Research ArticleExperimental Study on Stress Evolution and MicroseismicSignals underVibrationConditions ofCoal duringExcavation andSubsequent Waiting Time

Qifei Wang ,1 Chengwu Li ,1 Pingyang Lyu,2 Yuechao Zhao ,1 Dihao Ai ,1

and Beijing Xie 1

1College of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing),Beijing 100083, China2Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

Correspondence should be addressed to Chengwu Li; lcw@cumtb.edu.cn and Beijing Xie; bjxie1984@cumtb.edu.cn

Received 2 April 2019; Revised 8 June 2019; Accepted 17 June 2019; Published 2 July 2019

Academic Editor: Chao Tao

Copyright © 2019 Qifei Wang et al. /is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

An experiment designed to simulate coal during excavation was conducted. Microseismic signals of coal under vibrationconditions during excavation and subsequent waiting time of the coal roadway at different excavation speeds were collected andanalyzed. During the excavation and subsequent waiting time, the stress in coal is redistributed, and the concentrated stress isgradually transferred to the deeper section of the coal seam./eHilbert–Huang transform (HHT) is used to effectively denoise thecollected signals. According to the noise-reduced signal, the amplitude and pulse number of the microseismic signals emittedduring the excavation process are much larger than those of the waiting time process. During excavation, the energy and eventnumbers of microseismic signals increase first and then decrease as the excavation speed increases./e faster the excavation speed,the more the energy, and the higher the event numbers of the microseismic signals released during the subsequent waiting time.When the excavation speed is faster, more elastic potential accumulates in the coal seam and the concentration stress is greater. Asthe concentrated stress moves forward in time without excavation, more coal seams fail, and more microseismic signals arereleased. /e microseismic signal and the stress evolution law can provide a reasonable explanation for the forward movement ofthe concentrated stress and coal failure during roadway excavation.

1. Introduction

In underground mine mining, rock and coal are subjectedto tremendous overlying strata pressure and tectonic stress./e original stress balance state of the mine is disruptedduring the mining operation, which may trigger thetransfer of stress and the concentration of local stress. /isprocess is often accompanied by the rupture of coal, whichcan cause release of microseismic and acoustic emissionsignals./emain causes of microseismic events are slip anddislocation of existing cracks in mines under the action oftectonic and mining stress [1]. Real-time monitoring ofcoal-rock microseismic signals has become an importantmeans to predict coal-rock dynamic disasters in un-derground mining [2].

During the past 20 years, microseismic monitoring hasbeen widely used in major mineral-producing regions andcountries, such as Europe, South Africa, North America,Canada, and China [3–6]. Since the 1970s, the United Stateshas used microseismic monitoring in mines to ensure thesafety of underground personnel and developed supportingsoftware and hardware for this purpose [7]. As the tech-nology has matured, the microseismic technique has becomethe fundamental monitoring approach for mine safety andground control. Luo et al. studied the roof fracture processand stability using the microseismic method [8]. Mansurovanalyzed the microseismic data gathered by North UralBauxite Mines. Mansurov demonstrated that the micro-seismic signal is a highly efficient and accurate predictor ofstrong impact ground pressure [9]. Wang et al. conducted

HindawiShock and VibrationVolume 2019, Article ID 3235984, 13 pageshttps://doi.org/10.1155/2019/3235984

the 80-day monitoring at the Qixing Coal Mine through ahigh-precision microseismic monitoring system [10]. Zhanget al. studied the correlation length of mine microseismicsignals at different spatial scales with a single-link clustermethod and analyzed its mechanism [11].

