A rockburst microseismic monitoring system and monitoring method for railway tunnel construction

Abstract

The present invention relates to the field of tunnel construction safety monitoring, and specifically to a rockburst microseismic monitoring system and monitoring method for railway tunnel construction. The railway tunnel construction rockburst microseismic monitoring system at least includes: an acquisition and conversion module, an analysis and processing module, and an early warning module. The present invention first collects rockburst data such as rockburst vibration waves, blasting waves, and rupture waves at the tunnel target through the acquisition and conversion module; then, the analysis and processing module performs physical analysis on the rockburst data, specifically including using a waveform analysis model to analyze and process the rockburst vibration waves, blasting waves, and rupture waves into visual analysis data involving rockburst assessment projects, rockburst positioning projects, and rockburst filtering projects at the target; finally, the early warning module intervenes in the project and issues an early warning to prevent personal injury and property losses caused by sudden rockbursts in advance.

Description

Technical Field

[0001] The present invention relates to the field of tunnel construction safety monitoring, and in particular to a rockburst microseismic monitoring system and a monitoring method for railway tunnel construction. Background Art

[0002] A rockburst is a sudden, explosive failure that occurs in exposed rock masses deep underground or in areas of high tectonic stress. This phenomenon, also known as a rockburst, occurs when accumulated strain energy in the exposed rock mass is suddenly and violently released, causing a brittle fracture similar to an explosion. The resulting ground pressure causes massive rockfalls, generating loud noises and air waves. This can not only damage the mine shaft but also endanger surface structures through shock waves. Rockbursts are a major safety hazard in deep mines. Minor rockbursts involve only flaking rock fragments without ejection. Severe rockbursts can reach a magnitude of 4.6, with an intensity of 7-8, damaging surface structures and accompanied by loud noises. Rockbursts can occur suddenly or persist for days or months. Rockbursts occur when high ground stresses in the rock mass exceed its inherent strength and the rock is both brittle and elastic. Under these conditions, if underground engineering activities disrupt the rock mass's equilibrium, the release of accumulated energy can cause rock failure and ejection.

[0003] Rockburst has the following characteristics:

[0004] (1) Sudden

[0005] Before it happens, there are no obvious signs, and you may not even hear any empty sound. In places where it is generally believed that rocks will not fall, there will suddenly be a sound of rocks bursting. Sometimes the rocks fall down in response to the sound, and sometimes they will not fall for a while.

[0006] (2) Location concentration

[0007] Although there are some cases where rockbursts occur far from the newly excavated working face, most of them occur near the newly excavated working face. The most common rockburst locations are the arch or arch waist.

[0008] (3) Time concentration and continuity

[0009] Rockbursts appear one after another after excavation, usually within 24 hours after blasting, and usually last for 1 to 2 months, and some may last for more than 1 year. There are usually no obvious signs beforehand.

[0010] (4) Ejectability

[0011] During a rock burst, rock blocks are ejected from the surrounding rock matrix of the cave wall, generally in the form of irregular flakes with thick middle and thin edges.

[0012] In tunnel construction, rock burst is one of the important factors affecting construction safety. Therefore, the prediction of rock burst is urgent to avoid casualties and property losses. Summary of the Invention

[0013] The purpose of the present invention is to overcome the deficiency of the prior art in the lack of rock burst prediction analysis and to provide a railway tunnel construction rock burst microseismic monitoring system and monitoring method.

[0014] In its first aspect, the present invention provides a microseismic monitoring system for rockbursts during railway tunnel construction. The system comprises at least: an acquisition and conversion module for collecting rockburst data at target locations in the tunnel; an analysis and processing module for performing physical analysis on the rockburst data; and an early warning module for providing dynamic early warnings of potential rockburst risks during tunnel construction based on the results of the analysis and processing module. The physical analysis involves using a waveform analysis model to process the rockburst data into visual analysis data related to rockburst assessment, rockburst location, and rockburst filtering at the target locations. The rockburst data includes at least rockburst vibration waves, blasting waves, and rupture waves.

