A distributed intelligent microseismic monitoring method and device for rockburst in railway tunnels

Abstract

The present invention relates to the technical field of tunnel rockburst monitoring, and specifically discloses a distributed intelligent microseismic monitoring method and device for railway tunnel rockburst, which can obtain seismic wave signals from multiple angles based on distributed arrangement, improving the reliability of the recognition results; can adjust the energy level range and frequency bandwidth of the seismic wave analog signal, so that the seismic wave signal is maintained, improving the stability during signal transmission; at the same time, through the constructed D-S evidence theory model, different methods act together to obtain rock fracture signals from multiple signal categories, improving the accuracy of signal recognition, and further improving the safety of engineering construction.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel rockburst monitoring, and particularly to a distributed intelligent microseismic monitoring method and device for railway tunnel rockburst. Background Technique

[0002] In recent years, with the development of high-speed railways, especially the construction of railway tunnels with complex structures such as Sichuan-Tibet, Xinjiang-Tibet, and Yunnan-Tibet in the southwestern region, tunnel construction work has become increasingly complex. For the construction stage of complex and dangerous high ground stress deep tunnels, the characteristics of rockburst such as suddenness, fierceness, and strong destructiveness pose serious risks to the safety of personnel, equipment, and property. Microseismic monitoring technology has become one of the important early warning means in rockburst early warning.

[0003] Microseismic monitoring technology is a three-dimensional space monitoring technology for microfractures in rock masses. Through its characteristics of "time, space, and intensity", it can effectively monitor the occurrence time, spatial position, and energy magnitude of fracture points in rock masses, so as to predict the deformation and failure of surrounding rocks of deep-buried tunnels after excavation unloading by using microseismic monitoring. The existing microseismic monitoring systems are mostly mine microseismic monitoring systems. The entire system is interconnected by cable wires, ensuring stable data transmission while ensuring time synchronization of each sensor. However, this system structure is extremely inconvenient in tunnel rockburst monitoring. Especially the existence of communication cable wires makes installation difficult and the efficiency is relatively low. It is also easy to be cut off during the implementation process, thus causing the entire monitoring to stagnate. In actual engineering projects, there are difficulties in installation and recovery and low efficiency in microseismic rockburst monitoring of railway tunnels. Traditional vibration signal recognition has a strong dependence on expert experience and a relatively low accuracy rate.

[0004] Therefore, there is an urgent need for a microseismic monitoring method and device that can accurately identify tunnel microseismic signals and reduce the operation and maintenance risks of monitoring devices. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the existing microseismic monitoring, such as difficult installation and recovery of monitoring devices, low efficiency, strong dependence on expert experience and relatively low accuracy rate in traditional vibration signal recognition, and provide a distributed intelligent microseismic monitoring method and device for railway tunnel rockburst that combines distributed layout and monitors tunnel rockburst microseisms from multiple methods and angles.

[0006] In order to achieve the above invention purpose, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a distributed intelligent microseismic monitoring method for railway tunnel rockburst, and the method includes:

[0008] Distributively arranging acquisition devices;

[0009] Obtaining seismic wave analog signals through the acquisition devices;

[0010] Adjust the energy level range and frequency bandwidth of the seismic wave simulation signal;

[0011] Convert the adjusted seismic wave simulation signal into a digital signal to obtain a seismic wave digital signal;

[0012] Based on a time window, intercept the seismic wave digital signal to obtain the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio within the time window;

[0013] Based on the constructed D-S evidence theory model, identify the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio to obtain a rock fracture signal and achieve microseismic monitoring;

[0014] Among them, the construction process of the D-S evidence theory model includes:

[0015] Based on the seismic wave simulation signal and the acquisition device, establish a membership relationship and calculate the optimal membership degree weight value;

[0016] Calculate the support degree weight value according to the seismic wave simulation signal and the acquisition device of the seismic wave simulation signal;

[0017] Construct a D-S evidence theory model according to the optimal membership degree weight value and the support degree weight value.