With the advancement in signal analysis and processingtechnology, experts and scholars have formedmore in-depthresearch on microseismic signals in mines based on theanalysis of the relationship between the number of micro-seismic signal events and coal-rock dynamic disasters. Liet al. discussed weak signal detection using multiscalemorphology in microseismic monitoring which improvesdetection of more wave modes such as P and S waves, forbetter performance [12]. CAO et al. found that the averageP-wave velocity, the average maximum amplitude, and themain frequency of microseismic signals in the AustraliaSouthern Coal Mine decreased significantly with an increasein goaf and roof rupture [13]. /is outcome provides atechnical basis for the microseismic signal to be used tomonitor the stability of roadway surrounding rock. Ghoshand Sivakumar et al. monitored and analyzed the sequenceof microseismic events in coal mines in southeastern Indiaand studied the relationship between microseismic activityrate, potential energy, viscosity rate, seismic moment, energyindex, apparent volume, potential energy, and roof fall event[14]. Phillips et al. [15], Rowe et al. [16], and Gibowicz et al.[17] conducted in-depth research on the source of micro-seismic signals in the mine, the location of the rupture zone,and high-precision signal acquisition methods. Lu et al.analyzed the microseismic signals of mines with a band-stopfilter and studied the characteristics of low-frequency pre-cursor signals under coal bursting failure. Studies haveshown that the rock-burst intensity and signal intensitydisplay a significant positive correlation [18]. Sikora used avariety of mathematical methods to study the relationshipbetween coal mine microseismic signals and mine risk. Inthe study, the classification results achieved using decision-tree induction and fuzzy neural networks were more rea-sonable than those achieved using rule-based classification[19]. Based on a study of the characteristics of mine mi-croseismic signals, Vallejos and Mckinnon proposed aneural network model to classify the sources of microseismicevents [20]. Jiang et al. used velocity model optimization forsurface microseismic monitoring via amplitude stacking toenhance the signal-to-noise ratio in the coal-bed gas res-ervoir in Western China [21]. To investigate the generationmechanism of microseismic signals, most scholars use nu-merical simulation. Based on the traditional stress and coalrock failure model, numerical simulation has revealed thatthe concentrated stress zone and the damage zone of themining roadway are the areas in which microseismic eventsare concentrated [22].

In summary, the study of mine microseismic signalstypically focuses on the characteristics of on-site micro-seismic signals and the mechanism analysis is based onnumerical simulation. Laboratory research usually focuseson the characteristics of microseismic signals in the processof coal and rock loading and failure. /e experimental re-sults primarily reflect the mechanical characteristics of coal

and rock samples. /ere are few studies on the character-istics and energy trends of microseismic signals during coalseam excavation that employ similar simulation experi-ments. In this paper, through an original coal tunnel drivingsimulation experiment system, the microseismic signals atdifferent excavation speeds are synchronously monitored,and the characteristics and energy trends of the signalsduring simulated coal seam excavation are studied. /isapproach and its results are of substantial value to the theoryof applied research on early warning of coal-rock dynamicdisasters.

2. Experimental System for CoalRoadway Excavation

2.1. Experimental System. To study the characteristics ofmicroseismic signals during roadway excavation, the authordesigned an experiment that simulated the coal body underdifferent excavation speeds and collected the microseismicsignals that occurred during the experiment./e structure ofthe experimental system is shown in Figure 1. /e systemprimarily consists of three parts: stress loading, excavationequipment, and the signal acquisition system. /e coalroadway excavation simulation experiment system is shownin Figure 2.

In similar simulation tests of coal roadway excavation,the method of loading steel plates with jacks is typically usedto simulate the stress state of coal seams. With this stressloading method, the stress at the pressure head of the jack islarge, the stress at the edge is small, and the stress loading isnot uniform. In this experiment, the hydration expansionreaction of the expansion agent in the closed experimentaltank is used as the stress source. /e characteristics of thestress evolution law and microseismic signals in the processof coal seam excavation are studied by drilling the loadedcoal body.

/e production process of the pressed coal body used inthe experiment is as follows. (1)/e pulverized coal and thecoal tar are mixed uniformly. (2)/emixture is poured intoa circular cavity with an inner diameter of 128mm, and acylindrical steel pipe with a diameter of 28mm is placed inthe cavity. (3) /e pulverized coal in the cavity is com-pacted with servo press. (4) /e steel pipe is extracted fromthe coal body to form a hole. (5) /e prepared expansionagent is filled in the remaining cylindrical hole, and the twoends are blocked. /e volume expansion of the expansionagent after the hydration reaction causes the cavity toelastically deform and apply uniform stress to the internalcoal body, which is used to simulate the loaded coal body infront of the roadway. During the experiment, SF1500MEMS capacitive accelerometer was used to synchronouslycollect the microseismic signals. /e microseismic signalsthat occurred during the simulated excavation process werecollected by a HIOKI8860-50 storage recorder. /e fre-quency of the microseismic signal is generally below1000Hz. According to the Nyquist sampling theorem,when the sampling frequency is greater than 2000Hz, theacquired digital signal can completely retain the in-formation in the original microseismic signal. In the

2 Shock and Vibration

experiment, the sampling frequency of the microseismicsignal was set to 10240Hz. �e strain sensor was aDY2102E dynamic static strain gauge, and the signal ac-quisition system was a MEMORY HiCOROER 8860-50storage recorder manufactured by HIOKI, Japan.

2.2. Determination of Similar Experimental Parameters.�e parameters of the experimental model are determinedaccording to the design principles of similar simulationexperiments. �e similar experiments were required tosatisfy single-value similar conditions such as geometric

conditions, physical conditions, boundary conditions, andinitial conditions.

