A method for locating acoustic emission / microseismic events considering temperature effects

Abstract

This invention relates to a method for locating acoustic emission / microseismic events considering temperature effects, comprising: step 1, establishing the relationship between rock temperature and wave velocity; step 2, wave velocity field reconstruction; and step 3, acoustic emission / microseismic event location. This invention overcomes the problem of wave velocity variations caused by rock temperature changes affecting location accuracy. It combines the relationship between temperature and wave velocity with numerical simulation of rock temperature field distribution to reconstruct the wave velocity field of rocks during freeze-thaw cycles and spatially locate acoustic emission / microseismic events, significantly reducing the location error of acoustic emission / microseismic events induced by rock temperature changes.

Description

Technical Field

[0001] This invention relates to the technical field of acoustic emission / microseismic event location methods in mining and rock engineering, and particularly to an acoustic emission / microseismic event location method that takes into account temperature effects. Background Technology

[0002] China has a vast cold region with abundant metal mineral resources. With the continued rapid growth of China's economy, the cold region will inevitably become the most important metal mineral base in my country in the future.

[0003] Mining and rock excavation processes cause the formation of fracture surfaces within the rock mass, accompanied by the propagation of stress waves. This phenomenon is known as acoustic emission (high frequency, small amplitude) or microseismic activity (low frequency, large amplitude). Acoustic emission / microseismic activity is a concomitant phenomenon in the rock mass fracturing process and is closely related to the physical and mechanical behavior of the rock mass. Therefore, by analyzing acoustic emission / microseismic monitoring information, the stress state and degree of fracturing of the rock mass can be inferred, thereby providing early warning and control for rock mass failure and instability.

[0004] Acoustic emission / microseismic event localization is one of the core functions of this technology, and the ability to achieve accurate localization is a key evaluation indicator of the effectiveness of the acoustic emission / microseismic monitoring system. In open-pit mining projects in cold regions, due to the large annual temperature difference, the rock mass within a certain depth will experience drastic temperature fluctuations, causing corresponding drastic changes in wave velocity. Ignoring this influencing factor will lead to an increase in the error of the localization result, but currently widely used localization algorithms do not consider the impact of rock mass temperature changes on wave velocity and localization accuracy. Summary of the Invention

[0005] To address the above problems, the present invention aims to provide a method for locating acoustic emission / microseismic events that takes into account temperature effects.

[0006] The technical solution adopted in this invention is as follows:

[0007] The acoustic emission / microseismic event localization method considering temperature effects proposed in this invention includes the following steps:

[0008] Step 1: Establish the relationship between rock temperature and wave velocity: Drill holes in the rock and install temperature sensors at different depths of the holes. Then, grout the holes to seal them. Install wave velocity sensors near the holes, change the ambient temperature and measure the rock wave velocity when the temperature inside the rock is stable. Establish the fitting relationship between rock temperature and wave velocity v=f(T).

[0009] Step 2, Wave velocity field reconstruction: Using ambient temperature as the boundary condition, simulate the evolution of the temperature field inside the rock over time due to changes in ambient temperature. Based on the fitting relationship between rock temperature and wave velocity, quantify the wave velocity at different times and locations.

[0010] Step 3, Acoustic emission / microseismic event location: Conduct acoustic emission / microseismic monitoring. Assuming the propagation path is a straight line, discretize the propagation path into multiple sub-segments, calculate the average wave velocity corresponding to each sub-segment, accumulate the travel time of each sub-segment of the propagation path, and determine the location of the acoustic emission / microseismic event through grid search.

[0011] Furthermore, in step 2, the wave velocity field reconstruction specifically includes:

[0012] Step 2.1, Simulation of Temperature Field within Rock: A numerical model is established based on the research object, and a mesh is generated. Boundary conditions are applied to the surface of the numerical model according to the ambient temperature.

[0013]

[0014] In the formula, T w The boundary temperature; t For time; f 1( t (Regarding time) t The function.

[0015] Based on the energy conservation governing equation:

[0016]

[0017] In the formula, d The distance between a point inside the rock and its boundary; t For time; T(d,t) For about location d and time t Temperature function; Thermal diffusivity; L Latent heat of phase transition; This represents the phase transition progress of the water-ice phase transition.

[0018] Simulate the evolution of the temperature field within a rock over time and output the temperature corresponding to each grid node at different times. T i ( t ),in, i This refers to the grid node number.

