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Article

The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention

by
Jianju Ren
1,
Xin Zhang
1,*,
Qinghua Gu
1,*,
Wenlong Zhang
2,
Weiqin Wang
1 and
Long Fan
3
1
School of Resource Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2
School of Civil Engineering and Architecture, Qingdao Huanghai University, Qingdao 266427, China
3
College of Engineering and Mines, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5485; https://doi.org/10.3390/su16135485
Submission received: 22 May 2024 / Revised: 14 June 2024 / Accepted: 21 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Sustainability in the Mechanism and Prevention of Coal–Rock Dynamic Disaster and Rock Engineering)
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Abstract

:
The main frequency of microseismic signals has recently been identified as a dominant indicator for characterizing vibration events because it reflects the energy level of these events. Frequency information directly determines whether effective signals can be collected, which has a significant impact on the accuracy of predicting rock burst disasters. In this study, we adopted a characterizing method and developed a monitoring system for capturing rock failure events at various strata in an underground coal mine. Based on the rock break mechanism and energy release level, three types of rock failure events, namely, high roof breaking, low roof breaking, and coal fracture events, were evaluated separately using specific sensors and monitoring systems to optimize the monitoring accuracy and reduce the general cost. The captured vibration signals were processed and statistically analyzed to characterize the main frequency features for different rock failure events. It was found that the main frequency distribution ranges of low roof breaking, high roof breaking, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, these frequency ranges are proposed to monitor different vibration events to improve detection accuracy and reduce the test and analysis times. The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect both in the maximum amplitude and main frequency, which could be an indicator to distinguish failure locations in the vertical direction. This result can support the selection and optimization of the measurement range and main frequency parameters of microseismic monitoring systems. This study also discussed the distribution law of the maximum amplitude and main frequency of different events and the variation in test values with the measurement distance, which are of great significance in expanding the application of optimized microseismic monitoring systems for rock burst monitoring and prevention.

1. Introduction

Microseismic systems have been widely applied in subsurface engineering to monitor and identify early warning signs of potential rock failure and rock bursts [1,2,3]. Parameters such as amplitude [4], energy [5], event location [6], main frequency [7], and fractal dimension [8] are often used to characterize and analyze received signals, to monitor earthquakes [9], and to carry out fracture propagation [10] in oil and gas production [11]. In recent decades, microseismic systems have been adopted in mining engineering to monitor the failure events of surrounding rocks and potential rock bursts accompanied by the mining operation [12]. It has been proven that the greater the energy of a rock failure event, the lower the main frequency of the signal received from the microseismic system [3]. For example, the energy of an earthquake event is large, but its main frequency is only a few tenths of hertz [13], while the energy of fracturing and rock burst in artificial engineering is small, but the main frequency is large [14]. Therefore, the main frequency of received signals during mining activities can be used as an indicator to monitor and predict dynamic disasters. In addition, the main frequency value is related to factors such as propagation distance and the location of the event. An increase in the propagation distance causes the signal amplitude to decay [15,16], and the main frequency decreases accordingly [17,18]. The occurrence of multiple small-energy events at the initial rock failure period may increase the main frequency. While failure is accompanied by a large energy release, the main frequency gradually decreases [19]. Observing this phenomenon could provide mechanisms for identifying critical events to prevent dynamic disasters.
At present, most of China’s coal mines are deep underground, and rock bursts occur frequently [20]. These dynamic disasters seriously endanger miners’ safety [21]. To deal with rock burst disasters, scholars have conducted numerous studies to reveal their occurrence mechanisms [22] by focusing on developing monitoring methods [23,24] and treatment measures [25]. Methods such as microseism monitoring [26], stress monitoring [27,28,29], and electromagnetic radiation [30] have been used for monitoring and early warning purposes. Microseism monitoring is commonly adopted on a large scale due to its accuracy and relatively cheap cost. Historically, the signals received from microseism monitoring are processed to identify fracking locations and the energy released from the events [31]. Many successful studies have recently been conducted in the field to identify the precursor signals of rock bursts by analyzing the main frequency of the received microseismic signals, relying on the fact that the main frequency continuously decreases from its initiation to the dynamic failure of the rock burst [32,33].
There are three kinds of events used for early warning purposes regarding rock bursts in coal mines, namely, high roof breaking, low roof breaking, and coal fracturing events [34] (see Figure 1 for a schematic diagram of these three failure events). A high roof breaking event mainly occurs within hundreds of meters above the mining panel and is caused by beam breaks, with the high roof being in a large range for large-scale mining. The amount of energy released during such an event is quite large, but the main frequency of the monitored microseismic signals is quite low [35]. A low roof breaking event is mainly caused by the periodic collapse of the immediate roof in the mining panel; it is accompanied by medium energy release and an intermediate-level main frequency [36]. Primary coal fracturing events with small energy release and a high main frequency occur at locations close to the heading face because of high abutment stress [37,38]. To accurately identify these events, different microseism systems are required because of the energy gaps and differences in the received signals’ main frequency levels [39]. However, there is no guidance regarding equipment selection and parameter setup to monitor these events in practical applications, which is potentially preventing the extension of this advanced technology. The likelihood of these three events occurring simultaneously is relatively low. To simplify the complexity of this study, the mutual influence was ignored.
Currently, the equipment parameters for monitoring the three events are empirically determined with limited monitored data in the field. This leads to some critical events not being monitored due to the mismatch with the monitoring equipment parameters [40]. The main frequency of the monitoring equipment must be reasonably determined to ensure the accuracy of the early warnings. In practice, the whole mine monitoring system and local monitoring system should be used [41,42,43], respectively, to monitor high roof breaking, low roof breaking, and coal fracturing events with meaningful setup parameters. The main purpose of this study is to conduct a statistical data analysis on the three kinds of seismic events based on field data acquired using various equipment. The results will provide guidance for the practical application of seismic monitoring to prevent rock bursts in large-scale mines. This study will also provide a reference for the research and optimization of subsequent monitoring equipment by analyzing the distribution range of the main frequency of vibration events. It also provides the propagation law of the main frequency of underground vibration events, especially in the underground environment [44,45], which is of great significance in monitoring and identifying early warning signs of rock burst accidents.

