Acoustic emission / microseismic event positioning method considering temperature effect

Abstract

The invention relates to an acoustic emission / microseismic event positioning method considering a temperature effect. The method comprises the following steps: step 1, establishing a relationship between rock temperature and wave velocity; 2, reconstructing a wave velocity field; and step 3, positioning an acoustic emission / micro-seismic event. According to the method, the problem that the positioning precision is influenced by the wave velocity change caused by the rock temperature change is solved, the wave velocity field of the rock in the freezing and thawing cycle process can be reconstructed by combining the relationship between the temperature and the wave velocity and the rock temperature field distribution numerical simulation, and the acoustic emission / microseismic event is spatially positioned; and the acoustic emission / micro-seismic event positioning error induced by rock temperature change is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic emission / microseismic event locating methods in mines and rock mass engineering, and in particular to an acoustic emission / microseismic event locating method taking temperature effects into consideration. Background Art

[0002] China's cold regions are vast and rich in metal mineral resources. With the continued rapid growth of China's economy, the cold regions will surely become the most important metal mineral base in the future.

[0003] Mining and rock excavation will cause the formation of fracture surfaces inside the rock mass and the propagation of stress waves. This phenomenon is called acoustic emission (high frequency, small amplitude) or microseismic (low frequency, large amplitude). Acoustic emission / microseismic is a concomitant phenomenon of the rock mass fracture process and is closely related to the physical and mechanical behavior of the rock mass. Therefore, the stress state and fracture degree of the rock mass can be inferred by analyzing the acoustic emission / microseismic monitoring information, thereby warning and controlling the destabilization of the rock mass.

[0004] Acoustic emission / microseismic event location is one of the core functions of this technology. Whether accurate positioning can be performed is a key evaluation indicator of whether the acoustic emission / microseismic monitoring system is effective. 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 the corresponding wave velocity to change dramatically. Ignoring this influencing factor will lead to an increase in the error of the positioning result, but the currently widely used positioning algorithm does not consider the impact of rock temperature changes on wave velocity and positioning accuracy. Summary of the invention

[0005] In view of the above problems, an object of the present invention is to provide an acoustic emission / microseismic event location method taking temperature effects into account.

[0006] The technical solution adopted by the present invention is as follows:

[0007] The present invention proposes an acoustic emission / microseismic event location method considering temperature effect, comprising the following steps:

[0008] Step 1, establish the relationship between rock temperature and wave velocity: drill a hole in the rock and place temperature sensors at different depths in the hole, then grout the hole and seal it, place a wave velocity sensor near the hole, change the ambient temperature and measure the rock wave velocity when the temperature in the rock is stable, and establish a fitting relationship between rock temperature and wave velocity v=f(T);

[0009] Step 2: Reconstruction of wave velocity field: Taking the ambient temperature as the boundary condition, simulate the evolution of the temperature field in the rock due to the change of ambient temperature over time, and quantify the wave velocity at different times and locations based on the fitting relationship between rock temperature and wave velocity;

[0010] Step 3. Acoustic emission / microseismic event location: Carry out acoustic emission / microseismic monitoring, assume that 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, rock temperature field simulation: establish a numerical model and perform grid division according to the research object, and apply boundary conditions on the surface of the numerical model according to the ambient temperature:

[0013] When t>0, T w =f 1 (t)

[0014] Where, T w is the boundary temperature; t is the time; f 1 (t) is a function of time t.

[0015] According to the energy conservation control equation:

[0016]

[0017] Where d is the distance between a point in the rock and the boundary; t is time; T(d,t) is the temperature function with respect to position d and time t; α is the thermal diffusivity; L is the latent heat of phase change; ψ is the phase change progress of the water ice phase change.

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

[0019] Step 2.2, quantify the wave velocity at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, quantify the corresponding relationship between the temperature and wave velocity of each grid node at different times: v i =f(T i (t)), by considering the distance power inverse method of the time-space relationship, the velocity corresponding to any point in space at different times can be interpolated:

[0020]

[0021] Where v(t) is the interpolation velocity of any point in space at time t; n is the number of nodes; v i (t) is the wave velocity of grid node i at time t; d i is the spatial distance from the desired point to the grid node i; p is the power, which determines the influence of the distance on the weight.

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

[0023] Step 3.1, Discretization of propagation path: Assuming that the first-arrival AE wave propagates from the AE source to the AE sensor along a straight line, the AE wave from the grid node Q i Propagate to the acoustic emission sensor S j The propagation path equation can be given by the following formula:

[0024]

[0025] Where i is the grid node number; j is the sensor number; x i ,y i 、z i is the three-dimensional coordinate of the grid node; x j ,y j 、z j is the three-dimensional coordinate of the acoustic emission sensor.

