Microseismic Monitoring System Research Articles

Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground

Blasting technology is widely used to prevent coal bursts by presplitting the overburden in underground coal mines. The control of blasting intensity is important in achieving the optimal pre-split effectiveness and reducing the damage to roadway structures that are subjected to blasting vibrations. As a critical parameter to measure the blasting intensity, the peak particle velocity (PPV) of vibration induced by blasting, should be accurately predicted, and can provide a useful guideline for the design of blasting parameters and the evaluation of the damage. In this paper, various factors that influence PPV, induced by roof pre-split blasting, were analyzed using engineering blasting experiments and numerical simulations. The results showed that PPV was affected by many factors, including charge distribution design (total charge and maximum charge per hole), spacing of explosive centers, as well as propagation distance and path. Two parameters, average charge coefficient and spatial discretization coefficient were used to quantitatively characterize the influences of charge distribution and spacing of explosive centers on the PPV induced by roof pre-split blasting. Then, a model consisting of the combination of artificial neural network (ANN) and genetic algorithm (GA) was adopted to predict the PPV that was induced by roof pre-split blasting. A total of 24 rounds of roof pre-split blasting experiments were carried out in a coal mine, and vibration signals were collected using a microseismic (MS) monitoring system to construct the neural network datasets. To verify the efficiency of the proposed GA-ANN model, empirical correlations were applied to predict PPV for the same datasets. The results showed that the GA-ANN model had superiority in predicting PPV compared to empirical correlations. Finally, sensitivity analysis was performed to evaluate the impacts of input parameters on PPV. The research results are of great significance to improve the prediction accuracy of PPV induced by roof pre-splitting blasting.

Distribution characteristics and the evolution law of excavation damage zone in the large-span transition section of high-speed railway tunnel based on microseismic monitoring

PurposeThe microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.Design/methodology/approachIn situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.FindingsMicroseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.Originality/valueThe research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

Attenuation characteristics of impact-induced seismic wave in deep tunnels: An in situ investigation based on pendulum impact test

The radiated seismic energy is an important index for the intensity assessment of microseismic (MS) events and the early warning of dynamic disasters. However, the energy of MS signals is significantly attenuated due to the heterogeneity and viscous damping of rock media. Therefore, the study on attenuation characteristics of MS signals in underground engineering has practical significance for the accurately estimation of radiated seismic energy. Based on a pendulum impact test facility and MS monitoring system, an in situ investigation was carried out to explore attenuation characteristics at a deep tunnel. The results show that the seismic energy and peak particle velocity (PPV) attenuation are exponentially related to the propagation distance. The attenuation coefficient of energy is larger than that of PPV. With the increase in the input impact-energy, the seismic energy attenuation coefficient decreases as a power function. An empirical relationship between energy attenuation coefficient and wave impedance of rock mass was established in this scenario. Moreover, the time-frequency characteristics and energy distribution laws of impact-induced signals were investigated by the continuous wavelet transform (CWT) and wavelet packet analyses, respectively. The dominant frequency of signals decreases gradually as the propagation distance increases. Based on the energy attenuation characteristics, a new method was proposed to calculate the released source energy of MS events in the field. This study can provide an insight into energy attenuation characteristics of seismic waves and references for attenuation correction in seismic energy calculation.

Intelligent Microseismic Events Recognition in Fiber-Optic Microseismic Monitoring System Compared With Electronic One

Fiber-optic seismic sensor has become an emerging effective tool for microseismic monitoring, which is of importance for oil and gas production improvement. When processing the fiber-optic data, the recognition of microseismic events is the first crucial step, which is rarely reported due to the lack of such data. In this paper, based on the features of central data distribution, skewness, kurtosis, energy entropy, etc., the classification accuracy of microseismic (MS) events obtained by fiber-optic MS monitoring system and electronic one is compared using machine learning algorithm (e. g. SVM and KNN) for the first time. The results show that fiber-optic data has higher classification accuracy than electronic data when using the feature of central data distribution owing to the high signal-to-noise ratio of fiber-optic data. However, when choosing the features of energy entropy, zero-crossing rate and the energy proportion of specific frequency band, electronic data has higher classification accuracy than fiber-optic data benefiting from the longer events duration and the lower frequency components of electronic data. When using skewness and kurtosis, the classification performance in fiber-optic data is almost consistent with the electronic one. Moreover, the results indicate that the characteristics of MS signal itself have a greater impact on the discrimination ability of MS events than the applied machine learning classification algorithm.