[0015] According to a preferred embodiment, the acquisition and conversion module includes at least: a plurality of sensors positioned at the target for acquiring microvibration signals therefrom; a signal acquisition device for receiving the microvibration signals acquired by the sensors and converting them into initial vibration waveforms; and a central server for processing the initial vibration waveforms acquired by the signal acquisition device. The plurality of sensors are communicatively connected to the signal acquisition device, which is in turn communicatively connected to the central server, forming a sensor network.

[0016] According to a preferred embodiment, the central server converts the micro-vibration signal into the rockburst data by waveform conversion.

[0017] According to a preferred embodiment, the step of the central server performing the waveform conversion includes at least:

[0018] Read the period T and frequency f of rockburst vibration waves, blasting waves, and rupture waves involved in rockburst assessment projects, rockburst location projects, and rockburst filtering projects;

[0019] Calculate the phase difference of rockburst vibration wave, rockburst blasting wave and rockburst rupture wave;

[0020] Resonance processing is performed on the phase difference of the rockburst vibration wave, the phase difference of the rockburst blasting wave, and the phase difference of the rockburst fracture wave. Phase difference elimination processing is performed on the mechanical waves, thereby making an intuitive quantitative comparison of the three mechanical waves (rockburst vibration wave, blasting wave, and fracture wave).

[0021] According to a preferred embodiment, the sensor is wirelessly connected to the central server via a network connection module. The network connection module includes at least a local data processing unit and a remote data processing unit. The local data processing unit uses edge computing, and the remote data processing unit uses IoT computing.

[0022] According to a preferred embodiment, the visual analysis data at least includes the number of microfracture events and the magnitude of microseismic energy.

[0023] According to a preferred embodiment, the early warning module performs vector operations on the P-wave and S-wave of the rockburst point, and the report result of the early warning module is a set of vector operations, and a comprehensive evaluation can be performed through the set parameters.

[0024] According to a preferred embodiment, the vector operation formula of the rockburst point is:

[0025] ;

[0026] Among them, P is the longitudinal wave at the rock burst point, S is the shear wave at the rock burst point, is the angle between the P wave and the S wave;

[0027] The aggregate formula for the report results is:

[0028] .

[0029] According to a preferred embodiment, the early warning module performs early warning intervention based on at least the aggregated rockburst points.

[0030] The present invention also provides a method for monitoring rockburst microseismicity during railway tunnel construction, comprising at least the following steps:

[0031] S1. Collect rockburst data at a target location in a tunnel; wherein the rockburst data includes at least rockburst vibration waves, blasting waves, and rupture waves;

[0032] S2. performing physical analysis on the rockburst data;

[0033] S3. Providing a dynamic early warning of potential rock burst risks during tunnel construction based on the results of the physical analysis;

[0034] The physical analysis includes using a waveform analysis model to analyze and process the rockburst data into visual analysis data related to rockburst assessment items, rockburst positioning items, and rockburst filtering items at the target location.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention collects rockburst data such as rockburst vibration waves, blasting waves, and rupture waves from tunnel nodes, performs physical analysis on the rockburst data, and locates the waveforms, thereby detecting the location of each rockburst point and generating visual analysis data for rockburst assessment, rockburst location, and rockburst filtering projects. Finally, an early warning module intervenes in the project and issues an early warning based on the actual data, thereby preventing personal injury and property loss caused by sudden rockbursts in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the principle of a railway tunnel construction rockburst microseismic monitoring system according to a preferred embodiment of the present invention;

[0038] Figure 2 The figure is a flow chart of a method for monitoring rockburst and microseismicity during railway tunnel construction according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described in detail below in conjunction with test examples and specific embodiments. However, this should not be understood as limiting the scope of the present invention to the following embodiments, and all technologies implemented based on the present invention fall within the scope of the present invention.

[0040] Unless otherwise specified, in the description of the specific embodiments of the present invention, the terms indicating orientation or positional relationships such as "upper," "lower," "left," "right," "center," "inside," and "outside" are based on the orientation or positional relationships shown in the accompanying drawings, or are the orientation or positional relationships in which the inventive product / device / apparatus is typically placed when in use. These terms indicating orientation or positional relationships are merely for the purpose of facilitating the description of the present invention or simplifying the description of the specific embodiments to facilitate a quick understanding of the solutions by technicians. They do not indicate or imply that a particular device / component / element must have a specific orientation or be constructed and operated in a specific positional relationship, and therefore should not be construed as limiting the present invention.

