Coal rock bedding anisotropy kaiser geostress inversion method

Abstract

This invention relates to the fields of mine rock mechanics and geostress measurement technology, and particularly to a method for anisotropic Kaiser geostress inversion based on coal and rock bedding. The method includes directional coring and anisotropic sample preparation; anisotropic Kaiser effect acoustic emission testing; construction of anisotropic correction data model; field data acquisition and stress correction; and three-dimensional geostress tensor inversion analysis. This invention provides a method capable of quantitatively establishing a memory stress-bedding dip angle relationship model and using it as a basis to correct field measurement results, thereby achieving high-precision geostress inversion. This method is effectively adapted to the high-precision, high-stability geostress inversion method for layered rock mass structures.

Description

Technical Field

[0001] This invention relates to the field of mining rock mechanics and geostress measurement technology, and in particular to a method for inverting geostress based on anisotropic coal and rock bedding using the Kaiser method. Background Technology

[0002] In-situ stress is a natural stress inherent in the Earth's crust. It is the fundamental force causing disasters such as mine roadway deformation, roof collapse, and rock bursts. Accurately measuring the magnitude and direction of in-situ stress is of vital guiding significance for mine engineering design, disaster prevention, and safe production. Currently, the Kaiser effect, which utilizes the acoustic emission of rocks, is one of the commonly used methods for measuring in-situ stress. The principle is that rocks generate acoustic emission signals when subjected to stress. When the rock is unloaded and reloaded, acoustic emission activity will only increase significantly when the loaded stress exceeds the maximum stress it has historically endured, i.e., the memory stress. Theoretically, this memory stress corresponds to the original rock stress at the rock's location underground.

[0003] However, existing techniques for applying the Kaiser effect method typically rely on a fundamental assumption: that the rock is a homogeneous and isotropic material. In practical engineering, however, coal-bearing strata such as coal, shale, and sandstone commonly exhibit distinct bedding structures. These bedding planes, acting as natural structural weaknesses, cause significant anisotropy in the rock's mechanical properties, such as strength, elastic modulus, and acoustic emission behavior. When the angle between the core direction and the bedding plane differs, the measured Kaiser effect points will show significant differences.

[0004] Traditional methods neglect the anisotropic effect caused by the bedding dip angle, approximating the memory stress measured at different angles as the actual in-situ stress components. This leads to significant errors in the derived in-situ stress results, and may even result in incorrect judgments of the in-situ stress state, posing a huge hidden danger to mine safety. Therefore, a new method for measuring in-situ stress is needed that can quantitatively consider and correct for the bedding effect of coal and rock masses. Summary of the Invention

[0005] To address the issue of low measurement accuracy in existing Kaiser effect geostress measurement methods due to neglecting the anisotropy of coal and rock bedding, this invention provides a method for quantitatively establishing a memory stress-bedding dip relationship model and using it as a basis to correct field measurement results, thereby achieving high-precision geostress inversion. This method is effectively adapted to the high-precision and high-stability geostress inversion method for layered rock mass structures.

[0006] This invention is achieved through the following technical solution:

[0007] A method for inverting Kaiser in-situ stress based on anisotropic coal bedding includes the following steps:

[0008] S1. Directional coring and anisotropic sample preparation: Directional cores are obtained from the underground rock mass in the area to be tested. The spatial orientation parameters of the borehole and the attitude parameters of the core bedding plane are recorded by measuring equipment. The directional cores are processed into standard mechanical specimens. The physical angle between the axis of each specimen and the rock bedding plane is determined by measuring tools and defined as the bedding dip angle. The specimens are grouped according to the numerical range of the bedding dip angle.

[0009] S2. Anisotropic Kaiser effect acoustic emission test: Uniaxial compression acoustic emission test is performed on the specimens grouped by different bedding angles. Through the physical pressurization process of loading, unloading and reloading, the acoustic emission signal during the specimen fracture process is collected, and the acoustic emission signal characteristics are analyzed to determine the apparent memory stress value under different bedding angles.

[0010] S3. Construct an anisotropic correction data model. Based on the bedding dip angle data and the corresponding apparent memory stress value obtained in S2, perform data fitting processing to generate an anisotropic correction function relationship that characterizes the variation law of apparent memory stress with bedding dip angle.

[0011] S4. Field data acquisition and stress correction: Obtain field-oriented core samples of the target measurement points in the area to be measured, determine their bedding dip angle, and perform Kaiser effect testing to obtain uncorrected apparent memory stress detection values; call the anisotropic correction function relationship generated in S3 to determine the stress correction coefficient corresponding to the bedding dip angle, and use the correction coefficient to compensate and correct the apparent memory stress values ​​obtained in the field to obtain the corrected stress components in the sampling direction.

