A method and system for identifying the deterioration of the rock mass structure in a mined slope

Abstract

The present invention discloses a method and system for identifying the deterioration of the rock mass structure in a mined slope, specifically relating to the technical field of rock mass monitoring in mining engineering, and is used to solve the problem of missed or misjudged hidden damage caused by the existing cross-hole acoustic wave testing method relying on the overall statistical characteristics of signals; by collecting acoustic wave signals between boreholes and performing multi-scale wavelet decomposition, the energy distribution of sub-signals in the characteristic frequency band is extracted; combined with the skewness and kurtosis statistical maps of the acoustic wave travel time sequence, the complete rock mass reference map is compared section by section to mark the abnormal path section; the direction matching verification of the micro-fracture group observed by the borehole television is synchronously fused, the misjudged data is eliminated and the micro-fracture chain density model is generated; finally, the rock mass deterioration risk map is constructed through multi-source data fusion and three-dimensional visualization technology; the accurate positioning and dynamic assessment of the high-risk area of rock mass deterioration are realized, and the reliability and engineering applicability of the disaster warning of the mined slope are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rock mass monitoring in mining engineering, and more specifically, to a method and system for identifying the deterioration of the rock mass structure of a mined slope. Background Art

[0002] In mining engineering, the dynamic deterioration monitoring of the rock mass structure is one of the key technologies to ensure slope stability. The existing technology usually adopts the cross-hole acoustic wave testing method. By analyzing the wave velocity mean value or amplitude attenuation characteristics of the acoustic wave propagation between boreholes, the degree of rock mass fissure development is evaluated. This method relies on the overall statistical characteristics of the acoustic wave signal and combines borehole observation data to form a macroscopic judgment of the rock mass damage, which is applied to the rock mass stability monitoring in mining areas.

[0003] However, the above method has significant limitations in dealing with hidden rock mass damage. When there are microcrack chains that are continuously distributed but not formed into macroscopic fissures inside the rock mass, the acoustic wave signal will undergo overall attenuation due to the cumulative effect of microcracks on the cross-hole propagation path, resulting in the test data showing pseudo-uniformity characteristics. It is difficult to distinguish the acoustic response differences between such hidden damage and intact rock mass, causing missed or misjudged of high-risk areas of rock mass deterioration, which restricts the accuracy of disaster warning for mined slopes. Summary of the Invention

[0004] In order to overcome the above defects of the prior art, the embodiments of the present invention provide a method and system for identifying the deterioration of the rock mass structure of a mined slope to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for identifying the deterioration of the rock mass structure of a mined slope, comprising the following steps:

[0007] S1. Collect the acoustic wave signals between boreholes in the target rock mass area, and adjust the signal acquisition time window according to the adjacent borehole spacing;

[0008] S2. Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency bands corresponding to the microcrack chain response based on the energy gradient;

[0009] S3. Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the sub-signals in the characteristic frequency band to generate a statistical map, and perform time-domain sliding comparison with the reference map of the intact rock mass after segmenting according to the acoustic wave propagation path, and mark the abnormal path segments;

[0010] S4. Fit the microcrack chain density parameter according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model;

[0011] S5. Extract the coordinates of microcracks that do not penetrate the borehole wall from the borehole television observation data, and screen out the microcrack groups that match the acoustic wave propagation path direction;

[0012] S6. Compare and verify the spatial density of the microcrack groups with the preliminary density model, and eliminate the misjudged data in the areas with mismatched directions;

[0013] S7. Generate a three-dimensional rock mass deterioration risk map based on the verified microcrack chain density and the distribution of microcrack groups.

[0014] In a preferred embodiment, S1 includes:

[0015] Determine the spacing between adjacent boreholes, and calculate the theoretical propagation time of acoustic waves between adjacent boreholes according to the preset average acoustic velocity of the rock mass;

[0016] Based on the sum of the theoretical propagation time and the preset redundancy time threshold, determine the total duration of the signal acquisition time window;

[0017] According to the total duration of the signal acquisition time window, divide the start time and end time of the signal acquisition time window, and control the cross-hole acoustic wave device to collect acoustic wave signals according to the divided time window.

