Rock burst prediction method based on microseismic monitoring of spatiotemporal strong parameters of rupture source

Abstract

The application provides a rock burst prediction method based on microseismic monitoring of rupture source space-time strong parameters, which utilizes the detected microseismic monitoring time, space position and relative release energy parameters to preliminarily judge the microseismic activity degree and rock burst grade; according to the obtained space three-dimensional positioning coordinates and energy value energy level change conditions of the microseismic monitoring events, the event number aggregation trend and coordinate points are used to divide the disaster occurrence positions of different danger levels, and then the early warning index based on the microseismic energy parameters is proposed to carry out early warning. The method has more advantages in space continuity than the traditional two-dimensional analysis, and has good practical effect on the analysis of the space-time evolution law of the microseismic monitoring rupture source in the three-dimensional space of underground engineering, especially the aggregation degree in space.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underground engineering, and particularly relates to a rock burst prediction method based on microseismic monitoring of spatiotemporal strong parameters of a rupture source. BACKGROUND

[0002] How to effectively monitor and warn rock burst disasters has become a difficult problem to be solved in the safe construction of deep underground engineering. Rock burst warning is an important work in the construction of underground engineering in high ground stress areas, aiming to predict potential rock burst risk areas, so as to take appropriate protective measures in time and ensure the safety of underground structures, construction facilities and personnel. At present, microseismic monitoring technology, as a three-dimensional space monitoring technology of rock mass microfracture, is the most direct and effective monitoring means for studying the rock burst incubation process, and has been widely applied in rock burst monitoring and warning of deep underground engineering. The microseismic monitoring system uses multiple sensors installed in the rock mass to collect rock mass microfracture signals, and after waveform processing and source location by the microseismic system, the rupture position, microseismic source energy, magnitude and apparent stress of the microseismic source can be obtained. Then, the spatiotemporal evolution law of microseismic events in the rock burst incubation process is studied by using the source parameter information of the microseismic events, and the potential rock burst risk area is identified, so as to realize early warning of rock burst disasters. The invention patent with application number CN201910081165.9 discloses a rock fracture complexity characterization method based on fractal dimension, the invention patent with application number CN201910210964.1 discloses a comprehensive intelligent prediction method for predicting impact risk levels based on microseismic fractals, and the invention patent with application number CN112987087A discloses a microseismic monitoring / acoustic emission rupture source spatiotemporal distribution state and trend early warning method. The statistics and calculation of microfracture source fractal dimension are relatively mature, but in terms of fractal dimension and its statistical value, the statistics are difficult, the data are tedious, the calculation amount is large, the computing capacity is high, and especially in the analysis of three-dimensional real damage rock mass in space, it is more complex. SUMMARY

[0003] Based on the actual engineering needs, the application is dedicated to providing a simple, accurate and reasonable quantitative rock burst early warning index determination method, so as to provide a more reasonable prediction for rock burst risk assessment and disaster level determination.

[0004] To achieve the purpose of the application, the application is implemented as follows:

[0005] A rock burst prediction method based on microseismic monitoring of spatiotemporal strong parameters of a rupture source, the prediction method comprising the following steps:

[0006] S1, a microseismic monitoring system is arranged in a tunnel, and a layout scheme that can realize dynamic monitoring with the tunneling system is constructed, the number of sensors should not be less than 4, and 6-8 is appropriate;

[0007] S2, acquiring a tunnel microseismic monitoring data, performing a preliminary processing and screening, removing an interference waveform based on a wavelet time-frequency analysis, then removing an abnormal rock burst signal, calculating a spatial three-dimensional coordinate and an energy value of a microseismic event;

[0008] S3, collecting microseismic event data, forming a data file containing time, spatial three-dimensional positioning coordinates and energy values of the microseismic event, and determining a rock burst grade according to a rock burst grade division standard;

[0009] S4, reading a waveform data file of the microseismic event, calculating an apparent stress, a magnitude and an energy index of each microseismic event, and then calculating a cumulative apparent volume of the microseismic event in a certain region;

[0010] S5, constructing a microseismic early warning index based on the microseismic parameters calculated in step S4, and dividing the early warning grade into four levels: level I, defined as a particularly serious area, for red early warning; level II, defined as a serious area, for orange early warning; level III, defined as a relatively heavy area, for yellow early warning; and level IV, defined as a general area, for blue early warning;

[0011] S6, repeating the operation steps of S1 to S5 in the order of time of the microseismic event, so as to realize real-time early warning of the tunnel rock burst.

[0012] Further, the interference waveform in step S2 includes a mechanical knocking signal waveform, a mechanical vibration signal waveform and an electrical noise signal waveform.