[0012] S5. Three-dimensional geostress tensor inversion analysis: Obtain corrected stress component data from at least three non-parallel borehole directions, construct a set of stress state equations containing six independent stress components, determine the three-dimensional geostress tensor by solving the set of equations, and then analyze the magnitude and spatial direction of the maximum principal stress, intermediate principal stress and minimum principal stress.

[0013] Furthermore, in step S1, the acquisition of directional core samples must be carried out in at least three non-parallel directions. The grouping of samples according to the numerical range of bedding dip angle is divided according to multiple angle nodes including 0 degrees to 90 degrees, and multiple parallel samples are prepared at each angle node to obtain a statistical average value.

[0014] Furthermore, in step S2, the uniaxial compressed acoustic emission test specifically includes:

[0015] S201. The sample is installed on the loading device and acoustic emission monitoring system, and an axial load is applied at a constant loading rate until a preset stress threshold is reached. This threshold is set to be lower than the rock failure strength and higher than the estimated in-situ stress value.

[0016] S202. Control the loading device to completely remove the load on the specimen to a zero-stress state;

[0017] S203. Immediately start the secondary loading program, maintain the same loading rate as the initial loading until the sample is destroyed, and synchronously collect acoustic emission signal data throughout the process.

[0018] S204. By analyzing the changes in acoustic emission signal parameters during the secondary loading process, the inflection point where acoustic emission activity increases significantly is identified, and the stress value corresponding to the inflection point is determined as the apparent memory stress value.

[0019] Furthermore, in step S3, the anisotropy correction data model is constructed by fitting discrete data points of bedding dip angle and apparent memory stress using a nonlinear least squares regression method.

[0020] The anisotropic correction function relationship adopts an even-degree polynomial function structure or a trigonometric series function structure containing cosine terms, in order to quantitatively describe the variation law of apparent memory stress with bedding dip angle.

[0021] Furthermore, in step S4, the steps of determining the stress correction coefficient and the corrected stress components specifically include:

[0022] S401. Select the function value of the anisotropic correction function relationship when the bedding dip angle is 90 degrees as the reference value;

[0023] S402. Calculate the ratio of the reference value to the function value corresponding to the dip angle of the sample at the target measuring point, and define the ratio as the stress correction coefficient.

[0024] S403. Multiply the uncorrected apparent memory stress value obtained on site with the stress correction coefficient to obtain the corrected stress component after eliminating the stratification effect.

[0025] Furthermore, in step S5, the specific process of the three-dimensional geostress tensor inversion analysis includes:

[0026] S501. Establish a coordinate system and determine the direction cosine of the sampling direction corresponding to each corrected stress component in the coordinate system.

[0027] S502. Establish a system of linear equations between the corrected stress components and the six geostress tensor components to be determined.

[0028] S503. Solve the linear equation system using the least squares method to obtain six independent stress tensor components;

[0029] S504. Construct the stress tensor matrix and solve for its eigenvalues ​​and eigenvectors. Determine the magnitude of the principal stress by the eigenvalues ​​and the direction of the principal stress by the eigenvectors.

[0030] Furthermore, the method for identifying the inflection point of a significant increase in acoustic emission activity includes:

[0031] A curve of cumulative ring count of acoustic emission as a function of stress was constructed, and the inflection point where the slope of the curve changed significantly was selected as the apparent memory stress value.

[0032] Alternatively, analyze the curve of acoustic emission energy rate as a function of stress, and select the starting point where the energy rate begins to increase rapidly and continuously as the apparent memory stress value.

[0033] Furthermore, in step S1, the method for recording the spatial orientation parameters of the borehole and the attitude parameters of the core bedding plane includes selecting digital core scanning and image recognition technology, industrial CT scanning technology, or manual measurement technology using a geological compass combined with a core goniometer, depending on the accuracy requirements.

[0034] The beneficial effects of this invention are:

[0035] (1) This invention proposes a method for quantitatively establishing a memory stress-bedding dip angle anisotropy correction model. By calibrating the Kaiser effect response law under different bedding angles and generating a correction function, this invention can make targeted numerical corrections to the apparent memory stress obtained in the field, effectively eliminating the interference of weak surfaces of coal and rock bedding structure on the measurement results, and making the inverted three-dimensional geostress tensor closer to the real state of underground original rock stress.

