A method for quantifying calibration coefficients of acoustic emission / microseismic sensors

Abstract

The present invention provides a method for quantifying calibration coefficients of acoustic emission / microseismic sensors, comprising the following steps: performing a ball drop test and establishing a three-dimensional numerical model of the ball drop test; applying boundary conditions to the established three-dimensional numerical model, and simulating the propagation process of a ball falling, hitting a rock, and exciting stress waves in the rock; respectively extracting the initial amplitude of stress waves at corresponding positions of acoustic emission / microseismic sensors in the ball drop test and numerical simulation results to determine the calibration coefficients of each acoustic emission / microseismic sensor. The present invention combines physical experiments and numerical simulations, and simultaneously solves the attenuation degree of stress waves propagating in rocks and the calibration coefficients of each sensor, eliminating the errors caused by the different coupling effects between the sensor and the rock wall, which is conducive to improving the acquisition accuracy of acoustic emission / microseismic waves, and providing more accurate basic parameters for inverting the size and evolution law of cracks inside rocks.

Description

Technical Field

[0001] The invention belongs to the technical field of acoustic emission / microseismic sensor data calibration, and specifically relates to a method for quantifying calibration coefficients of acoustic emission / microseismic sensors. Background Art

[0002] With the continuous increase in the scale of large-scale civil engineering projects in my country, such as mining projects, nuclear power projects, tunnel projects, etc., the research on rock mechanics theory and the corresponding more sophisticated internal fracture mechanism of rock mass is becoming more and more important. Acoustic emission is a phenomenon in which the strain energy stored in rock materials is rapidly released in the form of stress waves due to its own deformation and fracture under external loads. The acoustic emission phenomenon reflects the initiation, expansion and penetration process of rock microcracks. It is an important monitoring method for exploring the fracture mechanism of rock. The use of acoustic emission waveform information is of great significance for in-depth research and understanding of rock failure characteristics and damage evolution.

[0003] With the continuous development of rock mechanics, the research on the internal fracture mechanism of rocks has gradually deepened. In recent years, more and more scholars have introduced the moment tensor theory into the research on the source mechanism of internal fracture of rocks, in order to obtain the internal damage evolution process and law of rock crack initiation, expansion, penetration and interaction and its corresponding relationship with stress, so as to be applied to engineering to guide production. The accuracy of waveform information acquisition is a necessary condition for the rationality of the moment tensor inversion method. The initial motion amplitude is the initial parameter of the moment tensor inversion, and its accuracy directly affects the inversion result.

[0004] However, when collecting the first-motion amplitude of the acoustic emission stress wave in the experiment, due to factors such as the poor coupling effect between the sensor and the rock wall, there is a difference between the first-motion amplitude of the stress wave received by the sensor and the actual first-motion amplitude of the stress wave. Therefore, a sensor calibration coefficient quantification method is urgently needed to improve the accuracy of the first-motion amplitude extraction and lay the foundation for truly solving the size and evolution law of internal cracks in the rock. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for quantifying calibration coefficients of an acoustic emission / microseismic sensor.

[0006] The present invention provides a method for quantifying calibration coefficients of an acoustic emission / microseismic sensor, comprising the following steps:

[0007] Step 1, ball drop experiment and establishment of its three-dimensional numerical model: Use acoustic emission / microseismic sensors to collect stress waves excited by the ball drop in the rock during the experiment, establish a three-dimensional solid model corresponding to the ball drop experiment, divide the grid and assign physical and mechanical parameters to the grid units of the ball drop and the rock, and build a three-dimensional numerical model;

[0008] Step 2: Numerical simulation of the propagation process of stress waves induced by falling balls: Boundary conditions are applied to the three-dimensional numerical model, and the propagation process of the falling ball, impacting the rock and induced stress waves in the rock is simulated;

[0009] Step 3: Compare the results of the falling ball experiment and the numerical simulation to determine the calibration coefficient of each acoustic emission / microseismic sensor.