[0018] In a preferred embodiment, S2 includes:

[0019] Calculate the energy proportion of each frequency sub-signal after multi-scale wavelet decomposition to generate an energy distribution sequence;

[0020] Generate an energy gradient sequence according to the absolute value of the difference in energy proportion between adjacent frequency bands;

[0021] Mark the frequency bands with the absolute value of the difference exceeding the preset gradient threshold in the energy gradient sequence as candidate frequency bands;

[0022] Extract the target frequency bands that match the frequency band characteristics of the microcrack chain response in the candidate frequency bands as the final characteristic frequency bands.

[0023] In a preferred embodiment, the final characteristic frequency bands are determined by the preset frequency band range of the microcrack chain response or the frequency band characteristic library statistically obtained from historical data.

[0024] In a preferred embodiment, S3 includes:

[0025] Divide the acoustic wave propagation path into multiple continuous sub-path segments according to the adjacent borehole spacing and the preset segmentation rule, and the length of each sub-path segment is dynamically adjusted based on the homogeneity of the rock mass structure;

[0026] Perform time-domain window segmentation on the acoustic wave travel time statistical map within each sub-path segment, and the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment;

[0027] Extract the reference data window corresponding to the current sub-path segment from the complete rock mass reference atlas, and calculate the combined difference value between the skewness and kurtosis of the statistical atlas within the current window and the reference data window;

[0028] According to the comparison result between the combined difference value and the preset difference threshold, mark the sub-path segment whose combined difference value exceeds the preset difference threshold as an abnormal path segment.

[0029] In a preferred embodiment, S4 includes:

[0030] Extract the skewness and kurtosis data of the acoustic wave travel time series corresponding to the abnormal path segment, and generate the statistical feature vector of the abnormal path segment;

[0031] Based on the mapping relationship between the acoustic wave data and the density parameter in the historical micro-fracture chain development area, construct a regression model of skewness-kurtosis and density parameter;

[0032] Input the statistical feature vector of the abnormal path segment into the regression model, and output the density parameter of the micro-fracture chain;

[0033] According to the spatial distribution of the density parameter, generate a preliminary density model through the Kriging interpolation algorithm, and use the acoustic wave propagation path of the abnormal path segment as the spatial constraint condition during the interpolation process.

[0034] In a preferred embodiment, S5 includes:

[0035] Extract the spatial coordinates of the micro-fractures that do not penetrate the borehole wall from the borehole television observation data, and generate a set of micro-fracture coordinates;

[0036] Calculate the corresponding extension direction for each micro-fracture in the set of micro-fracture coordinates, and the extension direction is determined by fitting the straight line direction of the coordinates of the two endpoints of the micro-fracture;

[0037] Calculate the direction of the acoustic wave propagation path in the borehole television observation coordinate system, and the direction of the acoustic wave propagation path is determined by connecting the spatial coordinates of the transmitting probe and the receiving probe;

[0038] Screen the micro-fractures whose included angle between the extension direction and the acoustic wave propagation path direction is less than the preset angle, and generate a group of micro-fractures with matching directions.

[0039] In a preferred embodiment, S6 includes:

[0040] Calculate the spatial density based on the number and distribution volume of the micro-fractures within the micro-fracture group, and the distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the micro-fracture group;

[0041] Define the verification index of the preliminary density model in the corresponding rock mass area, and the verification index is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the micro-fracture group;

[0042] Screen and verify the area where the verification index exceeds the preset error threshold and the direction of the microcrack group does not match the acoustic wave propagation path, and mark it as a misjudgment area;

[0043] Exclude the density parameter data of the misjudgment area from the preliminary density model and retain the corrected density model.

[0044] In a preferred embodiment, S7 includes:

[0045] Weightedly fuse the verified microcrack chain density parameter and the spatial density of the microcrack group according to the spatial coordinates to generate a fused density parameter;

[0046] Divide the three-dimensional grid units based on the borehole positions and acoustic wave propagation paths in the rock mass area, and determine the grid unit size according to the borehole spacing and the preset accuracy requirements;

[0047] Normalize the fused density parameter in each grid unit, and the normalization range is from 0 to 1, where 0 indicates no risk of deterioration and 1 indicates the highest risk;

[0048] Set the risk level threshold according to the normalization result, and divide the grid units into low-risk, medium-risk, and high-risk areas according to the threshold interval;

[0049] Map the risk level to a color gradient through three-dimensional visualization technology to generate a three-dimensional map containing spatial coordinates and risk levels.

