Rock burst position early warning method for tunneling in high-stress environment

Abstract

The invention discloses a rock burst position early warning method for tunneling in a high-stress environment, and relates to the technical field of tunnel rock burst early warning. The method comprises the following steps that sensors are arranged on three sections A, B and C in a tunnel, the sensors are arranged on each section, the sensors are placed in a sleeve, the sleeve is placed in a drilled hole formed in a rock mass in advance, the sleeve and surrounding rock are coupled into a whole, a sensor tail fiber is fixed to an opening of the sleeve through sound insulation cotton, and a sound insulation cover is added to the outermost layer of the sleeve; the sensor collects micro-vibration information and transmits the micro-vibration information to the monitoring host, the monitoring host filters the micro-vibration information to obtain a micro-vibration event, the micro-vibration information obtained by the sensor is filtered, obtained micro-vibration event data are more real and effective, automatic verification of an early warning result is achieved through the image terminal, and the accuracy of early warning is improved. The accuracy of the early warning result is improved, and the deviation of the early warning result is corrected.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel rock burst warning, and in particular to a rock burst position warning method for tunnel excavation in a high stress environment. Background Art

[0002] Rockburst is a common geological disaster during deep engineering excavation or mining. From the perspective of geological conditions and tectonic stress characteristics, rockburst is divided into strain-type rockburst, strain-structure surface-type rockburst and fault-slip type rockburst. It will not only damage underground structures and equipment, but also threaten the lives of construction workers. Tunnel excavation in a high-stress environment will inevitably cause stress concentration or energy accumulation at locations with poor rock structure. This is the root cause of rockburst. Even if the geological conditions are very complex, as long as the stress concentration location is determined, the location prone to rockburst can be predicted.

[0003] Existing rockburst prediction only uses sensors to capture microvibration information and cooperates with tunnel stress analysis to predict rockburst. Frequent microvibration information may overlap for a short time. Existing sensors lack the function of filtering and identifying microvibration waves, which leads to two small-energy microvibration events being identified as high-energy microvibration events. Incorrect data information will cause deviations in early warnings. At the same time, the existing rockburst early warning function is mainly achieved by system monitoring and manual verification. The early warning process is inefficient and cannot automatically correct the early warning results. Therefore, adding microvibration filtering functions and automatically verifying early warning results are technical problems that technical personnel in this field need to solve. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a rockburst location early warning method for tunnel excavation in a high-stress environment, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A rockburst location early warning method for tunnel excavation under high stress environment, based on sensors, monitoring host, image terminal and analysis unit, includes the following steps:

[0006] Step 1: Sensor arrangement: Arrange sensors at three sections A, B and C in the tunnel. Arrange at least 2 sensors in each section. Put the sensor into a steel sleeve and screw it into a whole with the sleeve. Place the sleeve in a pre-drilled hole on the rock mass. Use anchoring agent to couple the sleeve and surrounding rock into a whole. The sensor is 2 meters away from the tunnel wall. After the sensor is installed, fix the sensor pigtail with sound insulation cotton at the opening of the sleeve to isolate it from noise. Add a sound insulation cover to the outermost layer of the sleeve for secondary noise isolation.

[0007] Step 2: Data preprocessing: the sensor collects micro-vibration information and transmits it to the monitoring host. The monitoring host filters the micro-vibration information to obtain micro-vibration events, and records the energy Ep, occurrence location and occurrence time of the micro-vibration events;

[0008] Step 3: The monitoring host establishes a spatial coordinate system with the tunnel face D as the starting point. The unit of the spatial coordinate system is meter. The tunnel face D at the initial position is marked as X0, and the A section can be represented as X0+70. The monitoring host classifies the microvibration events according to the energy size and records the corresponding occurrence location and time of the event. The microvibration events are divided into large-energy microvibration hw and small-energy microvibration lw;

[0009] Step 4: Micro-vibration axial distribution characteristics. The monitoring host transmits the micro-vibration event to the analysis unit. The analysis unit distributes the micro-vibration event along the axis perpendicular to the three sections A, B and C according to the occurrence location to obtain the axial distribution characteristics. The diameter of the scattered points represents the energy of the micro-vibration event.

[0010] Step 5: Circumferential distribution characteristics of microvibration: the analysis unit distributes the microvibration events in the circumferential direction parallel to the three sections A, B and C according to the occurrence location to obtain the circumferential distribution characteristics;

[0011] Step 6: Rockburst warning. Rockburst is divided into strain-type rockburst and structural surface rockburst. The analysis unit executes the rockburst warning program according to the rockburst type to obtain the warning result. The on-site personnel view the warning result through the image terminal.

[0012] Step 7: Actual verification. On-site personnel feed back the actual rockburst location in the tunnel to the analysis unit. If the actual rockburst location falls within the warning range for three consecutive times, the feedback result is positive feedback, indicating that the analysis unit's warning result algorithm is gradually converging. At this time, the analysis unit reduces the warning range by 1%. If the actual rockburst location exceeds the warning result range, the feedback result is negative feedback, indicating that the analysis unit's warning result algorithm is biased and needs to be weighted. At this time, the analysis unit corrects the analysis process of the warning result.

[0013] Furthermore, the monitoring host filtering micro-vibration information specifically includes the following steps:

[0014] Step 201: The monitoring host defines the location where the micro-vibration event occurs as tA, tB, and tC, and defines the interval between sections A, B, and C as Δd. The monitoring host calculates the time when the sensor on section A receives the micro-vibration information according to the formula: Calculate the distance Xpd from position P to tunnel face D, where v is the P wave velocity of microvibration, and the specific value is determined according to the rock density of the tunnel. The distance Xpd is the occurrence position of position P relative to tunnel face D.

