Consider rock anisotropy deterioration sound signal high-precision spatial positioning method and device

Abstract

This invention provides a high-precision spatial positioning method and apparatus for acoustic signals considering the anisotropic degradation of rock masses, relating to the field of geotechnical engineering. Addressing the heterogeneity of rock mass structures and the anisotropy of acoustic signal propagation in existing technologies, a novel wave velocity attenuation model considering initial wave velocity anisotropy and the evolution of anisotropic fracture processes is proposed to achieve more accurate acoustic emission spatial positioning. This method deploys acoustic emission sensors on rock samples and collects source waveform data. Full stress-strain curves are obtained through true triaxial experiments, and a functional model of damage versus wave velocity is established. Through optimization algorithms, a source positioning objective function is constructed and iterated to ultimately determine the source location. This method can also analyze the energy and failure mechanism of acoustic emission signals, achieving high-precision quantitative characterization of failure location, microscopic mechanisms, and released energy during the fracture process of anisotropic rock masses.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, and in particular to a high-precision spatial positioning method and apparatus for acoustic signals considering the anisotropic degradation of rock mass. Background Technology

[0002] Due to the heterogeneity of natural rock mass structures, the inherent structural anisotropy of the rock mass leads to initial anisotropy. When the rock mass fractures under stress, the anisotropic evolution of wave velocity caused by anisotropic damage mechanisms results in anisotropic wave velocity during sound wave propagation within the rock mass. Current single-velocity models do not consider the anisotropy of wave velocity during rock mass propagation, leading to inaccurate localization. In the field of multiphysics monitoring of rock mass fractures, signals at different frequency bands exhibit significant differences: acoustic signals (SS) primarily exhibit low-frequency characteristics, microseismic signals (MS) show mid-to-low-frequency characteristics, while acoustic signals (AE) cover the high-frequency range. Traditional monitoring methods, limited by the physical characteristics of single-band acquisition equipment, often only acquire fracture information in local frequency bands, making it difficult to establish a complete fracture evolution characteristic spectrum.

[0003] Traditional moment tensor localization methods have significant limitations in practical engineering applications, requiring at least six acoustic emission probes to simultaneously receive valid signals for accurate positioning. However, due to complex geological conditions and limited sensor deployment, it is often difficult to meet the requirement of synchronous reception by multiple probes, leading to a significant reduction in positioning accuracy. These methods are prone to getting trapped in local minima, are susceptible to the influence of individual sensors, suffer from low stability, and lack universal applicability. Therefore, source localization algorithms and models considering the anisotropic degradation velocity of rock masses require further research.

[0004] In summary, current acoustic signal monitoring still has the following shortcomings: While existing velocity models can reflect the true wave velocity of rock masses, they cannot reflect the wave velocity attenuation law after rock deterioration. Existing positioning algorithms require a large number of acoustic emission probes, resulting in extremely high computational load and unstable positioning, which significantly reduces positioning efficiency. Furthermore, the signal energy responses received by different acoustic emission probes exhibit significant differences; however, a unified technical standard for determining the standardized energy response has not yet been established, making it difficult to guarantee the reliability and comparability of energy calculation results. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-precision spatial positioning method and device that considers the anisotropic degradation of rock mass acoustic signals. Specifically, it proposes an algorithm based on a small number of probe signals, considering the wave velocity evolution of the rock mass's initial anisotropy and the anisotropy of the fracturing process. Through innovative optimization of the signal processing algorithm and positioning model, only a small number of effective signals are needed to accurately determine the rock mass fracturing mechanism, significantly improving the method's engineering applicability and positioning reliability. Furthermore, it establishes a standardized determination method for energy response based on multi-probe signals. By constructing an energy correction model and a standardized processing flow, it achieves simultaneous and accurate identification of fracturing mechanisms and energy characteristics.

[0006] On the one hand, a high-precision spatial localization method for acoustic signals considering the anisotropic deterioration of rock mass includes the following steps:

[0007] Step S1: Deploy and bond full-band broadband sensors on the rock sample. There are four full-band broadband sensors, which are used to collect low, medium and high frequency micro-vibrations and acoustic emission signals. Establish four rectangular measurement areas for the sensors. The full-band broadband sensors collect source waveform data and calculate the arrival time difference between other full-band broadband sensors and the reference node.

[0008] In step S1, a full-band broadband sensor is deployed on the edge of the cuboid measurement area of ​​the rock mass. Adhesive is applied during deployment to establish a measurement area in the shape of a cuboid. The full-band broadband sensor collects the acoustic emission source waveform and obtains the time it takes for the source wave to travel from the source to the observation point. One of the full-band broadband sensors is selected as a reference node. By performing difference calculations, the arrival time difference between the other three full-band broadband sensors and the selected full-band broadband sensor is obtained.

[0009] Step S2: Conduct true triaxial experiments at different stress levels, obtain stress-strain curves, establish a damage deterioration equation, and simultaneously measure the wave velocity during rock fracture to establish a function of damage and wave velocity.

[0010] Specifically, the following steps were taken: When the rock was in its initial state, the wave velocity measurement function of a full-band broadband sensor was used. One full-band broadband sensor was selected as a reference node, and pairwise velocity measurements were performed between it and the other three full-band broadband sensors to obtain the wave velocity in the three directions of the initial rock. Subsequently, a true triaxial test of rock degradation under different stress conditions was conducted. During the experiment, measurements were performed between the sensor and the reference node sensor to obtain the wave velocity in the three directions and the total stress-strain curve during rock failure. The principal stress σ of the rock during the true triaxial test of rock degradation was also measured. i Anisotropic deformation modulus K in the direction i The calculation formula is defined as follows:

[0011] ;

[0012] Among them, K i The axial stress increment dσ and the three elastic principal strain increments dε during each loading and unloading cycle of the rock specimen. ie The absolute value of the ratio is used to determine the K value. i Define the Young's modulus E in the three principal directions i Let i = 1, 2, 3. Assume that the initial values ​​of Young's moduli E1, E2, and E3 in the three directions are the same. Then E i The definition is as follows:

[0013] ;

[0014] Where μ1 and μ2 are the lateral Young's modulus scaling factors, defined as:

[0015] ;

[0016] Where K 1,0 For the principal stress σ of the rock i Initial deformation modulus in the direction, K 2,0 and K 3,0 The initial deformation modulus in the lateral direction;

[0017] Based on the evolution law of mechanical parameters and nonlinear fitting methods, E1, E2, and E3 are fitted with the equivalent plastic strain ε. p Evolution equation of changing parameters:

[0018] ;

[0019] ;

[0020] ;

[0021] Among them, a i b i and c i All are fitting coefficients related to the second principal stress σ2 and the third principal stress σ3, i = 1, 2, 3, E i,0 Let ε be the initial elastic modulus. 1p ε 2p and ε 3p These represent the maximum, intermediate, and minimum principal plastic strains, respectively. 1i a 2i a 3i a 4i a 5i b is a weighting coefficient describing the evolution of the plastic strain component with respect to the elastic modulus E. 1i b 2ib 3i b 4i b 5i To control the nonlinear influence coefficient of plastic strain on modulus decay or hardening, c 1i c 2i c 3i c 4i c 5i The coefficients represent the quadratic or higher-order corrections to the modulus evolution due to plastic strain; wave velocity and Young's modulus E in three directions. i The relationship is as follows:

[0022] ;

[0023] in, It is Young's modulus. It's density. It is wave velocity, i =1,2,3 It is the Kronecker function, P ij It is a permutation matrix, and the wave velocity decreases as the rock deteriorates. Its evolution equation is as follows:

[0024] ;

[0025] Where t is the propagation time, The initial wave velocity is α, and the attenuation coefficient is α.

[0026] Step S3: Based on the time difference of arrival and considering the attenuation law of wave velocity with rock deterioration, establish the objective function for seismic source location, calculate the source location through the sensor arrival time, and optimize the residual formula using the L1 norm.

[0027] Specifically: Assume there exists a seismic source on a two-dimensional plane, with coordinates P=[xy]. T The full-band wideband sensor is located in S j = [x j y j ], j = 1,2,3...n, where n is the number of sensors; therefore, the time t is for the P-wave to be emitted from the source point and received by the j-th sensor. j for:

[0028] ;

[0029] Where v represents the propagation speed of the P-wave in the positioning model, d z Let (x0, y0) represent the distance between the z-th sensor and the earthquake source, where (x0, y0) are the coordinates of the earthquake source.

[0030] If we take the full-band broadband sensor T1 as the reference node, then the arrival time difference between the z-th sensor and T1 is expressed as:

[0031] ;

[0032] When the survey area is in three-dimensional space, the time for the i-th sensor to receive the source signal relative to the reference node is expressed as:

[0033] ;

[0034] Where (x0, y0, z0) are the coordinates of the seismic source in three-dimensional space, and (x1, y1, z1) are the coordinates of the reference full-band broadband sensor in three-dimensional space. i y i , z i () represents the coordinates of other full-band broadband sensors in three-dimensional space.

[0035] make , ;

[0036] The residual formula f based on the time difference positioning model is obtained as follows:

[0037] ;

[0038] Where R i R1 and R2 represent the earthquake source P to the full-band broadband sensor S, respectively. i The distance to the full-band broadband sensor S1, i=1,2,3...n, V i Let v be a function of damage and wave velocity, where k is the norm; based on the previously established function of damage and wave velocity, replace v in that function with the newly established function of damage and wave velocity, V. i The resulting formula is as follows:

[0039] ;

[0040] When there is data with an error greater than the set threshold, the minimum absolute value method, i.e., the L1 norm, is used to optimize the objective function. That is, the sum of the absolute values ​​of the residuals of the time difference between each full-band broadband sensor and the reference full-band broadband sensor is used as the objective function f, i.e., k is set to 1, as shown in the formula:

[0041] ;

[0042] The minimum value of the objective function f is obtained by iteratively optimizing the algorithm to obtain the location of the earthquake source (x0, y0, z0). When the objective function value f reaches its minimum value, the obtained (x0, y0, z0) is the optimal location of the earthquake source.

[0043] Step S4: Select four points within a threshold range around the center of the full-band broadband sensor, establish an initial tetrahedron and calculate the residuals. Compare the residuals of the four vertices, eliminate the point with the largest residual, and optimize the tetrahedron through iterative stretching and shrinking operations until the residual value reaches the threshold.

[0044] In step S4, four points are arbitrarily selected near the center of the sensor array, and an initial tetrahedron is established in three-dimensional space. Based on the coordinates of each vertex of the initial tetrahedron, considering the initial wave velocity anisotropy and the wave velocity attenuation to time difference during the anisotropic breakup process, the residual formula optimized by the L1 norm is used to calculate the residuals for these four vertices. The residual values ​​are calculated, and the residual values ​​of the four vertices are compared to find the minimum point f of the residual function at the vertex of the initial tetrahedron. min and the maximum point f max Then calculate the straightness d of each side of the initial tetrahedron, and then eliminate the point f with the largest residual difference. max The tetrahedron is continuously stretched and contracted, and new vertices are replaced and supplemented to construct new simplex tetrahedrons. It is determined whether the minimum residual value f reaches the predetermined threshold of the function iteration. If it does not reach the threshold, the search continues and a new simplex tetrahedron is constructed. If the residual value of the positioning target function is less than the predetermined threshold, the coordinate value of the vertex of the simplex is the positioning coordinate point.