[0050] On the other hand, the present invention provides an identification system for the deterioration of the rock mass structure of a mined slope, including:

[0051] Acoustic wave time window module: Collect the acoustic wave signals between boreholes in the target rock mass area and adjust the signal acquisition time window according to the adjacent borehole spacing;

[0052] Frequency band screening module: Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency bands corresponding to the microcrack chain response based on the energy gradient;

[0053] Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the sub-signals in the characteristic frequency band to generate a statistical map, and perform time-domain sliding comparison with the complete rock mass reference map after segmenting according to the acoustic wave propagation path, and mark the abnormal path segments;

[0054] Density modeling module: Fit the microcrack chain density parameter according to the acoustic wave travel time data of the abnormal path segments to generate a preliminary density model;

[0055] Fracture screening module: Extract the coordinates of the microcracks that do not penetrate the borehole wall in the borehole television observation data, and screen the microcrack groups that match the direction of the acoustic wave propagation path;

[0056] False judgment elimination module: Compare and verify the spatial density of the microcrack group with the preliminary density model, and eliminate the misjudged data in the area with mismatched directions;

[0057] Three-dimensional atlas module: Generate a three-dimensional rock mass deterioration risk atlas based on the verified microcrack chain density and the distribution of the microcrack group.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. Through the construction of the skewness and kurtosis statistical atlases of the acoustic wave travel time series and the time-domain comparison of path segmentation, the precise positioning of high-risk areas of rock mass deterioration is realized; at the same time, combined with the spatial matching verification of the direction of the microcrack group in the borehole television observation data and the acoustic wave propagation path, a misjudgment elimination mechanism for multi-source data collaboration is constructed, eliminating the limitations of single data source analysis from the physical relevance level and greatly improving the reliability of the detection results;

[0060] 2. Through dynamic density modeling and three-dimensional atlas generation technology, based on the spatial fusion analysis of the microcrack chain density parameter and the distribution of the microcrack group, the evolution trend of rock mass damage can be dynamically reflected, providing a forward-looking basis for the early warning of mining slope disasters; through the multi-level cross-verification of acoustic and optical data, not only the risk of missed judgment caused by isolated data in traditional methods is solved, but also the visual grading expression of the rock mass deterioration risk level is realized, providing intuitive and quantitative decision-making support for the formulation of engineering protection measures; while improving the monitoring accuracy, the engineering applicability of rock mass stability evaluation under complex geological conditions is significantly optimized. Description of the Drawings

[0061] Figure 1 It is a flowchart of a method for identifying the deterioration of the rock mass structure of a mining slope according to the present invention;

[0062] Figure 2 It is a schematic structural diagram of a system for identifying the deterioration of the rock mass structure of a mining slope according to the present invention. Detailed Embodiments

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment 1: Figure 1 A method for identifying the deterioration of the rock mass structure of a mining slope according to the present invention is given, which includes the following steps:

[0065] S1. Collect the acoustic wave signals between boreholes in the target rock mass area, and adjust the signal acquisition time window according to the spacing between adjacent boreholes;

[0066] S2. Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the energy distribution of sub-signals in each frequency band, and screen the characteristic frequency bands corresponding to the micro-fracture chain response based on the energy gradient;

[0067] S3. Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the sub-signals in the characteristic frequency band to generate a statistical map, and perform time-domain sliding comparison with the reference map of the intact rock mass after segmenting according to the acoustic wave propagation path, and mark the abnormal path segments;

[0068] S4. Fit the micro-fracture chain density parameter according to the acoustic wave travel time data of the abnormal path segments to generate a preliminary density model;

[0069] S5. Extract the coordinates of the micro-fractures that do not penetrate the borehole wall in the borehole television observation data, and screen the micro-fracture groups that match the direction of the acoustic wave propagation path;

[0070] S6. Compare and verify the spatial density of the micro-fracture groups with the preliminary density model, and eliminate the misjudged data in the areas with mismatched directions;

[0071] S7. Generate a three-dimensional rock mass deterioration risk map according to the verified micro-fracture chain density and the distribution of the micro-fracture groups.

