Deep tunnel time-delay rockburst comprehensive early warning method based on acoustoelectric technology

Abstract

The invention provides a deep tunnel time-delay rockburst comprehensive early warning method based on an acoustoelectric technology. The method is applied to the technical field of tunnel and underground engineering disaster early warning and comprises the steps that a micro-seismic sensor array is arranged in a deep tunnel rock engineering target area, and a micro-seismic monitoring system is built; positioning a micro-seismic event and seismic source parameter information for the collected micro-seismic signals, and determining a key monitoring area of a rock mass fracture damage area; carrying out point location monitoring on the key monitoring area of the rock mass fracture damage area by using an electromagnetic radiation monitoring instrument; establishing a time-delay rockburst early warning index system; a comprehensive early warning model is established, and fuzzy mathematics comprehensive evaluation is conducted on the time-delay rockburst; obtaining a rockburst main control factor membership degree function according to a micro-seismic and electromagnetic radiation monitoring data statistical result; obtaining a fuzzy matrix R of the time-delay rockburst according to the membership function of the main control factors of the rockburst; and outputting an early warning result. In this way, the accuracy of rockburst early warning can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel and underground engineering disaster early warning, and in particular to a deep-buried tunnel time-delay rockburst comprehensive early warning method based on acoustic and electrical technology. Background Art

[0002] During the excavation of deep buried tunnels, rock burst, as a common sudden rock disaster, poses a huge threat to tunnel construction safety and the lives and property of personnel. Rock burst is a violent rupture phenomenon caused by the sudden release of energy in the surrounding rock during the excavation process, usually accompanied by vibration, noise and flying rock blocks, and is extremely destructive. Rock bursts are usually divided into two categories: immediate rock bursts and time-delay rock bursts. Instant rock bursts occur suddenly, and their onset is related to tunnel excavation operations. They usually occur during the instantaneous release of rock layer stress; while time-delay rock bursts are manifested as rock mass experiencing a period of stress accumulation after excavation, and finally erupting suddenly after a period of time. The occurrence of time-delay rock bursts is related to many factors, including the degree of consolidation of rock layers, the stress state of surrounding rocks, and the seepage pressure of groundwater. This type of rock burst usually has a long incubation period and unpredictability, which greatly increases the safety hazards during construction. Time-delay rock bursts are one of the major risks that lead to casualties of tunnel construction personnel. Effective early warning can issue a warning before the rock burst occurs, evacuate construction personnel in advance, and avoid casualties and damage to facilities. Through early warning of time-delay rockburst, the construction plan of tunnel excavation can be adjusted in time, the probability of rockburst can be reduced, the construction can be stopped due to sudden accidents, the project stagnation and economic losses can be reduced, and the project can be advanced as planned; the support design of deep buried tunnels is usually adjusted according to the mechanical properties and stability of the rock mass. Early warning of time-delay rockburst can provide reference data for support design by real-time monitoring of the stress changes of the surrounding rock. When potential rockburst risks are detected, timely adjustments can be made in the support design to increase the support strength or change the support method to enhance the safety of the tunnel; through early warning of time-delay rockburst, the sustainability and long-term stability of tunnel construction can be effectively improved. Early warning can not only avoid sudden safety accidents during construction, but also provide data support for future tunnel operation and maintenance, ensuring the structural safety of the tunnel in long-term use. Therefore, early warning of time-delay rockburst in deep buried tunnels is of great significance.

[0003] At present, traditional time-lag rockburst early warning methods such as drill cuttings method, elastic rebound method, microgravity method and deformation monitoring and other conventional indicator prediction methods not only have the defects of consuming time and effort, but also have the phenomenon of low accuracy of rockburst prediction. Summary of the invention

[0004] The present invention provides a time-delay rockburst comprehensive early warning method for deep buried tunnels based on acoustic and electrical technology. The method comprises:

[0005] S1, arrange the microseismic sensor array in the target area of ​​the deep-buried tunnel rock project and build a microseismic monitoring system; arrange the microseismic sensor topology structure according to the scale and shape of the deep-buried tunnel rock project;

[0006] S2, filter and amplify the collected microseismic signals to remove noise and interference signals; use signal analysis technology to locate microseismic events and source parameter information, and determine the key monitoring areas of rock mass fracture damage areas;

[0007] S3, using portable, non-contact electromagnetic radiation monitoring instruments to conduct point monitoring of key monitoring areas in rock mass fracture and damage zones, and process and analyze waveforms;

[0008] S4, establish a time-lag rockburst early warning indicator system, in which the cumulative apparent volume ΣV is selected for microseismic monitoring A , energy index logEI and b value are selected as characteristic parameters; for electromagnetic radiation, electromagnetic radiation intensity EMR and pulse number N are selected as characteristic parameters;