[0018] According to a specific implementation manner, in the above monitoring method, the calculation formula of the D-S evidence theory model is:

[0019] ,

[0020] ,

[0021] Among them, m(A) is the confidence in the seismic wave digital signal A, m n (A n ) is the support degree of the nth acquisition device for the seismic wave digital signal A, f A is the optimal membership degree weight value, S A is the support degree weight value, K is the normalization factor, q(A) is the average value of the support degree of the acquisition device for the seismic wave digital signal A.

[0022] According to a specific embodiment, in the above monitoring method, the membership relationship adopts the optimal fuzzy statistical method. A membership function u(A) is established based on the support degree of the acquisition device obtained from the seismic wave simulation signal, and the optimal membership degree weight value is obtained according to the membership function.

[0023] According to a specific embodiment, in the above monitoring method, the calculation formula for the support degree weight value is:

[0024] ,

[0025] ;

[0026] Wherein, is the support norm of any two of the acquisition devices for the seismic wave digital signal A, , are the support degrees of any two of the acquisition devices for the seismic wave digital signal A, is the average value of the support norms of all acquisition devices for the seismic wave digital signal A, is the support degree weight value.

[0027] According to a specific embodiment, in the above monitoring method, the rock fracture signal is obtained after marking and screening out interference signals after obtaining the recognition result according to the constructed D-S evidence theory model.

[0028] According to a specific embodiment, in the above monitoring method, the interference signals include microseismic signals, blasting signals, drilling signals, forklift signals, and electrical pulse signals.

[0029] According to a specific embodiment, in the above monitoring method, the distributed arrangement of the acquisition devices includes:

[0030] Installation sections are set based on the tunnel excavation face at a preset spacing, and the acquisition devices are installed according to the installation sections at a preset angle.

[0031] In a second aspect, the present invention provides a distributed intelligent microseismic monitoring device for rock bursts in railway tunnels, and the device includes:

[0032] An acquisition device for acquiring seismic wave analog signals;

[0033] A signal optimization module for adjusting the energy level range and frequency bandwidth of the seismic wave analog signal;

[0034] A signal conversion module for converting the adjusted seismic wave analog signal into a digital signal to obtain a seismic wave digital signal;

[0035] A signal selection module is used to intercept the digital seismic wave signal based on a time window, obtain the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio within the time window; and identify the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio based on the constructed D-S evidence theory model to obtain a rock fracture signal and achieve microseismic monitoring.

[0036] Among them, the acquisition device is connected to the signal optimization module through an armored cable for manholes.

[0037] According to a specific embodiment, in the above monitoring device, the signal optimization module adopts an adjustable multi-stage signal amplifier and a follower circuit.

[0038] According to a specific embodiment, in the above monitoring device, the signal conversion module is further used to store the digital seismic wave signal so that the signal selection module can read and identify the signal; the signal conversion module adopts an MSP430F2730 chip.

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

[0040] 1. A distributed railway tunnel rockburst intelligent microseismic monitoring method provided by the present invention can obtain seismic wave signals from multiple angles based on distributed arrangement, improving the reliability of the identification result; it can adjust the energy level range and frequency bandwidth of the analog seismic wave signal, enabling the seismic wave signal to be maintained and improving the stability during signal transmission. By using the constructed D-S evidence theory model, different methods act together to obtain rock fracture signals from multiple signal categories, improving the accuracy of signal identification and further enhancing the safety of engineering construction.

[0041] 2. A distributed railway tunnel rockburst intelligent microseismic monitoring device provided by the present invention is connected through an armored cable for manholes, solving problems such as difficult installation, maintenance, and recovery of traditional wired microseismic monitoring equipment, and further improving the effectiveness and reliability of microseismic monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic flow chart of a distributed railway tunnel rockburst intelligent microseismic monitoring method provided by an embodiment of the present application;

[0043] Figure 2 is a schematic structural diagram of a distributed railway tunnel rockburst intelligent microseismic monitoring device provided by an embodiment of the present application;

[0044] Figure 3 is a schematic circuit diagram of a signal optimization module provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present invention will be further described in detail below in conjunction with test examples and specific embodiments. However, it should not be understood that the scope of the above-mentioned subject matter of the present invention is limited to the following embodiments. All technologies implemented based on the content of the present invention belong to the scope of the present invention.

