A microseismic monitoring and early warning system for rockburst during tunnel / cave operation

Abstract

This invention discloses a microseismic monitoring and early warning system for rockbursts during tunnel / tunnel operation, relating to the field of rockburst microseismic monitoring technology. Sensors are installed in a cross-distribution manner between the left and right tunnel sections to ensure effective acquisition of microseismic signals during tunnel operation. Sensor grouping using cross-numbering and adjacent numbering ensures that the monitoring ranges between monitoring sections do not overlap. Cross-verification of signals from multiple sensors improves the accuracy of seismic source location. The system extracts spectral characteristic parameters of the microseismic signals using Fourier transform, time-frequency analysis, and other methods, and performs microseismic activity intensity analysis to obtain the activity intensity index of the monitoring area. This index is then de-distanced to obtain the source activity index for each monitoring area. Based on the development trend of the source activity index of microseismic events in each monitoring area, the system assesses the rockburst risk of each monitoring area, obtains a risk warning value, and automatically triggers risk warnings or encryption processing accordingly, improving the safety and stability of tunnel operation.

Description

Technical Field

[0001] This invention relates to the field of rockburst microseismic monitoring technology, and in particular to a microseismic monitoring and early warning system for rockbursts during tunnel / cave operation. Background Technology

[0002] As underground engineering deepens and regional stress persists, the intensity of accumulated stress within the rock mass increases after tunnel lining is completed, gradually enhancing its stress-bearing capacity. This results in a time-dependent effect on the failure of previously identified geological defects, meaning that potential failures during construction are delayed until the operational phase. This could be fatal to trains or vehicles in operation, and while this phenomenon has already begun to appear on high-altitude railways, there are currently no reported cases of rockburst warnings via microseismic monitoring during operation, leaving the field largely unexplored. Therefore, there is an urgent need to establish a microseismic system operation method suitable for rockburst monitoring during operation. Summary of the Invention

[0003] Therefore, it is necessary to provide a microseismic monitoring and early warning system for rockbursts during tunnel / cave operation, addressing the problems mentioned in the background technology above.

[0004] The objective of this invention can be achieved through the following technical solution: a microseismic monitoring and early warning system for rockburst during tunnel / tunnel operation, the system comprising a data acquisition module, a memory, a microseismic monitoring module, and a risk early warning module;

[0005] The acquisition module is deployed by installing sensors in a crisscross pattern in the left and right holes, and the sensors are numbered. Based on the sensor numbers, the system is segmented to form several monitoring segments. Each monitoring segment contains three monitoring areas, and the location of the seismic source is determined based on the sensor numbers and quantities.

[0006] The memory communicates with each sensor to collect and store microseismic events and their corresponding microseismic signals;

[0007] The microseismic monitoring module performs microseismic activity intensity analysis based on microseismic events and their corresponding microseismic signals in each monitoring area to obtain the activity intensity index of the monitoring area. Then, it de-distances the activity intensity index according to the distance between each sensor and the center point of the monitoring area to obtain the source activity index of each monitoring area. Based on the development trend of the source activity index of microseismic events in each monitoring area, it analyzes the rockburst risk of each monitoring area, obtains the risk warning value, and sends it to the risk warning module.

[0008] The risk warning module processes the received risk warning values, specifically as follows:

[0009] The risk warning values ​​of the three monitoring areas in each monitoring segment are retrieved and compared with the set warning intervals. If the risk warning value of any monitoring area is greater than the upper limit of the set warning interval, a risk warning is triggered. If the risk warning values ​​of all three monitoring areas are less than the lower limit of the set warning interval, no action is required. Otherwise, the monitoring segment is designated as an encrypted segment, and sensor encryption processing is performed on the encrypted segment. The specific encryption processing is as follows:

[0010] Retrieve the risk warning values ​​of the three monitoring areas of the encryption segment, and calculate the average risk value by averaging them; multiply the average risk value by the set encryption conversion coefficient, round the product to obtain the encryption quantity, and output the encryption quantity; thus, the encryption quantity of each encryption segment can be obtained.

[0011] In some embodiments, sensors are arranged in a crisscross pattern on the left and right sides, and monitoring segments are formed accordingly:

[0012] Sensors are installed using a cross-distribution method in the left and right holes, and each sensor is numbered sequentially in a cross pattern, so that each sensor corresponds to a location and a number. The specific numbering order is as follows: the first sensor in the left hole is numbered 1, the first sensor in the right hole is numbered 2, the second sensor in the left hole is numbered 3, and so on, until all sensors are numbered. This numbering is denoted as n, where n = 1, 2, 3... N, and N is a positive integer. N represents the total number of sensors, and n represents the number of any one of the sensors.