[0009] S5, establish a comprehensive early warning model, the b value of microseismic monitoring, its change reflects the proportion of events of different magnitudes; logEI drops rapidly and ∑V A Rapid increase indicates that the probability of rock burst is increasing; the magnitude of EMR in electromagnetic radiation reflects the load degree of the rock mass, and the pulse number N reflects the frequency of rock mass failure;

[0010] Fuzzy mathematical comprehensive evaluation is performed on time-delay rockburst, and the above five parameters are used as the main control factors of the domain M, M = {EMR, N, Δb, ΔlogEI, Δ∑V A},

[0011] The time-delay rockburst evaluation set N is divided into five levels: no rockburst, slight, moderate, strong, and extremely strong, N = {none, slight, moderate, strong, extremely strong};

[0012] The membership function of the main controlling factors of rockburst is obtained from the statistical results of microseismic and electromagnetic radiation monitoring data; the fuzzy matrix R of time-delay rockburst is obtained from the membership function of the main controlling factors of rockburst;

[0013] The actual rockburst engineering data are analyzed and the inverse operation of fuzzy change is performed to obtain the main controlling factors of time-delay rockburst in the domain M, EMR, N, b value, ΔlogEI and Δ∑V A The weight distribution vector of ;

[0014] S6, output warning results, B is a fuzzy subset of the rockburst evaluation set N, and the sum of its components is equal to 1; the position of the one with the highest component vector in the evaluation set N can be used to determine the occurrence of time-delay rockburst; for the main controlling factor M and the weight distribution vector, a subdivided rockburst type database is established, and corresponding warning thresholds are established for different rockburst types, different geological conditions and different excavation methods; at the same time, historical data are used to train and verify the comprehensive warning model.

[0015] Furthermore, the main control factor M and the weight distribution vector in step S6 are based on the engineering analogy method to establish corresponding warning thresholds and weights for different rock burst types, different geological conditions and different excavation methods.

[0016] The present invention can comprehensively and accurately monitor the microseismic and electromagnetic radiation signals during the incubation of time-delay rockbursts by using a combined acoustic and electrical method, thereby improving the accuracy and reliability of rockburst warnings. A time-delay rockburst warning indicator system and a comprehensive warning model have been established, which can effectively predict and warn the occurrence of time-delay rockbursts, providing a strong guarantee for the safe construction of tunnels and underground projects.

[0017] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0019] Figure 1 A flowchart of a comprehensive early warning method for time-delay rockburst in a deep-buried tunnel based on acoustic-electric technology according to an embodiment of the present invention is shown;

[0020] Figure 2 A flow chart of fuzzy mathematical comprehensive evaluation of time-delay rockburst according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0023] See also Figure 1 and Figure 2 , a flow chart of a time-delay rockburst comprehensive early warning method for deep buried tunnels based on acoustic and electrical technology, the method comprising:

[0024] S1, arrange the microseismic sensor array in the target area of ​​the deep-buried tunnel rock project and build a microseismic monitoring system; arrange the microseismic sensor topology structure according to the scale and shape of the deep-buried tunnel rock project;

[0025] S2, filter and amplify the collected microseismic signals to remove noise and interference signals; use signal analysis technology to locate microseismic events and source parameter information, and determine the key monitoring areas of rock mass fracture damage areas;

[0026] S3, using portable, non-contact electromagnetic radiation monitoring instruments to conduct point monitoring of key monitoring areas in rock mass fracture and damage zones, and process and analyze waveforms;

[0027] S4, establish a time-lag rockburst early warning indicator system, in which the cumulative apparent volume ΣV is selected for microseismic monitoring A , energy index logEI and b value are selected as characteristic parameters; for electromagnetic radiation, electromagnetic radiation intensity EMR and pulse number N are selected as characteristic parameters;

[0028] S5, establish a comprehensive early warning model, the b value of microseismic monitoring, its change reflects the proportion of events of different magnitudes; logEI drops rapidly and ∑V A Rapid increase indicates that the probability of rock burst is increasing; the magnitude of EMR in electromagnetic radiation reflects the load degree of the rock mass, and the pulse number N reflects the frequency of rock mass failure;

[0029] Fuzzy mathematical comprehensive evaluation is performed on time-delay rockburst, and the above five parameters are used as the main control factors of the domain M, M = {EMR, N, Δb, ΔlogEI, Δ∑V A},

[0030] The time-delay rockburst evaluation set N is divided into five levels: no rockburst, slight, moderate, strong, and extremely strong, N = {none, slight, moderate, strong, extremely strong};

[0031] The membership function of the main controlling factors of rockburst is obtained from the statistical results of microseismic and electromagnetic radiation monitoring data; the fuzzy matrix R of time-delay rockburst is obtained from the membership function of the main controlling factors of rockburst;

[0032] The actual rockburst engineering data are analyzed and the inverse operation of fuzzy change is performed to obtain the main controlling factors of time-delay rockburst in the domain M, EMR, N, b value, ΔlogEI and Δ∑V A The weight distribution vector of ;

[0033] S6, output warning results, B is a fuzzy subset of the rockburst evaluation set N, and the sum of its components is equal to 1; the position of the one with the highest component vector in the evaluation set N can be used to determine the occurrence of time-delay rockburst; for the main controlling factor M and the weight distribution vector, a subdivided rockburst type database is established, and corresponding warning thresholds are established for different rockburst types, different geological conditions and different excavation methods; at the same time, historical data are used to train and verify the comprehensive warning model.

