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
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 source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techni...Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.展开更多
Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and...Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and used to locate modeled microseismic sources. The proposed method improves the precision and eliminates artifacts in location profiles. Numerical experiments based on a horizontally layered isotropic medium have shown that the method offers the following advantages： It can deal with Iow-SNR microseismic data with velocity perturbations as well as relatively sparse receivers and still maintain relatively high precision despite the errors in the velocity model. Furthermore, it is more efficient than conventional traveltime inversion methods because interferometric imaging does not require traveltime picking. Numerical results using a 2D fault model have also suggested that the weighted-elastic-wave interferometric imaging can locate multiple sources with higher location precision than the time-reverse imaging method.展开更多
The efficiency of an optimization method for acoustic emission/microseismic（AE/MS） source location is determined by the compatibility of its error definition with the errors contained in the input data.This compatib...The efficiency of an optimization method for acoustic emission/microseismic（AE/MS） source location is determined by the compatibility of its error definition with the errors contained in the input data.This compatibility can be examined in terms of the distribution of station residuals.For an ideal distribution,the input error is held at the station where it takes place as the station residual and the error is not permitted to spread to other stations.A comparison study of two optimization methods,namely the least squares method and the absolute value method,shows that the distribution with this character constrains the input errors and minimizes their impact,which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors.When the errors in the input data are systematic and/or extreme in that the basic data structure is altered by these errors,none of the optimization methods are able to function.The only means to resolve this problem is the early detection and correction of these errors through a data screening process.An efficient data screening process is of primary importance for AE/MS source location.In addition to its critical role in dealing with those systematic and extreme errors,data screening creates a favorable environment for applying optimization methods.展开更多
To solve the problem that the existing acoustic emission(AE) source location algorithms cannot always obtain accurate results for multilayer cylindrical media,a new acoustic emission source location method considering...To solve the problem that the existing acoustic emission(AE) source location algorithms cannot always obtain accurate results for multilayer cylindrical media,a new acoustic emission source location method considering refraction was proposed.AE source coordinates were solved by the complex method.Pencil-lead-break experiments were used to verify this method.The absolute distance errors of location results are less than 3 mm,much less than those by the traditional method.The numerical experiments were used to further analyze factors that affect location accuracy.The results of numerical experiments show that the location accuracy of the proposed method is not affected by the ratio of wave velocities but affected by the measurement accuracy of wave velocity.These results show that new method can obtain accurate AE source location in the two-layered cylindrical surface media such as the triaxial compression test.展开更多
Acoustic emission(AE)localization plays an important role in the prediction and control of potential hazardous sources in complex structures.However,existing location methods have less discussion on the presence of un...Acoustic emission(AE)localization plays an important role in the prediction and control of potential hazardous sources in complex structures.However,existing location methods have less discussion on the presence of unknown empty areas.This paper proposes an AE source location method for structures containing unknown empty areas(SUEA).Firstly,this method identifies the shape,size,and location of empty areas in the unknown region by exciting the active AE sources and using the collected AE arrivals.Then,the unknown AE source can be located considering the identified empty areas.The lead break experiments were performed to verify the effectiveness and accuracy of the proposed method.Five specimens were selected containing empty areas with different positions,shapes,and sizes.Results show the average location accuracy of the SUEA increased by 78%compared to the results of the existing method.It can provide a more accurate solution for locating AE sources in complex structures containing unknown empty areas such as tunnels,bridges,railroads,and caves in practical engineering.展开更多
Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to esta...Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.展开更多
Particle breakage is a common occurrence in granular systems when the external stress exceeds the individual particle strength.A large number of experimental evidences suggested that particle breakage may significantl...Particle breakage is a common occurrence in granular systems when the external stress exceeds the individual particle strength.A large number of experimental evidences suggested that particle breakage may significantly influence the soil behavior.In the case of pile foundations,the subsoil below the pile tip experiences considerable high stress and consequently prone to break.Due to the lack of sufficient understanding on particle breakage mechanism,there is currently no consentaneous theoretical background for particle breakage analysis during the pile penetration process.This study aims to clarify the location of particle breakage and its evolving characteristics with the aid of acoustic emission(AE)source location method.The spatial distribution of AE hypocenters is interpreted to be associated with the mechanism of particle breakage.Results showed that the AE sources were not uniformly distributed,but concentrated within certain zones below the pile tip.This AE concentration zone was pushed downward with the advancing pile tip,and its distance from the real time pile tip position decreased after certain depth of pile penetration.The location of particle breakage interpreted from AE source location was verified with posttest excavations and the insights on the particle breakage evolution zone were further discussed.展开更多