Floor Failure Characteristics in Deep Island Longwall Panel: Theoretical Analysis and Field Verification

Floor failure in deep coal mining above confined aquifers with high-water pressure may induce floor water inrush disasters. Considering the effects of mining stress and nonuniformly distributed water pressure, a mechanical calculation model of the island longwall panel in up-dip mining was established, and the stress distribution and floor failure characteristics were analyzed. The failure characteristics of the floor at NO. 2129 panel in Xingdong coal mine were detected by the borehole televiewer and microseismic monitoring system to validate the theoretical model. The results indicated that the floor failure characteristics along the strike and inclination of the island longwall panel in up-dip mining were “asymmetric inverted saddle-shaped” and “spoon-shaped,” respectively. The maximum floor failure depths before and after roof hydraulic fracturing (RHF) were 45.7 m and 29.1 m, respectively. The theoretical calculation results of the maximum depths of floor failure were 45.1 m and 29.9 m, respectively. The theoretical failure characteristics were consistent with those measured on site. The stress concentration magnitude and floor failure depth on the side of the isolated coal pillar were greater than those of other areas, and the water-inrush-prone zones were concentrated on the side of the isolated coal pillar near the intersection of the working face and the roadway. The research results could provide a certain reference for floor failure and water inrush mechanisms under complex geological conditions in deep mining.

Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine

The risks associated with underground mining at Fankou Lead/Zinc Mine in South China are growing due to the large-scale mining activities there. To recognize mining-induced earthquakes and assess the risk per mining level, a microseismic monitoring system, which is used to record microseismic events, is installed at multiple mining levels in Fankou Lead/Zinc Mine. The purpose of this study is to identify mining-induced earthquakes and to evaluate the risk per mining level by analyzing the spatiotemporal characteristics of microseismic activities in the Fankou Lead/Zinc Mine. In this study, the Gutenberg-Richter (G-R) relationship is applied to compute the b-value, which is used to obtain the maximum magnitude (M (max)) of microseismic event that probably occurs at each mining level. Then, the evaluation of the recurrence period for M (max) and the probability of the microseismic event with the magnitude M (max) is carried out and the M (max) at each mining level is determined based on the recording period of microseismic events. The results show that factors such as the maximum rock vibration velocity, source parameters, displacement, microseismic waveform and energy ratio (ES/EP) can be used to distinguish whether a recorded microseismic event is mining-induced earthquake. Additionally, we propose a method to assess the possibility of mining-induced earthquake at each mining level based on M (max) and predict the recurrence time of microseismic event with the magnitude M (max). The of two years results of microseismic events monitoring demonstrate that the current study is promising for identifying mining-induced earthquakes, assessing the risk of mining-induced earthquakes, predicting the potential maximum microseismic event in a region and estimating its recurrence period and its probability in the Fankou Lead/Zinc Mine.

Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment.

In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.

Microseismic monitoring system of the pit’s board array based on seismic sensors in deep boreholes beyond the final pit contour

Microseismic monitoring system of the pit’s board array based on seismic sensors in deep boreholes beyond the final pit contour

Design and application of microseismic monitoring system of underground optical fiber

为满足油气开发中微地震监测的需求，基于时分复用方案设计了一种新型井下3分量光纤微地震监测系统。结合室内测试，该系统本底噪声小于−101 dB，动态范围大于120 dB，级间串扰小于−67 dB，满足微地震信号探测的需求。该系统已成功应用于新疆油田水力压裂微地震现场探测。监测结果表明：该系统可清晰捕捉微地震的P波、S波信号，较好地揭示了水力压裂微地震信号的特征信息，为微地震事件的空间定位和裂缝特征参数解释建立了良好基础。

Rockburst Inducement Mechanism and Its Prediction Based on Microseismic Monitoring