2. Sensor Assembling and Data Acquisition System

In this study, three sets of vibration sensors and acquisition systems with various main frequency levels are adopted, namely, a whole mine monitoring system (WMMS), a working face monitoring system (WFMS), and a heading face monitoring system (HFMS). Each system is used for events with specific frequency and energy bands to ensure the successful collection of all underground vibration events. Because of their different data collection mechanisms, specific vibration sensors with designed installation numbers and sampling frequencies are selected to reduce the overall cost. The layout of measuring points is shown in Figure 2. According to the monitoring scopes of these systems, the test sensors of the WMMS are distributed in the roadways and gateways of the heading face and mining panel (Figure 2a). The sensors of the WFMS and HFMS are located in the gateways of the working panel and the gateways of the heading face, respectively (Figure 2b,c).
The WMMS adopts the SOS system from Poland, which is equipped with 15 sensors with a main frequency between 1 and 250 Hz. It can effectively identify and collect vibration signals caused by high roof breaking. Seven sensors are arranged in the WFMS. The spacing between the sensors is about 50–100 m, which can accurately monitor low roof breaking events with a main frequency between 1 and 500 Hz. The HFMS has a total of four sensors with an installation spacing of 50–60 m. They are arranged to ensure the effective identification of coal fracturing events with a main frequency between 1 and 1000 Hz. A summary of the three vibration signal monitoring systems’ acquisition parameters is shown in Table 1. Based on previous judgments and an understanding of the three types of events, the selection of these parameters referred to some conventional cognition.
The sensor receiving frequency is different from the data acquisition frequency. The true feedback of the three systems to the signal is half of the acquisition frequency. The receiving frequency of the sensor must exceed the acquisition frequency to be effective. After the signals are obtained by the sensors, they are transmitted to the acquisition instrument and then to the ground computer. The flow chart of signal acquisition is shown in Figure 3.
The employed sensor is a moving coil that can only capture vibration signals in one direction. The sensors are only installed on the roof or floor of the roadway in a vertical direction. When installed on the roof, the sensor is usually screwed at the end of the anchor bolt that is fully grouted. When installed on the floor, a concrete pile foundation must be set to ensure the transmission quality of the signal. In this study, the sensors are all installed on the roof at the end of fully grouted anchor bolts. The direct output of the WMMS is the speed signal, while the outputs of the WFMS and HFMS are voltage signals that can be converted into speed signals.
The signal captured for one vibration event is demonstrated in Figure 4. The indicators commonly used to characterize a vibration event include the arrival time, end time, duration, max amplitude, and main frequency, as shown in Figure 4. The waveform in the frequency domain can be used to identify the main frequency of a vibration event. As shown in Figure 4b, the amplitude increases to a high value in a short time and then slowly returns to the basic amplitude level. These vibration events were captured and identified by the algorithms published by the authors of [34] in 2021. This study mainly focuses on counting their main frequencies and analyzing their characteristics.
The proposed vibration signal monitoring system ensures that three types of underground vibration events (high roof breaking, low roof breaking, and coal fracturing events) can be effectively collected on a large scale, which lays a foundation to determine the main frequency in this study. To collect different target events, the layouts of the three systems’ measuring points are different, as shown in Figure 2, because the energy and propagation distance of events are different.