[0013] Further, the rock burst signal is generated by large-scale rupture of rock mass, has a large amplitude, a duration greater than 100 ms, and a tail wave developed; the mechanical knocking signal has a duration generally not greater than 70 ms and a tail wave decaying quickly; the mechanical vibration signal has a duration generally greater than 80 ms and a developed tail wave; and the electrical noise signal has a duration generally greater than 200 ms and an amplitude signal main frequency range mainly concentrated in 2000-2500 Hz.

[0014] Further, in step S2, after the sensor picks up the microseismic signal, the physical quantity is converted into a voltage quantity or a charge quantity, the time when each sensor receives the signal is determined through multi-point synchronous data acquisition, and the equation set is solved by substituting the coordinates of each sensor and the measured wave velocity, so as to determine the space-time parameters of the acoustic emission source and achieve the positioning purpose. Other formulas are as follows:

[0015]

[0016] In the formula, x, y and z are the coordinates of the microseismic event position, t is the time when the microseismic event occurs, x i , y i and zi Xi and Yj are the coordinates of the i-th sensor, respectively, v p is the p-wave velocity of the rock mass, t i is the time of the microseismic event signal arriving at the i-th sensor.

[0017] Further, in step S2, the energy of the microseismic event is calculated as follows:

[0018]

[0019] wherein E is the energy of the microseismic event, V(f) is the velocity spectrum of the seismic source signal, p is the density of the rock, c is the wave velocity, R is the propagation distance of the microseismic signal, and f is the frequency of the microseismic signal.

[0020] Further, in step S4, the apparent stress refers to the seismic energy emitted by a unit volume of inelastic co-seismic deformation, and reflects the stress at the seismic source. The calculation formula is as follows:

[0021]

[0022] wherein s α is the apparent stress, E is the energy of the microseismic event, M0 is the seismic moment, and u is the shear modulus of the rock mass.

[0023] The apparent volume V α reflects the deformation characteristics at the seismic source, and the calculation formula is as follows:

[0024]

[0025] The energy index EI is the ratio of the seismic energy of the microseismic event to the average energy of events with the same seismic moment:

[0026]

[0027] E(M0) is the average energy of events with the same seismic moment, which can be determined by the relationship curve of lgE-lgM0 within a certain spatial range, i.e.:

[0028]

[0029] Further, the rapid decrease of the energy index accompanied by the rapid increase of the apparent volume can be used as a warning indicator for rockburst in surrounding rock. The slope of the adjacent energy index-time is calculated, and then normalized to k E ; the slope of the adjacent apparent volume-time is also calculated, and then normalized to k V , and thus a rockburst warning indicator is proposed:

[0030]

[0031] Moderate rockburst {-0.4<kE xk V <-0.1

[0032] strong rockburst {-0.7 < k E xk V <-0.4

[0033] extremely strong rockburst {-1.0 < k E xk V <-0.7

[0034] Further, the adjacent energy index-time is sloped and normalized as k E Specifically,

[0035]

[0036] wherein K EI(i+1) is the slope calculated by subtracting the i+1th energy index EI(i+1) from the ith energy index EI(i) and then dividing by the time difference of the two energy indexes, t EI(i+1) is the time corresponding to the i+1th energy index EI(i+1), t EI(i) is the time corresponding to the ith energy index EI(i).

[0037] The following formula is used for normalization processing:

[0038] wherein, is the minimum value among all calculated slopes, is the maximum value among all calculated slopes.

[0039] Further, the adjacent visual volume-time is sloped and normalized as k V Specifically,

[0040]

[0041] wherein, is the slope calculated by subtracting the i+1th visual volume index V α (i+1) from the ith visual volume index V α (i) and then dividing by the time difference of the two visual volumes, is the time corresponding to the i+1th visual volume index V (i+1), is the time corresponding to the ith visual volume index V (i).

[0042] The following formula is used for normalization processing:

[0043] wherein, The minimum value among all the calculated slopes, The maximum value among all the calculated slopes.

[0044] Further, in combination with the rock burst early warning index, the main microseismic occurrence area is determined, and then early warning is carried out according to the four levels, wherein the rock burst corresponds to the first level, red early warning is carried out; the strong rock burst corresponds to the second level, orange early warning is carried out; the medium rock burst corresponds to the third level, yellow early warning is carried out; and the slight rock burst corresponds to the fourth level, blue early warning is carried out.