The rockburst disaster in the hard rock caused by excavation and unloading of deep underground caverns threatens the safety of engineering construction. In recent years, the microseismic monitoring technology, which can dynamically monitor the whole process of progressive failure of rock mass in real time, has been widely used in rockburst monitoring and early warning of underground engineering. In view of the slight rockburst in local surrounding rock during the excavation of underground powerhouse of Huanggou Pumped Storage Power Station, a rockburst microseismic monitoring system is constructed. And through the analysis of the temporal and spatial activity of microseisms during the monitoring period, the potential risk areas of rockbursts are identified and delineated. The monitoring results show that the microseismic system can effectively capture the blasting and microseismic signals during construction. The microseismic activity is closely related to the intensity of field blasting disturbance. The potential risk areas of rockburst are the upstream side arch shoulder and the intersection between lower drainage corridor and workshop installation room. The research results can provide technical support for later excavation and support of underground powerhouse caverns of Huanggou Hydropower Station.

Highly sensitive fiber optic microseismic monitoring system for tunnel rockburst

By capturing the micro-crack signals of rock mass structure, the microseismic (MS) monitoring technology is a good candidate for the forecasting and early warning of dynamic disaster. In this paper, MS sensors based on fiber optic interferometric scheme are studied and fabricated, and authors develop an 8-channel highly sensitive fiber optic MS system based on space division multiplexing technology to monitor the whole process of rock mass from micro-fracture to macro-damage. Field test of the system is carried out in Hanjiang River to Weihe River Water Diversion Project. The results show that it can realize continuously monitored with high accuracy and reliability in the tunnel and obtain the characteristic of temporal-spatio-strong of MS events, which are superior to the commercialized electrical monitoring equipment. This work shows that using fiber optic interference technology to develop accelerometer for MS monitoring system is both advantageous and feasible with the character of intrinsically safe.

Experimental Investigations on Charge Induction and Microseismic Characteristics of the Coal and Rock under Different Loading Rates

The research on charge induction and microseismic characteristics of coal and rock under different loading rates is of great significance for rockburst prediction. In this study, the coal and sandstone samples from the No. 11 mine of Pingdingshan Coal Mine are prepared. The charge induction and microseismic synchronous comprehensive monitoring system is built. The uniaxial compression tests of coal and sandstone samples under the different loading rates are conducted. The charge induction and microseismic signal characteristics in the deformation and fracture process of the coal and rock under the different loading rates are studied. The results show that, with the increase of loading rate, the compressive strength of the coal and rock samples increases and the time from the peak stress to instability failure becomes shorter. At the same loading rate, the softening failure stage time of coal is longer than that of sandstone. With the increase of loading rate, the duration of charge-induced signal and microseismic signal is longer and the events’ number and amplitude of charge signal and microseismic signal increase in the deformation and fracture process of the coal and rock. Before the instability failure, the charge-induced signal and microseismic signal have both synchronous and asynchronous signals, and the amplitude of charge-induced and microseismic signals in each channel is different, which is related to the distance from the position of each sensor to the fracture point of the sample. During the instability failure, the charge induction and microseismic signals of each channel are generated synchronously, and the signal amplitude reaches the maximum values of 50 pC and 6 × 10−3 m/s at the same time. With the increase of specimen stress, the dominant frequency of microseismic signals first increases and then decreases, while the amplitude of dominant frequency increases synchronously. The dominant frequency amplitude of microseismic signals is the largest in instability failure. With the increase of loading rate, the spectrum amplitude of microseismic signals changes little in the compaction stage, but the spectrum amplitude increases in other stages. At the same loading speed, the events’ number of the microseismic signal of coal samples after peak stress is more than that of sandstone samples, and the signal amplitude is also larger. However, the spectrum distribution range of microseismic signals of coal samples is wider than that of sandstone samples, and the spectrum amplitude of coal samples is lower than that of sandstone. With the increase of loading rate, the time of the first generation of high-amplitude signals is advanced, and the stress of specimen becomes smaller when the first generation of high-amplitude signals occurs. With the increase of loading rate, the duration of microseismic and charge signal is longer, and the mean square amplitude of charge signal is larger.

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.