[0041] In addition, if the terms "horizontal", "vertical", "overhanging", "parallel" and the like appear, it does not mean that the corresponding devices / components / elements are required to be absolutely horizontal or vertical or overhanging or parallel, but may be slightly tilted or have deviations. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but may be slightly tilted. Alternatively, it can be simply understood that the corresponding devices / components / elements are set in directions such as "horizontal", "vertical", "overhanging", and "parallel", and can have an error / deviation of ±10% relative to the corresponding direction setting, more preferably an error / deviation within ±8%, more preferably an error / deviation within ±6%, more preferably an error / deviation within ±5%, and more preferably an error / deviation within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its role in the solution of the present invention.

[0042] In addition, the expressions "first", "second", "third", etc. that appear in the terms are merely descriptions used to distinguish the same or similar components and should not be understood as emphasizing or implying the relative importance of specific components.

[0043] In addition, in the description of the embodiments of the present invention, "several," "plurality," and "a number" represent at least two. It can also be any number such as two, three, four, five, six, seven, eight, nine, or even more than nine.

[0044] Furthermore, in the description of the technical solution of the present invention, unless otherwise expressly specified, defined, or limited, the terms "disposed," "installed," "connected," "connected," "provided with," "laid," and "arranged" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections. They may be welded, riveted, bolted, threaded, or other commonly used connection methods in the art. Such connections may be mechanical, electrical, or communicative; they may be direct, indirect via an intermediate medium, or internally connected between two components.

[0045] Example 1

[0046] This embodiment provides a rockburst microseismic monitoring system for railway tunnel construction. The system can include: an acquisition and conversion module for collecting rockburst data at target locations in the tunnel; an analysis and processing module for performing physical analysis on the rockburst data; and an early warning module for providing dynamic early warnings of potential rockburst risks during tunnel construction based on the results of the analysis and processing module. The physical analysis involves using a waveform analysis model to process the rockburst data into visual analysis data related to rockburst assessment, rockburst location, and rockburst filtering at the target location. The rockburst data can include rockburst vibration waves, blasting waves, and rupture waves.

[0047] The present invention collects rockburst data such as rockburst vibration waves, blasting waves, and rupture waves from tunnel nodes, performs physical analysis on the rockburst data, and locates the waveforms, thereby detecting the location of each rockburst point and generating visual analysis data for rockburst assessment, rockburst location, and rockburst filtering projects. Finally, an early warning module intervenes in the project and issues an early warning based on the actual data, thereby preventing personal injury and property loss caused by sudden rockbursts in advance.

[0048] Example 2

[0049] This embodiment is a further improvement of Example 1, and repeated contents will not be repeated. This embodiment provides a rockburst microseismic monitoring system for railway tunnel construction. In this embodiment, the acquisition and conversion module may include: a number of sensors set at the target, for collecting microvibration signals at the target; a signal acquisition instrument, for receiving the microvibration signals collected by the sensors and converting them into initial vibration waveforms; a central server, for processing the initial vibration waveforms collected by the signal acquisition instrument. A number of sensors are communicatively connected to the signal acquisition instrument, and the signal acquisition instrument is communicatively connected to the central server to form a sensor network.

[0050] Preferably, the central server converts the micro-vibration signal into rock burst data by waveform conversion.

[0051] Preferably, the step of waveform conversion performed by the central server may include:

[0052] Read the period T and frequency f of rockburst vibration waves, blasting waves, and rupture waves involved in rockburst assessment projects, rockburst location projects, and rockburst filtering projects;

[0053] Calculate the phase difference of rockburst vibration wave, rockburst blasting wave and rockburst rupture wave;

[0054] Resonance processing is performed on the phase difference of the rockburst vibration wave, the phase difference of the rockburst blasting wave, and the phase difference of the rockburst fracture wave. Phase difference elimination processing is performed on the mechanical waves, thereby making an intuitive quantitative comparison of the three mechanical waves (rockburst vibration wave, blasting wave, and fracture wave).

[0055] Preferably, the sensor is wirelessly connected to the central server via a network connection module. The network connection module may include a local data processing unit and a remote data processing unit. The local data processing unit uses edge computing, and the remote data processing unit uses IoT computing.