3. Main Frequency Characterization and Statistical Analysis

We identify the main frequency for low roof breaking events based on the signals monitored via the WFMS. The histogram representing the count of events is quantified with a frequency bin of 20 Hz, as shown in Figure 5a. It can be seen that a total of 16,902 low roof fracture events occurred, and their main frequencies range from 30 Hz to 400 Hz. Overall, the main frequency of low roof breaking events is primarily concentrated between 40 and 280 Hz, and the count of events has peaks located between 60 and 80 Hz. Only a few events occur with a high main frequency and very limited events in the frequency ranges of 20–40 Hz and 280–300 Hz. Based on the statistical results of the main frequency and Nyquist’s law [46], we suggest 800 Hz as the sampling frequency to effectively monitor low roof breaking events.
Similarly, the statistical results of the main frequency for high roof breaking events are shown in Figure 5b with a count interval of 20 Hz. It can be seen that there are 5321 high roof breaking events occurring during the same monitoring period. The main frequency ranges from 1 to 180 Hz. There are limited events within the ranges of 60–100 Hz and 160–180 Hz. There are two peak counts with main frequencies between 20 and 40 Hz and between 120 and 140 Hz, namely, 1700 (~32%) and 2200 (~41% of all monitored events). Therefore, according to Nyquist’s law [47,48], the optimized sampling frequency is proposed to be 360 Hz instead of the 500 Hz value used in this study when monitoring high roof breaking events. That will reduce the time taken to carry out sample collection and data analysis in the future.
A total of 5060 coal fracturing events are monitored via the HFMS along the gateway of the heading face. The statistical results of the event number versus the main frequency are shown in Figure 5c. The main frequency of coal fracturing events spans from 1 Hz to 800 Hz. The number of events occurring with a main frequency greater than 520 Hz is quite small (less than 3% of all monitored events), especially in the range of 560–720 Hz. However, more than 1000 events (20% of all monitored events) occur in the frequency range of 80–100 Hz. In other words, the sampling frequency of coal fracturing events can be set to 1600 Hz according to Nyquist’s law [49].
A statistical comparison of the main frequency data for the three types of rock failure events monitored in the coal mine is also presented in Figure 6. The main frequency of high roof breaking events is concentrated in two frequency ranges: 20–40 Hz and 120–140 Hz. The main frequency distributions of low roof breaking and coal fracturing events are relatively scattered compared with those of high roof breaking events. The dominant ranges of the main frequency for low roof breaking events are 60–80 Hz and 160–180 Hz, and those for coal fracturing events are 80–100 Hz, 400–420 Hz, and 480–500 Hz. The average values of the main frequencies of high roof breaking, low roof breaking, and coal fracturing events are 85.7 Hz, 149.7 Hz, and 253.9 Hz, respectively, indicating an increasing trend. It is speculated that roof failure events are more likely to be caused by brittle failure and shear failure, while coal fracture is usually caused by tensile failure due to a low tensile strength [50].