[0013] Three sensors with adjacent numbers are grouped together, and their corresponding monitoring range is a monitoring segment. There is only one intersection sensor between two adjacent groups of sensors. Each monitoring segment is marked as m, where m = 1, 2, 3...M, M is a positive integer, M represents the total number of monitoring segments, and m represents the number of any one of the monitoring segments.

[0014] Within the same group of sensors in a certain monitoring segment, sensor n, located in the middle, is taken as the center. Monitoring areas are selected 50m away from sensor n on both sides, and are denoted as monitoring area n. The area from sensor n-1 to the edge of monitoring area n is monitoring area n-1, and similarly, the area from sensor n+1 to the edge of monitoring area n is monitoring area n+1. Thus, each monitoring segment can be divided into three monitoring areas. The location of the seismic source can be determined based on the microseismic signals received in each monitoring area.

[0015] In some embodiments, the specific method for determining the location of the earthquake source based on the sensor number and quantity is as follows:

[0016] There is a set of sensors (n-1, n, and n+1) within the monitoring section. If microseismic signals are received from sensors n-1 and n, the source of the earthquake is determined to be located in monitoring area n-1; if microseismic signals are received from sensors n and n+1, the source of the earthquake is determined to be located in monitoring area n+1; if microseismic signals are received from sensors n-1, n, and n+1, the source of the earthquake is determined to be located in monitoring area n. Therefore, the source of the earthquake can be determined based on the received microseismic signals and denoted as Lmn.

[0017] In some embodiments, the specific process of microseismic activity intensity analysis is as follows:

[0018] The microseismic signal is subjected to Fourier transform to convert the signal from the time domain to the frequency domain. The spectral characteristic parameters are extracted using time-frequency analysis methods. The specific spectral characteristic parameters include bandwidth, peak value, and signal duration.

[0019] The envelope of the microseismic signal is extracted, and the amplitude jump value is obtained by calculation and analysis using derivatives and calculus.

[0020] The bandwidth K, peak frequency P, duration T, and amplitude jump value ΔA are normalized and their values ​​are taken. The numerical values ​​are then used to perform formulaic calculations and analysis to obtain the activity intensity index KP of the microseismic signal. The specific calculation formula is as follows:

[0021]

[0022] β1, β2, β3, and β4 are the set weight constants, and their specific values ​​can be set by those skilled in the art according to actual needs.

[0023] In some embodiments, the distance reduction method is as follows:

[0024] The center point of each monitoring area is found using the minimum bounding rectangle. The distance between each sensor and the center point is calculated. The active intensity index is then divided by the distance to obtain a unitless intensity index. The source activity value is calculated by averaging the intensity indices in each monitoring area. Thus, the source activity value of each microseismic event in each monitoring area can be obtained and denoted as KPj, where j = 1, 2, 3... J, J is a positive integer, J represents the total number of microseismic events, and j represents any microseismic event in the monitoring area.

[0025] In some embodiments, the envelope extraction of the microseismic signal and the analysis using derivatives and calculus are performed as follows:

[0026] The envelope of the microseismic signal F(t) is extracted and denoted as |F|. HilbertThe specific envelope is the complex signal of F(t) after Hilbert transformation, where "||" represents the modulo operation, and t is the time dimension;

[0027] The amplitude variation of the microseismic signal is described by calculating the instantaneous rate of change of the envelope. The specific rate of change of amplitude is approximated by the derivative of the envelope, and the specific derivative formula is as follows:

[0028]

[0029] Where △A(t) represents the rate of change of amplitude at time t;

[0030] The amplitude jump value ΔA is obtained by calculus calculation of the overall change degree of the microseismic signal based on the amplitude change rate ΔA(t) of each unit time dimension. The specific calculation formula is as follows:

[0031]

[0032] Where T represents the total duration of the microseismic signal, and the amplitude jump value ΔA represents the total change of the microseismic signal during this duration.

[0033] In some embodiments, the analysis is performed based on the development trend of the focal activity index of microseismic events in each monitoring area as follows:

[0034] A two-dimensional rectangular coordinate system is constructed with time as the horizontal axis and focal activity value as the vertical axis. The focal activity value of each microseismic event in the monitoring area is input into the coordinate system according to its corresponding time. The position of the focal activity value in the coordinate axis is recorded as the activity point. The activity points are connected sequentially by line segments to obtain a line graph of focal activity index change.