[0056] Preferably, the visual analysis data may include the number of microfracture events and the magnitude of microseismic energy.

[0057] Preferably, the early warning module performs vector operations on the P-wave and S-wave of the rockburst point, and the report result of the early warning module is a set of vector operations, and a comprehensive evaluation can be performed through the set parameters.

[0058] Preferably, the vector calculation formula of the rockburst point is:

[0059] ;

[0060] Among them, P is the longitudinal wave at the rock burst point, S is the shear wave at the rock burst point, is the angle between the P wave and the S wave;

[0061] The aggregate formula for reporting results is:

[0062] .

[0063] Preferably, the early warning module can perform early warning intervention based on the aggregated rock burst points.

[0064] Example 3

[0065] This embodiment is a further improvement of embodiment 1 and embodiment 2, and the repeated contents will not be repeated here. This embodiment provides a rock burst microseismic monitoring system for railway tunnel construction. Figure 1 The railway tunnel construction rockburst microseismic monitoring system provided in this embodiment may include an acquisition and conversion module, an analysis and processing module, and an early warning module.

[0066] The acquisition and conversion module includes a sensor network set at the target location (tunnel node) and used for micro-vibration acquisition and conversion.

[0067] The sensor network consists of several sensors that collect micro-vibrations, a data collector that receives and processes the sensor signals, and a central server that processes the signals. The data collectors can obtain the parameters captured by the sensors. The central server connects the signals from the data collectors and centrally processes them.

[0068] The sensor and data collector are connected by signal. The data collector and the central server are wirelessly connected via a communication system built on a network connection module, forming a sensor network that enables signal transmission and time synchronization between the data collector and the central server. The sensor and data collector can be connected via a wired connection. The data collector continuously acquires the micro-vibration parameters captured by the sensor to obtain an initial vibration waveform. The data collector transmits the initial vibration waveform to the central server for waveform storage.

[0069] The rockburst assessment, rockburst location, and rockburst filtering projects all undergo waveform conversion via a central server. The converted waveforms are processed by the analysis and processing module and visualized for analytical data. The converted waveforms include rockburst vibration waves, blast waves, and rupture waves.

[0070] The central server performs waveform conversion on the stored initial vibration waveform to obtain rockburst vibration wave, blasting wave and rupture wave.

[0071] The analysis and processing module may include a processor for performing physical analysis on micro-vibrations. Specifically, the processor performs physical analysis on rockburst vibration waves, blasting waves, and rupture waves.

[0072] The rockburst assessment project mainly deals with the possibility and degree of rockburst at the target location; the rockburst positioning project mainly determines the target location; and the rockburst filtering project mainly processes the parameters of the generated rockburst.

[0073] The analysis and processing module is equipped with a waveform analysis model for tunnel rockburst, which is used to effectively analyze rockburst vibration waves, blasting waves and rupture waves.

[0074] The early warning module actively warns of potential or obvious rock burst risks in tunnels.

[0075] The network connection module consists of a local data processing unit and a remote data processing unit. The local data processing unit utilizes edge computing, while the remote data processing unit utilizes IoT computing. Both IoT and edge computing comply with the MQTT protocol, while IoT computing also complies with HTTP and CoAP protocols. This allows the network connection module to operate in a variety of network communication modes, maximizing communication compatibility and preventing signal interruptions and disconnections.

[0076] The rockburst assessment project, rockburst location project, and rockburst filtering project all undergo waveform conversion through the central server. The waveforms are then passed through the analysis and processing module to display visual analysis data. The waveform conversion includes the vibration wave, blasting wave, and rupture wave of the rockburst. The visual analysis data includes the number of microfracture events and the amount of microseismic energy.

[0077] Specifically, the central server performs waveform conversion by reading the period T and frequency f of the rockburst vibration wave, blasting wave, and rupture wave involved in the rockburst assessment project, rockburst location project, and rockburst filtering project, and then calculates the phase of the waveform. Then, the waveform conversion from the rockburst assessment project to the rockburst location project, the rockburst location project to the rockburst filtering project, and the rockburst assessment project to the rockburst filtering project is performed, including:

[0078] Phase calculation:

[0079] Where φ is the wavelength, X1 and X2 represent the positions of the two waves involved at the calculation time. X1 and X2 are two of the three waveforms of rockburst vibration wave, blasting wave, and rupture wave. That is, X1 is the wave that can be designated as a rockburst vibration wave, blasting wave, or rupture wave at time 1, and X2 is the wave that can be designated as a rockburst vibration wave, blasting wave, or rupture wave at time 2. The specific combination is selected based on the project content.