[0034] In some embodiments, step S1 is specifically as follows: S1.1 Composition of microseismic monitoring system: The microseismic monitoring system is mainly composed of a sensor array, a data acquisition unit, a data processing and analysis system, and an alarm system; microseismic sensors are mainly used to monitor tiny vibration signals of rock masses, and usually use accelerometers or speedometers and other equipment. The selection of sensors should be based on factors such as tunnel depth, rock type, and monitoring purpose. Each sensor in the array needs to be able to independently collect data and have high accuracy and a wide frequency response range; the data acquisition unit is responsible for receiving signals from the sensor array and performing preliminary processing. The data acquisition unit needs to have real-time data acquisition, storage, transmission and processing capabilities; the data processing and analysis system processes microseismic signals, performs event identification, source location, magnitude calculation, etc. This system can identify and locate microseismic events through specific algorithms (such as waveform coherence method, cepstrum analysis method, etc.), thereby evaluating the stability of the rock mass; the alarm system is used to determine the key rock burst area based on the monitoring results when a microseismic event occurs and automatically trigger an alarm, so that relevant personnel can take timely measures; S1.2 Arrangement of microseismic sensor array: According to the scale, shape and key monitoring area of ​​the tunnel project, the alarm system can automatically trigger an alarm when a microseismic event occurs, and the alarm system can automatically trigger an alarm when a microseismic event occurs, and can automatically trigger an alarm when a microseismic event occurs, so that relevant personnel can take timely measures; S1.2 Arrangement of microseismic sensor array: According to the scale, shape and key monitoring area of ​​the tunnel project, the alarm system can automatically trigger an alarm when a microseismic event occurs, and ... Different areas may face different geological environments and engineering risks, such as tunnel excavation locations, support structures, fracture zones, etc. Key monitoring areas may require a higher sensor density. For linear tunnels, the sensor array can be arranged along the tunnel axis, especially at the front of tunnel excavation, at surrounding rock supports, and in areas where fractures may occur. It can be arranged at equal intervals or distributed non-uniformly according to the actual risk areas. For curved or complex-shaped tunnels, the sensor layout is adjusted according to the curvature and direction of the tunnel to ensure that the microseismic signals in each area can be effectively captured. More sensors can be arranged in key locations such as corners and areas with large deformations; consider the vertical and horizontal distribution of sensors to detect microseismic events at different depths and azimuth angles; for the depth direction of the tunnel system, sensors can also be arranged in layers. Considering the vertical depth of the tunnel, there may be different levels of seismic wave propagation paths and intensities, so the sensitivity of sensors arranged in each layer may need to be adjusted; S1.3 Selection and technical requirements of microseismic sensors: The sensitivity of microseismic sensors should be high enough to detect small deformations or crack activities in the rock mass. The common sensitivity is 80.0V / m / s + 5% (for speedometer), or 10 -6 to 10 -9μstrain0.100V / g (for accelerometer); The frequency of microseismic signals is generally low, usually between 1Hz and several hundred Hz, so it is particularly important to select sensors with wide-band response; The equipment and environment in the tunnel may generate electromagnetic interference, vibration interference, etc. The sensor should have strong anti-interference ability to ensure the accuracy and stability of the data; The tunnel environment is complex, humid, and the pressure is high, so the waterproof, dustproof, and pressure-resistant properties of the sensor are very important, and packaging with protection level IP67 or above is usually required; S1.4 Data transmission and communication: For short-distance deployment, optical fiber communication or coaxial cable can be used for data transmission, which has lower latency and higher anti-interference ability; For long distances or complex deployment, In complex areas, wireless sensor network technology can be used. Common wireless communication technologies include Zigbee, LoRa, Wi-Fi, etc. It is crucial to choose a communication solution suitable for the tunnel environment; ensure that the monitoring data can be transmitted to the data center or cloud platform for processing in real time, and at the same time, have local storage function to prevent data loss; S1.5 Positioning and analysis of microseismic events: Use time difference method, waveform matching method, etc. to locate the microseismic source. The source position can be accurately determined by triangulation or four-sided positioning based on the arrival time difference of the microseismic signal received by the sensor; according to the amplitude and frequency of the signal received by the sensor, combined with the source model, the magnitude is evaluated, and the impact range of the microseismic event and possible changes in the rock structure can be determined based on the epicenter position.