Microseismic source location is the essential factor in microseismic monitoring technology, and its loca- tion precision has a large impact on the performance of the technique. Here, we discuss the problem of low-prec...Microseismic source location is the essential factor in microseismic monitoring technology, and its loca- tion precision has a large impact on the performance of the technique. Here, we discuss the problem of low-precision location identification for microseismic events in a mine, as may be obtained using conven-tional location methods that are based on arrival time. In this paper, microseismic location characteristics in mining are analyzed according to the characteristics of the mine＇s microseismic wavefield. We review research progress in mine-related microseismic source location methods in recent years, including the combination of the Geiger method with the linear method, combined microseismic event location method, optimization of relative location method, location method without pre-measured velocity, and location method without arrival time picking. The advantages and disadvantages of these methods are discussed, along with their feasible conditions. The influences of geophone distribution, first arrival time picking, and the velocity model on microseismic source location are analyzed, and measures are proposed to influence these factors. Approaches to solve the problem under study include adopting information fusion, combining and optimizing existing methods, and creating new methods to realize high-precision microseismic source location. Optimization of the velocity structure, along with applications of the time-reversal imaging technique, passive time-reversal mirror, and relative interferometric imag-ing, are expected to greatly improve microseismic location precision in mines. This paper also discusses the potential application of information fusion and deep learning methods in microseismic source location in mines. These new and innovative location methods for microseismic source location have extensive prospects for development.展开更多
In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through thr...In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through three stages： directional random routing, h-hop routing in the annular region and the shortest path routing. These stages provide two fold protections to prevent the source location from being tracked down by the adversary. The analysis and simulation results show that proposed scheme, besides providing longer safety period, can significantly reduce energy consumption compared with two baseline schemes.展开更多
A new source location method using wave-equation based traveltime inversion is proposed to locate microseismic events accurately. With a sourceindependent strategy, microseismic events can be located independently reg...A new source location method using wave-equation based traveltime inversion is proposed to locate microseismic events accurately. With a sourceindependent strategy, microseismic events can be located independently regardless of the accuracy of the source signature and the origin time. The traveltime-residuals-based misfit function has robust performance when the velocity model is inaccurate. The new Fréchet derivatives of the misfit function with respect to source location are derived directly based on the acoustic wave equation, accounting for the influence of geometrical perturbation and spatial velocity variation. Unlike the mostly used traveltime inversion methods, no traveltime picking or ray tracing is needed.Additionally, the improved scattering-integral method is applied to reduce the computational cost. Numerical tests show the validity of the proposed method.展开更多
The study presents a novel source location method based on EEMD （ensemble empirical mode decomposition） and optimized FastICA （independent component analysis） for determining the position of the AE （acoustic emis...The study presents a novel source location method based on EEMD （ensemble empirical mode decomposition） and optimized FastICA （independent component analysis） for determining the position of the AE （acoustic emission） sources in the damage structure of crane. Firstly, The AE signals are self-adaptive decomposed into a number of IMFs （intrinsic mode functions） by using EEMD algorithm. Then, the main feature IMFs signals are extracted as the effective AE source signal by optimized Fast-ICA method, the PSD （power spectral density） of each IMF and the real IMFs are obtained. According to the principle of spectrum similarity, time delay is computed at the different channels in combination with the Cross Correlation time delay estimation principle. Thirdly, a novel source location algorithm based on EEMD-FastICA is proposed and the results of AE source location are obtained. Finally, the three-point bending test for the crane is implemented in order to validate the efficiency of the proposed method. The experimental results indicate that the proposed method has the ability to determinate the position of the AE sources and reduce the interference noises. Moreover, compared with the traditional location algorithm, a considerable improvement is obtained.展开更多
The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods ...The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods have poor adaptability to the low frequency oscillation with time-varying steady-state points because of the limitations in the location criterion derivation.A disturbance source location method on a low frequency oscillation with good generality is presented in the paper.Firstly,the reasons why the steady-state points are time-varying on a low frequency oscillation are analyzed.Then,based on the energy function construction form,the branch transmission energy is decomposed into state energy,reciprocating energy and dissipation energy by mathematical derivation.The flow direction of the dissipation energy shows the source and destination of the disturbance energy,and the specific location of a disturbance source can be identified according to its flow direction.Meanwhile,to meet the needs of energy calculation,a recognition method on the electrical quantities steady-state points is also presented by using the cubic spline interpolation.Simulation results show the correctness of the derivation and analysis on energy structure in the paper,and the disturbance source can be located accurately according to the dissipation energy.展开更多