[0036] (2) This invention is specifically designed for coal-bearing strata rock masses with significant bedding structures, such as coal, shale, slate, and sandstone. By introducing the bedding dip angle as a key variable, it overcomes the technical bottleneck of poor application effect and large data dispersion of the traditional Kaiser effect method in such rock masses with significant anisotropy, and greatly expands the engineering application scope of acoustic emission geostress measurement technology under complex geological conditions.

[0037] (3) By providing high-precision geostress data after anisotropy correction, this invention provides a reliable scientific basis for the optimized layout of mine roadways, the rational design of support parameters, and the risk assessment of dynamic disasters such as rockburst. Engineering decisions based on the actual stress state can effectively reduce the risk of roof collapse and roadway deformation, thereby significantly preventing mine disasters and ensuring safe production.

[0038] (4) This invention provides a complete closed-loop technical solution from directional coring and sample preparation, anisotropic laboratory modeling to on-site data acquisition and correction and then to three-dimensional tensor inversion. It not only realizes the effective transformation from laboratory theoretical model to on-site engineering application, but also reduces the interference of human factors and random factors through standardized steps, ensuring the high reliability and good repeatability of geostress measurement results. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the process for the Kaiser in-situ stress inversion method based on coal and rock bedding anisotropy proposed in this invention.

[0041] Figure 2 This is a schematic diagram of the terminal equipment for the Kaiser geostress inversion method for anisotropic coal and rock bedding proposed in this invention.

[0042] Figure 3 This is a schematic diagram of a readable storage medium for a coal and rock bedding anisotropic Kaiser geostress inversion method proposed in this invention.

[0043] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0045] Example 1

[0046] refer to Figure 1 This invention proposes a Kaiser effect geostress inversion method that considers the anisotropic bedding effect of coal and rock masses. Its core implementation process is a closed-loop system engineering project, specifically including the following consecutive steps:

[0047] Step 1, Directional Core Sampling and Sample Preparation: This step is used to obtain representative rock materials and accurately determine their geometric and geological properties. Personnel must delve into the underground rock mass of the project area and use specialized drilling equipment to perform core sampling. To meet the mathematical requirements for spatial geometric relationships in subsequent three-dimensional geostress inversion, directional cores must be obtained from at least three non-parallel directions through drilling; that is, the spatial axes of the three boreholes cannot be coplanar. Typically, one vertical borehole and two horizontal boreholes are selected, or three oblique boreholes with different azimuth and dip angles are used.

[0048] During drilling, high-precision measuring tools, such as electronic multi-point photocatheter or fiber optic gyroscope, must be used to accurately record the azimuth and dip angles of each borehole at different depths. At the same time, directional coring tools or in-hole imaging technology are used to record in detail the attitude information of the bedding planes on the core, including the dip and dip angle of the bedding planes.

[0049] In the preferred operation of this embodiment, advanced digital core scanning and image recognition technology is used to measure the bedding attitude. This technology, through full cylindrical surface unfolding imaging and intelligent algorithms, can identify bedding plane traces and calculate their spatial orientation with extremely high accuracy. In scientific research applications with extremely high accuracy requirements, industrial CT scanning technology can also be used for non-destructive testing. Under basic engineering application conditions, manual measurement can also be performed using a geological compass and a dedicated core goniometer.

[0050] After the core is extracted, it is sealed to maintain its natural water content. The core is then processed into several cylindrical specimens conforming to the standards of the International Society for Rock Mechanics, typically 50 mm in diameter and 100 mm in height. The parallelism and perpendicularity of the end faces are ensured to meet specifications. Detailed geometric measurements are performed on each processed specimen, particularly measuring the angle between the specimen's axial geometric centerline and the rock bedding plane. In this embodiment, this angle is defined as the bedding dip angle, denoted as . .

[0051] The The value ranges from 0° to 90°, where 0° represents the loading direction being parallel to the bedding plane, and 90° represents the loading direction being perpendicular to the bedding plane. In order to construct a continuous and accurate anisotropic model, the specimen needs to be loaded according to the bedding dip angle. The samples are grouped by size. In this embodiment, the angle interval is set at 15 degrees, and the samples are divided into 7 angle groups: 0°, 15°, 30°, 45°, 60°, 75°, and 90°. In order to eliminate random errors caused by the inhomogeneity of the rock material itself and to ensure the reliability of statistical regularities, at least 5 parallel samples should be prepared for each angle group to complete the sample preparation and grouping.