[0046] In view of the characteristics of micro-seismic monitoring and early warning for rock bursts in railway tunnels in the embodiments of the present application, a distributed intelligent micro-seismic monitoring device for rock bursts in railway tunnels is designed according to the requirements of a face-pushing distributed micro-seismic node instrument. At the same time, a D-S evidence theory model based on the optimization of evidence chain weights is constructed. The maximum signal energy value within the time window, the integral value of the signal energy within the time window, the main frequency of the signal within the time window, and the maximum value of the long-short time window ratio within the time window are input into the improved D-S evidence theory model, and the micro-seismic signal recognition result is obtained. Furthermore, various vibration signals are automatically selected to obtain rock fracture signals, realizing micro-seismic monitoring.

[0047] The present application will be further introduced and described below in conjunction with specific embodiments.

[0048] Please refer to Figure 1 , which shows a schematic flowchart of a distributed intelligent micro-seismic monitoring method for rock bursts in railway tunnels provided by the embodiments of the present application. The method includes:

[0049] Step 1: Distributedly arrange acquisition devices.

[0050] Based on the tunnel excavation face, installation sections are set at a preset spacing, and the acquisition devices are installed according to a preset angle distribution based on the installation sections.

[0051] The preset spacing is set according to the actual situation of the tunnel. In a possible implementation, the preset spacing is 20 m to 50 m. The preset angle is set according to the interval of the installation sections, and 4 acquisition devices are equally angled on the installation section, so as to obtain a complete seismic wave simulation signal to the greatest extent.

[0052] Step 2: Obtain seismic wave simulation signals through the acquisition devices.

[0053] Step 3: Adjust the energy level range and frequency bandwidth of the seismic wave simulation signals.

[0054] Among them, the energy level range of the seismic wave simulation signals is automatically adjusted, and the frequency bandwidth is adjusted. The main purpose is to ensure the detection and holding of the seismic wave simulation signals.

[0055] Step 4: Convert the adjusted seismic wave simulation signals into digital signals to obtain seismic wave digital signals.

[0056] Step 5: Intercept the seismic wave digital signal based on the time window, and obtain the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio within the time window.

[0057] Specifically, the maximum signal energy value within the time window:

[0058] ,

[0059] where E max is the maximum signal energy value within the time window, and A max is the maximum amplitude of the seismic wave digital signal A within the time window.

[0060] The signal energy integral value within the time window:

[0061] ,

[0062] where E sum is the signal energy integral value within the time window, T0, T max are the minimum and maximum values of the time window, A i is the seismic wave digital signal A obtained by the i-th acquisition device, and N is the number of acquisition devices.

[0063] The signal main frequency within the time window .

[0064] The maximum long-short time window ratio within the time window:

[0065] ,

[0066] where N sta is the short time window, N lta is the long time window, and A i , A j respectively represent the seismic wave digital signals A collected by the i-th acquisition device and the j-th acquisition device.

[0067] Step 6: Identify the maximum signal energy value, signal energy integral value, signal main frequency, and maximum long-short time window ratio based on the constructed D-S evidence theory model, obtain the rock fracture signal, and realize microseismic monitoring.

[0068] Among them, the construction process of the D-S evidence theory model includes:

[0069] Establish a membership relationship based on the seismic wave digital signal and the acquisition device, and calculate the optimal membership degree weight value;

[0070] Calculate the support degree weight value according to the seismic wave digital signal and the acquisition device of the seismic wave analog signal;

[0071] Construct a D-S evidence theory model based on the optimal membership degree weight and the support degree weight.

[0072] According to a specific implementation manner, in the above monitoring method, the calculation formula of the D-S evidence theory model is:

[0073] ,

[0074] ,

[0075] where, m(A) is the degree of belief in the seismic wave digital signal A, m n (A n ) is the support degree of the nth acquisition device for the seismic wave digital signal A, f A is the optimal membership degree weight, S A is the support degree weight, K is the normalization factor, q(A) is the average value of the support degrees of the acquisition devices for the seismic wave digital signal A.

[0076] The membership relationship adopts the optimal fuzzy statistical method. The support degree of the acquisition device is obtained according to the seismic wave analog signal to establish a membership function u(A), and the optimal membership degree weight is obtained according to the membership function.