[0019] Step 2.2, Wave velocity quantification at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, the correspondence between temperature and wave velocity at each grid node at different times is quantified. v i = f ( T i ( tBy considering the inverse power law of distance in spatiotemporal relationships, the velocity at any point in space at different times can be interpolated to obtain the velocity corresponding to that point:

[0020]

[0021] In the formula, v ( t (time) t The interpolation rate at any point in spacetime; n The number of nodes; v i ( t (time) t Time grid nodes i wave speed; d i From the desired point to the grid node i Spatial distance; p The power of θ determines the degree to which distance affects the weight.

[0022] Furthermore, step 3, the acoustic emission / microseismic event localization, specifically includes the following steps:

[0023] Step 3.1, Discretization of the propagation path: Assuming the initial acoustic emission wave propagates in a straight line from the acoustic emission source to the acoustic emission sensor location, the acoustic emission wave from the grid node... Q i propagation to acoustic emission sensor S j The propagation path equation can be given by the following formula:

[0024]

[0025] In the formula, i For grid node numbers; j For sensor serial number; x i , y i , z i These are the three-dimensional coordinates of the grid nodes; x j , y j , z j The coordinates of the acoustic emission sensor are 3D.

[0026] The acoustic emission wave propagation path between the grid nodes and the sensor is set at intervals. l of N The endpoints are divided into N +1 sub-segments, endpoint coordinates on the propagation path P ijn (x ijn , y ijn , z ijn ) and their quantity N It is given by the following formula:

[0027]

[0028] In the formula, n The first acoustic emission wave propagation path starting from a grid node is the first... n endpoints ( n =1,2… N ), , , These are the angles between the propagation path of the acoustic emission wave and the coordinate axes.

[0029] Step 3.2: Calculate the average wave velocity corresponding to the sub-segment: Take the wave velocity at the midpoint of the sub-segment as the average wave velocity of that sub-segment. The average wave velocity of each sub-segment is shown in the following formula:

[0030]

[0031] In the formula, k The first acoustic emission wave propagation path starting from a grid node is the first... n Sub-segments ( k = n +1).

[0032] Step 3.3: Accumulate the travel time of each sub-segment of the propagation path:

[0033] The travel time of the acoustic emission waves excited by each grid node to each sensor can be obtained by piecewise summation, as given by the following formula:

[0034]

[0035] In the formula, T ij For grid nodes i The excited acoustic emission wave propagates to the sensor j The time of departure.

[0036] Step 3.4: Grid search to determine the location of acoustic emission / microseismic events: Using the travel time obtained from the above theoretical calculations, calculate the theoretical arrival time difference vector of each grid node, and then calculate the actual arrival time difference vector of the acoustic emission event using the measured arrival time. Select the grid node with the smallest difference between the two as the final location of the acoustic emission / microseismic event, and complete the localization.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] The method of this invention overcomes the problem that wave velocity changes caused by rock temperature variations affect positioning accuracy. It can reconstruct the wave velocity field of rocks during freeze-thaw cycles by combining the relationship between temperature and wave velocity and numerical simulation of rock temperature field distribution, thereby enabling spatial positioning of acoustic emission / microseismic events and significantly reducing the positioning error of acoustic emission / microseismic events induced by rock temperature variations. Attached Figure Description

[0039] Figure 1 A simplified model for deploying temperature and wave velocity sensors;

[0040] Figure 2 To establish the fitted curve of temperature T versus wave velocity v;

[0041] Figure 3 A numerical model of the freeze-thaw cycle was established;

[0042] Figure 4 The numerical model after meshing;

[0043] Figure 5 The function curve is the boundary condition.

[0044] Figure 6 The results are numerical simulations of the rock temperature field during freeze-thaw cycles, where... Figure 6 Numerical simulation results of the temperature field when a is 0.26h. Figure 6 b represents the numerical simulation result of the temperature field at 0.31 h. Figure 6 Numerical simulation results of the temperature field when c is 0.64h;

[0045] Figure 7 The numerical simulation results show the reconstruction of the wave velocity field during the freeze-thaw cycle. Figure 7 Numerical simulation results of wave velocity field reconstruction when a is 0.26h. Figure 7 b represents the numerical simulation result of the wave velocity field reconstruction at 0.31h. Figure 7 Numerical simulation results of wave velocity field reconstruction when c is 0.64h. Detailed Implementation

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] See appendix Figure 1-7 The specific implementation process of the acoustic emission / microseismic event localization method considering temperature effects proposed in this invention is as follows:

[0048] Step 1: Establish the relationship between rock temperature and wave velocity: Drill holes in the rock and install temperature sensors at different depths. Then, grout the boreholes to seal them. Install wave velocity sensors near the boreholes, change the ambient temperature, and measure the rock wave velocity when the temperature inside the rock stabilizes. Establish a fitting relationship between rock temperature and wave velocity. v = f ( T ).