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

[0027] P ijn (x ijn ,y ijn ,z ijn )=(x i +nlcosθ 1 sinθ 3 ,y i +nlcosθ 1 cosθ 3 ,z i +nlsinθ 1 )

[0028] Where n is the nth endpoint (n=1,2…N) on the acoustic emission wave propagation path starting from the grid node, θ 1 ,θ 2 ,θ 3 are the angles between the acoustic emission wave propagation path and the coordinate axis.

[0029] Step 3.2, calculate the average wave speed corresponding to the sub-segment: take the wave speed at the midpoint of the sub-segment as the average wave speed of the sub-segment, and the average wave speed of each sub-segment is shown in the following formula:

[0030]

[0031] Wherein, k is the nth sub-segment (k=n+1) on the acoustic emission wave propagation path starting from the grid node.

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

[0033] The travel time of the acoustic emission wave excited by each grid node to propagate to each sensor can be obtained by piecewise accumulation, which is given by the following formula:

[0034]

[0035] Where, T ij is the travel time of the acoustic emission wave excited by grid node i to propagate to sensor j.

[0036] Step 3.4, grid search to determine the location of the AE / microseismic event: Calculate the theoretical arrival time difference vector of each grid node through the travel time obtained by the above theoretical calculation, and then calculate the actual arrival time difference vector of the AE event through the measured arrival time, and select the grid node with the smallest difference between the two as the final AE / microseismic event location to complete the positioning.

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

[0038] The method of the present invention overcomes the problem of positioning accuracy being affected by wave velocity changes caused by rock temperature changes. It can combine the relationship between temperature and wave velocity and the numerical simulation of rock temperature field distribution to reconstruct the wave velocity field of rock during the freeze-thaw cycle, and then spatially locate the acoustic emission / microseismic events, significantly reducing the positioning error of acoustic emission / microseismic events induced by rock temperature changes. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0040] Figure 2 is the fitting curve diagram of temperature T and wave speed v established;

[0041] Figure 3 To establish a numerical model of freeze-thaw cycle;

[0042] Figure 4 is the numerical model after meshing;

[0043] Figure 5 is the function graph of boundary conditions;

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

[0045] Figure 7 is the numerical simulation result of wave velocity field reconstruction during freeze-thaw cycle, where Figure 7 a is the numerical simulation result of wave velocity field reconstruction at 0.26h, Figure 7 b is the numerical simulation result of wave velocity field reconstruction at 0.31h, Figure 7 c is the numerical simulation result of wave velocity field reconstruction at 0.64h. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] See attached Figure 1-7 The present invention proposes an acoustic emission / microseismic event location method considering temperature effect, and the specific implementation process is as follows:

[0048] Step 1, establish the relationship between rock temperature and wave velocity: drill holes in the rock and place temperature sensors at different depths in the holes, then grout the holes and seal them. Place wave velocity sensors near the holes, change the ambient temperature and measure the rock wave velocity when the temperature inside the rock is stable, and establish a fitting relationship v=f(T) between rock temperature and wave velocity.

[0049] Step 2: Reconstruction of wave velocity field: Taking the ambient temperature as the boundary condition, simulate the evolution of the temperature field in the rock due to the change of ambient temperature over time. 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, rock temperature field simulation: establish a numerical model and perform grid division according to the research object, and apply boundary conditions on the surface of the numerical model according to the ambient temperature:

[0052] When t>0, T w =f 1 (t)

[0053] Where, T w is the boundary temperature; t is the time; f 1 (t) is a function of time t.

[0054] According to the energy conservation control equation:

[0055]

[0056] Where d is the distance between a point in the rock and the boundary; t is time; T(d,t) is the temperature function with respect to position d and time t; α is the thermal diffusivity; L is the latent heat of phase change; ψ is the phase change progress of the water ice phase change.

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

[0058] Step 2.2, quantify the wave velocity at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, quantify the corresponding relationship between the temperature and wave velocity of each grid node at different times: v i =f(T i (t)), by considering the distance power inverse method of the time-space relationship, the velocity corresponding to any point in space at different times can be interpolated:

[0059]

[0060] Where v(t) is the interpolation velocity of any point in space at time t; n is the number of nodes; v i (t) is the wave velocity of grid node i at time t; d i is the spatial distance from the desired point to the grid node i; p is the power, which determines the influence of the distance on the weight.