[0015] Step 202: The monitoring host establishes a micro-vibration waveform diagram, wherein the ordinate of the micro-vibration waveform diagram is the vibration amplitude, and the abscissa is the time. The monitoring host inputs the micro-vibration information obtained by the sensor into the micro-vibration waveform diagram to obtain a micro-vibration curve;

[0016] Step 203: When the micro-vibration information is in a non-superposition state, the monitoring host normalizes all micro-vibration curves, extracts the time point of each peak and trough from the micro-vibration curve, the time point of the peak is t1, and the time point of the trough is t2. The monitoring host subtracts the peak time point t1 from all the trough time points t2 of the micro-vibration curve to obtain the peak-to-trough time difference t3. The monitoring host averages all the peak-to-trough time differences t3 in the micro-vibration curve and multiplies them by two to obtain the vibration period t4.

[0017] Step 204: The monitoring host moves the starting point of the vibration cycle t4 to the first peak of the micro-vibration curve. The monitoring host records the amplitude of the micro-vibration curve at the starting point of the vibration cycle t4 as A1, and records the amplitude of the micro-vibration curve at the end point of the vibration cycle t4 as A2. The monitoring host calculates the amplitude of the micro-vibration curve according to the formula Calculate the attenuation coefficient k1 of the first vibration cycle t4 of the micro-vibration curve, the monitoring host repeatedly calculates the attenuation coefficient k1 of each subsequent vibration cycle t4, when the amplitude A2 at the end point of the vibration cycle t4 multiplied by the square of k1 is less than the amplitude A1 at the starting point of the first vibration cycle t4, the monitoring host stops repeating the calculation, the monitoring host records the time end from the first vibration cycle t4 to the last vibration cycle t4 where the calculation is stopped as the standard interval, the monitoring host averages all the attenuation coefficients k1 to obtain the attenuation coefficient k2 of the micro-vibration curve, the monitoring host subsequently calculates the attenuation coefficient k3 of each micro-vibration curve, when the value of the attenuation coefficient k3 is within the range of the attenuation coefficient k2±20%, the monitoring host marks the micro-vibration curve as a micro-vibration event, and the occurrence time is the average value of tA, tB and tC;

[0018] Step 205: When the microvibration information is in a superposition state, the monitoring host divides the starting point of the microvibration curve with reference to the standard interval to obtain a superposition interval. The monitoring host marks the normalized microvibration curve in step 203 as a reference curve. The monitoring host adds the amplitudes of the two reference curves to obtain a double superposition curve qx1. The monitoring host compares the double superposition curve with the microvibration curve in the superposition interval to calculate the similarity f1. The monitoring host moves one of the reference curves of the double superposition curve backward. A new double superposition curve qx2 is generated in the interval, and the similarity f2 between the newly generated double superposition curve qx2 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx3 is generated in the interval, and the similarity f3 between the newly generated double superposition curve qx3 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx4 is generated in the interval, and the similarity f4 between the newly generated double superposition curve qx4 and the microvibration curve in the superposition interval is calculated. The similarities f1, f2, f3 and f4 are all calculated using the root mean square error RMSE;

[0019] Step 206: The monitoring host takes the double superposition curve corresponding to the maximum value from the similarities f1, f2, f3 and f4, and the monitoring host subtracts the micro-vibration curve in the superposition interval from one of the reference curves in the double superposition curve to obtain the first separation curve. The superposition state refers to the size of the interval of relative movement of the two reference curves, for example interval, the monitoring host then subtracts the microvibration curve in the superposition interval from another reference curve in the double superposition curve to obtain a second separation curve, the monitoring host respectively marks the two separation curves as two microvibration events, the monitoring host defines the time difference of the first peaks of the two separation curves as the occurrence time difference t5 of the two superposition microvibration events, the occurrence time of the first microvibration event is the average of tA, tB and tC, and the occurrence time of the first microvibration event plus the occurrence time difference t5 is the occurrence time of the second microvibration event;

[0020] Step 207: The monitoring host marks the value of the first peak of each micro-vibration curve as the energy Ep of the micro-vibration curve.

[0021] Furthermore, the rockburst early warning procedure specifically includes the following steps:

[0022] The blast pit of a typical strain-type rockburst is shallow and pit-shaped, with a depth of 0.5-0.7 meters. There are still rock pieces that have not separated from the parent body on the surface of the blast pit. There is no obvious structural surface in the blast pit and its surroundings. It is mainly controlled by stress. On the eve of the strain-type rockburst, microvibration events will gradually increase, accompanied by large-energy microvibrations hw. Large-energy microvibrations hw occur with rockbursts and do not appear before the rockburst. Therefore, before the rockburst, the warning level of the analysis unit is mainly slight, which makes the warning level half a level lower. Many microvibration events occurred during the process, but they were generally small-energy microvibrations lw, with low energy Ep, and there was no risk of high-level rockburst. The location range was between X0+259.6-244.7. The distribution of microvibration events was relatively scattered as a whole, with only small-scale events clustering, and the magnitude of the clustered events was small.

[0023] During the monitoring process, it was found that the occurrence of small-energy microvibration lw can reveal the direction of stress transfer or the location of stress concentration. When large-energy microvibration hw occurs in the concentrated area of ​​small-energy microvibration lw, the risk of high-level rock burst will increase.

[0024] At the same time, it was also found that in structural surface rockbursts, the location where rockbursts have occurred may accumulate stress again and become a potential rockburst point under the continuous disturbance of on-site construction, and there is a risk of secondary rockbursts. There may be deviations between the stress concentration point and the location of the rockburst, and they may not be completely consistent. The early warning of structural surface rockbursts is more difficult.