[0010] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, the step 1 is specifically:

[0011] Step 1.1, ball drop experiment: Apply vaseline to the surface marking point of a standard rock sample with a diameter of 5 cm and a height of 10 cm and attach an acoustic emission / microseismic sensor; place a steel ball with a diameter of 0.8 cm 10 cm above the center point of the upper surface of the rock sample; after preparation, let the steel ball fall freely to hit the center point of the upper surface of the rock sample and collect the stress wave waveform information excited by the falling ball in real time, and repeat the above experiment 3 times to reduce the experimental error;

[0012] Step 1.2, three-dimensional solid modeling: numerical simulation software is used to establish a three-dimensional solid model with a sphere and a cylinder as basic components, and the sphere is located 10 cm above the cylinder as the relative spatial position. The size of the sphere and the cylinder is consistent with the physical model size of the steel ball and the standard rock sample selected in the acoustic emission experiment;

[0013] Step 1.3, mesh division: The three-dimensional solid model has a simple structure and a small size, so the mesh scale is extremely refined to ensure the calculation accuracy of the model;

[0014] Step 1.4, assignment of physical and mechanical parameters: Use numerical simulation software to assign physical and mechanical parameters to the steel ball and standard rock sample, including Young's modulus, Poisson's ratio, and density. Since most numerical simulation software cannot directly assign wave velocity, the wave velocity is indirectly assigned by assigning the elastic modulus and density of the material. The relationship between wave velocity, elastic modulus, and density is expressed as follows:

[0015]

[0016] Where, E is the elastic modulus of the standard rock sample, unit is Pa; ρ is the density of the standard rock sample, unit is kg / m3.

[0017] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, the boundary conditions imposed on the three-dimensional numerical model in step 2 are specifically:

[0018] First, the steel ball hits the upper surface of the standard rock sample. The surface areas of the steel ball and the standard rock sample are independent and no surface penetration occurs. Therefore, the steel ball and the upper surface of the standard rock sample are set as a contact pair, the steel ball is set as the source, and the upper surface of the standard rock sample is set as the target.

[0019] Secondly, to simplify the simulation process, the steel ball is made to hit the center point of the upper surface of the standard rock sample at a uniform speed, and the initial speed of the steel ball is set to v P :

[0020]

[0021] Where: v P is the initial velocity of the steel ball, in m / s; d is the initial distance from the lower surface of the steel ball to the upper surface of the standard rock sample, in m; g is the gravitational acceleration;

[0022] Finally, the solid mechanics velocity field waveform data of the standard rock sample surface needs to be solved, and only fixed constraints are set on the bottom surface of the rock.

[0023] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, step 3 specifically includes the following steps:

[0024] Step 3.1: Extract the initial amplitude of stress wave in the falling ball experiment;

[0025] Step 3.2: Extract the initial amplitude of stress wave from the numerical simulation results;

[0026] Step 3.3: Calculate the attenuation rate of stress waves propagating in rocks and the attenuation coefficient of the sensor and rock wall coupling interface to stress waves respectively, and accurately calibrate the acoustic emission / microseismic sensor.

[0027] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, step 3.1 specifically includes the following steps:

[0028] Step 3.1.1: Preprocess the waveform data collected by the acoustic emission / microseismic sensor, convert the electrical signal waveform into a velocity waveform (sensor voltage-velocity conversion coefficient V / (m / s): 501.19), remove the tail quarter waveform data, and intercept the waveform data between the first non-zero point and the last non-zero point as the final waveform data;

[0029] Step 3.1.2: Use the Akaike Information Criterion (AIC) method to calculate the initial arrival time in sequence: select different waveform nodes, intercept waveform data of different lengths, calculate the AIC values ​​corresponding to different interception methods, and extract the waveform node corresponding to the minimum AIC value as the initial arrival time:

[0030]

[0031] Where: k is the length of the intercepted waveform, is the variance of the intercepted waveform data, k2 is the remaining waveform length, The variance of the remaining waveform data;

[0032] Step 3.1.3: Extract the initial amplitude of the velocity waveform according to the following formula:

[0033] Au' n =max||BO n || ∞

[0034] Where: Au' n is the initial amplitude of the stress wave collected by the nth acoustic emission / microseismic sensor in the ball drop experiment, BO n is the wave velocity matrix from the initial arrival time to the next non-zero data in the waveform data collected by the nth acoustic emission / microseismic sensor, ||BO n || ∞ Represents the wave velocity matrix BO n The elements in take the absolute value, max||BO n || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix.