[0045] Step S5: When the residual value reaches the termination threshold, within the space where the error of the optimal value is less than 1, use the random number generation command to randomly select hundreds of minimum spacing points and compare them with the optimal value residual value f. k The residual value f with random points i If f k <f i If f is the global optimum, then f is the global optimum. i If the value is greater than f, then the point is a local optimum. Exit the calculation, reselect the initial point, and jump to step S4 to perform the search calculation.

[0046] Step S6: Calculate the optimal location of the seismic source by back-calculating the source coordinate parameters corresponding to the minimum residual value;

[0047] Step S7: Based on the SS / MS / AE signal information received by each probe during the positioning event, determine the type of microscopic damage and the positioning energy;

[0048] Specifically as follows:

[0049] The SS / MS / AE wave signal information received by each full-band broadband sensor at the failure location is recorded and stored in the computer system. Inversion analysis is performed on the location events at the rock fracture location, and the fracture type is divided into tensile fracture, shear fracture and mixed tensile-shear fracture. The failure type of the rock at different time periods is determined as follows.

[0050] RA = Duration / Amplitude;

[0051] AF = Ringing count AE / Duration;

[0052] For the classification of RA and AF, the best boundary line method is adopted. The slope of the dividing line AF / RA is defined as N. Signals with AF / RA < N are defined as tensile fracture signals, and signals with AF / RA > N are defined as shear fracture signals. By conducting pure tension experiments and pure shear experiments on rocks, the slope of the dividing line N is determined;

[0053] The computer system determines the level of the positioning event energy. It is the set of SS, MS, and AE signals generated by the same fracture source received by the full-bandwidth broadband sensor. The positioning event energy is the area integral under the signal detection envelope from the start time of receiving the first signal to the end time of the last signal. The specific calculation is as follows:

[0054] ;

[0055] G5 = n1G1 + n2G2 + n3G3 + n4G4;

[0056] Where E is the positioning event energy, is the end time of the last signal, is the start time of the first signal, , , , are the weighting coefficients, G1, G2, G3, G4, G5 are the signals received by the full-bandwidth broadband sensor, A ring is the amplitude of the collected acoustic signal, f is the frequency of the collected acoustic signal; The computer system determines the level of the positioning event energy according to the above calculation. The higher the energy level, the larger the scatter point size and the darker the color.

[0057] On the other hand, a high-precision spatial positioning device for acoustic signals considering the anisotropic degradation of rock masses, which is used to implement the aforementioned positioning method, specifically includes:

[0058] Input module: It is used to collect the stress-strain data under true triaxial experiments at different stress levels and the original data of the SS, MS, and AE acoustic signal information of each full-bandwidth broadband sensor at the rock failure position;

[0059] Classification and storage module: Classify, screen, extract, integrate, and store the original data to obtain the damage degradation data and the set of SS, MS, and AE acoustic signal information generated by the fracture source;

[0060] Processing module: Based on damage and degradation data and wave velocity data from three directions, a damage-wave velocity function considering the anisotropy of initial wave velocity and the evolution of anisotropic fracture process is constructed. A source location objective function based on arrival time difference and considering the wave velocity attenuation law with rock degradation is established. SS / MS / AE acoustic signal information is trained, calculated, and analyzed and tested. Based on the analysis and processing results, location events are obtained, verified, and the acoustic signal information is classified and stored by individual location events.

[0061] Reprocessing module: Calculates, analyzes, optimizes, extracts and integrates the acoustic signal information of each location event, determines its microscopic damage type and location energy, and verifies and stores it again;

[0062] Output module: Outputs rock fracture location events, micro-damage types, location energy calculation results, and verification results in chart form for users to view.

[0063] The beneficial effects of adopting the above technical solution are as follows:

[0064] This invention provides a high-precision spatial positioning method and apparatus for acoustic signals considering the anisotropic deterioration of rock masses, which has the following beneficial effects:

[0065] 1. This invention improves the L1 norm positioning model based on arrival time difference, and solves the problem of inaccurate positioning caused by the initial anisotropy of natural rock masses and the anisotropy of wave velocity attenuation due to rock mass damage and deterioration. It proposes a wave velocity attenuation model that considers the initial wave velocity anisotropy and the evolution of the anisotropic fracture process, which is more accurate than the single velocity model, making the positioning more stable and accurate.

[0066] 2. This invention improves the termination condition of the simplex spatial positioning iterative algorithm. Based on the wave velocity attenuation model that considers the anisotropy of the initial wave velocity and the evolution of the anisotropic rupture process, an improved simplex acoustic emission spatial positioning method is proposed. This method reduces the influence of individual discrete points, has good solution stability and high accuracy. Compared with the traditional time difference positioning algorithm based on a single velocity model, the positioning accuracy is improved, and the number of sensors required is reduced to 4 to achieve positioning.

[0067] 3. This invention innovatively developed a full-band signal synchronous acquisition system, which achieved breakthrough collaborative sensing of wide-band signals. By establishing an energy coupling model for multi-band signals, it successfully solved the technical problems of poor spatiotemporal correlation and difficulty in feature fusion of signals in different frequency bands, providing a new technical paradigm for multi-scale characterization of the entire process of rock mass fracture.

[0068] 4. This invention establishes a standardized method for determining energy response based on multi-probe signals. By constructing an energy correction model and a standardized processing flow, it achieves simultaneous and accurate identification of fracture mechanisms and energy characteristics. This method can not only obtain the energy released at each location point during fracture but also easily assess the magnitude of this energy, accurately determine the fracture mechanism at each location point, solve the technical problem of standardized energy response determination, and significantly improve the accuracy and reliability of fracture mechanism analysis, providing a new technical means for the quantitative characterization of rock mass fracture processes. Attached Figure Description

[0069] Figure 1 This is a cuboid-shaped model diagram of the high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass in this invention.