[0061] Step 3. Acoustic emission / microseismic event location: Carry out acoustic emission / microseismic monitoring, assume that 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 location method in step 3 specifically comprises the following steps:

[0063] Step 3.1, Discretization of propagation path: Assuming that the first-arrival AE wave propagates from the AE source to the AE sensor along a straight line, the AE wave from the grid node Q i Propagate to the acoustic emission sensor S j The propagation path equation can be given by the following formula:

[0064]

[0065] Where i is the grid node number; j is the sensor number; x i ,yi 、z i is the three-dimensional coordinate of the grid node; x j ,y j 、z j is the three-dimensional coordinate of the acoustic emission sensor.

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

[0067] P ijn (x ijn ,y ijn ,z ijn )=(x i +nlcosθ 1 sinθ 3 ,y i +nlcosθ 1 cosθ 3 ,z i +nlsinθ 1 )

[0068] Where n is the nth endpoint (n=1,2…N) on the acoustic emission wave propagation path starting from the grid node, θ 1 ,θ 2 ,θ 3 are the angles between the acoustic emission wave propagation path and the coordinate axis.

[0069] Step 3.2, calculate the average wave speed corresponding to the sub-segment: take the wave speed at the midpoint of the sub-segment as the average wave speed of the sub-segment, and the average wave speed of each sub-segment is shown in the following formula:

[0070]

[0071] Wherein, k is the nth sub-segment (k=n+1) on the acoustic emission wave propagation path starting from the grid node.

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

[0073] The travel time of the acoustic emission wave excited by each grid node to propagate to each sensor can be obtained by piecewise accumulation, which is given by the following formula:

[0074]

[0075] Where, T ij is the travel time of the acoustic emission wave excited by grid node i to propagate to sensor j.

[0076] Step 3.4, grid search to determine the location of the AE / microseismic event: Calculate the theoretical arrival time difference vector of each grid node through the travel time obtained by the above theoretical calculation, and then calculate the actual arrival time difference vector of the AE event through the measured arrival time, and select the grid node with the smallest difference between the two as the final AE / microseismic event location to complete the positioning.

[0077] Example:

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

[0079] v=-6.7235T+4904.1358

[0080] 2. A freeze-thaw cycle numerical model with a diameter of 50 m and a height of 100 m was established according to the research object. To enhance the complexity of the wave velocity field in the model, a vertical crack with a length of 30 m and a width of 3 m was prefabricated on the upper part of the model, and an inclined crack with a length of 30 m and a width of 2 m and an angle of 45° with the horizontal plane was prefabricated on the side of the model, and water was filled in the vertical cracks, such as Figure 3 shown.

[0081] 3. Yes Figure 3 The numerical model is meshed, such as Figure 4 As shown, the boundary conditions are applied on the surface based on the ambient temperature, and its function curve with respect to time t is as follows Figure 5 shown.

[0082] 4. Simulate the evolution of the temperature field in the rock over time, and according to the simulation results (such as Figure 6 Output of each grid node Q at different time t i The corresponding temperature T i (t).

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

[0084] 6. Assume that there are 4 sensors S1, S2, S3, and S4, and the sensor position coordinates are shown in Table 1. Taking sensor S1 and grid node Q1 with coordinates (25, 25, 50) as an example, the propagation path equation of the acoustic emission wave is obtained:

[0085]

[0086] Table 1

[0087] sensor x y z S1 50 25 20 S2 25 50 30 S3 0 25 70 S4 25 0 80

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

[0089] Table 2

[0090] Endpoints x y z <![CDATA[P 111 ]]> 25.6401 25.0000 49.2318 <![CDATA[P 112 ]]> 26.2804 25.0000 48.4636 <![CDATA[P 113 ]]> 26.9206 25.0000 47.6953 <![CDATA[P 114 ]]> 27.5607 25.0000 46.9271

[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 velocity of some sub-segments is shown in Table 3.

[0092] Table 3

[0093] Sub-segment number Wave speed 1 4903.1300 2 4904.2185 3 4904.2185 4 4909.5146

[0094] 9. The travel time of the acoustic emission wave excited by each grid node propagating to each sensor is accumulated piecewise. Taking the grid node Q1 with coordinates (25, 25, 50) as an example, the travel time results are shown in Table 4.

[0095] Table 4

[0096] sensor Travel time S1 0.0081535 S2 0.0067244 S3 0.0067306 S4 0.0081587

[0097] 10. Subtract the shortest travel time from the travel time corresponding to each sensor in Table 4 to obtain the theoretical time difference vector of grid node Q1, as shown in Table 5.

[0098] Table 5

[0099] sensor Travel time S1 0.0014291 S2 0 S3 0.0000062 S4 0.0014343

[0100] 11. Table 6 shows the real arrival time difference vector of the acoustic emission / microseismic event monitored on site. A matching search is performed on the theoretical arrival time difference vector of all grid nodes, and it is found that it is closest to the theoretical arrival time difference vector with the grid node (25, 25, 50) as the acoustic emission source, that is, the difference between the two is the smallest. Therefore, the coordinates of the actual acoustic emission / microseismic event are determined to be (25, 25, 50).