[0035] In some embodiments, step S2 is specifically as follows: S2.1 Filtering and amplification of microseismic signals: S2.1.1 Signal filtering: Microseismic signals usually contain target signals (small vibrations) and various noises (such as electromagnetic interference, mechanical vibrations, temperature changes, etc.). The purpose of filtering is to remove unnecessary noise and interference and retain the effective information of the microseismic signals. The filtering process can be divided into the following steps: The frequency of microseismic signals is usually lower than 1kHz. Noise below this frequency (such as environmental noise, low-frequency vibration, etc.) may affect the signal quality. A high-pass filter (such as Butterworth filter, Chebyshev filter, etc.) is used to filter out noise below a predetermined frequency; high-frequency noise (such as equipment working noise, electromagnetic interference, etc.) usually affects the accuracy of the signal. A low-pass filter can remove the part of the signal that is higher than the frequency range of the microseismic signal. For most tunnel microseismic signals, the high-frequency cutoff frequency is usually set to 100Hz to 500Hz; a band-pass filter is usually used to filter out high and low-frequency noise that exceeds the frequency range of the microseismic signal. The band-pass filter can retain the signal The effective frequency range of the signal is determined by the adaptive filtering algorithm (such as Kalman filtering) to automatically adjust the filter parameters to remove unknown noise components. S2.1.2 Signal amplification: The amplitude of the microseismic signal is usually small and needs to be processed by signal amplification. The amplification process can not only enhance the detectability of the signal, but also improve the accuracy of subsequent analysis. The amplifier needs to have the characteristics of high gain, low noise and wide bandwidth. Commonly used amplifiers include low noise amplifiers (LNA) and broadband amplifiers. The gain should be set according to the actual signal amplitude collected to avoid excessive gain causing signal distortion or amplified noise. The gain is usually set between 10 and 100 times, and the specific value is adjusted according to the signal strength at the scene. For strong microseismic signals or sudden events, dynamic range control (such as automatic gain control AGC) can avoid signal saturation and maintain the linear output of the signal. S2.2 Microseismic signal analysis technology: S2.2.1 Microseismic event positioning: According to the arrival time difference (TDOA, TimeDifference) of the microseismic signals received by different sensors of Arrival), using triangulation or multilateral positioning technology to calculate the source location, using the known sensor location and time difference, using the least squares method and other algorithms to calculate, and obtain the spatial coordinates of the source; or extracting the source information of the signal through cepstrum analysis, which can decompose the signal into different frequency components to help identify microseismic events in the signal; or using the known microseismic waveform template to match the collected real-time signal to determine the location and source of the event; or in the rock of a deep buried tunnel, due to the complexity of the medium, the microseismic wave velocity is not constant, and the rock mass wave velocity model (such as the propagation velocity of P waves and S waves) is used to simulate the propagation path of microseismic signals, which can more accurately locate the source; S2.2.2 Source parameter estimation: Estimate the magnitude by analyzing the amplitude and duration of the signal (usually using ML (local magnitude) or mb (body wave magnitude)). In microseismic monitoring, the magnitude is generally small, and the waveform integration method or spectral analysis method is usually used to estimate the magnitude; Combined with geological exploration data, the time difference method and the wave velocity model are used to calculate the depth of the source. The depth can also be calculated by the epicenter position and the propagation time of the source recorded at multiple points; Use waveform inversion technology to analyze the source mechanism and determine the direction and type of rock rupture. Common inversion methods include matrix inversion method and least squares method S2.2.3 Analysis of microseismic events Classification and feature extraction: Microseismic events are classified according to parameters such as magnitude, focal mechanism, and spectrum characteristics, usually into rupture type, slip type, extension type, etc. By analyzing the waveform, spectrum, duration and other characteristics of the signal, potentially dangerous microseismic events can be further extracted. For example, long-lasting small vibrations may mean the gradual rupture of the surrounding rock, while short-term high-intensity microseisms may mean the sudden rupture of the rock mass. S2.3 Monitoring of rock mass rupture damage areas: S2.3.1 Identification and analysis of damage areas: By spatially analyzing all microseismic events, hot spots in the distribution of the earthquake sources can be found. If a certain The microseismic events in the region are dense and of large magnitude, indicating that there may be a risk of rock rupture in the region; monitor the trend of microseismic events over time to determine whether there is an aggravated rupture trend. For example, when the source location gradually approaches the tunnel excavation surface or support structure, it indicates that the area may need to be monitored more closely; use the data of microseismic events, combined with geological models and rock mechanics models, to establish a prediction model for rock rupture and further determine the key monitoring areas; S2.3.2 Delineation of key monitoring areas: Delineate key monitoring areas based on the location, magnitude, depth and source mechanism of microseismic events, combined with on-site geological exploration data. Monitoring areas, such areas may include high-risk areas such as tunnel excavation faces, fracture zones, and support structures; during the monitoring process, combined with newly emerging microseismic events, real-time adjustment of key monitoring areas, for example, if microseismic activity in a certain area is frequent and of large magnitude, consideration should be given to strengthening the monitoring density and accuracy of that area; S2.3.3 Feedback and early warning of monitoring data: Comprehensive microseismic data and monitoring data of support structures (such as displacement, stress, etc.) to achieve multi-dimensional data fusion and analysis, and accurately identify potential rupture risk areas: Set early warning thresholds based on information such as the magnitude, depth, and spatial distribution of microseismic events. When microseismic activity exceeds the threshold, an early warning is automatically triggered to remind engineering personnel to pay attention to potential rock mass rupture risks. .