[0035] By dividing the activity index into segments by two adjacent activity points, several activity segments can be cut from the line graph of the focal activity index variation. The mean of the focal activity index corresponding to the activity points at both ends of each activity segment is calculated and denoted as _____. The slope of each active line segment is calculated using data fitting and denoted as...

[0036] The average activity of each segment in the line graph of the focal activity index change. and slope The risk warning value S is obtained through formulaic calculation and analysis. R The specific formula for calculating isk is as follows:

[0037]

[0038] δ1, δ2, and δ3 are the set weight constants, and their specific values ​​can be set by those skilled in the art according to actual needs.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. Sensors are installed in a staggered manner on the left and right sides of the tunnel to ensure effective acquisition of microseismic signals during tunnel operation without damaging the original secondary lining structure. Each sensor has a unique location and number. By grouping sensors with cross-numbering and adjacent numbers, it is ensured that the monitoring ranges between monitoring sections do not overlap. In a quiet operating environment, the sensors can better receive microseismic signals, and cross-verification of signals from multiple sensors can improve the accuracy of seismic source location, providing accurate data support for subsequent risk assessment and treatment.

[0041] 2. Spectral characteristic parameters of microseismic signals are extracted using methods such as Fourier transform and time-frequency analysis. Microseismic activity intensity analysis is then performed to obtain the activity intensity index of the monitoring area. The activity intensity index is then de-distanced based on the distance between each sensor and the center point of the monitoring area to obtain the source activity index for each monitoring area. The development trend of the source activity index of microseismic events in each monitoring area is analyzed to determine the rockburst risk and obtain risk warning values. This effectively quantifies the energy concentration of microseismic signals and the intensity of source activity. This not only improves the accuracy of monitoring but also enables timely detection of potential risks in tunnels, thus providing reliable data support for tunnel risk prevention and management.

[0042] 3. By receiving risk warning values ​​from each monitoring area and combining them with the set risk range, the system can monitor and warn of potential rockburst risks of microseismic events in real time, automatically triggering risk warnings or intensive processing; adjusting the number of sensors in the intensive section according to different risk warning values ​​to ensure more intensive monitoring in high-risk areas; and realizing an automated risk processing flow, enabling rapid response to potential rockburst risks and improving the safety and stability of tunnel operation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the principle of the present invention;

[0045] Figure 2 This is a schematic diagram of the sensor layout according to the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] Example 1:

[0048] like Figure 1 As shown, a microseismic monitoring and early warning system for rockburst during tunnel / tunnel operation includes: a memory, a data acquisition module, and a microseismic monitoring module;

[0049] The data acquisition module is deployed by installing sensors in a staggered arrangement across the left and right tunnels (sensors are mounted using surface rivets; the tunnel is relatively quiet during operation, allowing for effective acquisition of microseismic signals without damaging the original secondary lining). The sensors are then sequentially numbered, assigning each sensor a unique location and number. The numbering order is as follows: the first sensor in the left tunnel is numbered 1, the first sensor in the right tunnel is numbered 2, the second sensor in the left tunnel is numbered 3, and so on, until all sensors are numbered. This numbering is denoted as n, where n = 1, 2, 3…N, and N is a positive integer representing the total number of sensors. This represents the number of any one of the sensors; three sensors with adjacent numbers (n-1, n, and n+1) are grouped together, and their corresponding monitoring range is a monitoring segment. Specifically, there is only one intersection sensor between any two adjacent groups of sensors. For example, there are three adjacent groups of sensors: (n-3, n-2, and n-1), (n-1, n, and n+1), and (n+1, n+2, and n+3). Therefore, the monitoring ranges between the monitoring segments do not overlap. Each monitoring segment is labeled m, specifically m = 1, 2, 3...M, where M is a positive integer, M represents the total number of monitoring segments, and m represents the number of any one of the monitoring segments.

[0050] Within a monitoring segment, using sensor n (located in the middle) as the center, two monitoring areas are selected 50m to the left and right of sensor n, denoted as monitoring area n. The area from sensor n-1 to the edge of monitoring area n is designated as monitoring area n-1, and similarly, the area from sensor n+1 to the edge of monitoring area n is designated as monitoring area n+1. Thus, each monitoring segment can be divided into three monitoring areas. Based on the microseismic signals received from each monitoring area, the location of the seismic source can be determined. The specific determination method is as follows:

[0051] Taking a monitoring section as an example, there is a set of sensors (n-1, n, and n+1) within the monitoring section. If microseismic signals are received from sensors n-1 and n, the source of the earthquake is determined to be located in monitoring area n-1; if microseismic signals are received from sensors n and n+1, the source of the earthquake is determined to be located in monitoring area n+1; if microseismic signals are received from sensors n-1, n, and n+1, the source of the earthquake is determined to be located in monitoring area n. Thus, the source of the earthquake can be determined based on the received microseismic signals and denoted as Lmn. This method is very reasonable for microseismic detection in tunnels during operation. By using cross-verification of signals from multiple sensors, the accuracy of earthquake source location can be improved.