[0049] Step 2, Wave velocity field reconstruction: Using ambient temperature as the boundary condition, simulate the evolution of the temperature field inside the rock over time due to changes in ambient temperature. Based on the fitting relationship between rock temperature and wave velocity, quantify the wave velocity at different times and locations.

[0050] The reconstruction of the wave velocity field in step 2 specifically includes:

[0051] Step 2.1, Simulation of Temperature Field within Rock: A numerical model is established based on the research object, and a mesh is generated. Boundary conditions are applied to the surface of the numerical model according to the ambient temperature.

[0052]

[0053] In the formula, T w The boundary temperature; t For time; f 1( t (Regarding time) t The function.

[0054] Based on the energy conservation governing equation:

[0055]

[0056] In the formula, d The distance between a point inside the rock and its boundary; t For time; T(d,t) For about location d and time t Temperature function; Thermal diffusivity; L Latent heat of phase transition; This represents the phase transition progress of the water-ice phase transition.

[0057] Simulate the evolution of the temperature field within a rock over time and output the temperature corresponding to each grid node at different times. T i ( t ),in, i This refers to the grid node number.

[0058] Step 2.2, Wave velocity quantification at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, the correspondence between temperature and wave velocity at each grid node at different times is quantified. v i = f ( T i ( t By considering the inverse power law of distance in spatiotemporal relationships, the velocity at any point in space at different times can be interpolated to obtain the velocity corresponding to that point:

[0059]

[0060] In the formula, v ( t (time) t The interpolation rate at any point in spacetime; n The number of nodes; v i ( t (time) t Time grid nodes i wave speed; d i From the desired point to the grid node i Spatial distance; p The power of θ determines the degree to which distance affects the weight.

[0061] Step 3, Acoustic emission / microseismic event location: Conduct acoustic emission / microseismic monitoring. Assuming the propagation path is a straight line, discretize the propagation path into multiple sub-segments, calculate the average wave velocity corresponding to each sub-segment, accumulate the travel time of each sub-segment of the propagation path, and determine the location of the acoustic emission / microseismic event through grid search.

[0062] The acoustic emission / microseismic event localization method in step 3 specifically includes the following steps:

[0063] Step 3.1, Discretization of the propagation path: Assuming the initial acoustic emission wave propagates in a straight line from the acoustic emission source to the acoustic emission sensor location, the acoustic emission wave from the grid node... Q i propagation to acoustic emission sensor S j The propagation path equation can be given by the following formula:

[0064]

[0065] In the formula, i For grid node numbers; j For sensor serial number; x i , y i , zi These are the three-dimensional coordinates of the grid nodes; x j , y j , z j The coordinates of the acoustic emission sensor are 3D.

[0066] The acoustic emission wave propagation path between the grid nodes and the sensor is set at intervals. l of N The endpoints are divided into N +1 sub-segments, endpoint coordinates on the propagation path P ijn ( x ijn , y ijn , z ijn ) and their quantity N It is given by the following formula:

[0067]

[0068] In the formula, n The first acoustic emission wave propagation path starting from a grid node is the first... n endpoints ( n =1,2… N ), , , These are the angles between the propagation path of the acoustic emission wave and the coordinate axes.

[0069] Step 3.2: Calculate the average wave velocity corresponding to the sub-segment: Take the wave velocity at the midpoint of the sub-segment as the average wave velocity of that sub-segment. The average wave velocity of each sub-segment is shown in the following formula:

[0070]

[0071] In the formula, k The first acoustic emission wave propagation path starting from a grid node is the first... n Sub-segments ( k = n +1).