[0036] In some embodiments, step S3 is specifically as follows: S3.1 Monitoring equipment selection: Select a portable electromagnetic radiation monitor suitable for rock mass monitoring, which must have the following characteristics: non-contact monitoring, high sensitivity, multi-band monitoring capability, and portability; S3.2 Monitoring area selection: Select key locations in the rock mass fracture damage zone for point monitoring, which generally include: above, below, and lateral extension areas of the fracture zone, "hotspot" areas of fracture damage, i.e., places with high stress concentration, complex geological structures, or locations with cracks and faults; Based on the characteristics of rock mass fracture, multiple monitoring points are reasonably arranged in key areas to ensure that the entire damage area can be fully covered; S3.3 Monitoring method: Deploy monitoring equipment at multiple points to collect electromagnetic radiation signals at different locations; the monitoring equipment can use wireless data transmission to transmit data back in real time; the monitoring frequency and time interval of each point can be dynamically adjusted according to the stress changes of the rock mass; S3.4 Waveform processing and analysis: The collected electromagnetic signals will have certain noise, which needs to be denoised by signal processing technology; analyze the waveform in time domain and frequency domain to extract useful features; pay attention to the waveform, duration, amplitude, etc. of the signal to determine the specific situation of rock mass rupture; analyze the frequency components of the signal to determine the rock mass rupture mechanism corresponding to different frequency bands (such as the increase in frequency may be related to the crack propagation speed or rupture type). S3.5 Data analysis and abnormality judgment: Use signal processing algorithms (such as filtering, Fourier transform, wavelet transform, etc.) to comprehensively analyze electromagnetic waveforms collected at different time points and different monitoring locations; Use machine learning or pattern recognition technology to automatically classify monitoring data to determine whether the electromagnetic signal belongs to a typical pattern in the process of rock mass fracture damage; Use real-time data to reconstruct the three-dimensional structure of the rock mass fracture and analyze the damage development trend; S3.6 Data analysis and abnormality judgment: Use signal processing algorithms (such as filtering, Fourier transform, wavelet transform, etc.) to comprehensively analyze electromagnetic waveforms collected at different time points and different monitoring locations; Use machine learning or pattern recognition technology to automatically classify monitoring data to determine whether the electromagnetic signal belongs to a typical pattern in the process of rock mass fracture damage; Use real-time data to reconstruct the three-dimensional structure of the rock mass fracture and damage; According to the analysis, predict the possible development trend of rupture and give early warning in time; combine the changes in electromagnetic radiation to analyze the possible abnormal signal patterns and give early warning prompts of rupture in time; compare the electromagnetic radiation background signal under normal conditions to identify the characteristic signals of rupture or damage; based on the early signals of rupture evolution, issue alarms or recommend further in-depth investigations; S3.6 Monitoring system integration: the data collected at each monitoring point is aggregated to the central control system, which can be transmitted in real time through wireless networks (such as 4G / 5G, LoRa, etc.) or wired networks (such as optical fiber); realize remote data viewing and monitoring, and support remote start, stop and parameter adjustment of electromagnetic radiation monitoring instruments.