[0052] like Figure 2 As shown in the diagram, the specific sensor installation locations are as follows: the first sensor is installed 10 meters from the entrance of the left tunnel. Subsequent sensors are installed every 500 meters along the left tunnel, for a total of 16 sensors. Similarly, 16 sensors are installed in the right tunnel, each positioned axially between the two sensors in the left tunnel, meaning each sensor is 250 meters axially from the left tunnel sensors. This installation ensures that each microseismic signal is received by at least two sensors. In quiet environments during operation (compared to the noise levels during construction), microseismic signals are well received, and compared to a single sensor, the threshold is raised, filtering out a significant amount of noise.

[0053] The location of a microseismic event will generate microfractures, which are potential rockburst risk areas. Determining the location is a crucial step in risk prevention and control. Depending on the sensor arrangement, there will be at least 2 to 3 sensors that trigger the microseismic waveform. Based on the sensor trigger numbers and quantities, the risk area can be preliminarily determined. When the microseismic event falls within area 1, sensors 2 and 3 will receive the signal. If the microseismic event falls within area 2, sensors 1 and 2 will receive the signal. If the microseismic event falls within area 3, sensors 1, 2, and 3 will all receive the signal.

[0054] It should be noted that compared to tunnel excavation during construction, the probability of rockbursts during tunnel operation is greatly reduced. Furthermore, the increased support strength due to the addition of secondary lining to the original rock further prolongs the development and occurrence time of rockbursts. Therefore, sensor deployment does not need to be as dense as during construction; it is sufficient to detect rockbursts as soon as they begin to show signs. Traffic tunnels are generally twin-track tunnels, with a cross passage connecting the two tunnels every 50-100 meters axially. These two tunnels are referred to as "left and right tunnels." A single all-fiber microseismic monitoring system can connect up to 32 sensors, with each sensor capable of monitoring at a distance of up to 300 meters. Moreover, the all-fiber microseismic monitoring system transmits signals via optical fiber, resulting in low loss and enabling long-distance signal transmission. This effect far surpasses that of piezoelectric sensors, making it more suitable for monitoring during operation.

[0055] The memory communicates with each deployed sensor (including all-fiber microseismic sensors) to collect microseismic signals from each monitoring segment, analyzes the signals to determine the location Lmn of the seismic source, and saves the data along with the corresponding microseismic signals, ensuring data traceability and subsequent analysis needs.

[0056] Sensors are installed in a staggered manner across the left and right tunnels to ensure effective acquisition of microseismic signals during tunnel operation without damaging the original secondary lining structure. Each sensor has a unique location and number, and the cross-numbering and grouping of sensors with adjacent numbers ensures that the monitoring ranges between monitoring sections do not overlap. In a quiet operating environment, the sensors can better receive microseismic signals, and cross-verification of signals from multiple sensors improves the accuracy of seismic source location, providing accurate data support for subsequent risk assessment and treatment.

[0057] The microseismic monitoring module performs time-frequency feature identification and analysis based on microseismic signals to determine the existence of potential risks, specifically:

[0058] Fourier transform is performed on the microseismic signal to convert it from the time domain to the frequency domain. Time-frequency analysis methods (specifically short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) are used to extract spectral characteristic parameters. These parameters include bandwidth (bandwidth refers to the frequency range occupied by the signal in the frequency domain, i.e., the difference between the lowest and highest frequencies in the signal spectrum), peak frequency, and signal duration, denoted as K, P, and T, respectively. From this, the spectral characteristic parameters of the microseismic signal at each source location Lmn can be obtained. It should be noted that rockburst signals typically have a high bandwidth. A larger bandwidth indicates a more complex, intense, and energy-concentrated microseismic signal, thus increasing the rockburst risk in the monitored area. A higher peak frequency indicates a greater likelihood of strong source activity, further increasing the rockburst risk in the monitored area. A longer signal duration indicates more sustained source activity, also increasing the rockburst risk in the monitored area.