[0045] Compared with the prior art, the present application has the following beneficial technical effects: the wavelet time-frequency analysis method is used to extract the time-frequency domain features of various typical signals, and the rock mass micro-fracture event is effectively identified; the original wave data and the microseismic energy data are fully utilized, the time, space and intensity information of the microseismic event are beneficial, the rock burst level is divided according to the time and space characteristics of the microseismic event; meanwhile, the potential law of the microseismic data is mined by using the microseismic energy and magnitude parameters, the quantitative rock burst prediction index is proposed, the rock burst prediction is strong in pertinence and high in accuracy, the method is simple to calculate and does not need to perform more operations, and various parameter calculation methods are mature and easy to use. The present application can be widely used for rock burst early warning in microseismic monitoring of underground powerhouse, tunnel, slope and roadway engineering. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 The present application is a whole flowchart;

[0048] Figure 2 The present application is a different signal waveform schematic diagram;

[0049] Figure 3 The present application is a sensor positioning principle diagram for microseismic events;

[0050] Figure 4 The present application is a logE-logM0 relationship diagram of the embodiment;

[0051] Figure 5 The present application is a warning index and strong rock burst relationship schematic diagram. DETAILED DESCRIPTION

[0052] The technical solutions of the present application will be described in detail below in combination with the drawings and embodiments.

[0053] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0054] The present application will be described in detail below with reference to the drawings. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0055] The present application proposes a rock burst prediction method based on microseismic monitoring of spatiotemporal strength parameters of rupture sources. In order to make the advantages and technical solutions of the present application clearer and more explicit, the present application will be described in detail below in combination with specific embodiments.

[0056] The present embodiment provides a rock burst prediction method based on microseismic monitoring of spatiotemporal strength parameters of rupture sources, which includes the following steps, as shown in Figure 1

[0057] S1, a microseismic monitoring system is arranged in a tunnel, and a layout scheme that can realize dynamic monitoring with the tunneling system is constructed. The number of sensors should not be less than 4, and 6-8 is appropriate;

[0058] S2, tunnel microseismic monitoring data is obtained, and preliminary processing and screening are performed. Interference waveforms are removed based on wavelet time-frequency analysis, and then abnormal rock burst signals are removed. The spatial three-dimensional coordinates and energy values of microseismic events are calculated. In the present embodiment, the interference waveforms are, for example, mechanical knocking signals, mechanical vibration signals, electrical noise signals, etc.

[0059] S3, microseismic event data is summarized to form a data file containing the time, spatial three-dimensional positioning coordinates and energy values of the microseismic events, and the rock burst grade is determined according to the rock burst grade division standard;

[0060] S4, the waveform data file of the microseismic event is read, and the apparent stress, magnitude and energy index of each microseismic event are calculated. Then, the cumulative apparent volume of microseismic events in a certain region is calculated.

[0061] S5, based on the microseismic parameters calculated in step S4, a microseismic early warning index is constructed, and the early warning grades are divided into four levels: level I, defined as a particularly serious area, for red early warning; level II, defined as a serious area, for orange early warning; level III, defined as a relatively heavy area, for yellow early warning; and level IV, defined as a general area, for blue early warning.

[0062] ​S6 repeats the operation steps from S1 to S4 based on the time sequence of microseismic events, thereby achieving real-time early warning of tunnel rockburst.

[0063] In the above embodiments, rockburst signals are generated by large-scale fracturing of the rock mass, with large amplitudes and durations typically greater than 100ms. The waveform exhibits obvious peak reduction and a well-developed tailwave. Mechanical impact signals have shorter durations, typically no more than 70ms, and the tailwave decays rapidly. Vibration signals typically last longer than 80ms, with a well-developed tailwave. Electrical noise signals have long durations (>200ms), lower amplitudes, and a dominant frequency range mainly concentrated between 2000 and 2500Hz. Figure 2 As shown.

[0064] After the sensor picks up the micro-vibration signal, this physical quantity is converted into a voltage or charge quantity. By simultaneously acquiring data from multiple points and determining the time when each sensor receives the signal, along with the coordinates of each sensor and the measured wave velocity, and substituting these values ​​into a system of equations, the spatiotemporal parameters of the acoustic emission source can be determined, thus achieving the purpose of localization. Figure 3 As shown. The specific formula is as follows:

[0065]

[0066] In the formula, x, y, and z are the location coordinates of the microseismic event, t is the time of the microseismic event, and x is the position coordinates of the microseismic event. i y i and z i Let v be the coordinates of the location of the i-th sensor. p The p-wave velocity of the rock mass is t. i The time it takes for the microseismic event signal to reach the i-th sensor.