[0037] In some embodiments, step S4 is specifically as follows: S4.1 microseismic monitoring indicators: cumulative apparent volume (ΣVA), ΣVA is the cumulative value of the total apparent volume of microseismic events in the rock mass. The increase in apparent volume usually means the process of rock mass rupture and energy accumulation. When there is a large apparent volume in the rock mass, it indicates that the stress inside the area is concentrated and a large-scale rock burst event may occur. This parameter can reflect the stress distribution of the rock mass and is an important indicator for predicting rock bursts; energy index (logEI), the energy index logEI reflects the amount of energy released by the microseismic event. When the rock mass is subjected to external pressure, the energy of the microseismic event gradually increases. The accumulation of stress will cause rock burst when it exceeds the bearing capacity of the rock mass. The sharp change of logEI (such as rapid decline) often indicates that the stress release of the rock mass is about to reach a critical point. The b value is an important parameter describing the frequency-magnitude distribution of microseismicity. Its change can reflect the rupture mechanism inside the rock mass and the proportion of events of different magnitudes. When the b value decreases, it usually means that large-magnitude events dominate, which is one of the signals of an impending rock burst. The change trend of the b value can effectively reflect the potential risk of rock burst. These characteristic parameters can be obtained through microseismic monitoring, and the stress state and rupture situation inside the rock mass can be evaluated in real time, providing information for the rock mass. S4.2 Electromagnetic radiation monitoring indicators: electromagnetic radiation intensity (EMR), electromagnetic radiation intensity reflects the degree of rock load. When the rock is subjected to external loads, tiny cracks will release electromagnetic waves. The increase in EMR value indicates that the stress inside the rock increases and the cracks begin to expand gradually, which may be a precursor to rock burst. High EMR value indicates that the rock is already in a dangerous state and has a high risk of rock burst. Pulse number (N), the pulse number N of the electromagnetic radiation signal reflects the frequency of rock rupture. When the pulse number increases, it means that the number of rock ruptures increases, and serious rock burst may occur. The sharp increase in the number of pulses may indicate that the rock mass is about to be severely damaged; Combining microseismic and electromagnetic radiation characteristic parameters: In the time-lag rockburst early warning indicator system, the characteristic parameters of microseismic and electromagnetic radiation are two complementary monitoring dimensions. Microseismic monitoring can provide early information on rock mass rupture, especially through the changes in b value, cumulative apparent volume ΣVA and logEI, it can be judged that the energy accumulation and release state of the rock mass in a short time. Electromagnetic radiation monitoring can supplement the dynamic information after the rock mass rupture, especially the changes in EMR and N can help judge the load degree and rupture frequency of the rock mass, thereby predicting the possibility of rockburst; S4.3 Construction of the time-lag rockburst early warning indicator system: The establishment of the time-lag rockburst early warning indicator system aims to generate a system that can provide early warning of rockbursts by integrating the monitoring data of microseismic and electromagnetic radiation. The system first analyzes the stress state and fracture trend inside the rock mass based on parameters such as ΣVA, logEI and b value of microseismic monitoring; combined with the EMR and N parameters of electromagnetic radiation, the load degree and damage frequency of the rock mass are further evaluated; through the joint analysis of these characteristic parameters, a dynamically updated rockburst early warning model can be established to provide timely and effective early warnings for mining, tunnel construction and other projects; when establishing this indicator system, the key lies in how to set the thresholds of each parameter and how to adjust and optimize it according to historical data and field monitoring results. For example, when ΣVA increases rapidly, logEI drops sharply, b value decreases significantly, EMR increases significantly, and the number of N pulses increases, the probability and timing of rockburst can be comprehensively judged based on past rockburst historical experience and field measured data. The comprehensive analysis of these parameters will provide clear early warning signals for engineering personnel to ensure that timely safety measures are taken. .

[0038] In some embodiments, step S5 is specifically as follows: rock burst, as a sudden rock mass fracture phenomenon, has a great safety risk in mining, tunnel construction and other projects. Microseismic monitoring and electromagnetic radiation monitoring are two commonly used means for early warning of rock burst. These means can be used to timely predict the occurrence of rock burst. When establishing an early warning model for time-lag rock burst, it is usually necessary to conduct multi-dimensional analysis through multiple monitoring parameters and use fuzzy mathematics methods for comprehensive evaluation. This paper will combine the key parameters of microseismic monitoring and electromagnetic radiation monitoring to establish a comprehensive early warning model based on fuzzy mathematics; S5.1 Microseismic monitoring and electromagnetic radiation monitoring Selection of electromagnetic radiation monitoring parameters: The occurrence of rock burst is closely related to the stress accumulation and crack development of the rock mass. Microseismic monitoring and electromagnetic radiation monitoring provide dynamic information on the stress state and fracture process of the rock mass. To effectively monitor the risk of rock burst, the following parameters are the main control factors: b value, logEI (logarithm of energy index), ΣVA (cumulative apparent volume), EMR (electromagnetic radiation intensity), N (number of electromagnetic radiation pulses); S5.2 Fuzzy mathematical comprehensive evaluation of time-delay rock burst; S5.2.1 Construction of domain M and evaluation set N. Domain M is the key factor in the occurrence of rock burst, including five parameters: EMR, N, b value, Δ logEI and ΣVA; the evaluation set N divides the risk of rockburst into five levels: no rockburst, slight, moderate, strong, and extremely strong; S5.2.2 Construction of the membership function of the main control factors: Based on the actual monitoring data, the EMR, N, b value, ΔlogEI and ΣVA are analyzed to construct the membership function of each monitoring parameter. The membership function is used to describe the membership of each parameter at different risk levels (i.e., the degree to which it belongs to a certain level). For example, as the EMR value increases, its membership function may gradually move from the "mild" level to the "intense" level. It is necessary to adjust the shape and parameters of the membership function based on the historical data and field data of rock bursts in combination with fuzzy theory to ensure the accuracy of the model. S5.2.3 Fuzzy matrix R of time-delay rock burst: The fuzzy matrix R is composed of the membership functions of various main control factors and is used to describe the comprehensive influence of different main control factors at different evaluation levels. The R matrix can be derived through the membership function. For example, when EMR, N, b value, Δ When the values ​​of logEI and ΣVA are within a certain range, the corresponding rockburst level may change; through the deduction of fuzzy rules, the R matrix can provide a comprehensive evaluation result for each rockburst risk level, thereby realizing comprehensive early warning; S5.2.4 Determination of weight allocation vector: In the actual rockburst early warning system, the contribution of each monitoring parameter to the rockburst risk is different. Therefore, it is necessary to determine the weight of each parameter based on the historical rockburst data through the inverse operation method in fuzzy mathematics; by analyzing the historical data and using fuzzy control theory, we can get EMR, N, b value, Δ The weight distribution vector A of logEI and ΣVA can reflect the relative importance of each monitoring parameter in the prediction of time-lag rockburst; S5.3 The inverse operation of fuzzy change adjusts the membership function and weight of each main control factor according to the actual monitoring data, so as to optimize the early warning model.