�e purpose of this experiment is to qualitatively analyzethe in�uence of excavation speed on the damage area infront of the roadway.�erefore, to avoid the boundary e�ect,the determination of the geometric similarity ratio mustmeet the requirements of the roadway excavation. Generally,the geometric similarity ratio of qualitative simulation ex-periments is 100–200. In this experiment, the determinedgeometric similarity ratio is αL � 150. Since the diameter ofthe on-site prototype roadway is 3m, the diameter of themodel roadway is 3m/150� 20mm.

Data acquisition system

Microseismic sensor #1

Experimental cavity

Strain gauge #1

Data acquisitioncomputer

Strain gauge #2

Strain gauge #3

Strain gauge #4

DY2102E dynamic static strain gauge HIOKI8860-50 storage recorder

Microseismic sensor #2

Figure 1: Experimental system.

Stress loading cavityData acquisition so�ware

Signal acquisition system

Power supply

Figure 2: Coal roadway excavation simulation experiment system.

Shock and Vibration 3

/e material used in this simulation experiment isbriquette, which is an approximately homogeneous materialmade by evenly mixing and compacting coal tar and pul-verized./e bulk density of the material is close to that of theraw coal. /us, the similarity ratio of bulk density αc isapproximately 1.

/e coal roadway excavation process conventionallyproceeds over three work shifts a day, whereby the overhaulshift does not perform excavation work for eight hours.During the two excavation shifts, part of the time is used forexcavation, while the remaining time is used to support sitecleaning and other operations. /e excavation period rep-resents approximately 15%–20% of the excavation shift,approximately 96minutes. Time similarity ratio αt can becalculated according to an acceleration similarity criterion:αt �

��αL

√�

���150

√≈ 12. /erefore, the duration of a single

excavation operation in the simulation experiment is96min/12� 8minutes.

When the depth of the simulated coal seam is 400m, thein-situ stress can reach approximately 5.6MPa. According tothe stress-strain relationship, the stress similarity ratio canbe calculated: ασ � αLαc � 150. /erefore, the stress aroundthe experimental model roadway is not less than 5.6MPa/150� 37KPa.

2.3. Experimental Program. /e roadway excavation simu-lation test can be used to study the influence of excavationspeed on the coal failure in front of the working face. Toorder to conform to the actual operation process, the ex-cavation-waiting process was repeated during the experi-ment. We performed a simulated excavation experiment atthree different excavation speeds: v1 �1.875mm/min,v2 � 3.125mm/min, and v3 � 4.375mm/min.

/e simulated excavation experiment process is dividedinto three stages: (1) 15mm of coal is excavated with speed v1and then allowed to wait for 32min, followed by excavatinganother 15mm and waiting for 40min; (2) 25mm of coal isexcavated with speed v2 and then allowed to wait for 32min,followed by excavating another 25mm and waiting for40min; (3) 35mm of coal is excavated with speed v3 andthen allowed to wait for 35min, followed by excavatinganother 35mm and waiting for 32min. /e schematic di-agram of the excavation scheme is shown as Figure 3.

As shown in Figure 3, the simulation excavation test canbe divided into four stages. /e first stage is to open theexcavation inlet reserved on the cavity, which is similar tothe process of uncovering the coal wall at the coal mine site./e second, third, and fourth stages simulate the excavationprocess under different excavation speeds. Two excavationsimulations were performed at each stage. /e excavationfootage, time, and speed of each stage are shown in Table 1.

3. Experimental Results and Signal Denoising

3.1. Stress Change during the Excavation and Waiting Time./e test chamber is a steel cylindrical tank, and the tankprovides stress to the inner coal seam based on the reactionof the expansion agent. /e stress at each tank position

represents the stress state of the coal seam at that position./e reserved tunneling holes must be opened before thetunneling process is simulated. A period of time afteropening the tunneling hole and the stress of the coal seam atpoints #1, #2, #3, and #4 aremeasured./e stress evolution isshown in Figure 4.

After the tunneling hole of the roadway is opened, thestress of the coal seam at point 1# (50mm from the initialheading face) displays a downward trend (Figure 4). /estress of the coal seam at point 2# (100mm from the initialheading face) and point 3# (150mm from the initial headingface) increases steadily, and the stress of the coal seam atpoint 2# increases more rapidly (Figure 4). /e stress of thecoal seam at point 4# (200mm from the initial heading face)scarcely changes (Figure 4). /erefore, in this stage, point 1#is located in the reduced stress zone, point 2# and point 3#are located in the increased stress zone, and the point 4# islocated in the stabled stress zone. /e stress decreases nearthe coal wall, and the stress concentration in the middle ofthe experimental coal during uncovering of the coal wall isobvious.

