Time delay type rockburst early warning method based on elastic wave interference

Abstract

The invention relates to a time-delay rockburst early warning method based on elastic wave interference, and the method comprises the steps: constructing a sensor array in a monitoring region through an elastic wave sensor, and carrying out the pairing of the sensor array to form a plurality of ray paths; collecting surrounding environment noise data, and preprocessing the surrounding environment noise data; performing cross-correlation processing on the environment noise data of the paired elastic wave sensors in the sensor array to obtain a time domain empirical Green function of a plurality of ray paths; extracting a tail wave, and calculating the relative speed variation of the tail wave under different ray paths; and according to the relative speed variation of the tail wave under different ray paths, carrying out abnormity judgment on the microstructure change in the rock mass, and carrying out early warning on an area in which time-delay rockburst possibly occurs. According to the method, the change of the microstructure in the rock mass is analyzed through the change of the relative speed of the tail wave, so that the stress adjustment process in the rock mass is further revealed, and finally accurate early warning of the time-delay rock burst is realized.

Description

Technical Field

[0001] The invention relates to the technical field of rock burst warning, and in particular to a time-delay rock burst warning method based on elastic wave interference. Background Art

[0002] Time-delay rockbursts are characterized by strong suddenness and difficulty in prediction, which makes them a very challenging form of geological disasters. This suddenness not only poses a major threat to mine safety, but also has a serious impact on the safety of underground engineering projects such as tunnel engineering and underground construction. Once a time-delay rockburst occurs, it often leads to serious consequences such as casualties, equipment loss, and project progress delays, which brings huge pressure to project construction and management.

[0003] At present, early warning methods for time-delay rockbursts have been developed, but their accuracy and practicality are still insufficient. The main problem is that existing methods are difficult to solve the monitoring problems caused by the complex environment inside the rock mass. Changes in stress state and microstructure inside the rock mass are important precursors to time-delay rockbursts. However, traditional monitoring methods can only monitor the surface or near the surface of the rock mass, making it difficult to fully and accurately understand the situation inside the rock mass.

[0004] Therefore, it is necessary to propose new measures to overcome the above-mentioned defects. Summary of the invention

[0005] The purpose of the present invention is to provide a time-lag rockburst early warning method based on elastic wave interference, so as to solve the problem of insufficient accuracy of the existing time-lag rockburst early warning methods.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A time-delay rockburst early warning method based on elastic wave interference, the method comprising:

[0008] Using elastic wave sensors, a sensor array is constructed in the monitoring area and paired to form multiple ray paths;

[0009] Collect surrounding environmental noise data in real time through elastic wave sensors;

[0010] Preprocessing environmental noise data;

[0011] The environmental noise data of paired elastic wave sensors in the sensor array are cross-correlated to obtain the time-domain empirical Green's functions of multiple ray paths;

[0012] Extract the coda wave from the empirical Green's function in the time domain and calculate the relative velocity variation of the coda wave under different ray paths;

[0013] According to the relative velocity change of the coda wave under different ray paths, the abnormal microstructural changes inside the rock mass are judged, so as to give early warning to the areas where time-delay rockburst may occur.

[0014] Furthermore, elastic wave sensors are used to construct a sensor array in the monitoring area, and paired to form multiple ray paths, including:

[0015] The tunnel's time-delay rockburst prone areas and high stress areas are selected as monitoring areas;

[0016] Two sections separated by a set distance are selected to cover the monitoring area, and multiple elastic wave sensors are arranged in a circumferential direction in each section to form a sensor array;

[0017] The elastic wave sensors of the two sections correspond to each other and are paired in pairs to form multiple sensor pairs, and construct multiple elastic wave ray paths to cover the monitoring area.

[0018] Further, preprocessing the environmental noise data includes:

[0019] Chopping and resampling the environmental noise data;

[0020] Then, linear trend processing and mean removal processing are performed;

[0021] Then, spectral whitening and bandpass filtering are performed;

[0022] Finally, time domain normalization is performed.