The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominat...The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominator.These outliers can result in inaccuracy and unreliability of harmonic source location.To address this issue,this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid outliers.The correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring data.The results show that the proposed method is not only simple and easy to implement but also highly accurate.展开更多
Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were freque...Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.展开更多
Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due...Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.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
A proposed computer model for predicting aerosol particle dispersion in indoor spaces was validated with experimental data found in the literature, and is then used to study the effect of the area and point source loc...A proposed computer model for predicting aerosol particle dispersion in indoor spaces was validated with experimental data found in the literature, and is then used to study the effect of the area and point source locations on particle dispersion in displacement ventilation （DV） rooms. The results show that aerosol source location has a strong impact on the spatial distribution and removal rate of indoor particles. Particle removal performance depends strongly on ventilation efficiency and particle deposition rate on indoor surfaces. Important consideration for both relative ventilation efficiency and deposition rate consists of the position of the aerosol source relative to the main airflow pattern and the occupied zone.展开更多
Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based inde...Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.展开更多
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.
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financial support of the Fundamental Research Funds for the Central Universities(Grant No.2022XSCX35)the National Natural Science Foundation of China(Grant Nos.51934007 and 52104230).
文摘Microseismic source location is crucial for the early warning of rockburst risks.However,the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy.Intelligent techniques,such as the full convolutional neural network(FCNN),can capture spatial information but struggle with complex microseismic sequence.Combining the FCNN with the long shortterm memory(LSTM)network enables better time-series signal classification by integrating multiscale information and is therefore suitable for waveform location.The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction.In this study,we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus.Initially,the method of short-time-average/long-time-average(STA/LTA)arrival time picking was employed to augment spatiotemporal information.Subsequently,oversampling the on-site data was performed to address the issue of data imbalance,and finally,the performance of LSTM-FCNN was tested.Meanwhile,we compared the LSTM-FCNN model with previous deep-learning models.Our results demonstrated remarkable location capabilities with a mean absolute error(MAE)of only 7.16 m.The model can realize swift training and high accuracy,thereby significantly improving risk warning of rockbursts.
基金supported by the R&D of Key Instruments and Technologies for Deep Resources Prospecting(No.ZDYZ2012-1)National Natural Science Foundation of China(No.11374322)
文摘Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weighted- elastic-wave （WEW） interferometric imaging are proposed and used to locate modeled microseismic sources. The proposed method improves the precision and eliminates artifacts in location profiles. Numerical experiments based on a horizontally layered isotropic medium have shown that the method offers the following advantages： It can deal with Iow-SNR microseismic data with velocity perturbations as well as relatively sparse receivers and still maintain relatively high precision despite the errors in the velocity model. Furthermore, it is more efficient than conventional traveltime inversion methods because interferometric imaging does not require traveltime picking. Numerical results using a 2D fault model have also suggested that the weighted-elastic-wave interferometric imaging can locate multiple sources with higher location precision than the time-reverse imaging method.
文摘The efficiency of an optimization method for acoustic emission/microseismic（AE/MS） source location is determined by the compatibility of its error definition with the errors contained in the input data.This compatibility can be examined in terms of the distribution of station residuals.For an ideal distribution,the input error is held at the station where it takes place as the station residual and the error is not permitted to spread to other stations.A comparison study of two optimization methods,namely the least squares method and the absolute value method,shows that the distribution with this character constrains the input errors and minimizes their impact,which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors.When the errors in the input data are systematic and/or extreme in that the basic data structure is altered by these errors,none of the optimization methods are able to function.The only means to resolve this problem is the early detection and correction of these errors through a data screening process.An efficient data screening process is of primary importance for AE/MS source location.In addition to its critical role in dealing with those systematic and extreme errors,data screening creates a favorable environment for applying optimization methods.