[0039] In some embodiments, step S6 is specifically as follows: S6.1 outputs the warning result: fuzzy subset B of rockburst evaluation set N = A*R; the warning result of rockburst is described by the output of the fuzzy subset, where the rockburst evaluation set N includes multiple risk levels, each level corresponds to a different rockburst probability (or intensity), specifically, the evaluation set N divides the rockburst risk into five levels: no rockburst, slight rockburst, moderate rockburst, strong rockburst, and extremely strong rockburst; each risk level corresponds to a fuzzy subset B, the sum of the components of these fuzzy subsets is 1, representing a complete risk assessment system, each fuzzy subset B contains a membership function for each monitoring parameter (such as EMR, N, b value, ΣVA, etc.), through these membership functions, it is possible to The changes of different monitoring parameters are quantitatively described; the specific output result is to map the monitoring data of rock burst to the fuzzy evaluation set B, and then calculate the sum according to the membership value of each risk level; S6.2 The highest component discrimination mechanism in the evaluation set N; in the output fuzzy subset B, each subset contains the membership of each monitoring parameter. By comprehensively analyzing these memberships, the evaluation level with the maximum membership value is obtained as the discrimination result of rock burst. For example, the early warning system of rock burst outputs the following fuzzy subsets: no rock burst: membership 0.1, slight rock burst: membership 0.2, moderate rock burst: membership 0.25, strong rock burst: membership 0.3, extremely strong rock burst: membership 0.15. In this case, strong rock burst The membership degree 0.3 corresponding to the rockburst is the largest, so the final judgment result is "strong rockburst", which is a comprehensive evaluation result based on the training of historical data and real-time data monitoring; S6.3 Establish a subdivided rockburst type database: In order to improve the accuracy and pertinence of early warning, it is necessary to establish a subdivided rockburst type database according to different rockburst types, geological conditions and excavation methods: S6.3.1 Rockburst type subdivision: Different types of rockbursts (for example: fissure type, block type, bedding type, etc.) have different occurrence mechanisms and risk characteristics. Therefore, different monitoring data and threshold standards must be established for each rockburst type; S6.3.2 Geological condition classification: The occurrence of rockburst is closely related to geological conditions. For example, different rock types have different occurrence mechanisms and risk characteristics. Different rockburst behavior patterns correspond to different types of rock (such as granite, sandstone, limestone, etc.). The database needs to define corresponding thresholds and warning rules for each type of rockburst based on these geological conditions; S6.3.3 Influence of excavation methods: Excavation methods (such as drilling and blasting, mechanical excavation, etc.) will affect the stress state and crack propagation behavior of the rock mass. Therefore, the database needs to be classified according to different excavation methods in order to make adaptive warnings for rockburst risks under different excavation methods; By comprehensively analyzing these factors in different dimensions, the database can provide adaptive warning standards for each specific rockburst scenario. Each warning rule may include: thresholds of each monitoring parameter, weights of each monitoring parameter, and the probability of occurrence of various rockburst types; S6.4. Warning thresholds for different rockburst types, geological conditions and excavation methods; According to the influence of different rockburst types, geological conditions and excavation methods, set appropriate warning thresholds. Each type of rockburst may have different risk characteristics, so a special warning threshold setting is required for each situation; Threshold setting for rockburst type: For fissure-type rockburst, more attention may be paid to the changes in stress concentration points; For bedding-type rockburst, more attention may be paid to the changes in microseismic frequency and magnitude; The influence of geological conditions: Different rocks have different rockburst performances under different pressure and stress conditions. For example, the rockburst mode and occurrence mechanism of hard rock and very hard rock are different, so the corresponding monitoring thresholds are also different; The influence of excavation method: Different excavation methods lead to different stress release and rock crack propagation modes, so the warning threshold should also be adjusted according to different excavation methods. For example, drilling and blasting may cause more violent vibrations, so its threshold may be lower, while mechanical excavation may produce less vibration and crack propagation; S6.5 Training and verification of historical data. In order to improve the accuracy and reliability of the model, historical data must be fully trained and verified. The training of historical data mainly includes the following aspects: data collection and preprocessing: by collecting historical data of different rockburst types, different geological conditions and different excavation methods, data preprocessing is carried out. Data preprocessing includes denoising, normalization and standardization to ensure the quality and consistency of data; fuzzy mathematical model training: the fuzzy mathematical model is trained using historical data, the shape and parameters of the membership function are adjusted, and the weights of various monitoring parameters are optimized. The training process needs to classify different rockburst scenarios and continuously adjust the warning parameters of the model according to the classification results; model verification and optimization: the accuracy and reliability of the model are verified by cross-validation, regression analysis and other methods. If it is found that the warning of certain types of rockburst is inaccurate or too slow in the existing model, the model needs to be adjusted. For example, the risk of a certain type of rockburst may be underestimated, resulting in insufficient advance warning. The model can be optimized by increasing the weight or adjusting the threshold. .