[0023] Furthermore, the environmental noise data of paired elastic wave sensors in the sensor array are cross-correlated to obtain time-domain empirical Green's functions of multiple ray paths, including:

[0024] The data between each sensor pair is cross-correlated every half hour to obtain the cross-correlation function;

[0025] The lag time is set, and the cross-correlation function is Hilbert transformed to obtain the empirical Green's function in the time domain.

[0026] Furthermore, the coda wave is extracted from the time-domain empirical Green's function, and the relative velocity changes of the coda wave under different ray paths are calculated, including:

[0027] Arrange the time-domain empirical Green's functions in time series;

[0028] Perform two-dimensional Wiener filtering on the arranged time-domain empirical Green's functions;

[0029] Extract the coda wave within the specified time window range of the time domain empirical Green's function;

[0030] Calculate the relative velocity changes of coda waves in different time series.

[0031] Furthermore, the relative velocity changes of coda waves of different time series are calculated, including:

[0032] Based on the coda waves of different time series, the superimposed time domain empirical Green's function is used as the reference signal, and the time domain empirical Green's function of different time series is used as the instantaneous signal;

[0033] Obtaining a cross-correlation signal between two signals;

[0034] Quantitatively analyze the similarity of two signals;

[0035] Based on the cross-correlation signal and similarity, the function of the relative speed change of the two signals to time is obtained, and then the relative speed change is calculated.

[0036] Further, obtaining a cross-correlation signal between the two signals includes:

[0037] Determine the moving window length, then the cross-correlation spectrum between the two signals in the i-th window is:

[0038]

[0039] in:

[0040] i is the selected window sequence;

[0041] f is the frequency point;

[0042] F ref_i (f) is the reference signal;

[0043] is an instantaneous signal;

[0044] X i (f) is the cross-correlation signal, expressed as is the phase of the cross-correlation signal.

[0045] Furthermore, the similarity of the two signals is quantitatively analyzed, including:

[0046] Construct the energy density correlation function:

[0047]

[0048] in:

[0049] The horizontal line represents the smoothing of the signal.

[0050] Furthermore, based on the cross-correlation signal and the similarity, the function of the relative speed change of the two signals over time is obtained, and then the relative speed change is calculated, including:

[0051] Introducing different frequency weight factors w i (f) Calculate the slope m of the linear regression estimate of the time difference between the two signals with respect to frequency i ;

[0052]

[0053] Calculate the time shift δt between the two signals i :

[0054] δt i =m i / 2π;

[0055] Get the relative speed change of the two signals as a function of time:

[0056]

[0057] in:

[0058] is the mean of all window sequences;

[0059] Calculate the relative speed change according to the function.

[0060] Furthermore, the microstructural changes inside the rock mass are judged based on whether the relative velocity change of the coda wave under different ray paths changes suddenly.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention provides a time-lag rockburst early warning method based on elastic wave interference. Based on the principle of elastic wave interference, the changes in the internal microstructure of the rock mass are analyzed by the change in the relative velocity of the coda wave, thereby further revealing the internal stress adjustment process, and ultimately achieving accurate early warning of time-lag rockbursts, overcoming the problem that the existing methods can only monitor the surface or near the surface of the rock mass, and improving the monitoring accuracy and timeliness of the internal state of the rock mass. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0064] Figure 1 It is a cross-sectional view of the on-site sensor arrangement of the method of the present invention.

[0065] Figure 2 is an empirical Green's function diagram of an embodiment of the present invention.

[0066] Figure 3 2 is a relative speed variation diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly understood.

[0068] It should be noted that similar reference numerals and letters represent similar items, and therefore, once an item is defined in one embodiment, it does not need to be further defined and explained in subsequent embodiments. In addition, the term "comprising" and the like and any variation thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0069] It should also be noted that although the order of steps is mentioned in the method description, in some cases, they may be performed in an order different from that shown here, and this should not be construed as a limitation on the order of the steps.