[0067] The energy calculation method for microseismic events in this embodiment is as follows:

[0068]

[0069] In the formula: E is the energy of the microseismic event; V(f) is the velocity spectrum of the source signal; ρ is the rock density; c is the wave velocity; R is the propagation distance of the microseismic signal; and f is the frequency of the microseismic signal.

[0070] Apparent stress refers to the seismic energy emitted per unit volume of inelastic coseismic deformation, reflecting the magnitude of stress at the earthquake source. The calculation formula is as follows:

[0071]

[0072] In the formula: σ α denoted as apparent stress; E as microseismic event energy; M0 as seismic moment; and u as rock mass shear modulus.

[0073] Apparent volume (V)a ) reflects the deformation characteristics at the source, and its calculation formula is as follows:

[0074]

[0075] The energy index (EI) is the ratio of the seismic energy of a microseismic event to the average energy of events with the same seismic moment:

[0076]

[0077] The average energy of events with the same seismic moment can be determined by the lgE-lgM0 relationship curve within a certain spatial range, as shown in Figure 4 , that is:

[0078]

[0079] The time series characteristics of the energy index and apparent volume reflect the stress and deformation state of the surrounding rock. An increase in the energy index indicates an increase in the stress level at the source, and uniform growth in the apparent volume means that the deformation of the surrounding rock gradually increases. When the energy index increases to a high level, the surrounding rock is close to failure. Thereafter, the energy index begins to decrease, and the growth rate of the apparent volume increases, and the surrounding rock experiences large deformation. The rapid decline in the energy index accompanied by rapid increase in the apparent volume can be used as an early warning indicator of rockburst, that is, the slope of the adjacent energy index-time is calculated, and then normalized (k E ); the slope of the adjacent apparent volume-time is also normalized (k V ), and thus a rockburst early warning indicator is proposed.

[0080] Further, the slope of the adjacent energy index-time is normalized to k E Specifically:

[0081]

[0082] where K EI(i+1) is the slope calculated by subtracting the i-th energy index EI(i) from the i+1-th energy index EI(i+1) and then dividing by the time difference between the two energy indices, t EI(i+1) is the time corresponding to the i+1-th energy index EI(i+1), and t EI(i) is the time corresponding to the i-th energy index EI(i).

[0083] Normalization is performed using the following formula:

[0084] where k is the minimum value of all calculated slopes, is the maximum value of all calculated slopes.

[0085] Further, the adjacent volume-time is differentiated and normalized as k V Specifically,

[0086]

[0087] wherein, is the i+1th volume index corresponding to the time, α (i) is differentiated and then divided by the slope calculated from the time difference of the two volumes, is the i+1th volume index corresponding to the time, is the i+1th volume index corresponding to the time,

[0088] Normalization is performed using the following formula:

[0089] wherein, is the minimum value of all calculated slopes, is the maximum value of all calculated slopes.

[0090] The rockburst early warning index is as follows:

[0091]

[0092] Moderate rockburst {-10 < kE x kV < -1

[0093] Strong rockburst {-100 < kE x kV < -10

[0094] Extremely strong rockburst {kE x kV < -100

[0095] In combination with the rockburst early warning index, the main microseismic occurrence area is determined, and then early warning is performed according to the four levels, wherein the rockburst corresponds to level I, red early warning is performed; the strong rockburst corresponds to level II, orange early warning is performed; the moderate rockburst corresponds to level III, yellow early warning is performed; and the slight rockburst corresponds to level IV, blue early warning is performed. Figure 5 As shown in the figure, the microseismic event energy of a certain area is plotted, and it can be seen from the figure that the energy distribution (black circles) of the microseismic event has no clear relationship with whether rockburst (black pentagons) occurs, and the prediction index (black squares) proposed in the present application can better predict the occurrence of rockburst.

[0096] The above examples are only illustrative of the technical solutions of the present application. The dry method involved in the present application is not limited to the content described in the above examples, but is subject to the scope defined in the claims. Any modification or supplement or equivalent replacement made by the skilled in the art on the basis of the examples is within the scope claimed by the claims of the present application.

