Classification of mine blasts and microseismic events using starting-up features in seismograms

To find discriminating features in seismograms for the classification of mine seismic events,signal databases of blasts and microseismic events were established based on manual identification.Criteria including the repetition of waveforms,tail decreasing,dominant frequency and occurrence time of day were considered in the establishment of the databases.Signals from databases of different types were drawn into a unified coordinate system.It is noticed that the starting-up angles of the two types tend to be concentrated into two different intervals.However,it is difficult to calculate the starting-up angle directly due to the inaccuracy of the P-wave arrival＇s picking.The slope value of the starting-up trend line,which was obtained by linear regression,was proposed to substitute the angle.Two slope values associated with the coordinates of the first peak and the maximum peak were extracted as the characteristic parameters.A statistical model with correct discrimination rate of greater than 97.1% was established by applying the Fisher discriminant analysis.

Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.

Automatic microseismic events detection using morphological multiscale top-hat transformation

The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise ratio.We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation.The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section.The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly.Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section,and the characteristic profile obtains the start point with a specific threshold value.Finally,the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions,and the filed data example also shows its reliability and adaptability.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

Tomographic inversion for microseismic source parameters in mining

We propose a new method for inverting source function of microseismic event induced in mining. The observed data from microseismic monitoring during mining are represented by a wave equation in a spherical coordinate system and then the data are transformed from the time-space domain to the time-slowness domain based on tomographic principle, from whichwe can obtain the signals related to the source in the time-slowness domain. Through analyzing the relationship between the signal located at the maximum energy and the source function, we derive the tomographic equations to compute the source function from the signals and to calculate the effective radiated energy based on the source function. Moreover, we fit the real amplitude spectrum of the source function computed from the observed data into the co-2 model based on the least squares principle and determine the zero-frequency level spectrum and the corner frequency, finally, the source rupture radius of the event is calculated and The synthetic and field examples demonstrate that the proposed tomographic inversion methods are reliable and efficient

Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.

Fast Marching Method for Microseismic Source Location in Cavern-Containing Rockmass: Performance Analysis and Engineering Application

Microseismic(MS)event locations are vital aspect of MS monitoring technology used to delineate the damage zone inside the surrounding rock mass.However,complex geological conditions can impose significantly adverse effects on the final location results.To achieve a high-accuracy location in a complex cavern-containing structure,this study develops an MS location method using the fast marching method(FMM)with a second-order difference approach(FMM2).Based on the established velocity model with three-dimensional(3D)discrete grids,the realization of the MS location can be achieved by searching the minimum residual between the theoretical and actual first arrival times.Moreover,based on the calculation results of FMM2,the propagation paths from the MS sources to MS sensors can be obtained using the linear interpolation approach and the Runge–Kutta method.These methods were validated through a series of numerical experiments.In addition,our proposed method was applied to locate the recorded blasting and MS events that occurred during the excavation period of the underground caverns at the Houziyan hydropower station.The location results of the blasting activities show that our method can effectively reduce the location error compared with the results based on the uniform velocity model.Furthermore,the obtained MS location was verified through the occurrence of shotcrete fractures and spalling,and the monitoring results of the in-situ multipoint extensometer.Our proposed method can offer a more accurate rock fracture location and facilitate the delineation of damage zones inside the surrounding rock mass.

Master event based backazimuth estimation and its application to downhole microseismic monitoring

Microseismic monitoring provides a valuable tool for evaluating the effectiveness of hydraulic fracturing operations.However,robust detection and accurate location of microseismic events are challenging due to the low signal to noise ratio(SNR)of their signals on seismograms.In a downhole monitoring setting,P-wave polarization direction measured from 3-component records is usually considered as the backazimuth of the microseismic event,i.e.,the direction of the event.The direction and arrival time difference between the P and S waves is used to locate the seismic event.When SNR is low,an accurate estimate of event backazimuth becomes very challenging with the traditional covariance matrix method.Here we propose to employ a master event and use a grid search method to find the backazimuth of a target event that maximizes the dot product of the two backazimuthal vectors of the master and target events.We compared the backazimuths measured with the proposed grid-search and the conventional covariance-matrix methods using a large synthetic dataset.We found that the grid-search method yields more accurate backazimuth estimates from low SNR records when measurements are made at single geophone level.When array data are combined,the proposed method also has some advantage over the covariance-matrix method,especially when the number of geophones is low.We also applied the method to a microseismic dataset acquired by a hydraulic fracturing project at a shale play site in southwestern China and found that the relocated microseismic events tend to align along existing faults more tightly than those in the original catalog.

Acoustic emission monitoring of rockbursts during TBM-excavated headrace tunneling at Jinping Ⅱ hydropower station

To better understand the mechanical properties of marble at Jinping II hydropower station, this paper examines the changes of brittle rocks in excavation damaged zones(EDZs) before and after excavation of tunnel with the tunnel boring machine(TBM). The paper attempts to employ the acoustic emission(AE) to study the AE characteristics and distribution of rockburst before and after TBM-excavated tunnel. It is known that the headrace tunnel #2, excavated by the drill-and-blast(D&B) method, is ahead of the headrace tunnel #3 that is excavated by TBM method. The experimental sub-tunnel #2–1, about 2000 m in depth and 13 m in diameter, between the two tunnels is scheduled. In the experimental sub-tunnel #2–1, a large number of experimental boreholes are arranged, and AE sensors are installed within 10 m apart from the wall of the headrace tunnel #3. By tracking the microseismic signals in rocks, the location, frequency, quantity, scope and intensity of the microseismic signals are basically identifed. It is observed that the AE signals mainly occur within 5 m around the rock wall, basically lasting for one day before tunnel excavation and a week after excavation. Monitoring results indicate that the rockburst signals are closely related to rock stress adjustment. The rock structure has a rapid self-adjustment capacity before and after a certain period of time during tunneling. The variations of rock stresses would last for a long time before reaching a fnal steady state. Based on this, the site-specifc support parameters for the deep tunnels can be accordingly optimized.

A review of the microseismic focal mechanism research

Reservoir reconstructions implemented in unconventional oil and gas exploration usually adopt hydraulic fracturing techniques to inject high-pressure fluid into the reservoir and change its pore-fracture connection structure to enhance production.Hydraulic fracturing changes the reservoir stress and causes the rocks to crack,thus generating microseismic events.One important component of microseismic research is the source mechanism inversion.Through the research on the microseismic focal mechanism,information on the source mechanisms and in-situ stress status variations can be quantitatively revealed to effectively optimize the reservoir reconstruction design for increasing production.This paper reviews the recent progress in hydraulic fracturing induced microseismic focal mechanism research.We summarize their main principles and provide a detailed introduction of the research advances in source modeling,microseismic data synthesis,and focal mechanism inversion.We also discuss the challenges and limitations in the current microseismic focal mechanism research and propose prospects for future research ideas and directions.