[0070] The monitoring methods used in existing time-lag rockburst early warning methods can only monitor the surface or near the surface of the rock mass, making it difficult to fully and accurately understand the internal conditions of the rock mass. By constructing a new elastic wave interference method, the changes in the internal microstructure of the rock mass are analyzed through the changes in the relative velocity of the coda wave, thereby further revealing its internal stress adjustment process, and ultimately achieving accurate early warning of time-lag rockbursts.

[0071] The method is a time-delay rockburst early warning method based on elastic wave interference, which specifically includes the following steps:

[0072] S1: Use elastic wave sensors to build a sensor array in the monitoring area and pair them to form multiple ray paths. Including:

[0073] S101: The tunnel’s time-delay rockburst prone areas and high stress areas are selected as monitoring areas;

[0074] S102: Select two sections separated by a set distance to cover the monitoring area, and arrange a plurality of elastic wave sensors (i.e., microseismic sensors) in a circumferential direction on each section to form a sensor array;

[0075] S103: The elastic wave sensors of the two sections correspond to each other and are paired in pairs to form multiple sensor pairs, and multiple elastic wave ray paths (i.e., elastic wave propagation paths between every two sensors) are constructed to cover the monitoring area. The elastic wave sensors belonging to the two sections can be paired with each other, and the two elastic wave sensors located in the same section can also be paired with each other.

[0076] S2: Collect surrounding environmental noise data in real time through elastic wave sensors.

[0077] S3: Preprocessing environmental noise data. Including:

[0078] S301: cutting and resampling environmental noise data;

[0079] S302: then perform linear trend processing and mean removal processing;

[0080] S303: Then perform spectrum whitening and bandpass filtering;

[0081] S304: Finally, perform time domain normalization processing.

[0082] S4: Perform cross-correlation processing on the environmental noise data of paired elastic wave sensors in the sensor array to obtain the time domain empirical Green's functions of multiple ray paths. Including:

[0083] S401: performing cross-correlation processing on the data between each sensor pair every half hour to obtain a cross-correlation function;

[0084] S402: Setting a lag time, performing a Hilbert transform on the cross-correlation function, and obtaining a time-domain empirical Green's function.

[0085] S5: Extract the coda wave from the empirical Green's function in the time domain and calculate the relative velocity change of the coda wave under different ray paths. Including:

[0086] S501: Arrange the time-domain empirical Green's functions according to a time series;

[0087] S502: performing two-dimensional Wiener filtering on the arranged time-domain empirical Green's functions;

[0088] S503: extracting the coda wave within the specified time window range of the time domain empirical Green's function;

[0089] S504: Calculate the relative velocity variation of coda waves in different time series. Including:

[0090] S50401: Based on the coda waves of different time series, the superimposed time domain empirical Green's function is used as the reference signal, and the time domain empirical Green's function of different time series is used as the instantaneous signal;

[0091] S50402: Obtaining a cross-correlation signal between two signals, including:

[0092] Determine the moving window length, then the cross-correlation spectrum between the two signals in the i-th window is:

[0093]

[0094] in:

[0095] i is the selected window sequence;

[0096] f is the frequency point;

[0097] F ref_i (f) is the reference signal;

[0098] is an instantaneous signal;

[0099] X i (f) is the cross-correlation signal, expressed as is the phase of the cross-correlation signal.

[0100] S50403: Quantitative analysis of the similarity of two signals, including:

[0101] Construct the energy density correlation function:

[0102]

[0103] in:

[0104] The horizontal line represents the smoothing of the signal.

[0105] S50404: Based on the cross-correlation signal and the similarity, a function of the relative speed change of the two signals with respect to time is obtained, and then the relative speed change amount is calculated, including:

[0106] Introducing different frequency weight factors w i (f) Calculate the slope m of the linear regression estimate of the time difference between the two signals with respect to frequency i ;

[0107]

[0108] Calculate the time shift δt between the two signals i :

[0109] δt i =m i / 2π;

[0110] Get the relative speed change of the two signals as a function of time:

[0111]

[0112] in:

[0113] is the mean of all window sequences;

[0114] Calculate the relative speed change according to the function.