[0025] The early warning results include the early warning range of strain-type rockburst and the early warning range of structural surface rockburst;

[0026] Step 601: The analysis unit counts the number of microvibration events n1, calculates the axial distance d1 between each microvibration event according to the axial distribution characteristics, calculates the circumferential distance d2 between each microvibration event according to the circumferential distribution characteristics, calculates the energy average value Epa of all microvibration events, and marks the microvibration events with energy Ep greater than or equal to 3Epa as large-energy microvibration hw, and the remaining microvibration events as small-energy microvibration lw;

[0027] Step 602: The analysis unit adds all axial distances d1 to obtain d1p, adds all circumferential distances d2 to obtain d2p, calculates the axial distance d3 between the small energy micro-vibrations lw, calculates the circumferential distance d4 between the small energy micro-vibrations lw, counts the number n2 of small energy micro-vibrations lw, adds all axial distances d3 to obtain d3p, adds all circumferential distances d4 to obtain d4p, and calculates the number n2 of small energy micro-vibrations lw according to the formula The aggregation degree g(lw) of the small energy micro-vibration lw is calculated. The smaller the aggregation degree g(lw) value is, the more the small energy micro-vibration lw is aggregated. When the aggregation degree g(lw) is less than or equal to 0.2, step 603 is executed;

[0028] Step 603: The analysis unit presets and initializes the strain weight index yb, the initial value of which is 2. The analysis unit counts the number of occurrences of large-energy micro-vibrations hw, n3. The analysis unit calculates the average axial distance d5 between all large-energy micro-vibrations hw, and the average circumferential distance d6 between all large-energy micro-vibrations hw. The analysis unit calculates the average axial distance d6 between all large-energy micro-vibrations hw according to the formula Calculate the occurrence position Pyb of the strain-type rock burst, and the analysis unit marks the range with a radius of 1 meter with the occurrence position Pyb as the center as the warning range of the strain-type rock burst;

[0029] Step 604: The analysis unit presets and initializes the structural surface weight index jgm, the initial value of which is 2. The analysis unit calculates the structural surface weight index jgm according to the formula Calculate the occurrence position Pjgm of the structural surface rock burst, and the analysis unit marks the range of 2 meters with the occurrence position Pjgm as the center as the warning range of the structural surface rock burst;

[0030] The analysis unit corrects the analysis process of the early warning result by specifically including the following steps:

[0031] Step 701: The camera of the image terminal obtains the image information of the rockburst and displays it on the touch screen of the image terminal. The on-site personnel manually mark the actual position Pyb2 in the image information. The image terminal calculates the error distance Δyb between the actual position Pyb2 and the occurrence position Pyb. The error distance Δyb is in meters. The image terminal transmits the error distance Δyb to the analysis unit. The analysis unit calculates the error distance Δyb according to the formula Recalculate the value of the strain weight index yb, and then calculate the occurrence location Pyb of the strain-type rockburst based on the changed strain weight index yb;

[0032] Step 702: The on-site personnel manually mark the actual position Pjgm2 in the image information, and the image terminal calculates the error distance Δjgm between the actual position Pjgm2 and the occurrence position Pjgm. The error distance Δjgm is in meters. The image terminal transmits the error distance Δjgm to the analysis unit. The analysis unit calculates the error distance Δjgm according to the formula The value of the strain weight index jgm is recalculated, and then the occurrence location Pjgm of the strain-type rockburst is calculated based on the changed structural surface weight index jgm.

[0033] Further, the intervals between the three sections A, B and C in the tunnel are equal to 30 meters, the distance between the section A and the working face D is greater than the interval between the three sections A, B and C, which is 70 meters, the sensors on the section A are respectively located at the right arch waist a1, the left arch waist a2 and the right arch shoulder a3, the sensors on the section B are respectively located at the right arch waist b1 and the left arch waist b2, and the sensors on the section C are respectively located at the right arch waist c1, the left arch waist c2 and the left arch shoulder c3;

[0034] The data acquisition of the sensors of the three sections A, B and C is based on the OF-MMS model all-fiber microseismic monitoring system. The communication optical fiber of the all-fiber microseismic monitoring system has a total of 12 channels, of which 8 channels are monitored and the remaining 4 channels are used as backup channels. The all-fiber microseismic monitoring system includes a monitoring host, a sensor, a wireless module, an image terminal, an analysis unit and an uninterruptible power supply. The uninterruptible power supply is used to power the monitoring host and the analysis unit. The output end of the sensor is connected to the input end of the monitoring host through the communication optical fiber. The port of the monitoring host establishes data communication with the port of the image terminal through the wireless module. The port of the monitoring host is connected to the port of the analysis unit. The monitoring host is used to calculate the occurrence location and time of microvibration. The analysis unit is used to analyze the location and time of rock burst and give early warning. The analysis unit transmits the warning results to the image terminal through the monitoring host and the wireless module for on-site personnel to watch. The image terminal is used to actually verify the warning results of the analysis unit and feedback the verification results to the analysis unit through the wireless module and the monitoring host in turn.

[0035] The sensor is an accelerometer with a voltage sensitivity of 40V / g, an observable frequency band of 20Hz-5kHz, a sampling accuracy of 16 bits, and a sampling frequency of 32KHz. Actual on-site tests show that when the P-wave velocity is set to 5420m / s, the average positioning error is 4.2 meters, which meets the on-site positioning requirements.