Figures 5 and 6 show the stress evolution during ex-cavation and waiting time in the second stage, respectively.During coal excavation, the stress at point #1 near theheading face is decreased, and the stress at the farther point#2 exhibits an upward trend (Figure 5). /is outcome in-dicates that the concentrated stress of the coal seam isshifting during excavation. However, due to the short time ofthe simulated excavation, the change in coal seam stress issmall. /e stress at points #3 and #4 far from the head isbasically unchanged (Figure 5).

As shown in Figure 6, the stress of the coal seam con-tinues to change during the waiting time. /e stress at point#1 is continuously decreasing./e stress of the coal seam in acertain range ahead of the heading face increases, wherebythe stress at point #2 increases obviously, and the stress at thepoint #3 increases slightly in the fluctuation. /e deep stressof the coal seam (point #4) is little changed. During thewaiting time and the excavation process, the concentratedstress is transferred to the deep part of the coal seam.

In order to describe more the changes of the stressduring the experiment intuitively, we subtract the initialvalue from the stress of all points. /e evolution law ofstress at each point during the whole experiment is shownin Figure 7. During the excavation and subsequent waitingtime of the simulation experiment, the stress of point #1showed a continuous downward trend with slight fluctu-ations in the medium term. During the first three tunnelingoperations, the stress of point #2 increased significantly.After the fourth excavation (the second excavation in thesecond stage), the stress of point #2 began to drop sig-nificantly. However, the stresses at points #3 and #4remained almost unchanged during the early stages of theexperiment and decreased significantly in the third stage.As a result, significant pressure relief has occurred in thecoal seam that have been excavated, significant stressconcentrations have occurred in front of the work face, andthe concentrated stresses have shifted to the deep coal asthe excavation work continues.

4 Shock and Vibration

3.2. Microseismic Signal Acquisition and Denoising. �emicroseismic signals released during the fracture process ofcoal mine rock and coal can be collected by the microseismicmonitoring system. However, the collected original signal isgenerally one in which a noise signal is superimposed on thegenuine microseismic signal. �e noise signal typically in-cludes stable background noise and a random transientinterference signal. Analysis and processing of the originalmicroseismic signals to identify the anomalous signals formsthe basis for studying the early warning of coal and rockdynamic disasters based on microseismic monitoring. Mine

microseismic signal waveforms primarily display the fol-lowing characteristics. (1)�e abnormal microseismic signalis a typical nonstationary signal, which contains informationon coal and rock damage; (2) the microseismic event istypically a short-term pulse signal, and abnormal signalrecognition is highly di�cult; and (3) many di�erent op-erations are involved in underground coal mining, whichresults in a variety of noises in the collected microseismicsignals, and the signal-to-noise ratio is relatively low. Toextract useful information from the recorded microseismicsignals, the time-frequency analysis method represented by

Table 1: Excavation load.

Stage and process Excavation footage (mm) Footage summed (mm) Excavation time (min) Excavation speed(mm/min)

First stage 10 10 — —

Second stage First excavation 15 25 8 1.875Second excavation 15 40 8 1.875

�ird stage First excavation 25 65 8 3.125Second excavation 25 90 8 3.125

Fourth stage First excavation 35 125 8 4.375Second excavation 35 160 8 4.375

t1 t6

v

t6

v1

v2

v3

T

t2 t4 t4

t4 t4

t4 t4

Figure 3: Schematic diagram of the excavation scheme. Note. v1, v2, and v3 are the excavation speeds; t1 is the time before excavation; t2 isthe time of each excavation; t3 is the waiting time of the excavation shift; and t4 is the waiting time of the overhaul shift.

1#2#

3#4#

–0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Stre

ss (M

Pa)

500 1000 1500 2000 2500 30000Time (s)

(a)

1000050

100150

200

Location (mm)

2000

Time (s)

0.25

0.2

0.15

0.1

0.05

0

0

0.10.15

0.05

0.20.25

Stre

ss (M

Pa)

(b)

Figure 4: Stress evolution after opening the tunneling hole: (a) stress-time curve of 4 measuring points; (b) diagram of stress distribution indi�erent locations over time.