[0072] S1. Collect the acoustic wave signals between boreholes in the target rock mass area, and adjust the signal acquisition time window according to the spacing between adjacent boreholes, including:

[0073] When determining the spacing between adjacent boreholes, measure the orifice coordinates of adjacent boreholes in the target rock mass area, and calculate the spacing between adjacent boreholes based on the three-dimensional space distance formula.

[0074] When calculating the theoretical propagation time of the acoustic wave between adjacent boreholes according to the preset average sound velocity of the rock mass, the preset average sound velocity of the rock mass is obtained by taking the average value of the historical acoustic wave test data in the target rock mass area.

[0075] When determining the total duration of the signal acquisition time window based on the sum of the theoretical propagation time and the preset redundancy time threshold, the preset redundancy time threshold is set according to the background noise level in the target rock mass area, and the background noise level is determined by the statistical energy of the acoustic wave signals during the period without mining operations.

[0076] When dividing the start time and end time of the signal acquisition time window according to the total duration of the signal acquisition time window, the start time is set as the acoustic wave emission time, and the end time is set as the sum of the acoustic wave emission time and the total duration.

[0077] When controlling the cross-hole acoustic wave device to collect acoustic wave signals according to the divided time window, the transmitting probe and the receiving probe of the cross-hole acoustic wave device are respectively placed at the set depths of adjacent drill holes. The transmitting probe emits acoustic wave signals at the set frequency, and the receiving probe synchronously collects acoustic wave signals within the time window.

[0078] S2. Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency bands corresponding to the micro-fracture chain response based on the energy gradient, including:

[0079] When performing multi-scale wavelet decomposition on the acoustic wave signals, use the db4 basis function in the Daubechies wavelet basis function to decompose the acoustic wave signals into three layers. The decomposed sub-signals include the first-layer high-frequency sub-signal, the second-layer high-frequency sub-signal, the third-layer high-frequency sub-signal, and the third-layer low-frequency sub-signal.

[0080] When extracting the sub-signal energy distribution of each frequency band, calculate the energy proportion of each sub-signal within the time window. The calculation formula for the energy proportion is the sum of the squared amplitudes of the sub-signal divided by the total sum of the squared amplitudes of all sub-signals. The length of the time window is the same as the signal acquisition time window in step S1.

[0081] When generating an energy gradient sequence based on the absolute value of the difference in energy proportions between adjacent frequency bands, the adjacent frequency bands are arranged in ascending order of frequency as the third-layer low-frequency sub-signal, the third-layer high-frequency sub-signal, the second-layer high-frequency sub-signal, and the first-layer high-frequency sub-signal. Calculate the absolute value of the difference in energy proportions between adjacent two sub-signals in turn.

[0082] When marking the frequency bands with the absolute value of the difference in the energy gradient sequence exceeding the preset gradient threshold as candidate frequency bands, the setting method of the preset gradient threshold is: collect the acoustic wave signals in the micro-fracture chain development area of the historical data of the target mining area, and calculate the 95% quantile value of its energy gradient sequence as the threshold.

[0083] When extracting the target frequency bands in the candidate frequency bands that match the frequency band characteristics of the micro-fracture chain response, the frequency band characteristic library is constructed through laboratory rock sample fracture tests and historical micro-fracture chain data in the mining area. The specific method is: collect acoustic wave signals when loading the rock sample to the micro-fracture chain development stage, perform wavelet decomposition, and then statistically analyze the frequency band range of the sudden change in energy gradient, and screen out 50Hz to 200Hz as the typical frequency band range.

[0084] The final characteristic frequency bands are determined by comparing the candidate frequency bands with the typical frequency band range in the frequency band characteristic library. If the overlapping part of the candidate frequency band and the typical frequency band range accounts for more than 70% of the length of the candidate frequency band, it is determined as the target frequency band, otherwise it is excluded.