[0035] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, step 3.2 specifically includes the following steps:

[0036] Step 3.2.1: According to the numerical simulation results, extract the velocity component matrix at each simulated sensor monitoring point in the numerical simulation, and calculate the velocity waveform matrix along the direction of the simulated sensor monitoring point in turn:

[0037] V m =[V xm ,V ym ,V zm ]

[0038] V′ m =I m ×V m

[0039] Where V m is the velocity component matrix at the mth simulated sensor monitoring point in the numerical simulation, V xm ,V ym ,V zm are the velocity components in the x, y, and z directions, in m / s, V′ m is the velocity matrix perpendicular to the direction of the mth simulated sensor monitoring point, I m is the direction vector of each simulated sensor monitoring point;

[0040] Step 3.2.2: Calculate the initial amplitude of each waveform data in the numerical simulation according to the following formula:

[0041] Au m =max||BO m|| ∞

[0042] In the formula, Au m is the initial amplitude of the stress wave collected by the mth simulated sensor monitoring point in the numerical simulation; m is the serial number of the simulated sensor monitoring point, BO m V′ m The wave velocity matrix between the initial arrival time and the next non-zero data point is intercepted, ||BO m || ∞ Represents the wave velocity matrix BO m The elements in take the absolute value, max||BO m || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix. The calculation method of the initial arrival time is the same as step 3.1.2.

[0043] In the acoustic emission / microseismic sensor calibration coefficient quantification method of the present invention, the step 3.3 is specifically as follows:

[0044] Step 3.3.1: Calculation of calibration coefficients for acoustic emission / microseismic sensors: In simulations, rock materials are often set as mean materials. However, the internal structure of real rock materials is very complex, and the attenuation of stress waves is more obvious during propagation inside the rock. The amplitude attenuation coefficient of stress waves per unit length propagating in the rock material is set as:

[0045]

[0046] Where: N represents the number of acoustic emission / microseismic sensors in the falling ball experiment and the number of simulated sensor monitoring points in the numerical simulation; d is the attenuation rate of stress wave propagation in rock; l i is the distance from the sound source to the i-th acoustic emission / microseismic sensor, l j is the distance from the sound source to the jth acoustic emission / microseismic sensor; Au i is the initial amplitude of the stress wave collected at the i-th simulated sensor monitoring point in the numerical simulation, Au j is the initial amplitude of the stress wave collected at the jth simulated sensor monitoring point in the numerical simulation; Au i ' is the initial amplitude of the stress wave collected by the ith acoustic emission / microseismic sensor in the falling ball experiment, Au' j is the initial motion amplitude of the stress wave collected by the jth acoustic emission / microseismic sensor in the falling ball experiment;

[0047] Step 3.3.2: In the experiment, the extracted initial vibration amplitude is reduced due to the poor coupling effect between the sensor and the rock wall. The attenuation coefficient of the amplitude is t, that is, the attenuation rate of the sensor and rock wall coupling interface to the stress wave. Therefore, the relationship between the initial vibration amplitude of the stress wave calculated by numerical simulation Au and the initial vibration amplitude of the stress wave extracted by the experiment Au' is defined as:

[0048] Au'=t×(Au-d×l)

[0049]

[0050] Where: t is the attenuation rate of the sensor and rock wall coupling interface to the stress wave; l n is the distance from the sound source to the nth acoustic emission / microseismic sensor n;

[0051] The attenuation rate of stress waves propagating in rocks and the attenuation coefficient of stress waves at the coupling interface between sensors and rock walls are calculated respectively, and the acoustic emission / microseismic sensors are accurately calibrated.

[0052] Step 3.3.3: The calibration calculation formula for the acoustic emission / microseismic sensor is:

[0053]

[0054] Wherein, d and t represent the calibration coefficients of the acoustic emission / microseismic sensor.