[0036] Furthermore, the image terminal is a mobile communication terminal, which has a camera and a touch screen integrated therein. The camera is used to obtain image information in the tunnel, and on-site personnel can manually determine the actual location of the rockburst using the touch screen. The touch screen is also used to display the warning results. Any mobile communication terminal on the market that can meet the image acquisition and data communication functions can be used, and the specific model is not limited here.

[0037] The present invention has the following beneficial effects:

[0038] 1. By filtering the microvibration information obtained by the sensor, the interference of superimposed microvibration information is removed, and the obtained microvibration event data is more real and effective, providing a data basis for subsequent rock burst warning.

[0039] 2. Automatic verification of early warning results through image terminals can continuously narrow the scope of early warning, improve the accuracy of early warning results, correct deviations in early warning results, and automatically optimize the early warning process.

[0040] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 This is a flow chart of a rockburst location early warning method for tunnel excavation in a high stress environment of the present invention;

[0043] Figure 2 This is a schematic diagram of the installation of the sensor of the present invention;

[0044] Figure 3 This is a block diagram of the all-fiber microseismic monitoring system of the present invention;

[0045] Figure 4 is the axial distribution diagram of micro-vibration events;

[0046] Figure 5 This is the annular distribution diagram of microvibration events. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0048] See also Figure 1-5 The present invention provides a technical solution: a rockburst location early warning method for tunnel excavation in a high-stress environment, which is implemented based on a sensor, a monitoring host, an image terminal and an analysis unit, such as Figure 1 As shown, the following steps are included:

[0049] Step 1: Sensor arrangement: Arrange sensors at three sections A, B and C in the tunnel. Arrange at least 2 sensors in each section. Put the sensor into a steel sleeve and screw it into a whole with the sleeve. Place the sleeve in a pre-drilled hole on the rock mass. Use anchoring agent to couple the sleeve and surrounding rock into a whole. The sensor is 2 meters away from the tunnel wall. After the sensor is installed, fix the sensor pigtail with sound insulation cotton at the opening of the sleeve to isolate it from noise. Add a sound insulation cover to the outermost layer of the sleeve for secondary noise isolation.

[0050] Step 2: Data preprocessing: the sensor collects micro-vibration information and transmits it to the monitoring host. The monitoring host filters the micro-vibration information to obtain micro-vibration events, and records the energy Ep, occurrence location and occurrence time of the micro-vibration events;

[0051] Step 3: The monitoring host establishes a spatial coordinate system with the tunnel face D as the starting point. The unit of the spatial coordinate system is meter. The tunnel face D at the initial position is marked as X0, and the A section can be represented as X0+70. The monitoring host classifies the microvibration events according to the energy size and records the corresponding occurrence location and time of the event. The microvibration events are divided into large-energy microvibration hw and small-energy microvibration lw;

[0052] Step 4: Micro-vibration axial distribution characteristics. The monitoring host transmits the micro-vibration event to the analysis unit. The analysis unit distributes the micro-vibration event along the axis perpendicular to the three sections A, B and C according to the occurrence location to obtain the axial distribution characteristics, such as Figure 4 As shown in the figure, each scattered point represents a micro-vibration event in sequence, and the diameter of the scattered point represents the energy of the micro-vibration event. X0+259.6 represents the tunnel section at a distance of 259.6 meters from the initial position of X0 of the tunnel face D, and X0+244.7 represents the tunnel section at a distance of 259.6 meters from the initial position of X0 of the tunnel face D. Figure 4 The medium-to-large energy microvibration hw1 occurs on the section at position X0+244.7;

[0053] Step 5: Circumferential distribution characteristics of microvibration: the analysis unit distributes the microvibration events in the circumferential direction parallel to the three sections A, B and C according to the occurrence location to obtain the circumferential distribution characteristics, such as Figure 5 As shown in the figure, each scattered point in the figure represents a micro-vibration event in sequence, and the diameter of the scattered point represents the energy of the micro-vibration event. Figure 5 The medium-to-large energy microvibration hw2 occurs to the right of the tunnel vault;

[0054] Step 6: Rockburst warning. Rockburst is divided into strain-type rockburst and structural surface rockburst. The analysis unit executes the rockburst warning program according to the rockburst type to obtain the warning result. The on-site personnel view the warning result through the image terminal.

[0055] Step 7: Actual verification. On-site personnel feed back the actual rockburst location in the tunnel to the analysis unit. If the actual rockburst location falls within the warning range for three consecutive times, the feedback result is positive feedback, indicating that the analysis unit's warning result algorithm is gradually converging. At this time, the analysis unit reduces the warning range by 1%. If the actual rockburst location exceeds the warning result range, the feedback result is negative feedback, indicating that the analysis unit's warning result algorithm is biased and needs to be weighted. At this time, the analysis unit corrects the analysis process of the warning result.

[0056] The monitoring host filtering micro-vibration information specifically includes the following steps:

[0057] Step 201: The monitoring host defines the location where the micro-vibration event occurs as tA, tB, and tC, and defines the interval between sections A, B, and C as Δd. The monitoring host calculates the time when the sensor on section A receives the micro-vibration information according to the formula: Calculate the distance Xpd from position P to tunnel face D, where v is the P wave velocity of microvibration, and the specific value is determined according to the rock density of the tunnel. The distance Xpd is the occurrence position of position P relative to tunnel face D.