[0070] Figure 2 This is a two-dimensional acoustic signal to time difference method positioning model diagram for the high-precision spatial positioning method of acoustic signals considering the anisotropic deterioration of rock mass in this invention;

[0071] Figure 3 A flowchart of a high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass provided in an embodiment of the present invention;

[0072] Figure 4 This is a schematic diagram of a cloud server device provided in an embodiment of the present invention;

[0073] Figure 5 This is a schematic diagram of the high-precision spatial positioning device for acoustic signals considering the anisotropic deterioration of rock mass provided in an embodiment of the present invention.

[0074] Figure 6 These are the full stress-strain curves simulated by the three-dimensional degradation model under different σ2 and σ3 stresses in the embodiments of the present invention;

[0075] Figure 7 These are the elastic modulus curves simulated by the three-dimensional degradation model under different σ2 and σ3 stresses in the embodiments of the present invention;

[0076] Figure 8 This is a diagram showing the acoustic signal and strain processing results of a true triaxial shear failure test of granite under multi-level disturbance in an embodiment of the present invention.

[0077] Figure 9 This is a diagram showing the location and energy processing results of the location event in the true triaxial shear failure test of granite under multi-stage disturbance in an embodiment of the present invention.

[0078] Figure 10 This is a diagram showing the results of microscopic damage type processing in a true triaxial shear failure test of granite under multi-level disturbance in an embodiment of the present invention. Detailed Implementation

[0079] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0080] On the one hand, a high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass, such as... Figure 3 As shown, it includes the following steps:

[0081] Step S1: Deploy and bond full-band broadband sensors on the rock sample. There are four full-band broadband sensors, which are used to collect low, medium and high frequency micro-vibrations and acoustic emission signals. Establish four rectangular measurement areas for the sensors. The full-band broadband sensors collect source waveform data and calculate the arrival time difference between other full-band broadband sensors and the reference node.

[0082] In step S1, a full-band broadband sensor is deployed on the edge of the cuboid measurement area of ​​the rock mass. Adhesive is applied during deployment to establish a measurement area in the shape of a cuboid. The full-band broadband sensor collects the acoustic emission source waveform and obtains the time it takes for the source wave to travel from the source to the observation point. One of the full-band broadband sensors is selected as a reference node. By performing difference calculations, the arrival time difference between the other three full-band broadband sensors and the selected full-band broadband sensor is obtained.

[0083] In this example, the rock sample selected was a massive, homogeneous, medium-to-coarse-grained granite. It was further finely cut and polished into 100mm × 100mm × 100mm specimens. Low, medium, and high frequency acoustic signal SS / MS / AE broadband sensors were deployed and bonded onto the rock sample to establish its cuboid measurement area. Figure 1 As shown, sensor T1 is used as the reference node sensor. A non-metallic ultrasonic detector is used to perform P-wave velocity measurements on the reference node sensor and three other sensors. Each group of velocities is measured three times, and the average value is taken. The results are shown in Table 1 below.

[0084] Table 1. Wave velocity measured by the sensor in this example:

[0085]

[0086] Step S2: Conduct true triaxial experiments at different stress levels, obtain stress-strain curves, establish a damage deterioration equation, and simultaneously measure the wave velocity during rock fracture to establish a function of damage and wave velocity.

[0087] Specifically, the following steps were taken: When the rock was in its initial state, the wave velocity measurement function of a full-band broadband sensor was used. One full-band broadband sensor was selected as a reference node, and pairwise velocity measurements were performed between it and the other three full-band broadband sensors to obtain the wave velocity in the three directions of the initial rock. Subsequently, a true triaxial test of rock degradation under different stress conditions was conducted. During the experiment, measurements were performed between the sensor and the reference node sensor to obtain the wave velocity in the three directions and the total stress-strain curve during rock failure. The principal stress σ of the rock during the true triaxial test of rock degradation was also measured. i Anisotropic deformation modulus K in the direction i The calculation formula is defined as follows:

[0088] ;

[0089] Among them, K i The axial stress increment dσ and the three elastic principal strain increments dε during each loading and unloading cycle of the rock specimen. ie The absolute value of the ratio is used to determine the K value. i Define the Young's modulus E in the three principal directions i Let i = 1, 2, 3. Assume that the initial values ​​of Young's moduli E1, E2, and E3 in the three directions are the same. Then E i The definition is as follows:

[0090] ;

[0091] Where μ1 and μ2 are the lateral Young's modulus scaling factors, defined as:

[0092] ;

[0093] Where K 1,0 For the principal stress σ of the rock i Initial deformation modulus in the direction, K 2,0 and K 3,0 The initial deformation modulus in the lateral direction;

[0094] Based on the evolution law of mechanical parameters and nonlinear fitting methods, E1, E2, and E3 are fitted with the equivalent plastic strain ε. p Evolution equation of changing parameters:

[0095] ;

[0096] ;

[0097] ;

[0098] Among them, a i b i and c iAll are fitting coefficients related to the second principal stress σ2 and the third principal stress σ3, i = 1, 2, 3, E i,0 Let ε be the initial elastic modulus. 1p ε 2p and ε 3p These represent the maximum, intermediate, and minimum principal plastic strains, respectively. 1i a 2i a 3i a 4i a 5i b is a weighting coefficient describing the evolution of the plastic strain component with respect to the elastic modulus E. 1i b 2i b 3i b 4i b 5i To control the nonlinear influence coefficient of plastic strain on modulus decay or hardening, c 1i c 2i c 3i c 4i c 5i The coefficients are quadratic or higher-order corrections to the modulus evolution due to plastic strain, and the results are as follows: Figure 6 and Figure 7 As shown; wave velocity and Young's modulus E in three directions i The relationship is as follows:

[0099] ;

[0100] in, It is Young's modulus. It's density. It is wave velocity, i =1,2,3 It is the Kronecker function, P ij This is a permutation matrix. Generally, the attenuation of wave velocity in rock masses may be affected by changes in the microstructure of the medium, such as the development and propagation of cracks. The wave velocity attenuates as the rock deteriorates, and its evolution equation is as follows:

[0101] ;