[0085] S3. Calculate the skewness and kurtosis of the acoustic wave travel time series of the sub-signals in the characteristic frequency band to generate a statistical map. After segmenting by the acoustic wave propagation path, perform a time-domain sliding comparison with the reference map of the intact rock mass, and mark the abnormal path segments, including:

[0086] When dividing the acoustic wave propagation path into multiple continuous sub-path segments according to the adjacent borehole spacing and the preset segmentation rule, the preset segmentation rule is determined according to the geological exploration data of the target rock mass area. The geological exploration data includes the distribution of rock mass elastic modulus, the location of historical fracture development, and the stress field distribution. For example, in areas with large differences in rock mass elastic modulus or dense historical fractures, the segmentation rule preferentially shortens the length of the sub-path segments to improve the monitoring accuracy, while in areas with better rock mass uniformity, the segmentation rule extends the length of the sub-path segments to improve efficiency.

[0087] When the length of each sub-path segment is dynamically adjusted based on the rock mass structure uniformity, the rock mass structure uniformity is evaluated by the coefficient of variation of the historical wave velocity in cross-hole acoustic wave testing. For example, if the coefficient of variation of the historical wave velocity in a certain area is less than 0.1, it is determined as a uniform rock mass, and the length of the sub-path segment is set to 1 / 5 to 1 / 3 of the adjacent borehole spacing; if the coefficient of variation is greater than 0.1, it is determined as a non-uniform rock mass, and the length of the sub-path segment is shortened to 1 / 10 to 1 / 5 of the adjacent borehole spacing.

[0088] When performing time-domain window segmentation on the acoustic wave travel time statistical map within each sub-path segment, the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment. For example, for a sub-path segment with a propagation time of 0.1 second, the window length is set to 0.02 second to 0.033 second, and the window sliding step size is set to 1 / 2 of the window length, that is, 0.01 second to 0.016 second; if the propagation time of the sub-path segment is 0.2 second, the window length is adjusted to 0.04 second to 0.066 second, and the sliding step size corresponds to 0.02 second to 0.033 second.

[0089] When extracting the reference data window corresponding to the current sub-path segment from the reference map of the intact rock mass, the reference map of the intact rock mass is generated by collecting acoustic wave data in the intact rock mass area of the mining area that is not affected by mining disturbances. For example, arrange cross-hole acoustic wave testing devices in the unmined area at the edge of the mining area, select positions more than 50 meters away from the goaf boundary and with no visible cracks on the borehole wall shown by borehole television observation, collect at least 10 groups of acoustic wave signals, calculate the mean values of the skewness and kurtosis of their travel time series, and generate a reference map.

[0090] When calculating the combined difference value between the skewness and kurtosis of the statistical atlas within the current window and the reference data window, the combined difference value is the weighted sum of the absolute value of the skewness difference and the absolute value of the kurtosis difference; the weight coefficients are determined according to the correlation between the micro-fracture chain response and skewness and kurtosis in historical data. For example, by analyzing the acoustic wave data in 10 regions with micro-fracture chain development, it is found that the sensitivity of skewness to micro-fracture chains is higher than that of kurtosis. Therefore, the skewness weight coefficient is assigned as 0.6, and the kurtosis weight coefficient is assigned as 0.4.

[0091] When marking abnormal path segments based on the comparison result between the combined difference value and the preset difference threshold, the preset difference threshold is determined through laboratory rock sample tests. For example, cross-hole acoustic wave tests are respectively carried out on intact granite samples and rock samples containing micro-fracture chains. The intact rock samples obtain data through tests in the unloaded state, and the rock samples containing micro-fracture chains are prepared and tested after being axially strained to 0.5% under triaxial loading. Calculate the distribution of the combined difference values of the two types of rock samples, and take the 95% quantile value of the difference value distribution of the intact rock samples as the threshold. If the combined difference value of the current sub-path segment exceeds this threshold, it is marked as an abnormal path segment.