[0058] Step 202: The monitoring host establishes a micro-vibration waveform diagram, wherein the ordinate of the micro-vibration waveform diagram is the vibration amplitude, and the abscissa is the time. The monitoring host inputs the micro-vibration information obtained by the sensor into the micro-vibration waveform diagram to obtain a micro-vibration curve;

[0059] Step 203: When the micro-vibration information is in a non-superposition state, the monitoring host normalizes all micro-vibration curves, extracts the time point of each peak and trough from the micro-vibration curve, the time point of the peak is t1, and the time point of the trough is t2. The monitoring host subtracts the peak time point t1 from all the trough time points t2 of the micro-vibration curve to obtain the peak-to-trough time difference t3. The monitoring host averages all the peak-to-trough time differences t3 in the micro-vibration curve and multiplies them by two to obtain the vibration period t4.

[0060] Step 204: The monitoring host moves the starting point of the vibration cycle t4 to the first peak of the micro-vibration curve. The monitoring host records the amplitude of the micro-vibration curve at the starting point of the vibration cycle t4 as A1, and records the amplitude of the micro-vibration curve at the end point of the vibration cycle t4 as A2. The monitoring host calculates the amplitude of the micro-vibration curve according to the formula Calculate the attenuation coefficient k1 of the first vibration cycle t4 of the micro-vibration curve, the monitoring host repeatedly calculates the attenuation coefficient k1 of each subsequent vibration cycle t4, when the amplitude A2 at the end point of the vibration cycle t4 multiplied by the square of k1 is less than the amplitude A1 at the starting point of the first vibration cycle t4, the monitoring host stops repeating the calculation, the monitoring host records the time end from the first vibration cycle t4 to the last vibration cycle t4 where the calculation is stopped as the standard interval, the monitoring host averages all the attenuation coefficients k1 to obtain the attenuation coefficient k2 of the micro-vibration curve, the monitoring host subsequently calculates the attenuation coefficient k3 of each micro-vibration curve, when the value of the attenuation coefficient k3 is within the range of the attenuation coefficient k2±20%, the monitoring host marks the micro-vibration curve as a micro-vibration event, and the occurrence time is the average value of tA, tB and tC;

[0061] Step 205: When the microvibration information is in a superposition state, the monitoring host divides the starting point of the microvibration curve with reference to the standard interval to obtain a superposition interval. The monitoring host marks the normalized microvibration curve in step 203 as a reference curve. The monitoring host adds the amplitudes of the two reference curves to obtain a double superposition curve qx1. The monitoring host compares the double superposition curve with the microvibration curve in the superposition interval to calculate the similarity f1. The monitoring host moves one of the reference curves of the double superposition curve backward. A new double superposition curve qx2 is generated in the interval, and the similarity f2 between the newly generated double superposition curve qx2 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx3 is generated in the interval, and the similarity f3 between the newly generated double superposition curve qx3 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx4 is generated in the interval, and the similarity f4 between the newly generated double superposition curve qx4 and the microvibration curve in the superposition interval is calculated. The similarities f1, f2, f3 and f4 are all calculated using the root mean square error RMSE;

[0062] Step 206: The monitoring host takes the double superposition curve corresponding to the maximum value from the similarities f1, f2, f3 and f4, and the monitoring host subtracts the micro-vibration curve in the superposition interval from one of the reference curves in the double superposition curve to obtain the first separation curve. The superposition state refers to the size of the interval of relative movement of the two reference curves, for example interval, the monitoring host then subtracts the microvibration curve in the superposition interval from another reference curve in the double superposition curve to obtain a second separation curve, the monitoring host respectively marks the two separation curves as two microvibration events, the monitoring host defines the time difference of the first peaks of the two separation curves as the occurrence time difference t5 of the two superposition microvibration events, the occurrence time of the first microvibration event is the average of tA, tB and tC, and the occurrence time of the first microvibration event plus the occurrence time difference t5 is the occurrence time of the second microvibration event;

[0063] Step 207: The monitoring host marks the value of the first peak of each micro-vibration curve as the energy Ep of the micro-vibration curve.

[0064] The rockburst early warning procedure specifically includes the following steps:

[0065] The blast crater of a typical strain-type rockburst is shallow and pit-shaped, with a depth of 0.5-0.7 meters. There are still rock pieces that have not separated from the parent body on the surface of the blast crater. There is no obvious structural surface in the blast crater and its surroundings. It is mainly controlled by stress. Before the occurrence of a strain-type rockburst, microvibration events will gradually increase, accompanied by large-energy microvibrations hw. Large-energy microvibrations hw occur with rockbursts and do not appear before the occurrence of rockbursts. Therefore, before the occurrence of the rockburst, the warning level of the analysis unit is mainly slight, which makes the warning level half a level lower. Figure 5 As shown in the figure, many microvibration events occurred on the way, but they were generally small-energy microvibrations lw, with low energy Ep, and no risk of high-level rock burst. The location range was between X0+259.6-244.7, and the distribution of microvibration events was relatively scattered as a whole, with only small-scale event aggregation, and the magnitude of the aggregated events was small;

[0066] During the monitoring process, it was found that the occurrence of small-energy microvibration lw can reveal the direction of stress transfer or the location of stress concentration. When large-energy microvibration hw occurs in the concentrated area of ​​small-energy microvibration lw, the risk of high-level rock burst will increase.

[0067] At the same time, it was also found that in structural surface rockbursts, the location where rockbursts have occurred may accumulate stress again and become a potential rockburst point under the continuous disturbance of on-site construction, and there is a risk of secondary rockbursts. There may be deviations between the stress concentration point and the location of the rockburst, and they may not be completely consistent. The early warning of structural surface rockbursts is more difficult.