[0055] The present invention provides a method for quantifying the calibration coefficient of an acoustic emission / microseismic sensor, which has the following characteristics:

[0056] Beneficial effects:

[0057] This method overcomes the difficulties of differences in coupling effects between different sensors and rock walls, and the attenuation of stress wave amplitude in complex rock materials, which leads to differences between the initial amplitude of stress wave received by the sensor and the actual initial amplitude of stress wave. It provides accurate basic parameters for acoustic emission positioning and moment tensor method to solve acoustic emission fracture types, and improves calculation accuracy. In addition, the development of simulation APP based on comsol simulation software can simulate and calculate physical experiments with different rock types, different sensor layouts and channel numbers, making this calculation method universally applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for quantifying calibration coefficients of an acoustic emission / microseismic sensor of the present invention;

[0059] Figure 2 is the location of the acoustic emission / microseismic sensor in the embodiment;

[0060] Figure 3 It is a three-dimensional numerical model after meshing;

[0061] Figure 4a is the numerical simulation result of the steel ball impact experiment in the embodiment;

[0062] Figure 4bis a top view of the numerical simulation results of the steel ball impact experiment in the embodiment;

[0063] Figure 5a 1 is a schematic diagram of the measured acoustic emission waveform in the embodiment;

[0064] Figure 5b Schematic diagram of simulated acoustic emission waveform in the embodiment. DETAILED DESCRIPTION

[0065] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0066] like Figure 1 As shown, the present invention provides a method for quantifying calibration coefficients of an acoustic emission / microseismic sensor, comprising the following steps:

[0067] Step 1: Establishment of the falling ball experiment and its three-dimensional numerical model: Use acoustic emission / microseismic sensors to collect stress waves excited by the falling ball in the rock during the experiment, establish a three-dimensional solid model corresponding to the falling ball experiment, divide the grid and assign physical and mechanical parameters to the grid cells of the falling ball and the rock, and build a three-dimensional numerical model, which specifically includes the following steps:

[0068] Step 1.1, ball drop experiment: Apply vaseline to the surface marking point of a standard rock sample with a diameter of 5 cm and a height of 10 cm and attach an acoustic emission / microseismic sensor; place a steel ball with a diameter of 0.8 cm 10 cm above the center point of the upper surface of the rock sample; after preparation, let the steel ball fall freely to hit the center point of the upper surface of the rock sample and collect the stress wave waveform information excited by the falling ball in real time, and repeat the above experiment 3 times to reduce the experimental error;

[0069] Step 1.2, three-dimensional solid modeling: numerical simulation software is used to establish a three-dimensional solid model with a sphere and a cylinder as basic components, and the sphere is located 10 cm above the cylinder as the relative spatial position. The size of the sphere and the cylinder is consistent with the physical model size of the steel ball and the standard rock sample selected in the acoustic emission experiment;

[0070] Step 1.3, mesh division: The three-dimensional solid model has a simple structure and a small size, so the mesh scale is extremely refined to ensure the calculation accuracy of the model;

[0071] Step 1.4, assignment of physical and mechanical parameters: Use numerical simulation software to assign physical and mechanical parameters to the steel ball and standard rock sample, including Young's modulus, Poisson's ratio, and density. Since most numerical simulation software cannot directly assign wave velocity, the wave velocity is indirectly assigned by assigning the elastic modulus and density of the material. The relationship between wave velocity, elastic modulus, and density is expressed as follows:

[0072]

[0073] Where, E is the elastic modulus of the standard rock sample, unit is Pa; ρ is the density of the standard rock sample, unit is kg / m3.

[0074] Step 2: Numerical simulation of the propagation process of stress waves induced by falling balls: applying boundary conditions to the three-dimensional numerical model established in step 1, and simulating the propagation process of the ball falling, hitting the rock and induced stress waves in the rock;

[0075] First, the steel ball hits the upper surface of the standard rock sample. The surface areas of the steel ball and the standard rock sample are independent and no surface penetration occurs. Therefore, the steel ball and the upper surface of the standard rock sample are set as a contact pair, the steel ball is set as the source, and the upper surface of the standard rock sample is set as the target.

[0076] Secondly, to simplify the simulation process, the steel ball is made to hit the center point of the upper surface of the standard rock sample at a uniform speed, and the initial speed of the steel ball is set to v P :

[0077]

[0078] Where: v P is the initial velocity of the steel ball, in m / s; d is the initial distance from the lower surface of the steel ball to the upper surface of the standard rock sample, in m; g is the gravitational acceleration;

[0079] Finally, the solid mechanics velocity field waveform data of the rock surface needs to be solved, so only fixed constraints are set on the bottom surface of the rock.