[0092] S4. Fit the micro-fracture chain density parameter according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model, including:

[0093] When extracting the skewness and kurtosis data of the acoustic wave travel time sequence corresponding to the abnormal path segment, the abnormal path segment is determined by the abnormal path segment marked in step S3. The skewness of the acoustic wave travel time sequence is the mean skewness of all acoustic wave travel time data within this path segment, and the kurtosis is the mean kurtosis of all acoustic wave travel time data within this path segment; for example, in a mining area with poor rock mass uniformity, the abnormal path segments may be concentrated in the stress concentration area at the edge of the goaf, and its mean skewness shows significant negative skewness, and the mean kurtosis is higher than that in the area of the intact rock mass.

[0094] When generating the statistical feature vector of the abnormal path segment, the statistical feature vector is a two-dimensional vector containing the mean skewness and the mean kurtosis. For example, in the historical data of a certain mining area, the statistical feature vectors in the regions with micro-fracture chain development are usually distributed in the skewness interval [-1.5, 0.5] and the kurtosis interval [3.0, 6.0], while the feature vectors in the area of the intact rock mass are concentrated in skewness [-0.2, 0.2] and kurtosis [2.8, 3.2].

[0095] When constructing a regression model based on the mapping relationship between the acoustic wave data and the density parameter in the historical micro-fracture chain development area, the historical data is selected from the cross-hole acoustic wave test data in the micro-fracture chain development area during the historical monitoring of the mining area and the corresponding borehole core density measurement results; for example, in a certain iron ore mining area, the historical data includes 10 sets of acoustic wave test records in the micro-fracture chain development area, and each set of data corresponds to the core density measurement values at different depths (50 meters to 200 meters). A mapping equation between skewness-kurtosis and density parameter is established through multiple linear regression, and the equation form is a linear combination.

[0096] Among them, the regression model is constructed based on the corresponding relationship between the acoustic characteristics of the micro-fracture chain development area and the core density parameters in historical data; the historical data includes the mean skewness and kurtosis of the acoustic travel time in the cross-hole acoustic test records in the mining area, and the micro-fracture chain density parameters measured in the core laboratory at the corresponding borehole positions. The core density is analyzed by CT scan images for the micro-fracture chain distribution and converted into physical density values.

[0097] The model adopts the multiple linear regression method, taking the mean skewness and kurtosis as input variables and the density parameter as the output variable, and determines the weight coefficients and constant terms of each variable by fitting the historical data with the least squares method; for example, in the historical data of a certain iron ore mining area, when the mean skewness increases by 1 unit, the density parameter increases by 0.8 units; when the kurtosis mean increases by 1 unit, the density parameter increases by 0.3 units, and the constant term is 0.5. The error between the predicted density value and the measured value of the model is less than 0.2 g / cm³.

[0098] If the lithology difference causes a non-linear relationship between the acoustic parameters and the density (such as in a sandstone mining area), the model is extended to include the square term of the mean skewness and the square term of the kurtosis mean, and the fitting accuracy is improved by adding higher-order terms; the weight coefficients are dynamically allocated according to the contribution degrees of skewness and kurtosis to the density parameter in the historical data. For example, in a granite mining area, the skewness weight accounts for 70% and the kurtosis accounts for 30%, and in the sandstone mining area, it is adjusted to 50% for both skewness and kurtosis; after the model is constructed, it needs to be cross-validated with historical data to ensure the stability of the prediction results.

[0099] If the non-linear relationship between the micro-fracture chain density and the acoustic parameters in a certain copper mining area is caused by lithology differences, the regression model can be extended to a polynomial form, such as introducing the square term of skewness to improve the fitting accuracy.

[0100] When the statistical feature vector of the abnormal path segment is input into the regression model, the output of the regression model is the estimated value of the micro-fracture chain density parameter; for example, the statistical feature vector of an abnormal path segment is (skewness -0.8, kurtosis 4.2), and the output density parameter is 1.8 g / cm³ after input into the model, while the feature vector of another path segment is (skewness 0.3, kurtosis 3.1), and the output density parameter is 1.2 g / cm³. When generating the preliminary density model based on the spatial distribution of the density parameter, the spatial distribution is a set of the density parameters corresponding to each abnormal path segment and their spatial coordinates, and the spatial coordinates are determined by the borehole positions and acoustic propagation paths of the cross-hole acoustic device; for example, an abnormal path segment is linearly distributed along the straight line from borehole A (coordinates X1, Y1, Z1) to borehole B (X2, Y2, Z2), and the spatial coordinates of its density parameter are equally distributed according to the path length, with the starting point coordinates being (X1, Y1, Z1), the end point being (X2, Y2, Z2), and the intermediate point coordinates being determined by linear interpolation.