Shock and Vibration 5

the Fourier transform has been widely used [23–26]. �eFourier transform is useful in analyzing the spectral char-acteristics of stationary signals at £xed time scales. However,it has substantial limitations in addressing nonstationarysignals [27]. Wavelet analysis is a nonstationary signalprocessing method based on wavelet decomposition andwidely used to denoise signals. Wavelet noise reduction isperformed by setting a certain threshold to process variouswavelet components obtained by wavelet decomposition andby reconstructing components containing useful signals tocomplete signal processing [28–30]. Wavelet functions, alsoknown as wavelet bases, are characterized by orthogonality

and symmetry. Wavelet analysis has been demonstrated tobe a useful means to address nonstationary signals, such asearthquakes and electromagnetic radiation [31, 32]. How-ever, the characteristic of wavelet £nite length will cause theleakage of signals energy, and the analysis results are sub-stantially in�uenced by the choice of wavelet basis functions[33].

In recent years, a new method termed the Hilbert–Huang transform (HHT) has been used increasing widelybecause of its powerful processing power for nonlinearnonstationary signals. �e HHT consists of empirical modedecomposition (EMD) and the Hilbert transform (Hilbert)

0.4

0.2

0.0

3000 3500 4000 4500Time (s)

5000 5500

Stre

ss (M

Pa)

1#2#

3#4#

(a)

–0.1

0

0.1

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6000400050

100

150200

–0.1–0.2

00.10.2

Stre

ss (M

Pa) 0.3

0.40.5

Location (mm)

8000

Time (s)

1000012000

(b)

Figure 6: Stress evolution in the waiting time of the second stage: (a) stress-time curve of 4 measuring points; (b) diagram of stressdistribution at di�erent locations over time.

1#2#

3#4#

–0.05

0.00

0.05

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Stre

ss (M

Pa)

2950 3000 3050 3100 3150 3200 3250 33002900Time (s)

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Location (mm)

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4000Time (s)

0.35

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–0.05

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Stre

ss (M

Pa)

(b)

Figure 5: Stress evolution during the £rst excavation in the second stage: (a) the stress-time curve of 4 measuring points; (b) diagram ofstress distribution at di�erent locations over time.

6 Shock and Vibration

with multiresolution and adaptive features. It provides anaccurate time resolution for the signal energy-frequencyrepresentation. Compared with the Fourier transform, theHilbert–Huang transform is simple and reliable in studyingthe microseismic wave before the occurrence of impactground pressure and can extract MS waveform feature in-formation more completely [34]. At present, the signalprocessing method has been applied in the £elds of earth-quake and blast prediction, biomedicine, £nance, and ge-ology [35, 36].

�e basis of the HHT transform is EMD decomposition,also known as signal sorting, which eliminates some of thesuperimposed waves in the signal. �e algorithm �ow ofEMD decomposition is as follows:

(i) De£ne the original signal and initialize it: r0 �x(t) and i � 1.

(ii) Calculate the i-th IMF component:

(a) Initialization: h0 � ri−1(t) and j � 1.(b) Find all extreme points in the signal hj−1(t);

connect all maxima points and perform cubicspline interpolation to obtain the upper envelopexmax(t); and connect all minima points andperform envelopes under cubic spline in-terpolation xmin(t).

(c) Calculate the average mj−1(t) of the upper andlower envelopes.

(d) Calculate the decomposed signal:hj(t) � hj−1(t)−mj−1(t).

(e) Determine whether hj(t) is an IMF function, ifyes: imf i(t) � hj(t); otherwise, j � j + 1.

�en, return step (b) until the calculated hj(t) satis£esthe IMF component criteria.

(iii) Calculate the residual component: ri(t) � ri−1(t)−imf i(t).

(iv) Calculate the number of extreme points of the se-quence ri(t); if the number exceeds 2, i � i + 1;

otherwise, the decomposition ends, and ri(t) isde£ned as the residual component. �e result of thedecomposition is x(t) � ∑ni�1imf i(t) + rn(t).

�e standard deviation between two consecutive pro-cessing results is typically used as the judgment calculationstandard of the IMF component. When the value of thestandard deviation is smaller than a given threshold (gen-erally 0.2 to 0.3), the data obtained by the screening areconsidered to be an IMF component. �e decomposed IMFcomponent constitutes a series of signal clusters from highfrequency to low frequency. Combining one or more IMFcomponents can obtain the required signal components.�etime-domain signal-processing method is based on variousIMF components, which can fully preserve the non-stationary characteristics of the original signal.

EMD has good adaptability, but its decomposed signal isprone to the mode-mixing problem.�e ensemble empiricalmode decomposition (EEMD) based on the EMD methodutilizes the uniform distribution of white noise, adds whitenoise to the original signal, and then performs EMD de-composition, which can e�ectively overcome the mode-mixing problem [37].