Comprehensive monitoring for rockburst at a pumped storage power station in northeast China

Rockburst is characterised by the wide occurrence areas and the long delayed time at a pumped storage power station in Heilongjiang province of China. For the study, a new method is proposed to identify the induced rockburst based on the combination of microseismic (MS) and electromagnetic radiation (EMR) monitoring methods. EMR monitoring method is introduced to make up for the shortcomings of the monitoring blind area caused by the propagation path of wave velocity during MS monitoring and a beneficial attempt is carried out for the comprehensive application of the two methods to monitor the rockburst. Firstly, a MS monitoring system is established around the underground powerhouse to recognize the propagation of micro-fracture during the deep excavation, while the typical five parameters including the frequency of the microseismic events, the total energy of daily events, energy index (EI), cumulative apparent volume (CAV) and microseismic signal b-value are analyzed to reveal the temporal and spatial evolution law of microseismic events. Secondly, an EMR monitoring system is also established near the MS events clusting area to study the characteristics of EMR produced from the fractured rock mass. The temporal and spatial variation of the two parameters including electromagnetic radiation intensity and pulse number are also analyzed and the distribution of higher values are compared with the geological structure. Finally, the correlation of electromagnetic signals, microseismic signals and local stress is studied based on theories of geophysics, electromagnetism and rock mechanics, and some credible results are obtained. Here are the following resluts: (1) The microseismic events are characterized by spatial clustering, and the joint action of the excavation disturbance and the weak structure body of the surrounding rock mass are the main factors for rockburst. The trend of sharp decrease of energy index and sudden increase of cumulative apparent volume generally indicates the occurrence of rockburst or macro-fracture. The magnitude of b value indicates that the rockburst failure type in the project belongs to fracture-slip rockburst. (2) The sudden change of electromagnetic radiation intensity in the fault fracture zone of the underground powerhouse can be used as the precursor information of rockburst or large-scale rupture events.(3) The micro-fracture activities revealed by EMR and MS methods are consistent in spatial distribution. The distribution of the peak values of electromagnetic radiation pulse number are basically consistent with the direction of the apparent stress migration disturbed by the field construction situation. So we can infer that it will play an important role to improve the accuracy of rockburst prediction based on the analysis of the multi-source singals.

Reasonable Determination of Terminal Mining Lines Using the Stress Field with Seismic Wave Excitation in Deep Coalfaces

Many field observations have shown that rock bursts occur frequently near the terminal mining line (TML) and dip coal pillar area in deep coalfaces. Taking the “7.26” rock burst in coalface 3302 in Xingcun Coal Mine as an example, the rock burst mechanism was investigated based on theoretical analysis and field observations, and a combined evaluation method using the stress field under seismic wave excitation was established to determine the reasonable TML of coalface 3302. Firstly, the static geological data revealed during roadway excavation were used for preevaluation of rock burst risk at the working face. By theoretically analyzing the stress transfer mechanism of the two types of the roof structure, the computational model of abutment pressure was established and the calculation method giving the abutment stress was proposed. Subsequently, a dynamic evaluation method that adopts microseismic and stress online monitoring system to monitor dynamic information, such as mine tremors and stress during coalface mining, was developed to define stress anomaly areas and then dynamically determine the TML. Finally, the proposed model was used to optimize the position of the TML of LW3302 in Xingcun Coal Mine; findings obtained in this study provide theoretical guidance for safe coal mining. Combined with the results of theoretical analysis (255 m), online stress monitoring (200 m), microseismic (MS) monitoring (262 m), and passive seismic velocity tomography (220–250 m), it can be finally determined that the width of the protective coal pillar for the TML of coalface 3302 should be at least 262 m.

An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts

To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.

Study on Response Characteristics of Surrounding Rock Rupture Microseismic Events during Coal Roadway Excavation