[0080] Step 3: Compare the ball drop test results and the numerical simulation results to determine the calibration coefficients of each acoustic emission / microseismic sensor, which specifically includes the following steps:

[0081] Step 3.1: Extract the initial amplitude of the stress wave in the falling ball experiment, specifically:

[0082] Step 3.1.1: Preprocess the waveform data collected by the acoustic emission / microseismic sensor, convert the electrical signal waveform into a velocity waveform (sensor voltage-velocity conversion coefficient V / (m / s): 501.19), remove the tail quarter waveform data, and intercept the waveform data between the first non-zero point and the last non-zero point as the final waveform data;

[0083] Step 3.1.2: Use the Akaike Information Criterion (AIC) method to calculate the initial arrival time in sequence: select different waveform nodes, intercept waveform data of different lengths, calculate the AIC values ​​corresponding to different interception methods, and extract the waveform node corresponding to the minimum AIC value as the initial arrival time:

[0084]

[0085] Where: k is the length of the intercepted waveform, is the variance of the intercepted waveform data, k2 is the remaining waveform length, The variance of the remaining waveform data;

[0086] Step 3.1.3: Extract the initial amplitude of the velocity waveform according to the following formula:

[0087] Au' n =max||BO n || ∞

[0088] Where: Au' n is the initial amplitude of the stress wave collected by the nth acoustic emission / microseismic sensor in the ball drop experiment, BO n is the wave velocity matrix from the initial arrival time to the next non-zero data in the waveform data collected by the nth acoustic emission / microseismic sensor, ||BO n || ∞ Represents the wave velocity matrix BO n The elements in take the absolute value, max||BO n || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix.

[0089] Step 3.2: Extract the initial amplitude of stress wave from the numerical simulation results, specifically:

[0090] Step 3.2.1: According to the numerical simulation results, extract the velocity component matrix at each simulated sensor monitoring point in the numerical simulation, and calculate the velocity waveform matrix along the direction of the simulated sensor monitoring point in turn:

[0091] V m =[V xm ,V ym ,V zm ]

[0092] V′ m =I m ×V m

[0093] Where V m is the velocity component matrix at the mth simulated sensor monitoring point in the numerical simulation, V xm ,V ym ,V zm are the velocity components in the x, y, and z directions, in m / s, V m ' m is the velocity matrix perpendicular to the direction of the mth simulated sensor monitoring point, I m is the direction vector of each simulated sensor monitoring point;

[0094] Step 3.2.2: Calculate the initial amplitude of each waveform data in the numerical simulation according to the following formula:

[0095] Au m =max||BO m || ∞

[0096] In the formula, Au m is the initial amplitude of the stress wave collected by the mth simulated sensor monitoring point in the numerical simulation; m is the serial number of the simulated sensor monitoring point, BO m V m ' m The wave velocity matrix between the initial arrival time and the next non-zero data point is intercepted, ||BO m || ∞ Represents the wave velocity matrix BO m The elements in take the absolute value, max||BO m || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix. The calculation method of the initial arrival time is the same as step 3.1.2.

[0097] Step 3.3: Calculate the attenuation rate of stress wave propagating in rock and the attenuation coefficient of stress wave at the sensor and rock wall coupling interface respectively, and accurately calibrate the acoustic emission / microseismic sensor, specifically:

[0098] Step 3.3.1: Calculation of calibration coefficients for acoustic emission / microseismic sensors: In simulations, rock materials are often set as mean materials. However, the internal structure of real rock materials is very complex, and the attenuation of stress waves is more obvious during propagation inside the rock. The amplitude attenuation coefficient of stress waves per unit length propagating in the rock material is set as:

[0099]

[0100] Where: N represents the number of acoustic emission / microseismic sensors in the falling ball experiment and the number of simulated sensor monitoring points in the numerical simulation; d is the attenuation rate of stress wave propagation in rock; l i is the distance from the sound source to the i-th acoustic emission / microseismic sensor, l j is the distance from the sound source to the jth acoustic emission / microseismic sensor; Au i is the initial amplitude of the stress wave collected at the i-th simulated sensor monitoring point in the numerical simulation, Au j is the initial amplitude of the stress wave collected at the jth simulated sensor monitoring point in the numerical simulation; Au i ' is the initial amplitude of the stress wave collected by the ith acoustic emission / microseismic sensor in the falling ball experiment, Au' j is the initial motion amplitude of the stress wave collected by the jth acoustic emission / microseismic sensor in the falling ball experiment;