[0072] Step 3.3: Accumulate the travel time of each sub-segment of the propagation path:

[0073] The travel time of the acoustic emission waves excited by each grid node to each sensor can be obtained by piecewise summation, as given by the following formula:

[0074]

[0075] In the formula, Tij For grid nodes i The excited acoustic emission wave propagates to the sensor j The time of departure.

[0076] Step 3.4: Grid search to determine the location of acoustic emission / microseismic events: Using the travel time obtained from the above theoretical calculations, calculate the theoretical arrival time difference vector of each grid node, and then calculate the actual arrival time difference vector of the acoustic emission event using the measured arrival time. Select the grid node with the smallest difference between the two as the final location of the acoustic emission / microseismic event, and complete the localization.

[0077] Example:

[0078] 1. Install temperature and wave velocity sensors on-site to simplify the model as follows: Figure 1 As shown, T1~T2 are temperature sensors, and S1~S4 are wave velocity sensors. The rock wave velocity corresponding to different ambient temperatures is obtained through these sensors, such as... Figure 2 As shown, scatter fitting is performed to establish the rock temperature. T Wave speed v Functional relationship between them:

[0079] v =-6.7235 T +4904.1358

[0080] 2. A freeze-thaw cycle numerical model with a diameter of 50m and a height of 100m was established based on the research object. To enhance the complexity of the wave velocity field within the model, a vertical crack 30m long and 3m wide was prefabricated in the upper part of the model, and an inclined crack 30m long and 2m wide, forming an angle of 45° with the horizontal plane, was prefabricated on the side of the model. Water was then filled into the vertical cracks. Figure 3 As shown.

[0081] 3. To Figure 3 The numerical model is meshed, such as... Figure 4 As shown, boundary conditions are applied to the surface based on ambient temperature, and their time-dependent properties are... t The function curve is as follows Figure 5 As shown.

[0082] 4. Simulate the evolution of the temperature field within the rock over time, and based on the simulation results (such as...) Figure 6 (As shown) Output different times t Each grid node Q i Corresponding temperature T i ( t ).

[0083] 5. Quantify the temperature and velocity corresponding to each grid node at different times, and interpolate to obtain the wave velocity corresponding to any point in space at different times, thereby reconstructing the rock wave velocity field at different times. Taking the rock at 0.26h, 0.31h, and 0.64h in the freeze-thaw cycle as examples, their corresponding wave velocity fields are as follows: Figure 7 As shown in a, b, and c.

[0084] 6. Assume there are four sensors S1, S2, S3, and S4, and their position coordinates are shown in Table 1. Consider sensor S1 and the grid node with coordinates (25, 25, 50). Q Taking 1 as an example, the propagation path equation of the acoustic emission wave is obtained:

[0085]

[0086] Table 1

[0087]

[0088] 7. The propagation path is divided into 40 sub-segments with 39 endpoints spaced 1m apart. The coordinates of some endpoints are shown in Table 2.

[0089] Table 2

[0090]

[0091] 8. The wave velocity at the midpoint of the sub-segment is taken as the average wave velocity of the sub-segment. Taking 0.26h as an example, the average wave velocities of some sub-segments are shown in Table 3.

[0092] Table 3

[0093]

[0094] 9. Segmentally accumulate the travel time of the acoustic emission waves excited by each grid node to each sensor, taking the grid node with coordinates (25, 25, 50) as an example. Q Taking 1 as an example, its timekeeping results are shown in Table 4.

[0095] Table 4

[0096]

[0097] 10. Subtract the shortest travel time from the travel time of each sensor in Table 4 to obtain the grid node. Q The theoretical time difference vector is shown in Table 5.

[0098] Table 5

[0099]

[0100] 11. Table 6 shows the actual time difference vectors of acoustic emission / microseismic events monitored on site. The theoretical time difference vectors of all grid nodes were matched and searched. It was found that the theoretical time difference vector of the grid node (25, 25, 50) as the acoustic emission source was the closest, that is, the difference between the two was the smallest. Therefore, the coordinates of the actual acoustic emission / microseismic event were determined to be (25, 25, 50).

[0101] Table 6

[0102]

[0103] All matters not covered in this invention are common knowledge.