[0102] Where t is the propagation time, It is the initial wave velocity, which depends on the angle θ, and α is the attenuation coefficient, which depends on the rock material;

[0103] In this example, based on the true triaxial compression test of granite under different stresses, and by measuring the changes in wave velocity during the failure process of the granite, the wave velocity and total stress-strain curves in three directions during the rock failure process were obtained. Table 2 below shows the experimental results:

[0104] Table 2. Wave velocity and total stress-strain data in three directions for this example:

[0105]

[0106] Based on the above experimental results and combined with theory, the evolution equation of wave velocity attenuation with rock degradation in this example is as follows:

[0107] ;

[0108] Step S3: Based on the time difference of arrival and considering the attenuation of wave velocity with rock deterioration, establish the objective function for seismic source location. Calculate the source location using the sensor arrival time. Refer to [reference needed] for the specific calculation model. Figure 2 As shown, the residual formula is optimized using the L1 norm;

[0109] Specifically: Assume there exists a seismic source on a two-dimensional plane, with coordinates P=[xy]. T The full-band wideband sensor is located in S j = [x j y j ], j = 1,2,3...n, where n is the number of sensors; therefore, the time t is for the P-wave to be emitted from the source point and received by the j-th sensor. j for:

[0110] ;

[0111] Where v represents the propagation speed of the P-wave in the positioning model, d z Let (x0, y0) represent the distance between the z-th sensor and the earthquake source, where (x0, y0) are the coordinates of the earthquake source.

[0112] If we take the full-band broadband sensor T1 as the reference node, then the arrival time difference between the z-th sensor and T1 is expressed as:

[0113] ;

[0114] When the survey area is in three-dimensional space, the time for the i-th sensor to receive the source signal relative to the reference node is expressed as:

[0115] ;

[0116] Where (x0, y0, z0) are the coordinates of the seismic source in three-dimensional space, and (x1, y1, z1) are the coordinates of the reference full-band broadband sensor in three-dimensional space. i y i , z i () represents the coordinates of other full-band broadband sensors in three-dimensional space.

[0117] make , ;

[0118] The residual formula f based on the time difference positioning model is obtained as follows:

[0119] ;

[0120] Where R i R1 and R2 represent the earthquake source P to the full-band broadband sensor S, respectively. i The distance to the full-band broadband sensor S1, i=1,2,3...n, V i Let v be a function of damage and wave velocity, where k is the norm; based on the previously established function of damage and wave velocity, replace v in that function with the newly established function of damage and wave velocity, V. i The resulting formula is as follows:

[0121] ;

[0122] When data with errors exceeding a set threshold exists, the L2 norm, by solving the objective function in a squared manner (k=2), will increase the dispersion of results with larger deviations. In contrast, the L1 norm uses the sum of absolute residuals from each station as the objective function, without emphasizing the role of any single station in the location calculation. Therefore, it effectively reduces the impact of individual outliers on the source calculation results and is more suitable for source location calculations. The minimum absolute value method, i.e., the L1 norm, will be used to optimize the objective function. Specifically, the sum of the absolute values ​​of the residuals between each full-band broadband sensor and the reference full-band broadband sensor is used as the objective function f, where k is set to 1, as shown in the formula:

[0123] ;

[0124] The location of the earthquake source (x0, y0, z0) is obtained by iteratively solving the objective function value f using an optimization algorithm. When the objective function value f reaches its minimum value, the obtained (x0, y0, z0) is the optimal location of the earthquake source, and only 4 sensors are needed to achieve the location.

[0125] Based on the experimental results above, this example categorizes the collected data into three types according to the normal, lateral, and shear directions, and correlates stress and strain accordingly. Based on this, a wave velocity attenuation model considering initial wave velocity anisotropy and the evolution of the anisotropic rupture process is constructed, combined with the following relationship: The location of the earthquake source (x0, y0, z0) can be obtained by iteratively solving the function f through optimization algorithm. When the objective function value f reaches its minimum value, the obtained (x0, y0, z0) is the optimal location of the earthquake source, as shown in Table 3 below.

[0126] Table 3 Initial source coordinates:

[0127]

[0128] Step S4: Select four points within a threshold range around the center of the full-band broadband sensor, establish an initial tetrahedron and calculate the residuals. Compare the residuals of the four vertices, eliminate the point with the largest residual, and optimize the tetrahedron through iterative stretching and shrinking operations until the residual value reaches the threshold.

[0129] In step S4, four points are arbitrarily selected near the center of the sensor array, and an initial tetrahedron is established in three-dimensional space. Based on the coordinates of each vertex of the initial tetrahedron, considering the initial wave velocity anisotropy and the wave velocity attenuation to time difference during the anisotropic breakup process, the residual formula optimized by the L1 norm is used to calculate the residuals for these four vertices. The residual values ​​are calculated, and the residual values ​​of the four vertices are compared to find the minimum point f of the residual function at the vertex of the initial tetrahedron. min and the maximum point f max Then calculate the straightness d of each side of the initial tetrahedron, and then eliminate the point f with the largest residual difference. max The tetrahedron is continuously stretched and contracted, and new vertices are replaced and supplemented to construct new simplex tetrahedrons. It is determined whether the minimum residual value f reaches the predetermined threshold of the function iteration. If it does not reach the threshold, the search continues and a new simplex tetrahedron is constructed. If the residual value of the positioning target function is less than the predetermined threshold, the coordinate value of the vertex of the simplex is the positioning coordinate point.

[0130] Step S5: When the residual value reaches the termination threshold, within the space where the error of the optimal value is less than 1, use the random number generation command to randomly select hundreds of minimum spacing points and compare them with the optimal value residual value f. k The residual value f with random points i If f k <f i If f is the global optimum, then f is the global optimum. i If the value is greater than f, then the point is a local optimum. Exit the calculation, reselect the initial point, and jump to step S4 to perform the search calculation.