[0101] Step 3.3.2: In the experiment, the extracted initial vibration amplitude is reduced due to the poor coupling effect between the sensor and the rock wall. The attenuation coefficient of the amplitude is t, that is, the attenuation rate of the sensor and rock wall coupling interface to the stress wave. Therefore, the relationship between the initial vibration amplitude of the stress wave calculated by numerical simulation Au and the initial vibration amplitude of the stress wave extracted by the experiment Au' is defined as:

[0102] Au'=t×(Au-d×l)

[0103]

[0104] Where: t is the attenuation rate of the sensor and rock wall coupling interface to the stress wave; l n is the distance from the sound source to the nth acoustic emission / microseismic sensor n;

[0105] The attenuation rate of stress waves propagating in rocks and the attenuation coefficient of stress waves at the coupling interface between sensors and rock walls are calculated respectively, and the acoustic emission / microseismic sensors are accurately calibrated.

[0106] Step 3.3.3: The calibration calculation formula for the acoustic emission / microseismic sensor is:

[0107]

[0108] Wherein, d and t represent the calibration coefficients of the acoustic emission / microseismic sensor.

[0109] During the specific implementation, in order to adapt to the simulation calculations of different rock materials, specimen sizes, and different sensor arrangements, a simulation APP was developed based on the Comsol simulation software. The interface parameter settings include the Young's modulus, density, Poisson's ratio of the rock / steel ball, as well as the monitoring point location information, model size, grid monitoring, etc.

[0110] Example:

[0111] 1. Establish a three-dimensional solid model according to the experimental design, such as Figure 2 As shown, it is assumed that there are 9 acoustic emission / microseismic sensors S1-S9, and the position coordinates of the acoustic emission / microseismic sensors are shown in Table 1.

[0112] Table 1: Acoustic emission / microseismic sensor locations

[0113]

[0114] 2. Yes Figure 2 The three-dimensional solid geological model is meshed, such as Figure 3 shown.

[0115] 3. Physical and mechanical parameter values ​​are assigned to the grid cells. For simplicity, a uniform medium with uniform lithology is used in this embodiment.

[0116] 4. Set boundary conditions and indirectly assign wave velocity values ​​using material elastic modulus and density.

[0117] 5. Simulate the process of steel ball hitting rock sample to stimulate stress wave. Figure 4a is the numerical simulation result of the steel ball impact experiment in the embodiment; Figure 4b is a top view of the numerical simulation results of the steel ball impact experiment in the embodiment; Figure 5b 9 simulated sensor monitoring points Z1-Z9 are set, and the initial amplitude of the velocity waveform at the nine monitoring points is extracted according to the simulation results, as shown in Table 2.

[0118] Table 2: Initial amplitude of stress waves collected at each simulated sensor monitoring point

[0119] Simulate sensor monitoring points Initial vibration amplitude (Au) Z1 9.02E-05 Z2 8.99E-05 Z3 9.04E-05 Z4 8.82E-05 Z5 8.79E-05 Z6 0.000088 Z7 0.000235 Z8 0.000235 Z9 0.000235

[0120] 6. Figure 5a 3 is a schematic diagram of the measured acoustic emission waveform in this embodiment, which collects the acoustic emission data of nine sensors in the steel ball impact experiment and extracts the initial amplitude of the stress wave.

[0121] Table 3 Initial amplitude of stress wave collected by each sensor

[0122] Acoustic Emission / Microseismic Sensor Initial vibration amplitude (Au') S1 6.70E-06 S2 3.17E-05 S3 2.56E-05 S4 3.90E-05 S5 8.53E-05 S6 5.54E-05 S7 1.35E-04 S8 2.21E-04 S9 2.02E-04

[0123] 7. According to the two initial vibration amplitudes, the amplitude attenuation rate per unit length of propagation in rock materials is calculated to be d=0.0011, and the amplitude attenuation coefficient t caused by the coupling interface between the sensor and the rock wall is obtained. As shown in Table 4.