[0115] S6: According to whether the relative velocity change of the coda wave under different ray paths changes suddenly, the microstructural changes inside the rock mass are judged, so as to give early warning to the areas where time-delay rockburst may occur.

[0116] The elastic wave sensor used in this method is a velocity-type microseismic sensor used in traditional tunnels, with a main frequency of 10-1000HZ. For example, the microseismic sensor SSS_GT10 produced by Zhongke Microseismic can be used. This type of sensor is a velocity-type sensor with a sensitivity of 100V / m / s, a natural frequency of 10Hz, a frequency response range of 10-1000Hz, and a sensor size of 39*120mm.

[0117] This method uses environmental noise data for early warning, without the need for active seismic sources, and can continuously monitor the environmental noise generated by human activities in the tunnel and its surroundings, and achieve high-time-resolution monitoring and early warning by reconstructing the empirical Green's function with a half-hour period. In addition, this method achieves accurate early warning of the time-delay rockburst area through the change in the relative velocity of the coda wave and the area covered by the ray path. The change in the relative velocity of the coda wave can reflect the microstructural changes inside the rock mass, which is unattainable by traditional monitoring methods. The ray path can accurately partition the monitoring area, thereby achieving accurate early warning of microstructural changes inside the rock mass.

[0118] In the above method, unless otherwise specified, the calculation methods used are all existing methods, and this method does not make any improvements to their specific processes.

[0119] The implementation of this method is described in detail below through specific examples:

[0120] S1: Deployment of sensors:

[0121] Elastic wave sensors are deployed in tunnel areas prone to time-delay rock bursts and high stress areas. The monitoring area is covered by two sections with an interval of 50 m. Five sensors are deployed in each section, one on the arch crown, two on the arch shoulders on both sides, and two on the arch foot, for a total of ten. The sensors between section A (sensors A1-A5) and section B (sensors B1-B5) are paired in pairs to cover the monitoring area with elastic wave ray paths, such as Figure 1 .

[0122] S2: Collect data:

[0123] On-site monitoring is carried out according to the sensor deployment plan, and environmental noise data is continuously collected during the monitoring process.

[0124] S3: Preprocessing environmental noise data:

[0125] Preprocess the continuously collected environmental noise data. The sampling rate and time window settings during data collection need to be comprehensively considered, such as the information that the data can provide, computer storage, and the simplicity of post-processing. The sampling rate of the instrument (i.e., elastic wave sensor) during data collection is set to 4000Hz. In order to ensure the time resolution of the relative velocity change and improve the calculation efficiency, the data needs to be cut and resampled by half an hour during actual data processing and analysis. The sampling rate of the resampled data is 2000HZ.

[0126] The data is processed by linear trend and de-averaging to remove non-zero mean or long-period linear trends in the waveform data.

[0127] The data is spectrally whitened to remove the interference of single-frequency fixed signals and multi-peaks and obtain background noise data with a wider frequency band.

[0128] The data were band-pass filtered at 5-100 Hz to remove interference from other frequency bands.

[0129] The data is normalized in the time domain to remove the interference of unstable noise signals.

[0130] S4: Get empirical Green's function:

[0131] The data between each sensor pair are cross-correlated every half hour, the lag time is set to -50s to 50s, and the cross-correlation function is Hilbert transformed to obtain the time domain empirical Green's function, such as Figure 2 .

[0132] S5: Extract the coda wave and calculate the relative velocity change:

[0133] The empirical Green's functions are arranged in time series, and the arranged time-domain empirical Green's functions are processed by two-dimensional Wiener filtering to remove the influence of incoherent noise on the empirical Green's functions. The coda waves within the time window of 3 to 40 s of the empirical Green's functions are extracted, and the relative velocity changes of the coda waves in different time series are calculated by the moving window cross-correlation spectrum analysis method.