[0068] The early warning results include the early warning range of strain-type rockburst and the early warning range of structural surface rockburst;

[0069] Step 601: The analysis unit counts the number of microvibration events n1, calculates the axial distance d1 between each microvibration event according to the axial distribution characteristics, calculates the circumferential distance d2 between each microvibration event according to the circumferential distribution characteristics, calculates the energy average value Epa of all microvibration events, and marks the microvibration events with energy Ep greater than or equal to 3Epa as large-energy microvibration hw, and the remaining microvibration events as small-energy microvibration lw;

[0070] Step 602: The analysis unit adds all axial distances d1 to obtain d1p, adds all circumferential distances d2 to obtain d2p, calculates the axial distance d3 between the small energy micro-vibrations lw, calculates the circumferential distance d4 between the small energy micro-vibrations lw, counts the number n2 of small energy micro-vibrations lw, adds all axial distances d3 to obtain d3p, adds all circumferential distances d4 to obtain d4p, and calculates the number n2 of small energy micro-vibrations lw according to the formula The aggregation degree g(lw) of the small energy micro-vibration lw is calculated. The smaller the aggregation degree g(lw) value is, the more the small energy micro-vibration lw is aggregated. When the aggregation degree g(lw) is less than or equal to 0.2, step 603 is executed;

[0071] Step 603: The analysis unit presets and initializes the strain weight index yb, the initial value of which is 2. The analysis unit counts the number of occurrences of large-energy micro-vibrations hw, n3. The analysis unit calculates the average axial distance d5 between all large-energy micro-vibrations hw, and the average circumferential distance d6 between all large-energy micro-vibrations hw. The analysis unit calculates the average axial distance d6 between all large-energy micro-vibrations hw according to the formula Calculate the occurrence position Pyb of the strain-type rock burst, and the analysis unit marks the range with a radius of 1 meter with the occurrence position Pyb as the center as the warning range of the strain-type rock burst;

[0072] Step 604: The analysis unit presets and initializes the structural surface weight index jgm, the initial value of which is 2. The analysis unit calculates the structural surface weight index jgm according to the formula Calculate the occurrence position Pjgm of the structural surface rock burst, and the analysis unit marks the range of 2 meters with the occurrence position Pjgm as the center as the warning range of the structural surface rock burst;

[0073] The analysis unit corrects the analysis process of the early warning result by specifically including the following steps:

[0074] Step 701: The camera of the image terminal obtains the image information of the rockburst and displays it on the touch screen of the image terminal. The on-site personnel manually mark the actual position Pyb2 in the image information. The image terminal calculates the error distance Δyb between the actual position Pyb2 and the occurrence position Pyb. The error distance Δyb is in meters. The image terminal transmits the error distance Δyb to the analysis unit. The analysis unit calculates the error distance Δyb according to the formula Recalculate the value of the strain weight index yb, and then calculate the occurrence location Pyb of the strain-type rockburst based on the changed strain weight index yb;

[0075] Step 702: The on-site personnel manually mark the actual position Pjgm2 in the image information, and the image terminal calculates the error distance Δjgm between the actual position Pjgm2 and the occurrence position Pjgm. The error distance Δjgm is in meters. The image terminal transmits the error distance Δjgm to the analysis unit. The analysis unit calculates the error distance Δjgm according to the formula The value of the strain weight index jgm is recalculated, and then the occurrence location Pjgm of the strain-type rockburst is calculated based on the changed structural surface weight index jgm.

[0076] Among them, Figure 2As shown, the intervals between the three sections A, B and C in the tunnel are equal to 30 meters, the distance between the section A and the working face D is greater than the interval between the three sections A, B and C, which is 70 meters, the sensors on the section A are respectively located at the right arch waist a1, the left arch waist a2 and the right arch shoulder a3, the sensors on the section B are respectively located at the right arch waist b1 and the left arch waist b2, and the sensors on the section C are respectively located at the right arch waist c1, the left arch waist c2 and the left arch shoulder c3;

[0077] The data collection of the three section sensors A, B and C is based on the OF-MMS model all-fiber microseismic monitoring system. The communication fiber of the all-fiber microseismic monitoring system has a total of 12 channels, of which 8 channels are used for monitoring and the remaining 4 channels are used as backup channels. Figure 3 As shown, the all-fiber microseismic monitoring system includes a monitoring host, a sensor, a wireless module, an image terminal, an analysis unit and an uninterruptible power supply. The uninterruptible power supply is used to power the monitoring host and the analysis unit. The output end of the sensor is connected to the input end of the monitoring host through a communication optical fiber. The port of the monitoring host establishes data communication with the port of the image terminal through the wireless module. The port of the monitoring host is connected to the port of the analysis unit. The monitoring host is used to calculate the occurrence position and occurrence time of microvibration. The analysis unit is used to analyze the location and occurrence time of rock burst and give early warning. The analysis unit transmits the warning result to the image terminal through the monitoring host and the wireless module in turn for on-site personnel to watch. The image terminal is used to actually verify the warning result of the analysis unit and feed the verification result back to the analysis unit through the wireless module and the monitoring host in turn.

[0078] The sensor is an accelerometer with a voltage sensitivity of 40V / g, an observable frequency band of 20Hz-5kHz, a sampling accuracy of 16 bits, and a sampling frequency of 32KHz. Actual on-site tests show that when the P-wave velocity is set to 5420m / s, the average positioning error is 4.2 meters, which meets the on-site positioning requirements.

[0079] Among them, the image terminal is a mobile communication terminal, which integrates a camera and a touch screen. The camera is used to obtain image information in the tunnel. On-site personnel can use the touch screen to manually determine the actual location of the rock burst. The touch screen is also used to display the warning results. Any mobile communication terminal on the market that can meet the image acquisition and data communication functions can be used, and the specific model is not limited here.