基金Project(2015CB060200)supported by the National Basic Research Program of ChinaProject(41772313)supported by the National Natural Science Foundation of China+1 种基金Project(MDPC201803)supported by the Open Fund Research Program of State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(2019zzts308)supported by the Fundamental Research Fund for the Central Universities of Central South University,China
文摘To solve the problem that the existing acoustic emission(AE) source location algorithms cannot always obtain accurate results for multilayer cylindrical media,a new acoustic emission source location method considering refraction was proposed.AE source coordinates were solved by the complex method.Pencil-lead-break experiments were used to verify this method.The absolute distance errors of location results are less than 3 mm,much less than those by the traditional method.The numerical experiments were used to further analyze factors that affect location accuracy.The results of numerical experiments show that the location accuracy of the proposed method is not affected by the ratio of wave velocities but affected by the measurement accuracy of wave velocity.These results show that new method can obtain accurate AE source location in the two-layered cylindrical surface media such as the triaxial compression test.
基金We are grateful for the financial support from the National Science Foundation for Excellent Young Scholars of China(51822407)the Natural Science Foundation of China(51774327)+1 种基金the Special Fund for Basic Scientific Research Operations in Universities(2282020cxqd055)the Fundamental Research Funds for the Central Universities of Central South University(2021zzts0875).
文摘Acoustic emission(AE)localization plays an important role in the prediction and control of potential hazardous sources in complex structures.However,existing location methods have less discussion on the presence of unknown empty areas.This paper proposes an AE source location method for structures containing unknown empty areas(SUEA).Firstly,this method identifies the shape,size,and location of empty areas in the unknown region by exciting the active AE sources and using the collected AE arrivals.Then,the unknown AE source can be located considering the identified empty areas.The lead break experiments were performed to verify the effectiveness and accuracy of the proposed method.Five specimens were selected containing empty areas with different positions,shapes,and sizes.Results show the average location accuracy of the SUEA increased by 78%compared to the results of the existing method.It can provide a more accurate solution for locating AE sources in complex structures containing unknown empty areas such as tunnels,bridges,railroads,and caves in practical engineering.
基金Project(cstc2020jcyj-bshX0106)supported by the Chongqing Postdoctoral Science Foundation,ChinaProject(2020M683247)supported by the China Postdoctoral Science Foundation+1 种基金Project(cstc2020jcyj-zdxmX0023)supported by the Key Natural Science Foundation Project of Chongqing,ChinaProject(551974043)supported by the National Natural Science Foundation of China。
文摘Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.
基金sponsored by the Shanghai Sailing Program (Grant No. 18YF1424000)Shanghai Education Commission (Peak Discipline Construction Program, Grant Nos. 0200121005/052 & 2019010206)
文摘Particle breakage is a common occurrence in granular systems when the external stress exceeds the individual particle strength.A large number of experimental evidences suggested that particle breakage may significantly influence the soil behavior.In the case of pile foundations,the subsoil below the pile tip experiences considerable high stress and consequently prone to break.Due to the lack of sufficient understanding on particle breakage mechanism,there is currently no consentaneous theoretical background for particle breakage analysis during the pile penetration process.This study aims to clarify the location of particle breakage and its evolving characteristics with the aid of acoustic emission(AE)source location method.The spatial distribution of AE hypocenters is interpreted to be associated with the mechanism of particle breakage.Results showed that the AE sources were not uniformly distributed,but concentrated within certain zones below the pile tip.This AE concentration zone was pushed downward with the advancing pile tip,and its distance from the real time pile tip position decreased after certain depth of pile penetration.The location of particle breakage interpreted from AE source location was verified with posttest excavations and the insights on the particle breakage evolution zone were further discussed.
基金This research was supported by the National Key Research and Development Program of China （2016YFC0801405 and 2017YFC0804105）, and the National Natural Science Foundation of China （51574250）. The authors also greatly indebted to Dr. Ye Chen, who is now working at the Research Centre of Photonics and Instrumentation at City, University of London, for his rigorous suggestions for this paper.