In order to solve the problems such as coal burst and abnormal gas overrun caused by the fracture of surrounding rock in the process of mining in the coal roadway, the ESG microseismic monitoring system is built on the driving face of 8005 transportation roadway of Wuyang Coal Mine to carry out a real-time, continuous, and omnidirectional dynamic state monitoring. In this way, the characteristics of time-frequency evolution and energy distribution of the acquired signals are systematically analyzed, and the location of the roadway microseismic events is studied. The results show the following: (1) influenced by mining activities, the localized microseismic events are mostly distributed in front and on both sides of the working face. Due to mining activities and geological changes, the equilibrium of original stress in coal and rock mass is broken. The stress thus is released accompanied by fracture before reaching a new equilibrium. (2) By comparing the coal and rock fracture signals with the interference signals, it is found that the fracture signals have short duration and large amplitude with obvious abrupt change characteristics. The interference signals have long duration and relatively small amplitude with less obvious change. (3) Fourier transform analysis shows that the main frequency of coal rock fracture signals is 100–200 Hz with large total energy concentrated in the first frequency band, while that of interference signals is mostly less than 100 Hz with small total energy. The research results can effectively identify the coal and rock dynamic disasters, provide technical support for the prediction and early warning of hidden danger, and guide the safe and efficient production.

Prediction of rockbursts in a typical island working face of a coal mine through microseismic monitoring technology

Coal has dominated the energy structure in China for a long time. In recent years, dynamic disasters such as rockbursts have affected the safety of coal mine in production with the increase of coal mining depth. Microseismic monitoring is a non-intrusive monitoring technique that can be used to collect much key information: it has been widely applied in coal mine safety monitoring. In this paper, the failure mechanism and energy conversion in a coal-rock mass during mining were investigated, revealing the controlling effect of the surrounding rock mass on the coal seam. microseismic events during mining were monitored by using a self-built high-precision microseismic monitoring system, a variety of analytical methods have been used to quantify the monitoring results, and two rockburst accidents have been identified. The responses of different parameters before the rockburst were analyzed, and the response characteristics of different parameters to rockburst were summarized. The functional relationship between microseismic energy and rock damage was established by classical Benioff strain theory. Combined with the microseismic energy, strain, the degree of damage and the damage, the model of coal-rock mass was verified and modified through physical experiments. A new parameter for rockburst prediction (the energy rockburst warning (ERW) index) was defined and the numerical relationship between ERW and microseismic energy was deduced. The monitoring data were analyzed using this model and results showed that compared with other analysis, the new model can effectively assess the stability of coal-rock mass to a certain extent.

Accurate identification of microseismic waveforms based on an improved neural network model

Microseismic waveform identification is the basis of rock burst risk assessment and rock burst warning in metal mines. This paper presents a method combining a genetic algorithm and neural network algorithm to classify the signals collected by a microseismic monitoring system during the mining process. After the analysis of the characteristics of the signals, six parameters, the duration time (DT), rise time (RT), ring number (RN), ring number of rise time (RRN), maximum amplitude (MA), and main frequency (MF), are selected for waveform recognition, that is, these six parameters are taken as the inputs of the waveform classification model. After establishing a fitness function, the numbers of the nodes in the two hidden layers were determined by a genetic algorithm. A 6–79-1 neural network waveform identification model was established, including one input layer, two hidden layers and one output layer. Only one blasting vibration waveform was misjudged as a rock mass fracture waveform when 114 samples were tested. Compared to the unordered multiple classification logistic regression method (UMCLRM), the improved neural network (INN) model had a higher classification accuracy. This model can effectively classify microseismic waveforms, mechanical vibration waveforms, electrical noise waveforms and blasting vibration waveforms. Additionally, the accuracy of waveform recognition is likely to be improved by using a two-layer hidden layer structure instead of by using a one-layer hidden layer structure. However, the complexity of the model increases. The waveform recognition model proposed in this paper is of great help to reduce the amount of manual work required to perform waveform recognition.

An Analysis Method for Time-, Frequency-, and Energy-Domain Characteristics of Downhole Microseismic Signals and Its Application

Based on SOS microseismic (MS) monitoring system of a certain mine, time-, frequency-, and energy-domain characteristics of MS signals are analyzed by comprehensively applying one-dimensional discrete Fourier transform (DFT) and one-dimensional discrete wavelet transform (DWT). The technology is employed to explore MS signals from incident and transmitted waves on the fault plane. The research results show that, within the time domain, the transmitted waves are found to have the rapidly reducing velocity and a few wave crests; within the frequency domain, the fault plane presents a low-pass filtering effect on incident waves; within the energy domain, the fault generally shows the vibration-attenuating effect on MS signals. The analysis method is used for the No. 3307 working face of a certain mine to realize the prediction on a buried fault in the middle part of the working face, and it is validated to have a favorable effect.