[0101] When generating a preliminary density model through the Kriging interpolation algorithm, the acoustic wave propagation path is used as a spatial constraint condition during the interpolation process; for example, if there are multiple intersecting acoustic wave propagation paths in a mining area, the interpolation range is limited to the rock mass area covered by all paths, and non-path areas (such as untested isolated rock strata) do not participate in the interpolation calculation.

[0102] When the distribution of acoustic wave propagation paths is uneven, the search radius of the interpolation algorithm is dynamically adjusted according to the path density. For example, the search radius is set to 5 meters in the area with dense paths (spacing less than 10 meters) and expanded to 10 meters in the sparse area (spacing greater than 20 meters).

[0103] The variogram parameters of the interpolation algorithm are determined according to the spatial variation characteristics of the microfracture chain density parameters in historical data. For example, the nugget value of a certain mining block is set to 0.1 (reflecting measurement error), the sill value is set to 1.5 (reflecting the total variation), and the range is set to 20 meters (reflecting the range of spatial autocorrelation).

[0104] S5. Extract the coordinates of the microfractures that do not penetrate the borehole wall from the borehole television observation data, and screen the microfracture groups that match the direction of the acoustic wave propagation path, including:

[0105] When extracting the spatial coordinates of the microfractures that do not penetrate the borehole wall from the borehole television observation data, the borehole television observation data collects the borehole wall images through an underground camera, and the image resolution meets the accuracy requirement of 0.1 mm corresponding to each pixel for the actual size.

[0106] The criterion for determining the microfractures that do not penetrate the borehole wall is that the fracture depth is less than the borehole radius and does not penetrate the borehole wall. For example, when the borehole radius is 50 mm, the microfractures with a fracture depth less than 50 mm are included in the coordinate set.

[0107] When generating the microfracture coordinate set, each microfracture coordinate in the coordinate set is converted from the local coordinate system of the borehole television to the global coordinate system of the mining area; the conversion method is: taking the borehole mouth as the origin of the global coordinate system, the borehole axis direction as the vertically downward direction, the due north direction as the positive direction of the X axis, and the due east direction as the positive direction of the Y axis, measuring the longitude, latitude and elevation of the borehole mouth through a total station or a global navigation satellite system device, and combining the borehole inclination and azimuth for coordinate conversion; for example, a certain microfracture has a radial distance of 1.2 meters, a circumferential angle of 30 degrees, and a vertical depth of 50 meters in the local coordinate system of the borehole, and the coordinates after conversion to the global coordinate system are X = 1000.5 meters, Y = 2000.3 meters, Z = -50.0 meters.

[0108] When calculating the corresponding extension direction for each microcrack in the microcrack coordinate set, the extension direction is determined by fitting the straight-line direction of the coordinates of the two endpoints of the microcrack. Specifically: Extract the local coordinates of the starting point and the ending point of the microcrack in the borehole wall image, convert them into three-dimensional global coordinates, and then calculate the horizontal coordinate difference and the vertical coordinate difference between the starting point and the ending point. The horizontal direction difference is the X coordinate of the ending point minus the X coordinate of the starting point, and the Y coordinate difference is the same. The vertical direction difference is the Z coordinate of the ending point minus the Z coordinate of the starting point.

[0109] The horizontal azimuth of the extension direction is obtained by calculating the arctangent value of the horizontal coordinate difference, and the vertical dip angle is obtained by calculating the arctangent value of the vertical direction difference and the horizontal distance. If the microcrack is in a curved shape, the main extension direction is extracted by piecewise fitting. For example, the crack image is segmented into multiple sub-segments, and the direction of each sub-segment is calculated and then averaged as the main extension direction.