[0052] Step 2, Anisotropic Kaiser Effect Test: This step is used to obtain the memory response characteristics of rocks to historical stresses at different bedding angles. The test is conducted on a rigid servo-controlled rock mechanics testing machine, in conjunction with a high-sensitivity acoustic emission monitoring system. The specific experimental procedure is as follows:

[0053] The specimen is mounted between the pressure plates of the press, and an acoustic emission sensor is coupled to the specimen surface. A special coupling agent is applied to the contact surface between the sensor and the specimen to ensure effective transmission of the acoustic signal. After the test begins, axial compressive stress is applied to the specimen at a constant loading rate. A loading rate that is too fast may lead to dynamic effects, while a rate that is too slow is inefficient and easily affected by environmental noise. In this embodiment, the preferred loading rate range is 0.05~0.5 MPa / s, and this rate must be strictly maintained in all comparative experiments within the same batch. During the initial loading stage, the specimen is loaded to a preset stress peak value. This preset value should be less than the macroscopic failure strength of the rock, but must be greater than the estimated maximum geostress value at that depth to ensure that the rock can generate new Kaiser memory. After reaching the preset value, the specimen is completely unloaded to a zero-stress state, allowing the microcracks to close. Immediately afterwards, the same specimen is loaded a second time at the exact same loading rate until macroscopic failure occurs.

[0054] Throughout the second loading process, the acoustic emission system continuously acquired and recorded various parameters of the acoustic emission signal, such as ring count, energy, and amplitude, while the testing machine recorded the stress-strain curve. The core of the test lies in determining the apparent memory stress, i.e., the Kaiser point.

[0055] The acoustic emission data during the second loading process were analyzed to identify the critical point where acoustic emission activity transitioned from a quiescent to an active phase. Two main methods were used: plotting the cumulative ring count of acoustic emission as a function of stress and using the tangent method to find the inflection point where the curve's slope changed significantly; and analyzing the curve of acoustic emission energy rate as a function of stress, identifying the initial stress point where the energy rate began to show a sustained and rapid increase. The stress value corresponding to this inflection point was defined as the apparent memory stress at that bedding angle, denoted as […]. The above process was repeated for 5 samples in each angle group. After removing outliers, the arithmetic mean was taken to obtain the dip angle of the bedding. Representative apparent memory stress .

[0056] Step 3: Establish an anisotropy correction model: Through step 2, a series of discrete data point pairs were obtained. These data intuitively reflect the influence of sampling angle on the measurement results. To achieve correction at arbitrary angles, these discrete points need to be fitted to a continuous mathematical function. Nonlinear least squares regression analysis is then performed on the above data points to establish apparent memory stress. with bedding dip angle The quantitative relationship function between them, i.e., the anisotropic Kaiser effect correction function: This function quantitatively describes the variation of apparent memory stress under the same geostress environment, caused solely by differences in the angle between the sampling direction and the bedding plane.

[0057] Regarding the specific form of the function, based on the symmetry principle of rock mechanics and the distribution characteristics of experimental data, this embodiment selects multiple fitting models, including the use of higher-order even-degree polynomials, such as quartic polynomials:

[0058] ;in The bedding dip angle is represented by A, B, C, D, and E, which are undetermined constant coefficients obtained by least squares fitting, reflecting the specific anisotropic intensity and variation morphology of the rock.

[0059] Using a trigonometric series in functional form that conforms to the physical symmetry of bedding rock masses, the specific expression is as follows:

[0060] Where a, b, and c are fitting coefficients. For example, in an experiment on sandy mudstone, the fitted relationship might be:

[0061] This means that even if the ground stress remains unchanged, the measured Kaiser point will fluctuate within a certain range simply by changing the sampling angle. This function accurately describes this fluctuation.

[0062] It should be clarified that the modeling and correction method proposed in this embodiment is universal and can be widely applied to various rock masses with significant anisotropic characteristics such as bedding and foliation, such as shale, slate, and gneiss. However, the specific correction function may vary depending on the lithology or region of the rock mass. It is specific and needs to be calibrated independently through the experimental steps described in this embodiment.

[0063] Step 4, On-site Core Measurement and Stress Correction: This step applies the theoretical model to engineering practice. First, one or more core samples are drilled from the target measurement point within the engineering area where the specific stress value needs to be determined. The bedding dip angle is then accurately determined using the aforementioned measurement methods and denoted as... .

[0064] Then, a standard Kaiser effect experiment (load-unload-reload) was performed on the sample to be tested, and its uncorrected apparent memory stress was measured and denoted as . The values ​​measured at this point contain errors caused by stratification effects and cannot be used directly.

[0065] To obtain the true stress, correction factors need to be calculated. First, the function established in step 3... Choose a reference datum, typically selecting one where the loading direction is perpendicular to the bedding plane. The state at that time is used as the standard reference state because this direction is usually least affected by bedding slip and is closest to the true bearing capacity of the rock matrix. A reference stress value is defined. .