4. Discussion

To further characterize the microseismic features of three different failure modes in underground mines, the signal amplitude of each vibration event, as another critical index parameter for predicting burst events, is also analyzed. Figure 7a shows the statistical results of the maximum amplitude versus the main frequency of low roof breaking events. The primary vibration events are concentrated in the region with a small amplitude and a low frequency. Both the maximum amplitude and main frequency distributions follow lognormal distributions with peaks close to the origin. The statistics for coal fracturing events (Figure 7c) show similar lognormal distributions for maximum attitude and main frequency. However, the distribution ranges for both dramatically expand compared to that of low roof breaking events (Figure 7b), indicating that more energy is released from coal fracturing events. This may be caused by a high abutment stress concentration in front of the working panel. High roof breaking events cluster within the range of a small main frequency and small amplitude. These events always refer to the go-through fractures within the thick roof beam.
When combining the distribution of the maximum amplitude and main frequency of the three types of events shown in Figure 8, it is observed that the failure mechanism in a high roof is quite different from that in low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect in both the maximum amplitude and the main frequency, which could be an indicator to distinguish failure locations in the vertical direction. The obvious trend change in coal fracturing occurs due to the expansion in the main frequency covering the range from 0 to ~800 Hz, which is almost double the region of the main frequency distribution for low roof breaking events. These high-frequency events occurring in the coal seam indicate brittle failure or cleat propagation within the coal. These failures may accompany rapid energy release, which could result in rock/coal bursts. However, the seismic events that occur in the high roof with rapid energy release are difficult to distinguish based on the amplitude alone, particularly for an event with a main frequency range below ~100 Hz.
The above analysis indicates that the main frequency ranges of the three types of events are different, which may help determine equipment parameters and identify vibration event types. To further determine the locations of vibration events, it is necessary to study the relationship between the main frequency and transmission distance of vibration events. In this study, the distance between the sensors (1#–4#) located at the gateway and the heading face gradually decreases. The distance from the location of coal fracture events to the location of sensors is shortened. It is interesting to compare the variations in the maximum amplitude and main frequency of coal fracture events as measured by these sensors. The statistical results in Figure 9 show that the maximum amplitude value is attenuated with the increase in the measurement distance, as previously reported in [15,16], while the main frequency values do not change regularly with the increase in the measurement distance. Therefore, the maximum amplitude may be a more suitable indicator to determine the locations of vibration events. A large amplitude event that conforms to the propagation law indicates that larger rock fractures have occurred, making it more prone to dynamic disasters; thus, it may be more suitable as a direct monitoring tool and early warning indicator.
Figure 10 shows the variation law of the main frequency from near to far during the propagation of all energy events (the change in amplitude continuously decreases during this process, which is consistent with conventional cognition). However, some of these events have more energy when propagating to the 4# sensor, and some have less energy, which should be classified for statistics. Therefore, the propagation law of the main frequency of events with different initial amplitudes is studied and counted. Figure 10 lists the main frequency variation law for different initial amplitude ranges of the 4# sensor: greater than 8000 mV, in the range of 6000–8000 mV, in the range of 4000–6000 mV, and smaller than 4000 mV. The results show that the main frequency change laws of events with initial amplitude ranges greater than 8000 mV and in the range of 6000–8000 mV are still chaotic, but the main frequency change laws of events with initial amplitude ranges of 4000–6000 mV and smaller than 4000 mV are relatively consistent, which reflects the phenomenon of continuous reduction, except for a few cases of distant recovery. There was no large-scale synergistic effect between the maximum amplitude and dominant frequency; this was unexpected in other studies, which laid the groundwork for further in-depth research. However, due to the single number of monitored mines, the applicability of the experimental results needs to be further verified. Therefore, future studies should increase the number of tested mines and expand the geographical scope of the mines.