[0080] By using different X values, the phase difference between the rockburst vibration wave - project A, the rockburst blast wave - project B, and the rockburst blast wave - project C is calculated. , and input it into the central server for conversion.

[0081] Table 1

[0082]

[0083] See Table 1, in which item A is the vibration wave of rock burst, item B is the blasting wave of rock burst, item C is the rupture wave of rock burst, and X1 and X2 are the positions of item A, item B, and item C at time 1 and time 2.

[0084] Referring to this table, the phase differences for Project A, Project B, and Project C are 0.897, 0.6484, and 0.981, respectively. These values ​​are input into the central server for resonance processing (i.e., using any one of the rockburst assessment, rockburst location, and rockburst filtering data as a benchmark for the calculation of the other two), resulting in three waveform conversion diagrams for user reference and use. Because the simple harmonic waveforms of Project A, Project B, and Project C differ, this embodiment uses mathematical calculations to calculate the phase differences between the simple harmonic waveforms of Project A, Project B, and Project C, aligning them for calculation.

[0085] As a further solution of the present invention: a rockburst microseismic monitoring system based on railway tunnel construction, the early warning module is subject to vector calculation, the vector calculation is the vector operation of the P wave and S wave of the rockburst point, the early warning module reports the result as a collection of vector operations, and the vector operation formula of the rockburst point is:

[0086] ;

[0087] Among them, P is the longitudinal wave at the rock burst point, S is the shear wave at the rock burst point, is the angle between the P wave and the S wave;

[0088] The aggregate formula for reporting results is

[0089] ;

[0090] Specifically, the method of calculating the rockburst point through vectors belongs to the formula for calculating rockburst points in the existing technology, but this formula only calculates one rockburst point, and the sample is relatively small. Therefore, this embodiment can expand the sample capacity and improve the accuracy of rockburst point calculation by performing collective calculation on the rockburst points.

[0091] Please see the attached Figure 1 The figure shows the paths of P waves and S waves when rock burst points occur. It can be seen from the figure that the paths of P waves and S waves are relatively disordered, and the instantaneous time when rock burst points occur is short and difficult to collect. Therefore, this application records the occurrence of each rock burst point and forms a collection, and then stores each data in the collection. After storage, waveform analysis is performed to effectively simulate and locate the rock burst point, and the early warning module can perform effective early warning intervention based on the rock burst point of the collection.

[0092] Specifically, the process of rockburst initiation in a tunnel is essentially a series of rock mass fracture events. After these fractures occur, the generated vibration waves propagate outward through the surrounding medium. Sensors placed within the borehole, close to the rock wall, receive these raw microvibration signals and convert them into electrical signals, which are then transmitted to a signal acquisition instrument. This data signal is then transmitted to a central server via a communication system. Analysis and processing software performs comprehensive processing and analysis on these microvibration signals, enabling the location of microseismic / acoustic emission events, the acquisition of source parameters, and trend tracking. The located microseismic / acoustic emission events can be visualized in three dimensions and time, ultimately identifying the rock mass fracture microseismic events involved in the rockburst initiation process. The early warning module issues warnings based on visualized data from rockburst assessment, location, and filtering. By analyzing the number of microseismic events and the spatiotemporal evolution of their corresponding source parameters (such as energy and apparent volume) during deep tunnel excavation, the early warning module provides dynamic early warnings of potential rockburst risks during tunnel excavation.