[0059] The envelope of the microseismic signal F(t) is extracted to obtain the envelope (i.e., the instantaneous amplitude), denoted as |F(t). Hilbert The specific envelope is the complex signal of F(t) after Hilbert transformation, where "||" represents the modulo operation, and t is the time dimension;

[0060] The amplitude variation of the microseismic signal is described by calculating the instantaneous rate of change of the envelope. The specific rate of change of amplitude is approximated by the derivative of the envelope, and the specific derivative formula is as follows:

[0061]

[0062] Where △A(t) represents the rate of change of amplitude at time t;

[0063] The amplitude jump value ΔA is obtained by calculus calculation of the overall change degree of the microseismic signal based on the amplitude change rate ΔA(t) of each unit time dimension. The specific calculation formula is as follows:

[0064]

[0065] Where T represents the total duration of the microseismic signal, and the amplitude jump value ΔA represents the total change of the microseismic signal during this duration, which is used to describe the degree of drastic change of the microseismic signal. The larger the value, the greater the risk of rockburst in the monitored area.

[0066] The bandwidth K, peak frequency P, duration T, and amplitude jump value ΔA are normalized and their values ​​are taken. The numerical values ​​are then used to perform formulaic calculations and analysis to obtain the activity intensity index KP of the microseismic signal. The specific calculation formula is as follows:

[0067]

[0068] β1, β2, β3, and β4 are the set weight constants, and their specific values ​​can be set by those skilled in the art according to actual needs.

[0069] From this, the activity intensity index of the microseismic signals received in each monitoring area can be obtained. The center point of each monitoring area is found using the minimum bounding rectangle (MBR), the distance between each sensor and the center point is calculated, and then the activity intensity index is divided by the distance to obtain a unitless intensity index. This effectively removes the influence caused by the different distances between the sensor and the seismic source, and can more accurately measure and evaluate the seismic source activity intensity of the monitoring area, which helps to improve the accuracy of the monitoring results. The seismic source activity value is obtained by averaging the intensity indices in each monitoring area. From this, the seismic source activity value of each microseismic event in each monitoring area can be obtained, and it is denoted as KPj, where j = 1, 2, 3... J, J is a positive integer, J represents the total number of microseismic events, and j represents any microseismic event in the monitoring area.

[0070] A two-dimensional rectangular coordinate system is constructed with time as the x-axis and focal activity value as the y-axis. The focal activity value of each microseismic event in the monitoring area is input into the coordinate system according to its corresponding time, and the position of the focal activity value on the coordinate axis is recorded as an activity point. Line segments are used to connect each activity point sequentially to obtain a line graph of focal activity index variation. Each adjacent activity point forms an activity line segment, thus several activity line segments can be cut from the focal activity index variation line graph. The mean of the focal activity index corresponding to the activity points at both ends of each activity line segment is calculated and recorded as the activity mean. The slope of each active line segment is calculated using data fitting and denoted as... It should be noted that when the slope is greater than zero and the larger the slope, it indicates that the focal activity index of the monitored area is increasing and the rate of increase is faster; when the slope is less than zero and the smaller the slope, it indicates that the focal activity index of the monitored area is decreasing and the rate of decrease is faster.

[0071] The average activity of each segment in the line graph of the focal activity index change. and slope The risk warning value S is obtained through formulaic calculation and analysis. R The specific formula for calculating isk is as follows:

[0072]

[0073] δ1, δ2, and δ3 are the set weight constants, and their specific values ​​can be set by those skilled in the art according to actual needs. As can be seen from the formula, when the slope is greater than zero, the larger the slope, the larger the risk warning value; when the slope is less than zero, the smaller the slope, the smaller the risk warning value; when the activity mean is larger, the risk warning value is larger. Thus, the risk warning value of each monitoring area can be obtained and sent to the risk warning module.

[0074] The spectral characteristic parameters of microseismic signals are extracted using methods such as Fourier transform and time-frequency analysis. Microseismic activity intensity analysis is then performed to obtain the activity intensity index of the monitoring area. The activity intensity index is then de-distanced based on the distance between each sensor and the center point of the monitoring area to obtain the source activity index for each monitoring area. The development trend of the source activity index of microseismic events in each monitoring area is analyzed to determine the rockburst risk and obtain risk warning values. This effectively quantifies the energy concentration of microseismic signals and the intensity of source activity. This not only improves the accuracy of monitoring but also enables timely detection of potential risks in tunnels, thus providing reliable data support for tunnel risk prevention and management.