[0124] Table 4

[0125]

[0126]

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the concept of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantifying calibration coefficients of acoustic emission / microseismic sensors, characterized in that: The steps include: Step 1, ball drop experiment and establishment of its three-dimensional numerical model: Use acoustic emission / microseismic sensors to collect stress waves excited by the ball drop in the rock during the experiment, establish a three-dimensional solid model corresponding to the ball drop experiment, divide the grid and assign physical and mechanical parameters to the grid units of the ball drop and the rock, and build a three-dimensional numerical model; Step 2: Numerical simulation of the propagation process of stress waves induced by falling balls: Boundary conditions are applied to the three-dimensional numerical model, and the propagation process of the falling ball, impacting the rock and induced stress waves in the rock is simulated; Step 3: Compare the falling ball experiment and the numerical simulation results to determine the calibration coefficient of each acoustic emission / microseismic sensor; Step 3 specifically includes the following steps: Step 3.1: Extract the initial amplitude of stress wave in the falling ball experiment; Step 3.2: Extract the initial amplitude of stress wave from the numerical simulation results; Step 3.3: Calculate the attenuation rate of stress wave propagating in rock and the attenuation coefficient of stress wave at the coupling interface between sensor and rock wall respectively, and accurately calibrate the acoustic emission / microseismic sensor; The step 3.1 specifically includes the following steps: Step 3.1.1: Preprocess the waveform data collected by the acoustic emission / microseismic sensor, convert the electrical signal waveform into a velocity waveform, the sensor voltage-velocity conversion coefficient is 501.19V / (m / s), remove the tail quarter waveform data, and intercept the waveform data between the first non-zero point and the last non-zero point as the final waveform data; Step 3.1.2: Use the Akaike Information Criterion (AIC) method to calculate the initial arrival time in sequence: select different waveform nodes, intercept waveform data of different lengths, calculate the AIC values ​​corresponding to different interception methods, and extract the waveform node corresponding to the minimum AIC value as the initial arrival time: Where: k is the length of the intercepted waveform, is the variance of the intercepted waveform data, k2 is the remaining waveform length, The variance of the remaining waveform data; Step 3.1.3: Extract the initial amplitude of the velocity waveform according to the following formula: Have n =max||BO n || ∞ Where: Au' n is the initial amplitude of the stress wave collected by the nth acoustic emission / microseismic sensor in the falling ball experiment, BO n is the wave velocity matrix from the initial arrival time to the next non-zero data in the waveform data collected by the nth acoustic emission / microseismic sensor, ||BO n || ∞ Represents the wave velocity matrix BO n The elements in take the absolute value, max||BO n || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix; The step 3.2 specifically includes the following steps: Step 3.2.1: According to the numerical simulation results, extract the velocity component matrix at each simulated sensor monitoring point in the numerical simulation, and calculate the velocity waveform matrix along the direction of the simulated sensor monitoring point in turn: V m =[V xm ,V ym ,V zm ] V' m =I m ×V m Where V m is the velocity component matrix at the mth simulated sensor monitoring point in the numerical simulation, V xm ,V ym ,V zm are the velocity components in the x, y, and z directions, in m / s, V' m is the velocity matrix perpendicular to the direction of the mth simulated sensor monitoring point, I m is the direction vector of each simulated sensor monitoring point; Step 3.2.2: Calculate the initial amplitude of each waveform data in the numerical simulation according to the following formula: Have m =max||BO m || ∞ In the formula, Au m is the initial amplitude of the stress wave collected by the mth simulated sensor monitoring point in the numerical simulation; m is the serial number of the simulated sensor monitoring point, BO m V' m The wave velocity matrix between the initial arrival time and the next non-zero data point is intercepted, ||BO m || ∞ Represents the wave velocity matrix BO m The elements in take the absolute value, max||BO m || ∞ Indicates the maximum absolute value of the elements of the wave velocity matrix. The calculation method of the initial arrival time is the same as step 3.1.2; The step 3.3 is specifically as follows: Step 3.3.1: Calculation of calibration coefficients for acoustic emission / microseismic sensors: In simulations, rock materials are often set as mean materials. However, the internal structure of real rock materials is very complex, and the attenuation of stress waves is more obvious during propagation inside the rock. The amplitude attenuation coefficient of stress waves per unit length propagating in the rock material is set as: Where: N represents the number of acoustic emission / microseismic sensors in the falling ball experiment and the number of simulated sensor monitoring points in the numerical simulation; d is the attenuation rate of stress wave propagation in rock; l i is the distance from the sound source to the i-th acoustic emission / microseismic sensor, l j is the distance from the sound source to the jth acoustic emission / microseismic sensor; Au i is the initial amplitude of the stress wave collected at the i-th simulated sensor monitoring point in the numerical simulation, Au j is the initial amplitude of the stress wave collected at the jth simulated sensor monitoring point in the numerical simulation; Au i ' is the initial amplitude of the stress wave collected by the ith acoustic emission / microseismic sensor in the falling ball experiment, Au' j is the initial motion amplitude of the stress wave collected by the jth acoustic emission / microseismic sensor in the falling ball experiment; Step 3.3.2: In the experiment, the extracted initial vibration amplitude is reduced due to the poor coupling effect between the sensor and the rock wall. The attenuation coefficient of the amplitude is t, that is, the attenuation rate of the sensor and rock wall coupling interface to the stress wave. Therefore, the relationship between the initial vibration amplitude of the stress wave calculated by numerical simulation Au and the initial vibration amplitude of the stress wave extracted by the experiment Au' is defined as: Au'=t×(Au-d×l) Where: t is the attenuation rate of the sensor and rock wall coupling interface to the stress wave; l n is the distance from the sound source to the nth acoustic emission / microseismic sensor n; Respectively solve the attenuation rate of stress wave propagating in rock and the attenuation coefficient of stress wave at the coupling interface between sensor and rock wall, and accurately calibrate the acoustic emission / microseismic sensor; Step 3.3.3: The calibration calculation formula for the acoustic emission / microseismic sensor is: Where d is the attenuation rate of stress wave propagating in rock; t is the attenuation rate of stress wave at the coupled interface between sensor and rock wall; d and t are used to represent the calibration coefficients of acoustic emission / microseismic sensor.