[0131] Step S6: Calculate the optimal location of the seismic source by back-calculating the source coordinate parameters corresponding to the minimum residual value;

[0132] In this embodiment, the optimal location of the earthquake source is obtained by back-calculating the source coordinate parameters corresponding to the minimum residual value, and the new location event coordinate points are obtained, as shown in Table 4 below.

[0133] Table 4. Updated source coordinates:

[0134]

[0135] Step S7: Based on the SS / MS / AE signal information received by each probe during the positioning event, determine the microscopic failure type and positioning energy;

[0136] Specifically as follows:

[0137] Record and store the SS / MS / AE wave signal information received by each full - frequency broadband sensor during the positioning event at the failure location in the computer system. Conduct an inversion analysis for the positioning event at the rock fracture location, classify the fracture types into tensile fracture, shear fracture, and mixed tensile - shear fracture, and determine the failure types of the rock at different time periods. The specific determination is as follows;

[0138] RA = Duration / Amplitude;

[0139] AF = Ring count AE / Duration;

[0140] For the classification of RA and AF, the best - dividing - line method is adopted. Define the slope of the dividing line AF / RA as N. Define the signal with AF / RA < N as the tensile fracture signal, and the signal with AF / RA > N as the tensile fracture signal. Through pure - tension experiments and pure - shear experiments on the rock, determine that the slope of the dividing line is N, and the obtained results are as Figure 10 shown;

[0141] The computer system determines the level of the positioning event energy. Since each positioning event is a set of SS, MS, and AE signals generated by the same fracture source received by multiple full - frequency broadband sensors, the positioning event energy is the area integral under the signal detection envelope from the start time of receiving the first signal to the end time of the last signal. The specific calculation is as follows:

[0142] ;

[0143] G5 = n1G1 + n2G2 + n3G3 + n4G4;

[0144] Where E is the energy of this positioning event, is the end time of the last signal, is the start time of the first signal, , , , are weighting coefficients, G1, G2, G3, G4, G5 are the signals received by the full - frequency broadband sensors, A ring is the amplitude of the collected acoustic signal, f is the frequency of the collected acoustic signal; The computer system determines the level of the positioning event energy according to the above calculation. The higher the energy level, the larger the scatter point size and the darker the color. The obtained results are as Figure 8 and Figure 9 shown.

[0145] On the other hand, a high-precision spatial positioning device for acoustic signals considering the anisotropic deterioration of rock mass, see [link to relevant documentation]. Figure 4 This invention proposes a cloud server device 10, which includes one or more processors 10-1, one or more storage devices 10-2, input devices 10-3, and output devices 10-4. These components are interconnected via a bus system 10-5 and / or other forms of connection mechanisms. It should be noted that... Figure 4 The components and structure of the cloud server device 10 shown are merely exemplary and not limiting. The cloud server device may also have other components and structures as needed.

[0146] The processor 10-1 may be a central processing unit or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the cloud server device 10 to perform desired functions.

[0147] Furthermore, the processor 10-1 can perform steps S2-S6, including preprocessing and reprocessing the raw data of full stress strain, wave velocity and acoustic signal in the method of the present invention, constructing a wave velocity attenuation model that considers the anisotropy of the initial wave velocity and the evolution of the anisotropic rupture process, establishing the source location objective function, and determining the micro-damage type and location energy.

[0148] Storage device 10-2 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory and / or cache memory. The non-volatile memory may include, for example, read-only memory, hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and processor 10-1 may execute the program instructions to implement the computer functions and / or other desired functions described in the embodiments of the present invention below. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs.

[0149] The input device 10-3 can be a device for receiving user input commands and collecting data, and its input method adopts a combination of wireless and wired transmission.

[0150] The output device 10-4 can output various information such as text data, images or sounds to the outside, and may include one or more of a display, a speaker, etc. The application output of this invention is image or text data.

[0151] Figure 5A schematic diagram of a high-precision spatial positioning device 20 considering the anisotropic degradation acoustic signal of rock mass, provided as an example of the present invention, is used to acquire rock sample positioning events and analyze rock failure types and energy. This device is used to execute the method provided in the above-described example of the present invention. The device 20 includes:

[0152] Input module 20-1 is used to receive the collected raw data from indoor rock experiments and obtain the initial raw sample set for the model. Input module 20-1 can be... Figure 4 The cloud server device 10 shown uses the input device 10-3 to collect data, the processor 10-1 to classify data, and the storage device 10-2 to store program instructions for execution. It can also execute the corresponding parts of steps S1-S3 of the high-precision spatial positioning method for considering the anisotropic deterioration of rock mass acoustic signals proposed in this embodiment of the invention.

[0153] The classification and storage module 20-2 is used to classify, filter, extract, integrate, and store the initial raw data to obtain a collection of damage and degradation data as well as SS / MS / AE acoustic signal information generated by the rupture source. The classification and storage module 20-2 can be composed of... Figure 4 The processor 10-1 in the cloud server device 10 shown runs the program instructions stored in the storage device 10-2 to implement the method, and can execute the corresponding parts of steps S2-S4 of the high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass in this invention.

[0154] Processing module 20-3 is used to construct a wave velocity attenuation model considering initial wave velocity anisotropy and the evolution of anisotropic fracturing processes based on damage degradation and wave velocity data in three directions. It also establishes a source location objective function based on the time difference of arrival and considering the wave velocity attenuation law with rock degradation. SS / MS / AE acoustic signal information is trained, calculated, and analyzed for testing. Based on the analysis and processing results, location events are obtained, verified, and the acoustic signal information is classified and stored as individual location events. Processing module 20-3 can be... Figure 4 The processor 10-1 in the cloud server device 10 shown runs the program instructions stored in the storage device 10-2 to implement the method, and can execute the corresponding parts of steps S2-S6 of the high-precision spatial positioning method for acoustic signals considering rock mass anisotropy deterioration proposed in this embodiment of the invention.