[0075] The risk warning module performs risk processing based on the received risk warning values ​​for each monitoring area, specifically as follows:

[0076] The risk warning values ​​of the three monitoring areas in each monitoring segment are retrieved and compared with the set warning intervals. If the risk warning value of any monitoring area exceeds the upper limit of the set warning interval, a risk warning is triggered. It should be noted that when a risk warning is triggered, staff will typically suspend operations such as cross-regional monitoring, multi-party detection, and fixed-point stress removal. If the risk warning values ​​of all three monitoring areas are less than the lower limit of the set warning interval, it indicates that the rockburst risk is very small and can be ignored, so no action is required. In other cases, the monitoring segment is marked as an encrypted segment, and sensor encryption processing is performed on the encrypted segment. The specific encryption processing is as follows:

[0077] The risk warning values ​​of the three monitoring areas of the encrypted section are retrieved and averaged to obtain the average risk value. The product of the average risk value and the set encryption conversion coefficient is rounded to obtain the encryption quantity, which is then output. Thus, the encryption quantity of each encrypted section can be obtained. Staff can promptly encrypt the sensors of the encrypted section according to the encryption quantity to achieve more accurate monitoring of rockburst risk in the encrypted section.

[0078] By receiving risk warning values ​​from various monitoring areas and combining them with the set risk intervals, the system can monitor and warn of potential rockburst risks from microseismic events in real time. It can automatically trigger risk warnings or intensify monitoring. Based on different risk warning values, the number of sensors in the intensified sections can be adjusted to ensure more intensive monitoring in high-risk areas. This automated risk handling process enables rapid response to potential rockburst risks and improves the safety and stability of tunnel operations.

[0079] Example 2: The data acquisition module is also used to deploy sensors in single-line tunnels. The specific deployment method is as follows:

[0080] For single-line tunnels, a linear distribution method is adopted. The first sensor is installed 10m away from the entrance, and then the next sensor is installed at fixed intervals (the fixed length is set according to actual needs, usually 100-300 meters, depending on the tunnel length and monitoring accuracy requirements). This installation method can also ensure that each microseismic signal is received by at least two sensors. When installing the sensors, they are installed on the secondary lining surface with expansion bolts, and the arch crown is preferred. If it is inconvenient to install at the arch crown, they can be installed at the arch shoulder. When selecting the arch shoulders on both sides of the tunnel, it is necessary to judge based on the geological conditions of the surrounding rock mass exposed during the construction period, and try to install them on the side with more exposed cracks, or on the side perpendicular to the direction of principal stress.

[0081] The sensors within the single-track tunnel are numbered individually as n, where n = 1, 2, 3...N, and N is a positive integer. N represents the total number of sensors, and n represents the number of any one of them. A monitoring area is defined as the area formed by two adjacent sensors.

[0082] If the monitoring area If two adjacent sensors receive microseismic signals, the arrival time difference between the two adjacent microseismic signals is calculated. If the arrival time difference is less than a set value, the seismic source is located in the monitoring area. The specific formula for calculating the time difference of arrival is as follows: Where Un and Un+1 are the times when the micro-seismic signal arrives at sensor n and sensor n+1, respectively;

[0083] If the arrival time difference is greater than or equal to the set difference, it indicates that the seismic source is not in the current monitoring area, but closer to one of the sensors. In this case, sensor encryption processing is required to accurately locate its monitoring area. The specific encryption process is as follows:

[0084] The sensor with the earlier arrival time among two adjacent sensors is selected as the target sensor. It should be noted that the earthquake source is closer to the target sensor. With the target sensor as the center point, sensors are installed on both sides at a certain distance. The specific number is set by the engineer in this field. As can be seen from the sensor installation, with the target sensor as the center point, the two adjacent monitoring areas involving the target sensor are all subjected to sensor densification processing to determine the area where the earthquake source is located.

[0085] This allows the location of the earthquake source to be determined, and the microseismic signals detected in each monitoring area are sent to the microseismic monitoring module; it should be noted that there are at least two microseismic signals in each monitoring area;

[0086] The microseismic monitoring module identifies and analyzes the time-frequency characteristics of the microseismic signals in each monitoring area to obtain the risk warning value for each monitoring area, and then sends it to the risk warning module.

[0087] The risk warning module is also used to handle risk warning values ​​for each monitoring section of a single-track tunnel, specifically:

[0088] Monitoring area The risk warning value is compared and analyzed with the set warning range. If the risk warning value is greater than the upper limit of the set warning range, a risk warning is triggered. If the risk warning value of the monitored area is less than the lower limit of the set warning range, it indicates that the rockburst risk is very small and can be ignored, so no action is required. If the risk warning value is within the set warning range, the monitored area is marked as an encrypted area, and sensor encryption processing is performed on the encrypted area. The specific encryption processing is as follows:

[0089] The risk warning value of the encrypted area is retrieved, multiplied by the set encryption conversion coefficient, and the product is rounded to obtain the encryption quantity, which is then output. This gives the encryption quantity for each encrypted area, allowing staff to promptly encrypt the sensors in the encrypted section based on the encryption quantity, thus achieving more accurate monitoring of rockburst risk in the encrypted section.