2. The method for quantifying the calibration coefficient of an acoustic emission / microseismic sensor according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1.1, ball drop experiment: Apply vaseline to the surface marking point of a standard rock sample with a diameter of 5 cm and a height of 10 cm and attach an acoustic emission / microseismic sensor; place a steel ball with a diameter of 0.8 cm 10 cm above the center point of the upper surface of the rock sample; after preparation, let the steel ball fall freely to hit the center point of the upper surface of the rock sample and collect the stress wave waveform information excited by the falling ball in real time, and repeat the above experiment 3 times to reduce the experimental error; Step 1.2, three-dimensional solid modeling: numerical simulation software is used to establish a three-dimensional solid model with a sphere and a cylinder as basic components, and the sphere is located 10 cm above the cylinder as the relative spatial position. The size of the sphere and the cylinder is consistent with the physical model size of the steel ball and the standard rock sample selected in the acoustic emission experiment; Step 1.3, mesh division: The three-dimensional solid model has a simple structure and a small size, so the mesh scale is extremely refined to ensure the calculation accuracy of the model; Step 1.4, assignment of physical and mechanical parameters: Use numerical simulation software to assign physical and mechanical parameters to the steel ball and standard rock sample, including Young's modulus, Poisson's ratio, and density. Since most numerical simulation software cannot directly assign wave velocity, the wave velocity is indirectly assigned by assigning the elastic modulus and density of the material. The relationship between wave velocity, elastic modulus, and density is expressed as follows: Where, E is the elastic modulus of the standard rock sample, in Pa; ρ is the density of the standard rock sample, in kg / m 3 .

3. The method for quantifying the calibration coefficient of an acoustic emission / microseismic sensor according to claim 1, characterized in that: In step 2, the boundary conditions imposed on the three-dimensional numerical model are specifically: First, the steel ball hits the upper surface of the standard rock sample. The surface areas of the steel ball and the standard rock sample are independent and no surface penetration occurs. Therefore, the steel ball and the upper surface of the standard rock sample are set as a contact pair, the steel ball is set as the source, and the upper surface of the standard rock sample is set as the target. Secondly, to simplify the simulation process, the steel ball is made to hit the center point of the upper surface of the standard rock sample at a uniform speed, and the initial speed of the steel ball is set to v P : Where: v P is the initial velocity of the steel ball, in m / s; d is the initial distance from the lower surface of the steel ball to the upper surface of the standard rock sample, in m; g is the gravitational acceleration; Finally, the solid mechanics velocity field waveform data of the standard rock sample surface needs to be solved, and only fixed constraints are set on the bottom surface of the rock.

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