[0134] Specifically, in the moving window cross-correlation spectrum analysis method, the superimposed empirical Green's function is used as the reference signal, the empirical Green's function of different time series is used as the instantaneous signal, and the length of the moving window is selected as 1.5s. The cross-correlation spectrum between the two signals in the i-th window can be expressed as:

[0135]

[0136] In the above formula, F ref_i (f) is the reference signal in the frequency domain, is the instantaneous signal in the frequency domain, X i (f) is the frequency domain cross-correlation signal of the two, f is the frequency point in the frequency domain, and X i (f) can be expressed as:

[0137]

[0138] In the above formula, the phase of the cross-correlation signal is is the frequency f and the time difference δt between the two signals i The higher the similarity between the two signals, the more accurate the time shift represented by this phase. Here, the correlation of energy density is used to quantitatively analyze this similarity:

[0139]

[0140] In the above formula, the horizontal line represents the smoothing of the signal, which is smoothed by the cross-correlation signal using the Hanning window function. The correlation range is between 0 and 1, and C i (f) The maximum value corresponds to the frequency close to where the energies of the two spectra are highly similar. It is linearly related to the frequency f and has:

[0141]

[0142] In the above formula, m i It can be used directly to calculate the time shift between signals. However, since the correlation of signals at different frequencies is different, different frequency weight factors w are introduced by using the correlation of the two signals. i (f) to calculate m i , expressed as:

[0143]

[0144] Then calculate m after introducing the weight factor i :

[0145]

[0146] Then in the i-th sliding window, the time shift δt between the two signals is i for:

[0147] δt i =m i / 2π;

[0148] The relative speed change between the signals is calculated and expressed as:

[0149]

[0150] In the above formula, i represents the selected window sequence, is the mean of all window sequences. Finally, the change of relative velocity change over time under different ray paths (i.e., between sensor pairs) is obtained through the above calculation, as shown in Figure 3 .

[0151] S6: The microstructure change inside the rock mass is judged based on whether the relative velocity change under different ray paths has a sudden change. The monitoring area is divided into several areas for analysis based on the ray path coverage area between sensors in different sections. It is mainly divided into the following situations:

[0152] (1) If the relative velocity of ray paths A1-A2, A1-B1, and A2-B1 relative to other ray paths changes suddenly, the microstructure of region A1A2B1 changes relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high;

[0153] (2) If the relative velocity of ray paths A1-A2, A1-B2, and A2-B2 relative to other ray paths changes suddenly, the microstructure of region A1A2B2 changes relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high;

[0154] (3) If the relative velocity changes of ray paths A1-B2, A1-B1, and B2-B1 relative to other ray paths change suddenly, the microstructure of region A1B1B2 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0155] (4) If the relative velocity changes of ray paths A2-B2, A2-B1, and B2-B1 relative to other ray paths change suddenly, the microstructure of region A2B1B2 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0156] (5) If the relative velocity changes of ray paths A1-B1, A1-A2, A2-B2, and B2-B1 relative to other ray paths change suddenly, the microstructure of the central area of ​​region A1A2B1B2 will change relative to other areas, indicating that the time-delay rockburst is more likely to occur in the center of this area;

[0157] (6) If the relative velocity of ray paths A1-A5, A1-B1, and A5-B1 relative to other ray paths changes suddenly, the microstructure of region A1A5B1 changes relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high, and the bottom plate uplift disaster is prone to occur;

[0158] (7) If the relative velocity of ray paths A1-A5, A1-B5, and A5-B5 relative to other ray paths changes suddenly, the microstructure of region A1A5B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high, and the bottom plate uplift disaster is prone to occur;

[0159] (8) If the relative velocity changes of ray paths A1-B5, B1-B5, and A1-B1 relative to other ray paths change suddenly, the microstructure of region A1B1B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high, and the bottom plate uplift disaster is prone to occur;

[0160] (9) If the relative velocity changes of ray paths A5-B5, B1-B5, and A5-B1 relative to other ray paths change suddenly, the microstructure of region A5B1B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is high, and the floor uplift disaster is prone to occur;

[0161] (10) If the relative velocity of ray paths A1-B1, A1-A5, A5-B5, and B5-B1 relative to other ray paths changes suddenly, then the microstructure of the central area of ​​region A1A5B1B5 changes relative to other areas, indicating that the possibility of floor uplift in the center of this area is relatively high;