文摘Microseismic source location is the essential factor in microseismic monitoring technology, and its loca- tion precision has a large impact on the performance of the technique. Here, we discuss the problem of low-precision location identification for microseismic events in a mine, as may be obtained using conven-tional location methods that are based on arrival time. In this paper, microseismic location characteristics in mining are analyzed according to the characteristics of the mine＇s microseismic wavefield. We review research progress in mine-related microseismic source location methods in recent years, including the combination of the Geiger method with the linear method, combined microseismic event location method, optimization of relative location method, location method without pre-measured velocity, and location method without arrival time picking. The advantages and disadvantages of these methods are discussed, along with their feasible conditions. The influences of geophone distribution, first arrival time picking, and the velocity model on microseismic source location are analyzed, and measures are proposed to influence these factors. Approaches to solve the problem under study include adopting information fusion, combining and optimizing existing methods, and creating new methods to realize high-precision microseismic source location. Optimization of the velocity structure, along with applications of the time-reversal imaging technique, passive time-reversal mirror, and relative interferometric imag-ing, are expected to greatly improve microseismic location precision in mines. This paper also discusses the potential application of information fusion and deep learning methods in microseismic source location in mines. These new and innovative location methods for microseismic source location have extensive prospects for development.
基金Supported by the National Natural Science Foundation of China(61170065)the Natural Science Foundation of Jiangsu Province(BK20130882)the Scientific Research Foundation of Nanjing University of Posts and Telecommunications(NY214118)
文摘In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through three stages： directional random routing, h-hop routing in the annular region and the shortest path routing. These stages provide two fold protections to prevent the source location from being tracked down by the adversary. The analysis and simulation results show that proposed scheme, besides providing longer safety period, can significantly reduce energy consumption compared with two baseline schemes.
文摘A new source location method using wave-equation based traveltime inversion is proposed to locate microseismic events accurately. With a sourceindependent strategy, microseismic events can be located independently regardless of the accuracy of the source signature and the origin time. The traveltime-residuals-based misfit function has robust performance when the velocity model is inaccurate. The new Fréchet derivatives of the misfit function with respect to source location are derived directly based on the acoustic wave equation, accounting for the influence of geometrical perturbation and spatial velocity variation. Unlike the mostly used traveltime inversion methods, no traveltime picking or ray tracing is needed.Additionally, the improved scattering-integral method is applied to reduce the computational cost. Numerical tests show the validity of the proposed method.
基金Acknowledgments This work is supported by the National Natural Science Foundation of China （51575101, 51305176）, the funds of High-level talents of JIT （JIT6201623, FHXM201608）.
文摘The study presents a novel source location method based on EEMD （ensemble empirical mode decomposition） and optimized FastICA （independent component analysis） for determining the position of the AE （acoustic emission） sources in the damage structure of crane. Firstly, The AE signals are self-adaptive decomposed into a number of IMFs （intrinsic mode functions） by using EEMD algorithm. Then, the main feature IMFs signals are extracted as the effective AE source signal by optimized Fast-ICA method, the PSD （power spectral density） of each IMF and the real IMFs are obtained. According to the principle of spectrum similarity, time delay is computed at the different channels in combination with the Cross Correlation time delay estimation principle. Thirdly, a novel source location algorithm based on EEMD-FastICA is proposed and the results of AE source location are obtained. Finally, the three-point bending test for the crane is implemented in order to validate the efficiency of the proposed method. The experimental results indicate that the proposed method has the ability to determinate the position of the AE sources and reduce the interference noises. Moreover, compared with the traditional location algorithm, a considerable improvement is obtained.
基金This work was supported in part by National Natural key R&D Program of China(2016YFB0900100).
文摘The low frequency oscillation is a serious threat to security and stability of a power grid.How to locate the disturbance source accurately is an important issue to low frequency oscillation disposal.Existing methods have poor adaptability to the low frequency oscillation with time-varying steady-state points because of the limitations in the location criterion derivation.A disturbance source location method on a low frequency oscillation with good generality is presented in the paper.Firstly,the reasons why the steady-state points are time-varying on a low frequency oscillation are analyzed.Then,based on the energy function construction form,the branch transmission energy is decomposed into state energy,reciprocating energy and dissipation energy by mathematical derivation.The flow direction of the dissipation energy shows the source and destination of the disturbance energy,and the specific location of a disturbance source can be identified according to its flow direction.Meanwhile,to meet the needs of energy calculation,a recognition method on the electrical quantities steady-state points is also presented by using the cubic spline interpolation.Simulation results show the correctness of the derivation and analysis on energy structure in the paper,and the disturbance source can be located accurately according to the dissipation energy.