We used DataDemon software for HHT signal denois-ing. �e software is used to decompose the original signaland then reconstruct the useful signal using the MATHfunction to obtain the denoised signal. In order to betterdemonstrate the signal processing process, we selected a 5ssignal for analysis. �e original microseismic signal is shownin Figure 8. After the EEMD decomposition of the originalsignal, 14 IMF components and one residual component areobtained (shown in Figure 9). Comparing the di�erent IMFcomponent waveforms, it can be found that the noise signalis mainly in the high frequency components (IMF1–IMF5).�e IMF6–IMF14 and residual components are recon-structed by the MATH function to obtain the denoisedsignal shown in Figure 10. Compared with denoised andoriginal signals, it can be found that the HHTmethod has agood denoising e�ect on the microseismic signal. �e high-frequency noise signal is e�ectively £ltered out when theamplitude of the useful signal does not change much whichindicates that the useful signal is su�ciently preserved.

�e microseismic signals collected during the experimentare shown in Figure 11. Compared with the signals before andafter denoising, the HHT can e�ectively remove the noisesignals that occur during the continuous and stopping ex-cavation. As shown in Figure 11, obviousmicroseismic signalsare generated during excavation and waiting time. However,the signals in these two processes are highly di�erent. �emicroseismic signals generated by coal destruction during thesimulated excavation process are single-pulse signals, and thesignals exhibit obvious periodicity. During the experiment, adrilling process was used to simulate the coal excavation, andthe vibration of the drilling machine has obvious periodicity,which results in the periodicity of the coal destruction.Comparing Figures 11(b) and 11(f), one can note that theamplitude and pulse number of the microseismic signals thatoccur during excavation aremuch larger than those that occurduring the waiting time.�is phenomenon is primarily due to

0 5000

Stre

ss (M

Pa)

0.5

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–0.5

–1.0

–1.5

–2.0

–2.510000 15000

Time (s)20000 25000

1#2#3#

Waiting timeExcavation

4#

Figure 7: Stress evolution diagram during the whole experiment.

Shock and Vibration 7

the severe destruction of the coal seam during the coalroadway excavation, and the released signals are also relativelylarge. However, the forward movement of the concentratedstress in front of the head face after coal excavation causes thelocal coal body to be destroyed, which is the main source ofthe microseismic signal. In this process, the signal amplitudeis lower and the number of microseismic pulses is small.

4. Discussion

According to the described experimental results, thecharacteristics of microseismic signals that occur during

excavation and those that occur during waiting time arehighly di�erent. In the coal mining, how to increase theexcavation speed as much as possible while avoiding coaland rock dynamic disasters is a key problem. In thissection, the event numbers and energy trends of mi-croseismic signals at di�erent excavation speeds arestudied. �e STA/LTA algorithm is used to identify themicroseismic event and then to calculate the eventnumbers of the microseismic signal. In the STA/LTAalgorithm, £rst a short window is taken in a given slidinglong window, and the starting point or the end point ofthe two windows coincides. �en, the amplitude change

0 1IMF13

Am

plitu

de (m

V)

2 3Time (s)

4 5

10–4

–10–4

IMF14 Time (s)0 1 2 3 4 5

10–5

–10–5

Residual Time (s)0 1 2 3 4 5

5 × 10–5

5 × 10–5

Am

plitu

de (m

V)

IMF10 Time (s)0 1 2 3 4 5

0.0005

–0.0005

0

IMF11 Time (s)0 1 2 3 4 5

0.0002

–0.0002

0

IMF12 Time (s)0 1 2 3 4 5

0.0001

–0.0001

Am

plitu

de (m

V)

IMF7 Time (s)0 1 2 3 4 5

0.005

–0.005

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0.002

–0.002

IMF9 Time (s)0 1 2 3 4 5

0.001

–0.001

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plitu

de (m

V)

IMF4 Time (s)0 1 2 3 4 5

0.001

–0.001

0

IMF5 Time (s)0 1 2 3 4 5

0.001

–0.001

IMF6 Time (s)0 1 2 3 4 5

0.005

–0.005

Am

plitu

de (m

V)

IMF1 Time (s)0

0.001

–0.001

1 2 3 4 5

0.001

0

–0.001

IMF2 Time (s)0 1 2 3 4 5

0.001

–0.0005

IMF3 Time (s)0 1 2 3 4 5

Figure 9: IMF components obtained by EEMD.

0 1 2 3 4 5Time (s)

–0.01

–0.005

0

0.005

0.01

Am

plitu

de (m

V)

Figure 8: Original microseismic signal.