[0162] (11) If the relative velocity of ray paths A2-A3, A2-B2, and A3-B2 relative to other ray paths changes suddenly, the microstructure of region A2A3B2 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0163] (12) If the relative velocity changes of ray paths A2-A3, A2-B3, and A3-B3 relative to other ray paths change suddenly, the microstructure of region A2A3B3 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0164] (13) If the relative velocity changes of ray paths A2-B3, A2-B2, and B3-B2 relative to other ray paths change suddenly, the microstructure of region A2B2B3 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0165] (14) If the relative velocity changes of ray paths A3-B3, A3-B2, and B3-B2 relative to other ray paths change suddenly, the microstructure of region A3B2B3 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0166] (15) If the relative velocity changes of ray paths A2-B2, A2-A3, A3-B3, and B3-B2 relative to other ray paths change suddenly, the microstructure of the central area of ​​region A2A3B2B3 will change relative to other areas, indicating that the time-delay rockburst is more likely to occur in the center of this area;

[0167] (16) If the relative velocity changes of ray paths A3-A4, A3-B3, and A4-B3 relative to other ray paths change suddenly, the microstructure of region A3A4B3 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0168] (17) If the relative velocity of ray paths A3-A4, A3-B4, and A4-B4 relative to other ray paths changes suddenly, the microstructure of region A3A4B4 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0169] (18) If the relative velocity changes of ray paths A3-B3, A3-B4, and B4-B3 relative to other ray paths change suddenly, the microstructure of region A3B3B4 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0170] (19) If the relative velocity changes of ray paths A4-B4, A4-B3, and B4-B3 relative to other ray paths change suddenly, the microstructure of region A4B3B4 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0171] (20) If the relative velocity changes of ray paths A3-B3, A3-A4, A4-B4, and B4-B3 relative to other ray paths change suddenly, then the microstructure of the central area of ​​region A3A4B3B4 changes relative to other areas, indicating that the time-delay rockburst is more likely to occur in the center of this area;

[0172] (21) If the relative velocity changes of ray paths A4-A5, A4-B4, and A5-B4 relative to other ray paths change suddenly, the microstructure of region A4A5B4 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0173] (22) If the relative velocity changes of ray paths A4-A5, A4-B5, and A5-B5 relative to other ray paths change suddenly, the microstructure of region A4A5B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0174] (23) If the relative velocity changes of ray paths A4-B4, A4-B5, and B5-B4 relative to other ray paths change suddenly, the microstructure of region A4B4B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0175] (24) If the relative velocity changes of ray paths A5-B5, A5-B4, and B5-B4 relative to other ray paths change suddenly, the microstructure of region A5B4B5 will change relative to other regions, indicating that the possibility of time-delay rockburst occurring in this region is relatively high;

[0176] (25) If the relative velocity changes of ray paths A4-B4, A4-A5, A5-B5, and B5-B4 relative to other ray paths change suddenly, then the microstructure of the central area of ​​region A4A5B4B5 changes relative to other areas, indicating that the time-delay rockburst is more likely to occur in the center of this area;

[0177] In summary, the present invention uses the elastic wave interference method to divide the rockburst-prone area into 25 areas through two monitoring sections and ten sensors, and determines the area prone to time-lag rockburst according to the sudden change of relative velocity under the ray path by different sensors, thereby providing an early warning for time-lag rockburst.

[0178] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A time-delay rockburst early warning method based on elastic wave interference, characterized by: The method comprises: Using elastic wave sensors to construct a sensor array in the monitoring area, pairing them to form multiple ray paths; Collect surrounding environmental noise data in real time through elastic wave sensors; Preprocessing environmental noise data; The environmental noise data of paired elastic wave sensors in the sensor array are cross-correlated to obtain the time-domain empirical Green's functions of multiple ray paths; Extract the coda wave from the empirical Green's function in the time domain and calculate the relative velocity variation of the coda wave under different ray paths; According to the relative velocity change of the coda wave under different ray paths, the abnormal microstructural changes inside the rock mass are judged, so as to give early warning to the areas where time-delay rockburst may occur.