[0077] Regarding the optimal membership degree weight f A , it is defined as: in the process of multiple acquisition devices detecting multiple objects, if the number of acquisition devices that support a certain object A (the degree of belief is greater than a certain threshold) is n, and the total number of acquisition devices is N, the membership degree can be calculated by the membership function u(A), where u(A) = (A - n) / (N - n), f A = u(A), the larger the membership degree value, the higher the probability of object A. Generally speaking, the more acquisition devices indicate support for a certain object.

[0078] The calculation formula of the support degree weight is:

[0079] ,

[0080] ;

[0081] where, is the support norm of any two of the acquisition devices for the seismic wave digital signal A, , is the support degree of any two of the said acquisition devices for the seismic wave digital signal A, is the average value of the support norms of all acquisition devices for the seismic wave digital signal A, S A is the support degree weight.

[0082] Specifically, the signal analysis results from different methods and angles are classified into an improved D-S evidence theory model. This model conducts intelligent analysis and identification, synthesizes multi-category signal index parameters, and finally obtains the identification result of the micro-seismic signal and completes the discrimination of various vibration signals.

[0083] Among various micro-seismic signals, the truly effective signal is the rock fracture signal. Due to the complex operation environment of the tunnel, the rock fracture signal is hidden among numerous interference signals and is difficult to identify. Usually, before signal analysis, a certain procedure is used to exclude the interference part in order to screen out micro-seismic signals, blasting signals, drilling signals, forklift signals, and electrical pulse signals. These signals are significantly different from the rock fracture signal on the original waveform diagram and can be easily distinguished and screened by manual wave reading, as shown in Table 1.

[0084] Table 1 shows the classification results of various signals based on time windows

[0085]

[0086] Further, please refer to Figure 2 which shows a schematic structural diagram of a distributed railway tunnel rockburst intelligent micro-seismic monitoring device provided by an embodiment of the present application. The device includes:

[0087] An acquisition device for acquiring seismic wave analog signals;

[0088] A signal optimization module for adjusting the energy level range and frequency bandwidth of the seismic wave analog signal;

[0089] A signal conversion module for converting the adjusted seismic wave analog signal into a digital signal to obtain a seismic wave digital signal;

[0090] A signal selection module for intercepting the seismic wave digital signal based on a time window to obtain the maximum signal energy value, signal energy integral value, signal main frequency, and maximum value of the long and short time window ratio within the time window; and for identifying the maximum signal energy value, signal energy integral value, signal main frequency, and maximum value of the long and short time window ratio based on the constructed D-S evidence theory model to obtain the rock fracture signal and achieve micro-seismic monitoring;

[0091] Among them, the acquisition device is connected to the signal optimization module through an armored cable with an access hole.

[0092] Specifically, the core of signal optimization acquisition lies in the automatic adjustment and amplification of the on-site signal energy level range and frequency bandwidth. The signal optimization module adopts an adjustable multi-stage signal amplifier and a follower circuit to ensure the detection and holding of seismic wave analog signals. Please refer to Figure 3 , which shows the circuit structure schematic diagram of the signal optimization module provided by the embodiment of the present application.

[0093] Specifically, another core of on-site microseismic signal acquisition lies in the rapid conversion of on-site signals from analog signals to digital signals, and at the same time, it can be transmitted to the large-capacity memory of the device, which is convenient for the next-step signal selection. In a possible implementation manner, the signal conversion module uses the MSP430F2730 chip as the acquisition and processing core, adopts an internal ADC with 24-bit resolution and a rate of 0.25 ms, and has an internal storage capacity of up to 200 M Bytes. At the same time, it uses UART for high-speed communication with the wireless WIFI module.

[0094] In summary, a distributed railway tunnel rockburst intelligent microseismic monitoring method provided by the embodiment of the present application can obtain seismic wave signals from multiple angles based on distributed arrangement, improving the reliability of the recognition result; it can adjust the energy level range and frequency bandwidth of the seismic wave analog signal, enabling the seismic wave signal to be held, improving the stability during signal transmission, and through the constructed D-S evidence theory model, realizing the joint action of different methods to obtain rock fracture signals from multiple signal categories, improving the accuracy of signal recognition, and further improving the safety of engineering construction.