[0155] The reprocessing module 20-4 is used to calculate, analyze, optimize, extract, and integrate the acoustic signal information of each location event, determine its microscopic damage type and location energy, and verify and store it again. The reprocessing module 20-4 can be... Figure 4 The processor 10-1 in the cloud server device 10 shown runs the program instructions stored in the storage device 10-2 to implement the high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass proposed in this embodiment of the invention, and can execute step S6.

[0156] Output module 20-5 is used to output the rock fracture location event, micro-damage type, location energy calculation results, and verification results in graphical form for user viewing. Output module 20-5 can be... Figure 4 The output device 10-4 in the cloud server device 10 shown executes the program instructions stored in the storage device 10-2 to achieve this.

[0157] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass, characterized in that, Includes the following steps: Step S1: Deploy and bond full-band broadband sensors on the rock sample. There are four full-band broadband sensors, which are used to collect low, medium and high frequency micro-vibrations and acoustic emission signals. Establish four rectangular measurement areas for the sensors. The full-band broadband sensors collect source waveform data and calculate the arrival time difference between other full-band broadband sensors and the reference node. Step S2: Conduct true triaxial experiments at different stress levels, obtain stress-strain curves, establish a damage deterioration equation, and simultaneously measure the wave velocity during rock fracture to establish a function of damage and wave velocity. Step S2 is as follows: When the rock is in its initial state, the wave velocity measurement function of a full-band broadband sensor is used. One full-band broadband sensor is selected as a reference node, and the wave velocity is measured in pairs with the other three full-band broadband sensors to obtain the wave velocity in the three directions of the initial rock. Subsequently, a true triaxial test of rock degradation under different stress conditions is conducted. During the test, measurements are taken between the sensor and the reference node sensor to obtain the wave velocity in the three directions and the full stress-strain curve during rock failure. The principal stress σ of the rock during the true triaxial test of rock degradation is also measured. i Anisotropic deformation modulus K in the direction i The calculation formula is defined as follows: Among them, K i The axial stress increment dσ and the three elastic principal strain increments dε during each loading and unloading cycle of the rock specimen. ie The absolute value of the ratio is used to determine the K value. i Define the Young's modulus E in the three principal directions i Let i = 1, 2, 3. Assume that the initial values ​​of Young's moduli E1, E2, and E3 in the three directions are the same. Then E i The definition is as follows: Where μ1 and μ2 are the lateral Young's modulus scaling factors, defined as: ;where K 1,0 For the principal stress σ of the rock i Initial deformation modulus in the direction, K 2,0 and K 3,0 The initial deformation modulus in the lateral direction; Based on the evolution law of mechanical parameters and nonlinear fitting methods, E1, E2, and E3 are fitted with the equivalent plastic strain ε. p Evolution equation of changing parameters: ； ； ； Among them, a i b i and c i All are fitting coefficients related to the second principal stress σ2 and the third principal stress σ3, i=1,2,3,E i,0 Let ε be the initial elastic modulus. 1p ε 2p and ε 3p These represent the maximum, intermediate, and minimum principal plastic strains, respectively. 1i a 2i a 3i a 4i a 5i b is a weighting coefficient describing the evolution of the plastic strain component with respect to the elastic modulus E. 1i b 2i b 3i b 4i b 5i To control the nonlinear influence coefficient of plastic strain on modulus decay or hardening, c 1i c 2i c 3i c 4i c 5i The coefficients represent the quadratic or higher-order corrections to the modulus evolution due to plastic strain; wave velocity and Young's modulus E in three directions. i The relationship is as follows: ； in, It is Young's modulus. It's density. This is the wave velocity, i=1,2,3. It is the Kronecker function, P ij It is a permutation matrix, and the wave velocity decreases as the rock deteriorates. Its evolution equation is as follows: ； Where t is the propagation time, The initial wave velocity is α, and the attenuation coefficient is α. Step S3: Based on the time difference of arrival and considering the attenuation law of wave velocity with rock deterioration, establish the objective function for seismic source location, calculate the source location through the sensor arrival time, and optimize the residual formula using the L1 norm. Step S3 specifically involves: Assuming there exists a seismic source on a two-dimensional plane, with coordinates P = [xy]. T The full-band wideband sensor is located in S j = [x j y j ], j = 1,2,3...n, where n is the number of sensors; therefore, the time t is for the P-wave to be emitted from the source point and received by the j-th sensor. j for: ； Where v represents the propagation speed of the P-wave in the positioning model, d z Let (x0, y0) represent the distance between the z-th sensor and the earthquake source, where (x0, y0) are the coordinates of the earthquake source. If we take the full-band broadband sensor T1 as the reference node, then the arrival time difference between the z-th sensor and T1 is expressed as: ； When the survey area is in three-dimensional space, the time for the i-th sensor to receive the source signal relative to the reference node is expressed as: ； Where (x0, y0, z0) are the coordinates of the seismic source in three-dimensional space, and (x1, y1, z1) are the coordinates of the reference full-band broadband sensor in three-dimensional space. i y i , z i ( ) represents the coordinates of other full-band broadband sensors in three-dimensional space; make , ; The residual formula f based on the time difference positioning model is obtained as follows: ； Where R i R1 and R2 represent the earthquake source P to the full-band broadband sensor S, respectively. i The distance to the full-band broadband sensor S1, i=1,2,3...n, V i Let v be a function of damage and wave velocity, where k is the norm; based on the previously established function of damage and wave velocity, replace v in that function with the newly established function of damage and wave velocity, V. i The resulting formula is as follows: ； When there is data with an error greater than the set threshold, the minimum absolute value method, i.e., the L1 norm, is used to optimize the objective function. That is, the sum of the absolute values ​​of the residuals of the time difference between each full-band broadband sensor and the reference full-band broadband sensor is used as the objective function f, i.e., k is set to 1, as shown in the formula: ； The minimum value of the objective function f is obtained by iteratively optimizing the algorithm to obtain the location of the earthquake source (x0, y0, z0). When the objective function value f reaches its minimum value, the obtained (x0, y0, z0) is the optimal location of the earthquake source. Step S4: Select four points within a threshold range around the center of the full-band broadband sensor, establish an initial tetrahedron and calculate the residuals. Compare the residuals of the four vertices, eliminate the point with the largest residual, and optimize the tetrahedron through iterative stretching and shrinking operations until the residual value reaches the threshold. Step S5: When the residual value reaches the termination threshold, within the space where the error of the optimal value is less than 1, use the random number generation command to randomly select hundreds of minimum spacing points and compare them with the optimal value residual value f. k The residual value f with random points i If f k < f i If f is the global optimum, then f is the global optimum. i If the value is greater than f, then the point is a local optimum. Exit the calculation, reselect the initial point, and jump to step S4 to perform the search calculation. Step S6: Calculate the optimal location of the seismic source by back-calculating the source coordinate parameters corresponding to the minimum residual value; Step S7: Based on the SS / MS / AE signal information received by each probe during the positioning event, determine the type of microscopic damage and the positioning energy.