[0101] Table 6

[0102]

[0103]

[0104] Matters not described in detail in the present invention are all known technologies.

[0105] The embodiments described above are merely descriptions of preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for locating acoustic emission / microseismic events considering temperature effects, characterized in that: The steps include: Step 1, establish the relationship between rock temperature and wave velocity: drill a hole in the rock and place temperature sensors at different depths in the hole, then grout the hole and seal it, place a wave velocity sensor near the hole, change the ambient temperature and measure the rock wave velocity when the temperature in the rock is stable, and establish a fitting relationship between rock temperature and wave velocity v=f(T); Step 2: Reconstruction of wave velocity field: Taking the ambient temperature as the boundary condition, simulate the evolution of the temperature field in the rock due to the change of ambient temperature over time, and quantify the wave velocity at different times and locations based on the fitting relationship between rock temperature and wave velocity; Step 3. Acoustic emission / microseismic event location: Carry out acoustic emission / microseismic monitoring, assume that 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 location method considering temperature effect according to claim 1, characterized in that: In step 2, the wave velocity field reconstruction specifically includes: Step 2.1, rock temperature field simulation: establish a numerical model and perform grid division according to the research object, and apply boundary conditions on the surface of the numerical model according to the ambient temperature: When t > 0, T w = f1(t) Where, T w is the boundary temperature; t is the time; f1(t) is the function of time t. According to the energy conservation control equation: Where d is the distance between a point in the rock and the boundary; t is time; T(d,t) is the temperature function with respect to position d and time t; α is the thermal diffusivity; L is the latent heat of phase change; ψ is the phase change progress of the water ice phase change. Simulate the evolution of the temperature field in the rock over time and output the temperature T corresponding to each grid node at different times i (t), where i is the grid node number. Step 2.2, quantify the wave velocity at different times and locations: Based on the fitting relationship between rock temperature and wave velocity, quantify the corresponding relationship between the temperature and wave velocity of each grid node at different times: v i =f(T i (t)), by considering the distance power inverse method of the time-space relationship, the velocity corresponding to any point in space at different times can be interpolated: Where v(t) is the interpolation velocity of any point in space at time t; n is the number of nodes; v i (t) is the wave velocity of grid node i at time t; d i is the spatial distance from the desired point to the grid node i; p is the power, which determines the influence of the distance on the weight.

3. The acoustic emission / microseismic event location method considering temperature effect according to claim 1, characterized in that: The acoustic emission / microseismic event positioning in step 3 specifically includes the following steps: Step 3.1, Discretization of propagation path: Assuming that the first-arrival AE wave propagates from the AE source to the AE sensor along a straight line, the AE wave from the grid node Q i Propagate to the acoustic emission sensor S j The propagation path equation can be given by the following formula: Where i is the grid node number; j is the sensor number; x i ,y i 、z i is the three-dimensional coordinate of the grid node; x j ,y j 、z j is the three-dimensional coordinate of the acoustic emission sensor. The acoustic emission wave propagation path between the grid node and the sensor is divided into N+1 sub-segments with N endpoints at intervals of l. The endpoint coordinates P on the propagation path are ijn (x ijn ,y ijn ,z ijn ) and its number N is given by: P ijn (x ijn ,y ijn ,z ijn )=(x i +nlcosθ1sinθ3,y i +nlcosθ1cosθ3,z i +nlsinθ1) Where n is the nth endpoint (n=1, 2…N) on the acoustic emission wave propagation path starting from the grid node, and θ1, θ2, and θ3 are the angles between the acoustic emission wave propagation path and the coordinate axis, respectively. Step 3.2, calculate the average wave speed corresponding to the sub-segment: take the wave speed at the midpoint of the sub-segment as the average wave speed of the sub-segment, and the average wave speed of each sub-segment is shown in the following formula: Wherein, k is the nth sub-segment (k=n+1) on the acoustic emission wave propagation path starting from the grid node. Step 3.3: Accumulate the travel time of each sub-segment of the propagation path: The travel time of the acoustic emission wave excited by each grid node to propagate to each sensor can be obtained by piecewise accumulation, which is given by the following formula: Where, T ij is the travel time of the acoustic emission wave excited by grid node i to propagate to sensor j. Step 3.4, grid search to determine the location of the AE / microseismic event: Calculate the theoretical arrival time difference vector of each grid node through the travel time obtained by the above theoretical calculation, and then calculate the actual arrival time difference vector of the AE event through the measured arrival time, and select the grid node with the smallest difference between the two as the final AE / microseismic event location to complete the positioning.

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