8 Shock and Vibration

Time (s)5 10 15 20 25 30 35

Am

plitu

de (m

V)

–0.5

0

0.5

(a)

Time (s)5 10 15 20 25 30 35

Am

plitu

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V)

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(b)

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0 5 10 15 20 25 30 35Time (s)

0

1000

2000

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5000

Freq

uenc

y (H

z)

(c)

Figure 11: Continued.

0 1 2 3 4 5

–0.005

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Time (s)

Am

plitu

de (m

V)

Figure 10: Denoised microseismic signal.

Shock and Vibration 9

of the signal is re�ected by the ratio R of the short-timeaverage (STA) to long-time average (LTA). STA primarilyre�ects the characteristics of microseismic signals, whileLTA primarily re�ects the characteristics of backgroundsignals. When R is increased beyond the set threshold,abnormal microseismic signals are considered to be re-leased. Indicator R can be calculated by the followingformula:

R �STALTA

�∑N

i�1X(i)/N

∑M

j�1X(j)/M, (1)

where X(i) is the eigenfunction values of microseismicsignals, which is typically the absolute of the signal ampli-tude or energy; N is the length of the short window; andM islength of the long window.

To improve signal identi£cation accuracy, a more sen-sitive signal sequence is constructed, which can respond tothe change in amplitude and frequency of the microseismic

signal. �e eigenfunction CF(t) of the microseismic signalcan be described as follows:

CF(t) � Y(i)2 +K(i)[Y(i) −Y(i− 1)]2, (2)

where Y(i) is the original amplitude of the signal andK(i) isthe weighting factor of the amplitude and £rst derivative.K(i) can be calculated as follows:

K(i) �SUM|Y(i)|

SUM|Y(i)−Y(i− 1)|. (3)

In this paper, the R value is corrected by the energy ratiomethod. �e £nal microseismic signal determination se-quence MR can be described as follows:

MR � abs(Y(i)) × Ri( )3. (4)

In the calculation, the length of the SATwindow is set to40 (approximately 3.90ms) and the length of the LATwindow is set to 360 (approximately 35.16ms).

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35Time (s)

0

1000

2000

3000

4000

5000

Freq

uenc

y (H

z)

(d)

Time (s)0 200 400 600 800 1000 1200

Am

plitu

de (m

V)

–0.01

–0.005

0

0.005

0.01

0.015

(e)

Time (s)0 200 400 600 800 1000 1200

Am

plitu

de (m

V)

–0.01

–0.005

0

0.005

0.01

0.015

(f )

Figure 11: Original microseismic signals and denoised signals collected in the experiment: (a) signal before denoising during excavation;(b) denoised signal during excavation; (c) signal spectrum before denoising during excavation; (d) denoised signal spectrum duringexcavation; (e) original signal during waiting time; (f ) signal after denoising during waiting time.

10 Shock and Vibration

/e trend of the energy and event numbers ofmicroseismicsignals over time at a range of excavation speeds can be ob-tained by calculation. /e calculation results are shown inFigure 12.

As shown in Figure 12, the energy and the event numbersof the microseismic signals exhibit an upward trend.However, the upward curve is very different. /e energy ofthe microseismic signal first increases rapidly and thenchanges slightly, while the event numbers increase linearlywith time. /e rapid increase in the microseismic energy inthe early stage indicates that the rupture of the coal seam inthe experimental cavity gradually increases with the progressof the excavation. However, as the internal stress of the coalseam is removed after the excavation, the energy of the singlemicroseismic pulse is substantially greatly reduced. /isleads to a continuous and rapid increase in the number ofmicroseismic events, while the total energy of the micro-seismic signals changes slowly. Comparing the energychange curves under different excavation speeds reveals that

the faster the excavation speed is, the earlier the time of thephase is when the energy of themicroseismic signal increasessharply.

Microseismic signal indicators such as energy persecond and event numbers per second, during excavationand subsequent waiting time at different excavationspeeds, are counted. /e calculation results are shown inTable 2.

As shown in Table 2, the energy of the microseismicsignal per second increases first and then decreases with theincrease in the excavation speed. /e event numbers persecond at different speeds behave similarly./is is consistentwith the previous conclusion that excessive excavation speeddoes not lead to the larger microseismic energy.