[0095] In addition, a distributed railway tunnel rockburst intelligent microseismic monitoring device provided by the embodiment of the present application is connected through an armored cable for manholes, solving the problems of difficult installation, maintenance, and recovery of traditional wired microseismic monitoring equipment, and further improving the effectiveness and reliability of microseismic monitoring.

[0096] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed railway tunnel rockburst intelligent microseismic monitoring method, characterized in that: The method comprises: Distributed arrangement of acquisition equipment; Acquire seismic wave simulation signals through acquisition equipment; Adjusting the energy level range and frequency bandwidth of the seismic wave simulation signal; Converting the adjusted seismic wave analog signal into a digital signal to obtain a seismic wave digital signal; Based on the time window, the seismic wave digital signal is intercepted to obtain the maximum signal energy value, signal energy integral value, signal main frequency and maximum value of the long-short time window ratio within the time window; Based on the constructed DS evidence theory model, the maximum signal energy value, the signal energy integral value, the signal main frequency and the maximum value of the long-short time window ratio are identified, and the interference part is eliminated through artificial wave reading to screen out the microseismic signal and realize microseismic monitoring; The construction process of the DS evidence theory model includes: Establishing a membership relationship between the seismic wave digital signal and the acquisition device, and calculating an optimal membership weight; Calculate the support weight according to the acquisition equipment of the seismic wave digital signal and the seismic wave analog signal; A DS evidence theory model is constructed according to the optimal membership weight and the support weight.

2. A distributed railway tunnel rockburst intelligent microseismic monitoring method according to claim 1, characterized in that: The calculation formula of the DS evidence theory model is: ， ， in, m(A) is the trust in the seismic wave digital signal A, m n (A n ) is the support degree of the nth acquisition device for the seismic wave digital signal A, f A is the optimal membership weight, S A is the support weight, K is the normalization factor, q(A) It is the average value of the support of the acquisition equipment for the seismic wave digital signal A.

3. A distributed railway tunnel rockburst intelligent microseismic monitoring method according to claim 1, characterized in that: The membership relationship adopts an optimal fuzzy statistical method, and establishes a membership function u(A) based on the support of the acquisition equipment obtained according to the seismic wave simulation signal, and obtains the optimal membership weight according to the membership function.

4. A distributed railway tunnel rockburst intelligent microseismic monitoring method according to claim 1, characterized in that: The calculation formula of the support weight is: ， ； in, is the support norm of any two of the acquisition devices for the seismic wave digital signal A, , is the support degree of any two acquisition devices for the seismic wave digital signal A, is the average value of the support norm of all acquisition devices for the seismic wave digital signal A, is the support weight.

5. A distributed railway tunnel rockburst intelligent microseismic monitoring method according to claim 1, characterized in that: The distributed arrangement acquisition device comprises: The installation sections are set according to preset intervals based on the tunnel excavation surface, and the collection equipment is installed according to preset angle distribution based on the installation sections.

6. A distributed railway tunnel rockburst intelligent microseismic monitoring device, characterized in that: The device comprises: Acquisition equipment, used for collecting seismic wave simulation signals; A signal optimization module, used for adjusting the energy level range and frequency bandwidth of the seismic wave simulation signal; A signal conversion module, used for converting the adjusted seismic wave analog signal into a digital signal to obtain a seismic wave digital signal; A signal selection module is used to intercept the seismic wave digital signal based on the time window, obtain the maximum signal energy value, signal energy integral value, signal main frequency and maximum value of the long-short time window ratio in the time window; and identify the maximum signal energy value, signal energy integral value, signal main frequency and maximum value of the long-short time window ratio based on the constructed DS evidence theory model, exclude the interference part through manual wave reading, screen out the microseismic signal, and realize microseismic monitoring; Wherein, the acquisition device is connected to the signal optimization module via an armored cable.

7. A distributed railway tunnel rockburst intelligent microseismic monitoring device according to claim 6, characterized in that: The signal optimization module adopts an adjustable multi-stage signal amplifier and a follower circuit.

8. A distributed railway tunnel rockburst intelligent microseismic monitoring device according to claim 6, characterized in that: The signal conversion module is also used to store the seismic wave digital signal so that the signal selection module can read and identify the signal; the signal conversion module adopts the MSP430F2730 chip.

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