[0093] The working principle of the present invention is as follows: In this application, sensors are placed in tunnel nodes to collect rockburst data, and then a signal acquisition instrument is used to collect rockburst vibration waves, blasting waves, and rupture waves on a large scale. Each rockburst point collected is recorded and formed into a collection. Then, a central server converts and stores all waveforms. After analyzing the waveforms, a post-processor intervenes to locate the waveforms, thereby detecting the location of each rockburst point and generating visual data of the rockburst project. Finally, an early warning module intervenes in the project and issues an early warning based on the actual data. The specific process is as follows: after these rock mass fractures occur, the generated vibration waves propagate outward along the surrounding medium. Sensors placed in the hole, close to the rock wall, receive the original microvibration signals and convert them into electrical signals, which are then sent to the signal acquisition instrument. The data signals are then transmitted to the central server through a communication system. The microvibration signals can be processed and analyzed in various aspects by analysis and processing software to achieve the location of microseismic / acoustic emission events, obtain source parameters, and track trends. The located microseismic / acoustic emission events can also be physically demonstrated in three-dimensional space and time, ultimately obtaining the rock mass fracture microseismic events in the rockburst incubation process. By real-time analyzing the number of microseismic events and the spatiotemporal evolution characteristics of their corresponding source parameters (such as energy and apparent volume) during deep tunnel excavation, dynamic early warning of potential rockburst risks during tunnel excavation can be provided, thus preventing personal injury and property loss caused by sudden rockbursts in advance.

[0094] Example 4

[0095] This embodiment provides a method for monitoring rockburst and microseismic monitoring of railway tunnel construction, which can be implemented using the railway tunnel construction rockburst and microseismic monitoring systems provided in Embodiments 1, 2, and 3.

[0096] Example 5

[0097] This embodiment is a further improvement of embodiment 4, and repeated contents are not repeated here. This embodiment provides a method for monitoring rockburst microseismicity during railway tunnel construction.

[0098] Please see the attached Figure 2 Specific methods include:

[0099] Step S1: sensors are placed in tunnel nodes to collect rockburst data;

[0100] Step S2: The signal acquisition instrument works to collect waveforms;

[0101] Step S3: The central server performs waveform conversion and storage;

[0102] Step S4: The processor intervenes to perform waveform positioning;

[0103] Step S5: The processor analyzes and generates visual data;

[0104] Step S6: The early warning module intervenes to issue an early warning.

[0105] Among them, physical analysis includes rockburst assessment projects at the target location, rockburst positioning projects, and rockburst filtering projects. These three projects are reference factors for active early warning. Among them, the rockburst assessment project specifically requires a comprehensive rockburst risk assessment of the mine before formulating a rockburst plan to understand the current risk situation and provide a basis for formulating appropriate prevention and control plans.

[0106] Rockburst risk assessment mainly includes the following steps:

[0107] Determine the assessment object: Select the object of risk assessment, which can be the entire mine, a specific working face or a rock mass area.

[0108] Collect information: Collect data related to rock burst, including geological features, groundwater conditions, mining methods, rock properties, etc.

[0109] Assess risk level: Based on the collected information, conduct a qualitative or quantitative assessment of the rockburst risk and give a corresponding risk level.

[0110] Identify the main risk factors: Analyze the assessment results and determine the main factors affecting rockburst risk to facilitate subsequent prevention and control work.

[0111] Make recommendations: Make corresponding rock burst prevention and control recommendations based on the assessment results, including engineering measures, management measures, etc.

[0112] According to the above-mentioned conditions, it is necessary to make prevention and control measures for rock burst risk assessment and reduce the probability and degree of harm of rock burst. For example, in the existing technology, the probability of rock burst is prevented by optimizing mining methods and strengthening support measures. Therefore, this application creatively proposes a systematic rock burst assessment scheme, which does not interfere with the progress of rock burst operations and has the advantages of high prediction accuracy and good prediction effect.