/e percentage of time required for the energy of themicroseismic signal to reach 90% of the total time during theexcavation process is positively correlated with the exca-vation speed. With the increase in the excavation speed, thetime consumption increases. Comparing the microseismic

Number of eventsEnergy

0

100

200

300N

umbe

r of e

vent

s

0

40

80

120

Ener

gy (1

0–6J)

5 10 15 20 25 30 35 400Time (s)

(a)

Number of eventsEnergy

0

100

200

300

400

Num

ber o

f eve

nts

5 10 15 20 25 30 35 400Time (s)

020406080100

Ener

gy (1

0–6J)

(b)

Number of eventsEnergy

0500

1000150020002500

Num

ber o

f eve

nts

010002000300040005000

Ener

gy (1

0–6J)

50 100 150 200 250 300 3500Time (s)

(c)

Number of eventsEnergy

0

2000

4000

Num

ber o

f eve

nts

50 100 150 200 250 300 3500Time (s)

0

200

400

Ener

gy (1

0–6J)

(d)

Number of eventsEnergy

0

2000

4000

Num

ber o

f eve

nts

0

1000

2000

Ener

gy (1

0–6J)

50 100 150 200 250 300 3500Time (s)

(e)

Number of eventsEnergy

0

2000

4000

Num

ber o

f eve

nts

0

200

400

Ener

gy (1

0–6J)

50 100 150 200 250 300 3500Time (s)

(f )

Figure 12: Microseism signal change trend of energy and event numbers at a range of excavation speeds: (a) microseismic sensor 1# atexcavation speed v1; (b) microseismic sensor 2# at excavation speed v1; (c) microseismic sensor 1# at excavation speed v2; (d) microseismicsensor 2# at excavation speed v2; (e) microseismic sensor 1# at excavation speed v3; (f ) microseismic sensor 2# at excavation speed v3.

Shock and Vibration 11

signals in the waiting time process after excavation at dif-ferent speeds reveals that the signal energy and eventnumbers are greater in the waiting time process after ex-cavation at a faster speed. Specifically, when the drivingspeed v1 � 1.875mm/min, v2 � 3.125mm/min, andv3 � 4.375mm/min, the total energy of the microseismicsignal is 2.424mJ, 6.255mJ, and 53.344mJ, respectively, andthe total number of events is 27, 53, and 102, respectively./is outcome indicates that when the excavation speed is toofast, the stress distribution of the coal seam does not haveenough time to reach equilibrium, and a large amount ofunstable deformation energy remain accumulated. Whenthe excavation stops, along with the forward movement ofconcentrated stress, unstable deformation energy is released,causing the fracture of the coal body and the release of themicroseismic signal. /e faster the excavation speed is, thestronger the unstable energy is accumulated, and the morethe microseismic signal is released during excavation.

During the excavation process, the degree of coal failure infront of the working face does not always increase as theexcavation speed increases. /e release of the microseismicsignal is affected by the excavation speed and the stress ad-vancement speed. When the excavation speed is too fast, thestress at the head will not affect the deeper coal in a short time,and the increase in the coal body damage range is no longerobvious. However, after stopping the excavation (typical forthe roadway support process in a coal mine), the stress of thecoal seam continues to change drastically, causing large-scaleinstability and damage to the coal body and triggering a coal-rock dynamic disaster. A large number of microseismicsignals continue to be released during this process, and themonitoring of these signals can be used for the prediction andearly warning of coal and rock dynamic disasters.

5. Conclusion

In this paper, a coal roadway excavation simulation ex-periment systemwas designed. In addition, the microseismicsignals emitted by coal during excavation and by the coal

roadway during the subsequent waiting time at differentexcavation speeds were collected and analyzed. /e mainconclusions are as follows:

(1) During the excavation and subsequent waiting time,the stress in the coal seam is redistributed, and theconcentrated stress is gradually transferred deeperinto the coal seam.

(2) /e HHT transform could effectively denoise themicroseismic signals during the simulated excava-tion. Compared with the microseismic signalsemitted during the waiting time, the microseismicsignals emitted during excavation are more denselyreleased and their amplitude is higher. /is findingindicates that the stress in front of the working facechanges substantially during excavation and thatcoal is violently destroyed across a wide range.

(3) During the excavation, the energy and event num-bers of the microseismic signals first increase andthen decrease with the increases in excavation speed.

(4) /e faster the excavation speed, the more the energy,and the higher the event numbers of the micro-seismic signals released during the subsequentwaiting time. When the excavation speed is faster,more elastic potential accumulates in the coal seam,and the concentration stress will be greater. As theconcentrated stress moves forward in the timewithout excavation, more coal seams fail, and moremicroseismic signals are released.

Data Availability

/e data used to support the findings of this study areavailable from the corresponding author upon request.

Conflicts of Interest

/e authors declare that there are no conflicts of interestregarding the publication of this paper.

Acknowledgments

/is research was financially supported by the NaturalScience Foundation of China (Grant nos. 51274206 and51404277).

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12 Shock and Vibration

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