Projects(52222810,52178383)supported by the National Natural Science Foundation of China。
文摘Axial chain rockbursts(ACRs)repeatedly occur in deep tunnels during drilling and blasting methodology(D&B)within locked-in stress zones,severely hindering construction progress.In extremely cases,ACRs can persist for 7−10 d and affect areas exceeding 20 m along tunnel axis.Through integrated geological investigations and microseismic(MS)monitoring,the geological characteristics,MS activity patterns,and formation mechanisms of ACRs were analyzed.In tectonically active regions,locked-in stress zones arise from interactions between multiple structural planes.Blasting dynamic disturbances during tunnel excavation in these zones trigger early slippage along structural planes and fractures in the surrounding rock,with MS events developing ahead of the working face.High-energy MS events dominate during the development and occurrence stages of ACRs,extending 20−30 m(3−4 tunnel diameters)ahead of the working face.Following the ACRs,low-energy MS events primarily occur behind the working face.Tensile fracturing is the predominant failure mode during ACRs.Shear and mixed fractures primarily occur within the ACRs zone during the intra-ACR phase.Monitoring MS event locations ahead of the working face provides a reliable approach for prewarning potential ACR-prone zones.
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 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 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.