[0090] The above formulas are all derived from software simulation using a large amount of data, and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art based on the actual situation.

[0091] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A microseismic monitoring and early warning system for rockburst during tunnel / hole operation period, characterized in that, The device comprises a layout collection module, a memory, a microseismic monitoring module and a risk early warning module. The layout collection module installs sensors through left and right hole cross distribution and numbers the sensors; the sensors are segmented according to the numbers to form several monitoring sections, each monitoring section contains three monitoring areas, and the position of the seismic source is determined according to the numbers and quantity of the sensors; The memory is connected with each sensor to collect and store microseismic events and corresponding microseismic signals; The microseismic monitoring module analyzes the microseismic activity intensity of each monitoring area based on the microseismic events and corresponding microseismic signals to obtain the activity intensity index of the monitoring area, and then removes the distance of the activity intensity index according to the distance between each sensor and the center point of the monitoring area to obtain the seismic source activity index of each monitoring area; the development trend of the seismic source activity index of each monitoring area is analyzed to determine the rock burst risk of each monitoring area, and a risk early warning value is obtained and sent to the risk early warning module; The risk early warning module processes the risk early warning based on the received risk early warning value, specifically: The risk early warning values of the three monitoring areas of each monitoring section are called and compared with the set early warning interval; if the risk early warning value of any one monitoring area is greater than the upper limit of the set early warning interval, the risk early warning is triggered; if the risk early warning values of the three monitoring areas are all less than the lower limit of the set early warning interval, no operation is needed; otherwise, the monitoring section is marked as an encrypted section, and the sensor of the encrypted section is encrypted; the specific encryption processing is as follows: The risk early warning values of the three monitoring areas of the encrypted section are called and the mean value is calculated to obtain the risk mean value; the product of the risk mean value and the set encryption conversion coefficient is calculated to obtain the encryption quantity, which is output; thus the encryption quantity of each encrypted section is obtained.

2. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation according to claim 1, characterized in that, The sensors are installed through left and right hole cross distribution, and the monitoring sections are formed by segmentation as follows: Each sensor corresponds to a position and a number by cross numbering; the specific numbering order is as follows: the first sensor in the left hole is numbered 1, the first sensor in the right hole is numbered 2, the second sensor in the left hole is numbered 3, and so on; the numbering of all sensors is recorded as n, n=1, 2, 3……N, N is a positive integer, N represents the total number of sensors, and n represents the number of any sensor; Each group of three adjacent sensors corresponds to a monitoring section, and there is only one intersection sensor between each two adjacent groups of sensors; each monitoring section is marked as m, specifically m=1, 2, 3……M, M is a positive integer, M represents the total number of monitoring sections, and m represents the number of any monitoring section. In the same group of sensors in a certain monitoring section, take sensor n in the middle position as the center, and select 50 m away from sensor n on the left and right as a monitoring area, which is recorded as monitoring area n; take the edge position of monitoring area n from sensor n-1 as monitoring area n-1, and take the edge position of monitoring area n from sensor n+1 as monitoring area n+1; thus, each monitoring section can be divided into three monitoring areas; according to the microseismic signals received by each monitoring area, the position of the seismic source can be determined.

3. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation period according to claim 2, characterized in that, The specific way of determining the position of the seismic source according to the sensor number and quantity is as follows: There is a group of sensors (n-1, n and n+1) in the monitoring section, if microseismic signals from sensors n-1 and n are received, it is determined that the position of the seismic source is in monitoring area n-1; if microseismic signals from sensors n and n+1 are received, it is determined that the position of the seismic source is in monitoring area n+1; if microseismic signals from sensors n-1, n and n+1 are received, it is determined that the position of the seismic source is in monitoring area n; thus, the position of the seismic source can be determined according to the received microseismic signals, which is recorded as Lmn.

4. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation according to claim 1, characterized in that, The specific process of microseismic activity intensity analysis is as follows: Perform Fourier transform on the microseismic signal to convert the signal from time domain to frequency domain, extract the frequency spectrum characteristic parameters using time-frequency analysis method, and the specific frequency spectrum characteristic parameters include frequency band width, frequency peak value and signal duration; Perform envelope extraction on the microseismic signal and calculate the amplitude jump value using derivative and calculus analysis; Normalize the amplitude jump value, frequency band width, frequency peak value and signal duration and take their values, and perform formula calculation and analysis on the values to obtain the activity intensity index of the microseismic signal; thus, the activity intensity index of the microseismic signal received by each monitoring area can be obtained.

5. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation period according to claim 4, characterized in that, The de-distance method is as follows: Find the center point of each monitoring area using the minimum circumscribed rectangle, calculate the distance between each sensor and the center point, and then divide the activity intensity index by the distance to obtain a unitless intensity index; perform mean value calculation on the intensity indexes in each monitoring area to obtain the seismic source activity value; thus, the seismic source activity value of each microseismic event in each monitoring area can be obtained, which is recorded as KPj, where j=1, 2, 3…J, J is a positive integer, J represents the total number of microseismic events, and j represents any microseismic event in the monitoring area.

6. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation period according to claim 5, characterized in that, The method of performing envelope extraction on the microseismic signal and using derivative and calculus analysis is as follows: The microseismic signal F(t) is subjected to envelope extraction to obtain an envelope line denoted as The specific envelope line is a complex signal after Hilbert transform of F(t) The symbol represents a modulo operation, wherein t is a time dimension. The instantaneous change rate of the envelope is calculated to describe the amplitude change of the microseismic signal, and the specific amplitude change rate is approximately calculated by the derivative of the envelope, and the specific derivative formula is as follows: ； Where △A(t) represents the amplitude change rate at time dimension t; According to the amplitude change rate △A(t) of each unit time dimension, the overall change degree of the microseismic signal is calculated by integral calculus to obtain the amplitude jump value △A, and the specific calculation formula is as follows: ； Where T represents the total duration of the microseismic signal, and the amplitude jump value △A represents the total change amount of the microseismic signal in the duration.

7. The microseismic monitoring and early warning system for rock burst during tunnel / cave operation period according to claim 6, characterized in that, The method of analyzing the development trend of the seismic source activity index of each monitoring area is as follows: A two-dimensional rectangular coordinate system is constructed with time as the horizontal axis and focal activity value as the vertical axis. The focal activity value of each microseismic event in the monitoring area is input into the coordinate system according to its corresponding time. The position of the focal activity value in the coordinate axis is recorded as the activity point. The activity points are connected sequentially by line segments to obtain a line graph of focal activity index change. A segment of active line is formed by two adjacent active points. Thus, several segments of active line can be cut from the variation broken line graph of the seismic activity index of the source, and the average of the seismic activity index of the source corresponding to the two active points at the two ends of each segment of active line is calculated as an active average, denoted as , and the slope of each segment of active line is calculated by data fitting, denoted as . The activity average of each activity segment in the seismic activity index change broken line graph is calculated and slope The risk early warning value is obtained by formula calculation and analysis The specific calculation formula is: ； Where δ1 and δ2 are the set weight constants.

8. The microseismic monitoring and early warning system for rockburst during tunnel / tunnel operation as described in claim 1, wherein the data acquisition module is further used for sensor deployment, microseismic signal acquisition, and source location determination for a single-line tunnel, specifically: For single-line tunnels, a linear distribution method is adopted. The first sensor is installed 10m away from the entrance, and then the next sensor is installed at fixed intervals. The sensors are installed on the secondary lining surface using expansion bolts. The sensors in the single-wire tunnel are numbered one by one as n, n = 1, 2, 3…N, N is a positive integer, N represents the total number of sensors, and n represents the number of any one sensor; two adjacent sensors form a monitoring area, which is recorded as ; If the monitoring area If the monitoring area ; If the arrival time difference is greater than or equal to the set difference, sensor encryption processing is required to accurately locate its monitoring area. The specific encryption process is as follows: The sensor with the earlier arrival time among two adjacent sensors is selected as the target sensor. Sensors are installed at intervals on both sides of the target sensor to further determine the monitoring area where the earthquake source is located.

9. The microseismic monitoring and early warning system for rockburst during tunnel / tunnel operation according to claim 7, wherein the risk early warning module is further used to perform risk processing on the risk early warning values ​​of each monitoring section of a single-line tunnel, specifically as follows: Monitoring area The risk warning value is compared and analyzed with the set warning range. If the risk warning value is greater than the upper limit of the set warning range, a risk warning is triggered; if the risk warning value is within the set warning range, the monitoring area is marked as an encrypted area, and sensor encryption processing is applied to the encrypted area. The specific encryption processing is as follows: Retrieve the risk warning value of the encrypted area, multiply it by the set encryption conversion coefficient, round the product to obtain the encryption quantity, and output the encryption quantity; thus, the encryption quantity of each encrypted area can be obtained.

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