[0040] In some embodiments, the main control factor M and the weight distribution vector in step S6 are based on the engineering analogy method to establish corresponding warning thresholds and weights for different rock burst types, different geological conditions and different excavation methods.

[0041] According to the embodiments of the present invention, by adopting a method combining sound and electricity, microseismic and electromagnetic radiation signals in the process of time-lag rockburst incubation can be fully and accurately monitored, thereby improving the accuracy and reliability of rockburst early warning. A time-lag rockburst early warning indicator system and a comprehensive early warning model have been established, which can effectively predict and warn the occurrence of time-lag rockbursts, providing a strong guarantee for the safe construction of tunnels and underground projects.

[0042] It should be understood that the above-mentioned various forms of processes can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0043] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A time-delay rockburst comprehensive early warning method for deep buried tunnels based on acoustic and electrical technology, characterized in that: include: S1, arrange microseismic sensor arrays in the target area of ​​deep tunnel rock engineering and build a microseismic monitoring system; Arrange the topology of microseismic sensors according to the scale and shape of the deep tunnel rock project; S2, filter and amplify the collected microseismic signals to remove noise and interference signals; use signal analysis technology to locate microseismic events and source parameter information, and determine the key monitoring areas of rock mass fracture damage areas; S3, using portable, non-contact electromagnetic radiation monitoring instruments to conduct point monitoring of key monitoring areas in rock mass fracture and damage zones, and process and analyze waveforms; S4, establish a time-lag rockburst early warning indicator system, in which the cumulative apparent volume ΣV is selected for microseismic monitoring A , energy index logEI and b value are selected as characteristic parameters; for electromagnetic radiation, electromagnetic radiation intensity EMR and pulse number N are selected as characteristic parameters; S5, establish a comprehensive early warning model, the b value of microseismic monitoring, its change reflects the proportion of events of different magnitudes; logEI drops rapidly and ∑V A Rapid increase indicates that the probability of rock burst is increasing; the magnitude of EMR in electromagnetic radiation reflects the degree of load on the rock mass; the number of pulses N reflects the frequency of rock mass failure; Fuzzy mathematical comprehensive evaluation is performed on time-delay rockburst, and the above five parameters are used as the main control factors of the domain M, M = {EMR, N, Δb, ΔlogEI, Δ∑V A }, The time-delay rockburst evaluation set N is divided into five levels: no rockburst, slight, moderate, strong, and extremely strong, N = {none, slight, moderate, strong, extremely strong}; The membership function of the main controlling factors of rockburst is obtained from the statistical results of microseismic and electromagnetic radiation monitoring data; the fuzzy matrix R of time-delay rockburst is obtained from the membership function of the main controlling factors of rockburst; The actual rockburst engineering data are analyzed and the inverse operation of fuzzy change is performed to obtain the main controlling factors of time-delay rockburst in the domain M, EMR, N, b value, ΔlogEI and Δ∑V A The weight distribution vector of ; S6, output warning results, B is a fuzzy subset of the rockburst evaluation set N, and the sum of its components is equal to 1; the position of the one with the highest component vector in the evaluation set N can be used to determine the occurrence of time-delay rockburst; for the main controlling factor M and the weight distribution vector, a subdivided rockburst type database is established, and corresponding warning thresholds are established for different rockburst types, different geological conditions and different excavation methods; at the same time, historical data are used to train and verify the comprehensive warning model.

2. The deep-buried tunnel time-delay rockburst comprehensive early warning method based on acoustic and electrical technology according to claim 1 is characterized in that: The main control factor M and the weight distribution vector in step S6 are determined by engineering analogy to establish corresponding warning thresholds and weights for different rock burst types, different geological conditions and different excavation methods.

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