[0080] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A rockburst location early warning method for tunnel excavation in a high stress environment, characterized by: Based on sensors, monitoring host, image terminal and analysis unit, it includes the following steps: Step 1: Sensor arrangement: Arrange sensors at three sections A, B and C in the tunnel. Arrange several sensors at each section. Put the sensors into a sleeve. Place the sleeve in a pre-drilled hole on the rock mass. Combine the sleeve with the surrounding rock to form a whole. Fix the sensor pigtail with sound insulation cotton at the opening of the sleeve. Add a sound insulation cover to the outermost layer of the sleeve. Step 2: Data preprocessing: the sensor collects micro-vibration information and transmits it to the monitoring host. The monitoring host filters the micro-vibration information to obtain micro-vibration events, and records the energy Ep, occurrence location and occurrence time of the micro-vibration events; Step 3: The monitoring host establishes a spatial coordinate system with the tunnel face D as the starting point. The tunnel face D at the initial position is marked as X0. The monitoring host classifies the microvibration events according to the energy size and records the corresponding occurrence location and time of the event. The microvibration events are divided into large-energy microvibration hw and small-energy microvibration lw; Step 4: Micro-vibration axial distribution characteristics. The monitoring host transmits the micro-vibration event to the analysis unit. The analysis unit distributes the micro-vibration event along the axis perpendicular to the three sections A, B and C according to the occurrence location to obtain the axial distribution characteristics. The diameter of the scattered points represents the energy of the micro-vibration event. Step 5: Circumferential distribution characteristics of microvibration: the analysis unit distributes the microvibration events in the circumferential direction parallel to the three sections A, B and C according to the occurrence location to obtain the circumferential distribution characteristics; Step 6: Rockburst warning. Rockburst is divided into strain-type rockburst and structural surface rockburst. The analysis unit executes the rockburst warning program according to the rockburst type to obtain the warning result. The on-site personnel view the warning result through the image terminal. Step 7: Actual verification: On-site personnel will feed back the actual rockburst location in the tunnel to the analysis unit. If the actual rockburst location falls within the warning range for three consecutive times, the analysis unit will reduce the warning range by 1%. If the actual rockburst location exceeds the warning result range, the analysis unit will correct the analysis process of the warning result.

2. The rockburst location early warning method for tunnel excavation in a high stress environment according to claim 1, characterized in that: The monitoring host filters the micro-vibration information in the following steps: Step 201: The monitoring host defines the location where the micro-vibration event occurs as follows: the time when the sensor on section A receives the micro-vibration information is marked as tA, the time when the sensor on section B receives the micro-vibration information is marked as tB, the time when the sensor on section C receives the micro-vibration information is marked as tC, and the interval distance between sections A, B and C is defined as Δd. According to the formula Calculate the distance Xpd from position P to tunnel face D, where v is the P wave velocity of microvibration, and the specific value is determined according to the rock density of the tunnel. The distance Xpd is the occurrence position of position P relative to tunnel face D. Step 202: The monitoring host establishes a micro-vibration waveform diagram, wherein the ordinate of the micro-vibration waveform diagram is the vibration amplitude, and the abscissa is the time, and the micro-vibration information obtained by the sensor is input into the micro-vibration waveform diagram to obtain a micro-vibration curve; Step 203: When the micro-vibration information is in a non-superposition state, the monitoring host normalizes all micro-vibration curves, extracts the time points of each peak and trough from the micro-vibration curve, the time point of the peak is t1, the time point of the trough is t2, and the peak time point t1 is subtracted from all the trough time points t2 of the micro-vibration curve to obtain the peak-to-trough time difference t3, and the average of all the peak-to-trough time differences t3 in the micro-vibration curve is multiplied by two to obtain the vibration period t4; Step 204: The monitoring host moves the starting point of the vibration cycle t4 to the first peak of the micro-vibration curve, records the amplitude of the micro-vibration curve at the starting point of the vibration cycle t4 as A1, and records the amplitude of the micro-vibration curve at the end point of the vibration cycle t4 as A2. According to the formula Calculate the attenuation coefficient k1 of the first vibration cycle t4 of the micro-vibration curve, and repeatedly calculate the attenuation coefficient k1 of each subsequent vibration cycle t4. When the amplitude A2 at the end point of the vibration cycle t4 multiplied by the square of k1 is less than the amplitude A1 at the starting point of the first vibration cycle t4, stop the repeated calculation, and record the time from the first vibration cycle t4 to the last vibration cycle t4 where the calculation is stopped as the standard interval. Average all the attenuation coefficients k1 to obtain the attenuation coefficient k2 of the micro-vibration curve. Calculate the attenuation coefficient k3 of each micro-vibration curve subsequently. When the value of the attenuation coefficient k3 is within the range of the attenuation coefficient k2±20%, mark the micro-vibration curve as a micro-vibration event, and the occurrence time is the average value of tA, tB and tC; Step 205: When the microvibration information is in a superposition state, the monitoring host divides the starting point of the microvibration curve with the standard interval as a reference to obtain a superposition interval, marks the normalized microvibration curve in step 203 as a reference curve, adds the amplitudes of the two reference curves to obtain a double superposition curve qx1, compares the double superposition curve with the microvibration curve in the superposition interval to calculate the similarity f1, and moves one of the reference curves of the double superposition curve backward A new double superposition curve qx2 is generated in the interval, and the similarity f2 between the newly generated double superposition curve qx2 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx3 is generated in the interval, and the similarity f3 between the newly generated double superposition curve qx3 and the microvibration curve in the superposition interval is calculated. One of the reference curves of the double superposition curve is moved backward. A new double superposition curve qx4 is generated in the interval, and the similarity f4 between the newly generated double superposition curve qx4 and the microvibration curve in the superposition interval is calculated. The similarities f1, f2, f3 and f4 are all calculated using the root mean square error RMSE; Step 206: The monitoring host takes the double superposition curve corresponding to the maximum value from the similarities f1, f2, f3 and f4, subtracts the microvibration curve in the superposition interval from one of the reference curves in the double superposition curves to obtain the first separation curve, and then subtracts the microvibration curve in the superposition interval from the other reference curve in the double superposition curve to obtain the second separation curve, and marks the two separation curves as two microvibration events respectively, and defines the time difference of the first peaks of the two separation curves as the occurrence time difference t5 of the two superposition microvibration events, the occurrence time of the first microvibration event is the average value of tA, tB and tC, and the occurrence time of the first microvibration event plus the occurrence time difference t5 is the occurrence time of the second microvibration event; Step 207: The monitoring host marks the value of the first peak of each micro-vibration curve as the energy Ep of the micro-vibration curve.