[0066] Next, calculate the angle of the sample to be tested. Function prediction value Define the bedding angle as dimensionless correction coefficient The ratio of the reference value to the predicted value, i.e.:

[0067] .

[0068] Finally, this coefficient is used to correct the field-measured data, thus correcting the apparent memory stress measured in the field. Multiply by the correction factor The corrected stress components after eliminating the influence of bedding anisotropy are obtained, denoted as . :

[0069] This operation unifies the measurement values ​​obtained from different directions and with varying degrees of stratification interference into the same equivalent mechanical reference surface, thus providing the physical basis for subsequent tensor synthesis.

[0070] Step 5: Three-dimensional geostress tensor inversion: The stress state at any point in the Earth's crust is described by a second-order symmetric tensor, which contains six independent components: three normal stress components. and three shear stress components .

[0071] To solve these six unknowns, a system of equations must be established. This step requires obtaining core measurement data from at least three non-parallel boreholes, covering at least six independent directions with different spatial orientations. These six data points must be corrected according to step 4. The specific mathematical process of inversion calculation is as follows:

[0072] First, establish a unified mine coordinate system, typically with the X-axis pointing due east, the Y-axis pointing due north, and the Z-axis pointing vertically upward;

[0073] For each core sample Based on the borehole azimuth angle, dip angle, and the relative attitude of the core within the borehole, calculate the unit direction vector of the axial loading direction in the coordinate system:

[0074] ;in Let be the direction cosines of the direction vector on the X, Y, and Z axes, respectively, and satisfy . In this embodiment, the direction cosine of a sample taken from a vertically upward drilled hole is (0, 0, 1); and the direction cosine of a sample facing east horizontally is (1, 0, 0).

[0075] In practical engineering, the vertical direction, the horizontal due east, the horizontal due north, and three different tilt directions (such as 45° northeast, 45° XZ plane, 45° YZ plane, etc.) are usually selected as sampling directions to form a spatial three-dimensional coverage.

[0076] According to the Cauchy stress formula in elasticity, in any direction in space... Normal stress on The relationship with the stress tensor components is as follows:

[0077] ;

[0078] The calibrated measurement value It is approximated as the true normal stress in this direction. Therefore, for N samples, the following system of linear equations can be established:

[0079] .

[0080] For ease of calculation, the above system of equations is written in matrix form. Where A is an N×6 coefficient matrix, and each row consists of the geometric parameters of the corresponding sample:

[0081] ;

[0082] S is a 6×1 column vector of unknowns to be solved:

[0083] ;

[0084] B is an N×1 column vector of observations containing all corrected stress values:

[0085] ;

[0086] Since N > 6 is typically used in actual measurements to increase redundancy and improve accuracy, this system of equations is an overdetermined system. This embodiment uses the least squares method to find the optimal solution, minimizing the sum of squared residuals of all equations. The standard solution takes the form of:

[0087] in It is a matrix The transpose of the matrix, It is a matrix The inverse matrix of the matrix can be used to perform the above matrix operations through a computer program to uniquely determine the six geostress tensor components.

[0088] Obtaining the stress tensor Then, it is assembled into a 3×3 stress tensor matrix. Solve the eigenvalue problem of this matrix.

[0089] The three real eigenvalues ​​of this matrix correspond to the maximum principal stresses. Intermediate principal stress and minimum principal stress The magnitude of the eigenvalue. Simultaneously, the eigenvector corresponding to each eigenvalue represents the direction of action of the three principal stresses in space (usually expressed as azimuth and dip angles).

[0090] This embodiment completes a full geostress inversion process that considers the anisotropic effects of coal and rock bedding. This method not only theoretically overcomes the shortcomings of traditional methods that neglect the effects of medium structure, but also provides rigorous quantitative means for implementation. Through basic calibration in the laboratory and mathematical correction of field data, the final geostress data accurately reflects the stress state of the original underground rock, eliminating human measurement errors caused by different rock bedding inclinations. This high-precision measurement result has extremely important engineering value for early warning of dynamic disasters in deep mines, roadway stability analysis, and long-term safety maintenance of underground engineering projects. For example, when determining the roadway axis, the layout can be based on the accurately measured direction of the maximum horizontal principal stress, making it as parallel as possible to the principal stress direction, thereby improving the stress conditions of the surrounding rock; in anti-scour design, accurate stress values ​​help to reasonably set pressure relief parameters, avoiding insufficient protection due to underestimation of stress values. In summary, this invention provides a scientific, systematic, and effective technical solution for solving the problem of geostress measurement in layered rock masses.