2. The time-delay rockburst early warning method based on elastic wave interference according to claim 1 is characterized in that: Elastic wave sensors are used to construct a sensor array in the monitoring area and paired to form multiple ray paths, including: The tunnel's time-delay rockburst prone areas and high stress areas are selected as monitoring areas; Two sections separated by a set distance are selected to cover the monitoring area, and multiple elastic wave sensors are arranged in a circumferential direction in each section to form a sensor array; The elastic wave sensors of the two sections correspond to each other and are paired in pairs to form multiple sensor pairs, and construct multiple elastic wave ray paths to cover the monitoring area.

3. The time-delay rockburst early warning method based on elastic wave interference according to claim 2 is characterized in that: Preprocess the environmental noise data, including: Chopping and resampling the environmental noise data; Then, linear trend processing and mean removal processing are performed; Then, spectral whitening and bandpass filtering are performed; Finally, time domain normalization is performed.

4. The time-delay rockburst early warning method based on elastic wave interference according to claim 3 is characterized in that: The environmental noise data of paired elastic wave sensors in the sensor array are cross-correlated to obtain the time-domain empirical Green's functions of multiple ray paths, including: The data between each sensor pair is cross-correlated every half hour to obtain the cross-correlation function; The lag time is set, and the cross-correlation function is Hilbert transformed to obtain the empirical Green's function in the time domain.

5. The time-delay rockburst early warning method based on elastic wave interference according to claim 4 is characterized in that: Extract the coda wave from the time-domain empirical Green's function and calculate the relative velocity changes of the coda wave under different ray paths, including: Arrange the time-domain empirical Green's functions in time series; Perform two-dimensional Wiener filtering on the arranged time-domain empirical Green's functions; Extract the coda wave within the specified time window range of the time domain empirical Green's function; Calculate the relative velocity changes of coda waves in different time series.

6. The time-delay rockburst early warning method based on elastic wave interference according to claim 5 is characterized in that: Calculate the relative velocity changes of coda waves in different time series, including: Based on the coda waves of different time series, the superimposed time domain empirical Green's function is used as the reference signal, and the time domain empirical Green's function of different time series is used as the instantaneous signal; Obtaining a cross-correlation signal between two signals; Quantitatively analyze the similarity of two signals; Based on the cross-correlation signal and similarity, the function of the relative speed change of the two signals to time is obtained, and then the relative speed change is calculated.

7. The time-delay rockburst early warning method based on elastic wave interference according to claim 6 is characterized in that: Obtaining a cross-correlation signal between two signals, including: Determine the moving window length, then the cross-correlation spectrum between the two signals in the i-th window is: in: i is the selected window sequence; f is the frequency; F ref_i (f) is the reference signal; is an instantaneous signal; X i (f) is the cross-correlation signal, expressed as is the phase of the cross-correlation signal.

8. The time-delay rockburst early warning method based on elastic wave interference according to claim 7 is characterized in that: Quantitative analysis of the similarity of two signals, including: Construct the energy density correlation function: in: The horizontal line represents the smoothing of the signal.

9. The time-delay rockburst early warning method based on elastic wave interference according to claim 8 is characterized in that: Based on the cross-correlation signal and similarity, the function of the relative speed change of the two signals over time is obtained, and then the relative speed change is calculated, including: Introducing different frequency weight factors w i (f) Calculate the slope m of the linear regression estimate of the time difference between the two signals with respect to frequency i ; Calculate the time shift δt between the two signals i : δt i =m i / 2π; Get the relative speed change of the two signals as a function of time: in: is the mean of all window sequences; Calculate the relative speed change according to the function.

10. The time-delay rockburst early warning method based on elastic wave interference according to claim 9, characterized in that: The microstructural changes inside the rock mass are judged based on whether the relative velocity change of the coda wave under different ray paths occurs suddenly.

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