[0113] As for the rockburst location project, the existing technology includes a positioning model through machine learning. This model builds the MSlocation Net fully convolutional neural network, uses the waveform as the input of the neural network, and uses the three-dimensional Gauss function of the study area as the output of the neural network, ultimately achieving rockburst location and rockburst level prediction. However, the cost of this rockburst location project is relatively high. Therefore, the rockburst location and rockburst risk assessment in this solution can effectively save R&D and use costs.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A railway tunnel construction rockburst microseismic monitoring system, characterized by: The railway tunnel construction rockburst microseismic monitoring system at least includes: The acquisition and conversion module is used to collect rock burst data at the tunnel target; An analysis and processing module, configured to perform physical analysis on the rockburst data; An early warning module, configured to provide a dynamic early warning of potential rock burst risks during tunnel construction based on the results of the analysis and processing module; The physical analysis includes analyzing and processing the rockburst data into visual analysis data related to rockburst assessment items, rockburst location items, and rockburst filtering items at the target location using a waveform analysis model; The rockburst data at least includes rockburst vibration wave, blasting wave and rupture wave; The early warning module issues early warning based on the visual data related to the rockburst assessment project, the rockburst location project, and the rockburst filtering project; The early warning module provides dynamic early warning of potential rock burst risks during tunnel excavation by real-time analysis of the number of microseismic events and the spatiotemporal evolution characteristics of their corresponding source parameters during deep tunnel excavation. The early warning module is subject to vector calculation, which is the vector operation of the P wave and S wave of the rock burst point. The early warning module reports the result as a collection of vector operations; The vector calculation formula of the rockburst point is: ； Among them, P is the longitudinal wave at the rock burst point, S is the shear wave at the rock burst point, is the angle between the P wave and the S wave; The aggregate formula for reporting results is: ； The early warning module performs early warning intervention based on a collection of rockburst points.

2. A railway tunnel construction rockburst microseismic monitoring system according to claim 1, characterized in that: The acquisition and conversion module at least includes: A plurality of sensors arranged at the target, for collecting micro-vibration signals at the target; A signal collector, configured to receive the micro-vibration signal collected by the sensor and convert it into an initial vibration waveform; A central server, configured to process the initial vibration waveform collected by the signal collector; A plurality of the sensors are communicatively connected to the signal acquisition instrument, and the signal acquisition instrument is communicatively connected to the central server to form a sensor network.

3. A railway tunnel construction rockburst microseismic monitoring system according to claim 2, characterized in that: The central server converts the initial vibration waveform collected by the signal acquisition instrument into the rock burst data by waveform conversion.

4. A railway tunnel construction rockburst microseismic monitoring system according to claim 3, characterized in that: The steps of the central server performing the waveform conversion include at least: Read the period T and frequency f of rockburst vibration waves, blasting waves, and rupture waves involved in rockburst assessment projects, rockburst location projects, and rockburst filtering projects; Calculate the phase difference of rockburst vibration wave, rockburst blasting wave and rockburst rupture wave; Resonance processing is performed on the phase difference of the rockburst vibration wave, the phase difference of the rockburst blasting wave, and the phase difference of the rockburst rupture wave.

5. The railway tunnel construction rockburst microseismic monitoring system according to claim 2, characterized in that: The sensor is wirelessly connected to the central server via a network connection module; The network connection module at least includes a local data processing unit and a remote data processing unit; The proximal data processing unit adopts edge computing, and the remote data processing unit adopts IoT computing.

6. The railway tunnel construction rockburst microseismic monitoring system according to claim 1, characterized in that: The visual analysis data at least includes the number of microfracture events and the magnitude of microseismic energy.

7. A method for monitoring rockburst microseismicity during railway tunnel construction, characterized in that: At least the following steps are included: S1. Collect rockburst data at a target location in a tunnel; wherein the rockburst data includes at least rockburst vibration waves, blasting waves, and rupture waves; S2. performing physical analysis on the rockburst data; S3. Providing a dynamic early warning of potential rock burst risks during tunnel construction based on the results of the physical analysis; The physical analysis includes analyzing and processing the rockburst data into visual analysis data related to rockburst assessment items, rockburst location items, and rockburst filtering items at the target location using a waveform analysis model; Provide early warning based on visual data from rockburst assessment, rockburst location, and rockburst filtering projects; By analyzing the number of microseismic events and the temporal and spatial evolution characteristics of their corresponding source parameters during deep tunnel excavation in real time, a dynamic early warning of potential rockburst risks during tunnel excavation can be provided. Perform vector operations on the P and S waves at the rockburst point. The vector operation formula for the rockburst point is: ； Among them, P is the longitudinal wave at the rock burst point, S is the shear wave at the rock burst point, is the angle between the P wave and the S wave; The set of vector operations to obtain rockburst points is as follows: ； Early warning intervention is carried out based on the collection of rockburst points.

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