[0091] Example 2

[0092] This embodiment proposes an optimal method for the anisotropic Kaiser geostress inversion method of coal and rock bedding, based on Embodiment 2.

[0093] A preferred step 2 is an anisotropic Kaiser effect test based on VMD-fractal dimension coupling characteristics. This step is used to obtain the memory response characteristics of rocks to historical stresses at different bedding angles. Considering that rock acoustic emission signals are non-stationary and nonlinear, and easily affected by mechanical noise, this implementation uses a joint algorithm of VMD decomposition-phase space reconstruction-fractal dimension mutation to accurately identify Kaiser points. The specific process is as follows:

[0094] (1) Variational Mode Decomposition (VMD) and Denoising of Acoustic Emission Signal: The original acoustic emission signal acquired is denoted as... .

[0095] To separate the real rock fracture signal from the environmental white noise, a variational mode decomposition algorithm is used. Decomposed into A single unit with a specific center frequency The intrinsic mode functions (IMFs) of the intrinsic mode functions are denoted as follows: .

[0096] In this embodiment, default parameters are not used. Instead, a constrained variational problem is constructed and solved to ensure the physical meaning of the decomposition:

[0097] ;

[0098] ;in The result of decomposition The set of intrinsic mode function (IMF) components represents the vibration modes of the original signal in different frequency bands; This represents the set of center frequencies corresponding to each modal component; Indicates time Partial derivative operations; This represents the Dirac distribution function, used to construct analytic signals; Represents the imaginary unit; This represents the exponential term used to modulate the spectrum to baseband; This represents the L2 norm, where the squared form is used to measure the smoothness of the signal gradient after demodulation. This indicates the preset number of modal decompositions.

[0099] To solve the above constrained variational problem, a quadratic penalty factor is introduced. and Lagrange multipliers The constrained variational problem is transformed into an unconstrained problem, and an augmented Lagrangian function is constructed. Its expression is:

[0100] ;in Denotes the regularization term for a variational problem. This is a secondary penalty factor used to adjust the weight of the bandwidth constraint. The larger the value, the narrower the modal bandwidth of the decomposed mode; Represents the Lagrange multiplier, used to strictly enforce the iteration process. This constraint.

[0101] Subsequently, the alternating direction multiplier method (ADMM) was used to... Solve by alternating updates and Find the saddle point of the augmented Lagrangian function to obtain the modal components. and center frequency Through the above iterative solution, the original acoustic emission signal, which is mixed with various noise sources, is adaptively decomposed into a series of independent components with different frequency centers and limited bandwidths. This is equivalent to physically isolating the real signal of rock fracture from environmental mechanical noise and current white noise in the frequency domain, providing an operable independent unit for the next step of accurately removing noise components, and avoiding the accidental damage to the effective signal by traditional filters.

[0102] In step (1), the ADMM algorithm was used to decompose the original signal into a series of intrinsic mode functions with discrete center frequencies and minimum bandwidth. However, this does not mean that every decomposed mode component contains effective rock mechanics information. Since VMD is essentially a lossless decomposition, some mode components objectively carry environmental noise and mechanical interference from the original signal. If all optimal modes are used directly for reconstruction without screening, signal-to-noise separation cannot be achieved. Therefore, feature evaluation indicators must be introduced to identify the physical properties of the optimal mode components.

[0103] (2) Screening effective components and reconstructing the signal, calculating each kurtosis value of the component and permutation entropy Define screening criteria :

[0104] in This represents the kurtosis value of the k-th modal component, used to characterize the impact characteristics of the signal. Rock fracture signals typically have high kurtosis. The permutation entropy of the k-th modal component is used to characterize the complexity and randomness of the signal. Noisy signals typically have high permutation entropy.

[0105] Select Components exceeding a preset threshold (set to 2.5 in this embodiment) are considered effective components containing rich fracture information, and these components are superimposed and reconstructed to obtain a pure acoustic emission signal sequence. ,in This represents the total number of signal sampling points.

[0106] Based on the joint screening mechanism of kurtosis and permutation entropy, a feature filter was constructed to accurately identify and discard the high-entropy and low-kurtosis components separated in step (1), and the reconstructed signal was obtained. It is a pure waveform after removing coarse and refined elements, which solves the problem of feature submersion in low signal-to-noise ratio environments and lays a highly reliable data foundation for subsequent steps to reveal the rock memory characteristics from the perspective of nonlinear dynamics.