[0104] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for locating acoustic emission / microseismic events considering temperature effects, characterized in that, Includes the following steps: Step 1: Establish the relationship between rock temperature and wave velocity: Drill holes in the rock and install temperature sensors at different depths of the holes. Then, grout the holes to seal them. Install wave velocity sensors near the holes, change the ambient temperature and measure the rock wave velocity when the temperature inside the rock is stable. Establish the fitting relationship between rock temperature and wave velocity v=f(T). Step 2, Wave velocity field reconstruction: Using ambient temperature as the boundary condition, simulate the evolution of the temperature field inside the rock over time due to changes in ambient temperature. Based on the fitting relationship between rock temperature and wave velocity, quantify the wave velocity at different times and locations. Step 3, Acoustic emission / microseismic event location: Conduct acoustic emission / microseismic monitoring. Assuming the propagation path is a straight line, discretize the propagation path into multiple sub-segments, calculate the average wave velocity corresponding to each sub-segment, accumulate the travel time of each sub-segment of the propagation path, and determine the location of the acoustic emission / microseismic event through grid search.

2. The acoustic emission / microseismic event localization method considering temperature effects according to claim 1, characterized in that: Step 2, specifically the wave velocity field reconstruction, includes: Step 2.1, Simulation of Temperature Field within Rock: A numerical model is established based on the research object, and a mesh is generated. Boundary conditions are applied to the surface of the numerical model according to the ambient temperature. In the formula, T w The boundary temperature; t For time; f 1( t (Regarding time) t The function; Based on the energy conservation governing equation: In the formula, d The distance between a point inside the rock and its boundary; t For time; T(d,t) For about location d and time t Temperature function; Thermal diffusivity; L Latent heat of phase transition; The phase transition progress of water-ice phase transition; Simulate the evolution of the temperature field within a rock over time and output the temperature corresponding to each grid node at different times. T i ( t ),in, i For grid node numbers; Step 2.2, Wave velocity quantification at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, the correspondence between temperature and wave velocity at each grid node at different times is quantified. v i = f ( T i ( t By considering the inverse power law of distance in spatiotemporal relationships, the velocity at any point in space at different times can be interpolated to obtain the velocity corresponding to that point: In the formula, v ( t (time) t The interpolation rate at any point in spacetime; n The number of nodes; v i ( t (time) t Time grid nodes i wave speed; d i From the desired point to the grid node i Spatial distance; p The power of θ determines the degree to which distance affects the weight.

3. The acoustic emission / microseismic event localization method considering temperature effects according to claim 1, characterized in that: Step 3, the location of acoustic emission / microseismic events, specifically includes the following steps: Step 3.1, Discretization of the propagation path: Assuming the initial acoustic emission wave propagates in a straight line from the acoustic emission source to the acoustic emission sensor location, the acoustic emission wave from the grid node... Q i propagation to acoustic emission sensor S j The propagation path equation can be given by the following formula: In the formula, i For grid node numbers; j For sensor serial number; x i , y i , z i These are the three-dimensional coordinates of the grid nodes; x j , y j , z j The three-dimensional coordinates of the acoustic emission sensor; The acoustic emission wave propagation path between the grid nodes and the sensor is set at intervals. l of N The endpoints are divided into N +1 sub-segments, endpoint coordinates on the propagation path P ijn ( x ijn , y ijn , z ijn ) and their quantity N It is given by the following formula: In the formula, n The first acoustic emission wave propagation path starting from a grid node is the first... n endpoints ( n =1,2… N ), , , These are the angles between the propagation path of the acoustic emission wave and the coordinate axes, respectively. Step 3.2: Calculate the average wave velocity corresponding to the sub-segment: Take the wave velocity at the midpoint of the sub-segment as the average wave velocity of that sub-segment. The average wave velocity of each sub-segment is shown in the following formula: In the formula, k The first acoustic emission wave propagation path starting from a grid node is the first... n Sub-segments ( k = n +1); Step 3.3: Accumulate the travel time of each sub-segment of the propagation path: The travel time of the acoustic emission waves excited by each grid node to each sensor can be obtained by piecewise summation, as given by the following formula: In the formula, T ij For grid nodes i The excited acoustic emission wave propagates to the sensor j The time of departure; Step 3.4: Grid search to determine the location of acoustic emission / microseismic events: Using the travel time obtained from the above theoretical calculations, calculate the theoretical arrival time difference vector of each grid node, and then calculate the actual arrival time difference vector of the acoustic emission event using the measured arrival time. Select the grid node with the smallest difference between the two as the final location of the acoustic emission / microseismic event, and complete the localization.

Images