基金supported by the National Natural Science Foundation of China(No.51777035).
文摘The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic sources.However,the existing calculation methods suffer from outliers caused by the zero-crossing of the denominator.These outliers can result in inaccuracy and unreliability of harmonic source location.To address this issue,this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid outliers.The correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring data.The results show that the proposed method is not only simple and easy to implement but also highly accurate.
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(No.NIER-2019-04-02-039)supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.
基金supported by the National Natural Science Foundation of China(Nos.U23A2016,U1704241,and 42007175).
文摘Water-soluble organic nitrogen(WSON)affects the formation,hygroscopicity,acidity of organic aerosols,and nitrogen biogeochemical cycles.However,qualitative and quantitative characterizations of WSON remain limited due to its chemical complexity.In the study,1-year field samples of particulate matter 2.5(PM_(2.5))were collected fromJune 2022 to May 2023 to analyze the WSON concentration in PM_(2.5),and correlation analysis,positive matrix factor(PMF),and potential source contribution function(PSCF)modelswere employed to elucidate WSON source apportionment and transport pathways.The results revealed that the mean WSON concentrations reached 1.98±2.64μg/m^(3) with a mean WSON to water-soluble total nitrogen(WSTN)ratio of 21%.Further,WSON concentration exhibited a seasonal variation trend,with higher values in winter and lower in summer.Five sources were identified as contributors to WSON in PM_(2.5) within the reservoir area through a comprehensive analysis including correlation analysis,PSCF and concentration weighted trajectory(CWT),and PMF analyses.These sources were agricultural,dust,combustion,traffic,and industrial sources,of which agricultural source emerged as the primary contributor(76.69%).The atmosphere in the reservoir area were primarily influenced by the transport of northeastern air masses,local agricultural activities,industrial cities along the trajectory,and coastal regions,exerting significant influences on the concentration of WSON in the reservoir area.The findings of this study addressed the research gap concerning organic nitrogen in PM_(2.5) within the reservoir area,thereby offering a theoretical foundation and data support in controlling nitrogen pollution in the Danjiangkou Reservoir area.
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the National Natural Science Foundation of China(Grant No.50578034)Shanghai Educational Development Foundation,titled"Shuguang Project",PR.China(Grant No.03SG30).
文摘A proposed computer model for predicting aerosol particle dispersion in indoor spaces was validated with experimental data found in the literature, and is then used to study the effect of the area and point source locations on particle dispersion in displacement ventilation （DV） rooms. The results show that aerosol source location has a strong impact on the spatial distribution and removal rate of indoor particles. Particle removal performance depends strongly on ventilation efficiency and particle deposition rate on indoor surfaces. Important consideration for both relative ventilation efficiency and deposition rate consists of the position of the aerosol source relative to the main airflow pattern and the occupied zone.
基金This work was partly supported by National Natural Science Foundation of China(No.61903296)Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(No.2019ZDLGY18-03)+1 种基金Thousand Talents Plan of Shaanxi Province for Young Professionals,Project of Shaanxi Science and Technology(No.2019JQ-329)Doctoral Scientific Research Foundation of Xi’an University of Technology(No.103-451116012).
文摘Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality.In this paper,an attention-based independently recurrent neural network(IndRNN)for sag source location and sag type recognition in sparsely monitored power system is proposed.Specially,the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system,and the desired outputs simultaneously contain the following information:the located lines where sag occurs;the corresponding sag types,including motor starting,transformer energizing and short circuit;and the fault phase for short circuit.In essence,the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible.A favorable feature of the proposed method is that it can be realized without system parameters or models.The proposed method is validated by IEEE 30-bus system and a real 134-bus system.Experimental results demonstrate that the accuracy of sag source location is higher than 99%for all lines,and the accuracy of sag type recognition is also higher than 99%for various sag sources including motor starting,transformer energizing and 7 different types of short circuits.Furthermore,a comparison among different monitor placements for the proposed method is conducted,which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.