Claims

1. A rockburst prediction method based on spatiotemporal intensity parameters of the rupture source monitored by microseismic monitoring, characterized in that: The prediction method includes the following steps: S1. Install a microseismic monitoring system in the tunnel and construct a deployment scheme that enables dynamic monitoring as the tunneling system progresses. The number of sensors should not be less than 4. S2, acquire tunnel microseismic monitoring data, perform preliminary processing and screening, remove interference waveforms based on wavelet time-frequency analysis, then remove abnormal rockburst signals, and calculate the spatial three-dimensional coordinates and energy values ​​of microseismic events; S3, summarize the microseismic event data to form a data file containing the time, spatial three-dimensional positioning coordinates and energy values ​​of the microseismic events, and determine the rockburst level according to the rockburst level classification standard; S4 reads the waveform data file of microseismic events, calculates the apparent stress, magnitude, and energy index of each microseismic event, and then calculates the cumulative apparent volume of microseismic events in a certain area. Apparent stress refers to the seismic energy emitted per unit volume of inelastic coseismic deformation, reflecting the stress magnitude at the epicenter. The calculation formula is as follows: In the formula: For apparent stress; E Energy of microseismic events; M 0 represents the seismic moment; u This refers to the rock mass shear modulus. See volume This reflects the deformation characteristics at the earthquake source, and its calculation formula is as follows: Energy Index EI This is the ratio of the seismic energy of a microseismic event to the average energy of an event with the same seismic moment. The average energy of events with the same seismic moment can be expressed as lgE-lg within a certain spatial range. M The relationship curve for 0 is determined, that is: A rapid decrease in the energy index, accompanied by a rapid increase in apparent volume, can be used as an early warning indicator for rockburst in the surrounding rock. This is achieved by calculating the slope of adjacent energy indices minus time, and then normalizing the result. k E The slope of adjacent apparent volume-time is also normalized. k V Therefore, rockburst early warning indicators were proposed: S5. Based on the microseismic parameters calculated in step S4, construct a microseismic early warning index and divide the early warning level into 4 levels: Level I, defined as an area with particularly severe seismic activity, is given a red warning. Level II, defined as a severe area, is subject to an orange alert; Level III is defined as a relatively severe area and is subject to a yellow alert; Level IV is defined as a general area and is subject to a blue alert. S6 repeats the operation steps from S1 to S5 in chronological order of the microseismic events to achieve real-time early warning of tunnel rockbursts.

2. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: The interference waveforms in step S2 include mechanical impact signal waveforms, mechanical vibration signal waveforms, and electrical noise signal waveforms.

3. A rockburst prediction method based on spatiotemporal intensity parameters of the rupture source according to claim 2, characterized in that: The rockburst signal is generated by large-scale fracturing of the rock mass. It has a large amplitude, a duration of more than 100 ms, and the waveform shows obvious peak elimination and a well-developed tail wave. The mechanical impact signal usually has a duration of no more than 70 ms and the tail wave decays quickly. The mechanical vibration signal usually has a duration of more than 80 ms and a well-developed tail wave. The electrical noise signal generally has a duration of more than 200 ms and the amplitude signal's main frequency range is mainly concentrated in the 2000~2500 Hz range.

4. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: In step S2, after the sensor picks up the micro-vibration signal, this physical quantity is converted into voltage or charge. The time when each sensor receives the signal is determined by multi-point synchronous data acquisition. The coordinates of each sensor and the measured wave velocity are substituted into the equation system to solve the problem, thereby determining the spatiotemporal parameters of the acoustic emission source and achieving the purpose of positioning.

5. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: In step S2, the energy calculation method for microseismic events is as follows: In the formula: E Energy of microseismic events; V ( f () represents the velocity spectrum of the seismic source signal; ρ Density of the rock; c Wave speed; R This represents the propagation distance of the microseismic signal. f The frequency of the microseismic signal is denoted as .

6. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: The slope of adjacent energy exponents versus time is calculated and normalized. k E Specifically: in, For the first i +1 energy index With the i Energy Index The slope is calculated by subtracting the two energy indices and then dividing by the time difference between them. For the first i +1 energy index The corresponding time, For the first i Energy Index The corresponding time; Normalization is performed using the following formula: in, The minimum value among all calculated slopes. This is the maximum value among all calculated slopes.

7. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: The slopes of adjacent apparent volumes-time are calculated and normalized. k V Specifically: in, For the first i +1 visual volume index With the i Individual volume index The slope is calculated by subtracting the values ​​from the time difference between the two apparent volumes. For the first i +1 visual volume index The corresponding time, For the first i Individual volume index The corresponding time; Normalization is performed using the following formula: in, The minimum value among all calculated slopes. This is the maximum value among all calculated slopes.

8. The rockburst prediction method based on the spatiotemporal intensity parameters of the rupture source according to claim 1, characterized in that: By combining rockburst early warning indicators, the main areas where microseismic events occur are determined, and then early warnings are issued according to four levels: Level I corresponds to rockburst and a red warning is issued; Level II corresponds to strong rockburst and an orange warning is issued. Moderate rockbursts correspond to Level III and are subject to a yellow alert; minor rockbursts correspond to Level IV and are subject to a blue alert.

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