[0107] (3) Phase space reconstruction and correlation integral calculation: In order to reveal the nonlinear dynamic characteristics of rock fracture, the reconstructed signal is... Perform phase space reconstruction and construct a phase space vector set:

[0108] ,in Represents the i-th phase space vector; This represents the i-th data point in the reconstructed signal sequence; This indicates the embedding dimension, which is the dimension of the reconstructed phase space; This indicates the delay time, used to eliminate the effects of sequence autocorrelation; The range of values ​​is ,in Let be the total number of vectors in the phase space. The correlation integral is calculated using the Grassberger-Procaccia (GP) algorithm. :

[0109] ,in This represents the correlation integral, reflecting the fact that the distance between points in phase space is less than... The probability of; Represents a given set of hypersphere radii; Represents phase space vectors and The Euclidean distance between them; This represents the Heaviside step function, which has a value of 1 when the input variable is greater than 0, and 0 otherwise.

[0110] Since the evolution of microcracks within rocks is a complex, nonlinear, and chaotic process, it is difficult to discover its inherent laws by observing the waveform only on a one-dimensional time axis. Through phase space reconstruction, this embodiment maps the one-dimensional signal to a higher-dimensional space, recovering the dynamic trajectory of rock damage evolution and calculating the correlation integral. This is a mathematical measure of the density of this high-dimensional geometric structure, which directly determines whether the next step can quantify the degree of chaos in the rock.

[0111] (4) Evolution of correlation dimension and precise determination of Kaiser point: Calculation of correlation dimension based on fractal theory : ,in The correlation dimension (fractal dimension) is used to quantitatively describe the degree of chaos in a rock damage evolution system. Represents the natural logarithm operation.

[0112] The loading process is calculated using a sliding window to obtain the correlation dimension. With stress The evolutionary curve of the change, defining the dimensional mutation rate. :

[0113] When the applied stress reaches the historical maximum principal stress (Kaiser point), the microcracks inside the rock change from a disordered, random distribution to a cooperative, ordered propagation, leading to a significant reduction in the system's fractal dimension. Therefore, taking... The stress value that first exceeds the threshold (set to 3 times the standard deviation of background noise in this embodiment) is the precise apparent memory stress. .

[0114] By quantifying the correlation dimension This embodiment captures, in its physical essence, the critical state of the rock's transition from disordered microcrack activity in the elastic stage to ordered coordinated fracturing at the Kaiser point. The values ​​no longer depend on the waveform inflection points observed by the naked eye, but are based on the abrupt change points of the system's degree of chaos. They have extremely high physical clarity and anti-interference ability, thus providing high-precision core input parameters for subsequent inversion of geostress tensors by substituting them into anisotropic models.

[0115] Example 3

[0116] refer to Figure 2 Based on Example 1, this example proposes a terminal device for the coal and rock bedding anisotropic Kaiser in-situ stress inversion method. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0117] The memory 210 may include a readable medium in the form of volatile memory, such as RAM 211 and / or cache memory 212, and may further include ROM 213.

[0118] The memory 210 also stores a computer program that can be executed by the processor 220, causing the processor 220 to perform any of the above-described applications of the coal and rock bedding anisotropy Kaiser in-situ stress inversion method in this application. The specific implementation method and the achieved technical effects are consistent with those described in the above-described application embodiments, and some details will not be repeated here. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0119] Accordingly, processor 220 can execute the aforementioned computer program, as well as executable program / utility 214.

[0120] Bus 230 can represent one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0121] Terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with it, and / or with any device that enables it to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via I / O interface 250. Furthermore, terminal device 200 can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0122] Example 4

[0123] This embodiment proposes a readable storage medium for a coal and rock bedding anisotropic Kaiser in-situ stress inversion method. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned coal and rock bedding anisotropic Kaiser in-situ stress inversion methods. The specific implementation method and the technical effects achieved are consistent with those described in the above application embodiments, and some details will not be repeated.