3. The rockburst location early warning method for tunnel excavation in a high stress environment according to claim 1, characterized in that: The rockburst early warning procedure specifically includes the following steps: The early warning results include the early warning range of strain-type rockburst and the early warning range of structural surface rockburst; Step 601: The analysis unit counts the number of microvibration events n1, calculates the axial distance d1 between each microvibration event according to the axial distribution characteristics, calculates the circumferential distance d2 between each microvibration event according to the circumferential distribution characteristics, calculates the energy average value Epa of all microvibration events, marks the microvibration events with energy Ep greater than or equal to 3Epa as large-energy microvibration hw, and marks the remaining microvibration events as small-energy microvibration lw; Step 602: The analysis unit adds all axial distances d1 to obtain d1p, adds all circumferential distances d2 to obtain d2p, calculates the axial distance d3 between the small energy micro-vibrations lw, calculates the circumferential distance d4 between the small energy micro-vibrations lw, counts the number n2 of small energy micro-vibrations lw, adds all axial distances d3 to obtain d3p, adds all circumferential distances d4 to obtain d4p, and calculates the number n2 of small energy micro-vibrations lw according to the formula The concentration degree g(lw) of the small energy microvibration lw is calculated. When the concentration degree g(lw) is less than or equal to 0.2, step 603 is executed; Step 603: The analysis unit presets and initializes the strain weight index yb, counts the number of occurrences of large-energy micro-vibrations hw n3, calculates the average axial distance d5 between all large-energy micro-vibrations hw, calculates the average circumferential distance d6 between all large-energy micro-vibrations hw, and calculates the average axial distance d6 between all large-energy micro-vibrations hw according to the formula Calculate the occurrence position Pyb of strain-type rock burst, and mark the range with a radius of 1 meter with the occurrence position Pyb as the center as the warning range of strain-type rock burst; Step 604: The analysis unit presets and initializes the structural surface weight index jgm according to the formula Calculate the occurrence position Pjgm of the structural surface rock burst, and mark the range with a radius of 2 meters with the occurrence position Pjgm as the center as the warning range of the structural surface rock burst; The analysis unit corrects the analysis process of the early warning result by specifically including the following steps: Step 701: The camera of the image terminal obtains the image information of the rockburst and displays it on the touch screen of the image terminal. The on-site personnel manually mark the actual position Pyb2 in the image information. The image terminal calculates the error distance Δyb between the actual position Pyb2 and the occurrence position Pyb. The error distance Δyb is in meters. The image terminal transmits the error distance Δyb to the analysis unit. According to the formula Recalculate the value of the strain weight index yb, and then calculate the occurrence location Pyb of the strain-type rockburst based on the changed strain weight index yb; Step 702: The on-site personnel manually mark the actual position Pjgm2 in the image information, and the image terminal calculates the error distance Δjgm between the actual position Pjgm2 and the occurrence position Pjgm. The error distance Δjgm is in meters. The image terminal transmits the error distance Δjgm to the analysis unit. According to the formula The value of the strain weight index jgm is recalculated, and then the occurrence location Pjgm of the strain-type rockburst is calculated based on the changed structural surface weight index jgm.

4. The rockburst location early warning method for tunnel excavation in a high stress environment according to claim 1, characterized in that: The distances between the three sections A, B and C are equal. The distance between the section A and the tunnel face D is greater than the distances between the three sections A, B and C. The sensors on the section A are located at the right arch waist a1, the left arch waist a2 and the right arch shoulder a3, respectively. The sensors on the section B are located at the right arch waist b1 and the left arch waist b2, respectively. The sensors on the section C are located at the right arch waist c1, the left arch waist c2 and the left arch shoulder c3, respectively. The data acquisition of the sensor is based on the OF-MMS model all-fiber microseismic monitoring system. The all-fiber microseismic monitoring system includes a monitoring host, a sensor, a wireless module, an image terminal, an analysis unit and an uninterruptible power supply. The output end of the sensor is connected to the input end of the monitoring host through a communication optical fiber. The port of the monitoring host establishes data communication with the port of the image terminal through the wireless module. The port of the monitoring host is connected to the port of the analysis unit. The monitoring host is used to calculate the occurrence location and time of microvibration. The analysis unit is used to analyze the location and time of rockburst and give early warning. The analysis unit transmits the warning result to the image terminal. The image terminal is used to actually verify the warning result and feed the verification result back to the analysis unit.

5. The rockburst location early warning method for tunnel excavation in a high stress environment according to claim 1, characterized in that: The image terminal is a mobile communication terminal with an integrated camera and touch screen. The camera is used to obtain image information inside the tunnel. On-site personnel can use the touch screen to manually determine the actual location of the rockburst. The touch screen is also used to display the warning results.

Images