2. The high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass according to claim 1, characterized in that, In step S1, a full-band broadband sensor is deployed on the edge of the cuboid-shaped rock mass measurement area. Adhesive is applied during deployment to establish a measurement area in the shape of a cuboid. The full-band broadband sensor collects the acoustic emission source waveform and obtains the time it takes for the source wave to travel from the source to the observation point. One of the full-band broadband sensors is selected as a reference node. By performing difference calculations, the arrival time difference between the other three full-band broadband sensors and the selected full-band broadband sensor is obtained.

3. The high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass according to claim 1, characterized in that, In step S4, four points are arbitrarily selected near the center of the sensor array, and an initial tetrahedron is established in three-dimensional space. Based on the coordinates of each vertex of the initial tetrahedron, considering the initial wave velocity anisotropy and the wave velocity attenuation to time difference during the anisotropic breakup process, the residual formula optimized by the L1 norm is used to calculate the residuals for these four vertices. The residual values ​​are calculated, and the residual values ​​of the four vertices are compared to find the minimum point f of the residual function at the vertex of the initial tetrahedron. min and the maximum point f max Then calculate the straightness d of each side of the initial tetrahedron, and then eliminate the point f with the largest residual difference. max The process involves continuously stretching and shrinking the tetrahedron, replacing and supplementing new vertices to construct new simplex tetrahedrons. It then determines whether the minimum residual value f reaches the predetermined threshold for function iteration. If not, the search continues and new simplex tetrahedrons are constructed. If the residual value of the target function is less than the predetermined threshold, the coordinates of the tetrahedron vertices are the location coordinates.

4. The high-precision spatial positioning method for acoustic signals considering the anisotropic deterioration of rock mass according to claim 1, characterized in that, In step S7, the SS / MS / AE wave signal information received by each full-band broadband sensor at the failure location is recorded and stored in the computer system. An inversion analysis is performed on the location events at the rock fracture location, classifying the fracture type into tensile fracture, shear fracture, and mixed tensile-shear fracture. The failure type of the rock at different time periods is determined as follows: RA = Duration / Amplitude; AF = Ring count AE / Duration; For the classification of RA and AF, the best dividing line method is adopted. The slope of the dividing line AF / RA is defined as N. Signals with AF / RA < N are defined as tensile fracture signals, and signals with AF / RA > N are defined as shear fracture signals. By conducting pure tensile experiments and pure shear experiments on the rock, the slope of the dividing line N is determined. The computer system determines the level of the positioning event energy, which is the set of SS, MS, and AE signals generated by the same fracture source received by the full-frequency broadband sensor. The positioning event energy is the integral of the area under the signal detection envelope during the period from the start time of receiving the first signal to the end time of the last signal. The specific calculation is as follows: ； G5 = n1G1 + n2G2 + n3G3 + n4G4; Where E represents the energy of the location event. The end time of the last signal. The start time of the first signal. , , , For weighting coefficients, G1, G2, G3, G4, and G5 are the signals received by the full-band broadband sensor, and A is the weighting coefficient. ring To collect the amplitude of the sound signal, f is the frequency of the sound signal collected; the computer system determines the energy level of the location event based on the above calculations. The higher the energy level, the larger the scatter plot size and the darker the color.

5. A high-precision spatial positioning device for acoustic signals considering anisotropic deterioration of rock mass, used to implement the high-precision spatial positioning method for acoustic signals considering anisotropic deterioration of rock mass as described in claim 1, characterized in that... Specifically, it includes: Input module: used to collect the original data of stress-strain data under true triaxial experiments at different stress levels and the SS, MS, and AE acoustic signal information of each positioning event at the rock failure location received by the full-frequency broadband sensor. Classification and storage module: classifies, screens, extracts, integrates, and stores the original data to obtain the damage deterioration data and the set of SS, MS, and AE acoustic signal information generated by the fracture source. Processing module: constructs a function of damage and wave velocity considering the initial wave velocity anisotropy and the evolution of the anisotropic fracture process based on the damage deterioration data and wave velocity data in three directions, and establishes a source location objective function based on the time difference of arrival considering the attenuation law of wave velocity with rock deterioration. The SS / MS / AE acoustic signal information is trained, calculated, and analyzed and tested. According to the results of the analysis and processing, the positioning event is obtained, and it is checked and rechecked. The acoustic signal information is classified and stored according to individual positioning events. Re-processing module: calculates, analyzes, optimizes, extracts, and integrates the acoustic signal information included in each positioning event, determines its microscopic failure type and positioning energy, and checks and rechecks and stores it again. Output module: outputs the rock fracture positioning event, microscopic failure type, and the calculation results and recheck results of the positioning energy in the form of charts for users to view.

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