[0124] Figure 3The present embodiment illustrates a program product 300 for implementing the above-described applications. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited thereto. In this embodiment, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product 300 may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0125] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0126] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding, characterized in that, Includes the following steps: S1. Directional coring and anisotropic sample preparation: Directional cores are obtained from the underground rock mass in the area to be tested. The spatial orientation parameters of the borehole and the attitude parameters of the core bedding plane are recorded by measuring equipment. The directional cores are processed into standard mechanical specimens. The physical angle between the axis of each specimen and the rock bedding plane is determined by measuring tools and defined as the bedding dip angle. The specimens are grouped according to the numerical range of the bedding dip angle. S2. Anisotropic Kaiser effect acoustic emission test: Uniaxial compression acoustic emission test is performed on the specimens grouped by different bedding angles. Through the physical pressurization process of loading, unloading and reloading, the acoustic emission signal during the specimen fracture process is collected, and the acoustic emission signal characteristics are analyzed to determine the apparent memory stress value under different bedding angles. S3. Construct an anisotropic correction data model. Based on the bedding dip angle data and the corresponding apparent memory stress value obtained in S2, perform data fitting processing to generate an anisotropic correction function relationship that characterizes the variation law of apparent memory stress with bedding dip angle. S4. Field data acquisition and stress correction: Obtain field-oriented core samples of the target measuring points in the area to be measured, determine their bedding dip angle, and perform Kaiser effect testing to obtain uncorrected apparent memory stress detection values. The anisotropic correction function relationship generated by S3 is called to determine the stress correction coefficient corresponding to the bedding dip angle. The apparent memory stress value obtained in the field is compensated and corrected using the correction coefficient to obtain the corrected stress component in the sampling direction. S5. Three-dimensional geostress tensor inversion analysis: Obtain corrected stress component data from at least three non-parallel borehole directions, construct a set of stress state equations containing six independent stress components, determine the three-dimensional geostress tensor by solving the set of equations, and then analyze the magnitude and spatial direction of the maximum principal stress, intermediate principal stress and minimum principal stress.

2. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S1, the acquisition of directional core samples must be carried out in at least three non-parallel directions. The grouping of samples according to the numerical range of bedding dip angle is divided according to multiple angle nodes including 0 degrees to 90 degrees, and multiple parallel samples are prepared at each angle node to obtain a statistical average value.

3. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S2, the uniaxial compressed acoustic emission test specifically includes: S201. The sample is installed on the loading device and acoustic emission monitoring system, and an axial load is applied at a constant loading rate until a preset stress threshold is reached. This threshold is set to be lower than the rock failure strength and higher than the estimated in-situ stress value. S202. Control the loading device to completely remove the load on the specimen to a zero-stress state; S203. Immediately start the secondary loading program, maintain the same loading rate as the initial loading until the sample is destroyed, and synchronously collect acoustic emission signal data throughout the process. S204. By analyzing the changes in acoustic emission signal parameters during the secondary loading process, the inflection point where acoustic emission activity increases significantly is identified, and the stress value corresponding to the inflection point is determined as the apparent memory stress value.

4. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S3, the anisotropy correction data model is constructed by fitting discrete data points of bedding dip angle and apparent memory stress using a nonlinear least squares regression method. The anisotropic correction function relationship adopts an even-degree polynomial function structure or a trigonometric series function structure containing cosine terms, in order to quantitatively describe the variation law of apparent memory stress with bedding dip angle.

5. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S4, the steps of determining the stress correction coefficient and the corrected stress components specifically include: S401. Select the function value of the anisotropic correction function relationship when the bedding dip angle is 90 degrees as the reference value; S402. Calculate the ratio of the reference value to the function value corresponding to the dip angle of the sample at the target measuring point, and define the ratio as the stress correction coefficient. S403. Multiply the uncorrected apparent memory stress value obtained on site with the stress correction coefficient to obtain the corrected stress component after eliminating the stratification effect.

6. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S5, the specific process of the three-dimensional geostress tensor inversion analysis includes: S501. Establish a coordinate system and determine the direction cosine of the sampling direction corresponding to each corrected stress component in the coordinate system. S502. Establish a system of linear equations between the corrected stress components and the six geostress tensor components to be determined. S503. Solve the linear equation system using the least squares method to obtain six independent stress tensor components; S504. Construct the stress tensor matrix and solve for its eigenvalues ​​and eigenvectors. Determine the magnitude of the principal stress by the eigenvalues ​​and the direction of the principal stress by the eigenvectors.

7. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 3, characterized in that, The method for identifying the inflection point of a significant increase in acoustic emission activity includes: A curve of cumulative ring count of acoustic emission as a function of stress was constructed, and the inflection point where the slope of the curve changed significantly was selected as the apparent memory stress value. Alternatively, analyze the curve of acoustic emission energy rate as a function of stress, and select the starting point where the energy rate begins to increase rapidly and continuously as the apparent memory stress value.

8. The method for inverting Kaiser in-situ stress based on anisotropic coal and rock bedding according to claim 1, characterized in that, In step S1, the method for recording the spatial orientation parameters of the borehole and the attitude parameters of the core bedding plane includes selecting digital core scanning and image recognition technology, industrial CT scanning technology, or manual measurement technology using a geological compass combined with a core goniometer, depending on the accuracy requirements.

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