5. Conclusions

This study statistically analyzes three vibration events that are commonly used to prevent and monitor rock bursts: low roof breaking events, high roof breaking events, and coal fracture events. The main conclusions are summarized as follows:
(1)
The main frequency distribution ranges of low roof breaking events, high roof breaking events, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, the corresponding frequency distribution range can be set to monitor different vibration events to improve detection accuracy and reduce the test and analysis durations.
(2)
The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect in both the maximum amplitude and the main frequency.
(3)
Amplitude is a more suitable indicator for determining the locations of vibration events. The main frequency change laws of larger initial amplitude events are chaotic, but the main frequency change laws of smaller initial amplitude events are relatively consistent.
These results lay a foundation to optimize microseismic monitoring system parameters, which are conducive to improving the monitoring efficiency of rock bursts and the safety of underground operations to a certain extent.

Author Contributions

Conceptualization, W.Z.; methodology, X.Z. and Q.G.; software, X.Z.; validation, W.Z.; formal analysis, W.Z.; investigation, J.R. and Q.G.; resources, W.W.; data curation, J.R. and L.F.; writing—original draft preparation, W.Z. and W.W.; writing—review and editing, L.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 52204109), the Shaanxi Provincial Natural Science Foundation (Grant Nos. 2022JQ-333; 2024JC-YBQN-0453), and the Shaanxi Key Laboratory of Safety and Durability of Concrete Structures (Grant No. SZ02202).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. A schematic diagram of three kinds of events in a longwall panel.
Figure 1. A schematic diagram of three kinds of events in a longwall panel.
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Figure 2. The layout of measuring points of three systems: (a) WMMS, (b) WFMS, and (c) HFMS.
Figure 2. The layout of measuring points of three systems: (a) WMMS, (b) WFMS, and (c) HFMS.
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Figure 3. Flow chart of signal acquisition.
Figure 3. Flow chart of signal acquisition.
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Figure 4. Waveform and indicators of the vibration event.
Figure 4. Waveform and indicators of the vibration event.
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Figure 5. Main frequency distribution of (a) low roof breaking events, (b) high roof breaking events, and (c) coal fracturing events.
Figure 5. Main frequency distribution of (a) low roof breaking events, (b) high roof breaking events, and (c) coal fracturing events.
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Figure 6. A statistical comparison of the main frequency percentage distribution of the three types of events.
Figure 6. A statistical comparison of the main frequency percentage distribution of the three types of events.
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Figure 7. Relationship between main frequency and maximum amplitude of three types of seismic events: (a) low roof breaking events, (b) high roof breaking events, and (c) coal fracture events.
Figure 7. Relationship between main frequency and maximum amplitude of three types of seismic events: (a) low roof breaking events, (b) high roof breaking events, and (c) coal fracture events.
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Figure 8. Comparison of main frequency and maximum amplitude of three types of seismic events.
Figure 8. Comparison of main frequency and maximum amplitude of three types of seismic events.
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Figure 9. Relationship between maximum amplitude and transmission distance.
Figure 9. Relationship between maximum amplitude and transmission distance.
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Figure 10. Relationship between main frequency of coal fracture events and transmission distance for different initial amplitude ranges of 4# sensor.
Figure 10. Relationship between main frequency of coal fracture events and transmission distance for different initial amplitude ranges of 4# sensor.
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Table 1. Parameters of three monitoring systems.
Table 1. Parameters of three monitoring systems.
SystemsNumber of Measuring PointsSpacing of Measuring Points (m)Sampling Frequency (Hz)Sensor Receiving Frequency (Hz)
WMMS15100–5005001–600
WFMY750–100100010–800
HFMY450–60200010–800
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MDPI and ACS Style

Ren, J.; Zhang, X.; Gu, Q.; Zhang, W.; Wang, W.; Fan, L. The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention. Sustainability 2024, 16, 5485. https://doi.org/10.3390/su16135485

AMA Style

Ren J, Zhang X, Gu Q, Zhang W, Wang W, Fan L. The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention. Sustainability. 2024; 16(13):5485. https://doi.org/10.3390/su16135485

Chicago/Turabian Style

Ren, Jianju, Xin Zhang, Qinghua Gu, Wenlong Zhang, Weiqin Wang, and Long Fan. 2024. "The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention" Sustainability 16, no. 13: 5485. https://doi.org/10.3390/su16135485

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