[0110] When calculating the direction of the acoustic wave propagation path in the borehole televiewer observation coordinate system, the direction of the acoustic wave propagation path is determined by connecting the spatial coordinates of the transmitting probe and the receiving probe. For example, the transmitting probe is located at the global coordinates of borehole A (Xa = 1000.0 m, Ya = 2000.0 m, Za = -50.0 m), and the receiving probe is located at the global coordinates of borehole B (Xb = 1050.0 m, Yb = 2050.0 m, Zb = -60.0 m). The horizontal azimuth of the acoustic wave propagation path is 45 degrees east of north, and the vertical dip angle is 5 degrees downward.

[0111] If the acoustic wave path is a broken line crossing multiple layers of boreholes, calculate the direction of each segment separately. For example, from borehole A to borehole C is the first segment, and from borehole C to borehole B is the second segment. Calculate the horizontal azimuth and the vertical dip angle of each segment respectively.

[0112] When screening for microcracks with an included angle between the extension direction and the acoustic wave propagation path direction less than a preset angle, the preset angle is set according to the development characteristics of microcrack chains in the target rock mass area. For example, in a layered rock mass area, microcrack chains usually extend along bedding planes, and the preset angle is set to 30 degrees; in a massive rock mass area, the preset angle is relaxed to 45 degrees. The calculation method for the included angle is as follows: Calculate the absolute value of the horizontal azimuth difference and the absolute value of the vertical dip difference between the microcrack extension direction and the acoustic wave propagation path direction. If both are less than the preset angle, it is determined that the directions match. For example, if the horizontal azimuth of a microcrack extension direction is 50 degrees north of east and the vertical dip is 10 degrees downward, and the horizontal azimuth of the acoustic wave path direction is 55 degrees north of east and the vertical dip is 8 degrees downward, with a preset angle of 30 degrees, then the horizontal azimuth difference is 5 degrees and the vertical dip difference is 2 degrees, both less than 30 degrees, so it is determined to match. If the horizontal azimuth of a microcrack extension direction is 80 degrees north of east and the vertical dip is 15 degrees downward, and the acoustic wave path direction is 30 degrees north of east and the vertical dip is 5 degrees downward, then the horizontal azimuth difference is 50 degrees, exceeding the preset angle, so it is determined not to match.

[0113] When generating a group of microcracks with matching directions, the spatial distribution of the microcrack group is combined through spatial proximity and direction consistency. Spatial proximity is determined when the global coordinate distance between two microcracks is less than the preset clustering radius. For example, when the borehole spacing is 20 meters, the clustering radius is set to 5 meters. Direction consistency is determined when the included angle between the microcrack extension directions is less than the preset angle and the vertical dip difference is less than 10 degrees. For example, if the global coordinate distance between two microcracks is 3 meters, the horizontal azimuth difference of the extension directions is 10 degrees, and the vertical dip difference is 5 degrees, then they are combined into the same microcrack group. If a microcrack group contains 10 microcracks and its spatial distribution is concentrated along the acoustic wave propagation path direction, it is marked as a high-risk microcrack chain area.

[0114] S6. Compare and verify the spatial density of the microcrack group with the preliminary density model, and eliminate misjudged data in the areas with non-matching directions, including:

[0115] When calculating the spatial density based on the number and distribution volume of microcracks in the microcrack group, the distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the microcrack group.

[0116] The method for determining the three-dimensional boundary is as follows: Extract the global coordinates of all microcracks in the microcrack group, and calculate the length, width, and height of its minimum enclosing cube as an approximation of the distribution volume. For example, if the microcrack coordinates of a certain microcrack group range from 1000.0 m to 1005.0 m in the X direction, from 2000.0 m to 2008.0 m in the Y direction, and from -50.0 m to -45.0 m in the Z direction, then the distribution volume is 5 m (length) × 8 m (width) × 5 m (height) = 200 cubic meters, and the spatial density is the number of microcracks (such as 50) divided by 200 cubic meters, resulting in 0.25 cracks per cubic meter. If the microcrack group is irregularly distributed, the actual occupied volume is calculated through the three-dimensional convex hull algorithm. For example, the minimum convex polyhedron is generated using the microcrack coordinate point set in the borehole television observation data, and its volume is calculated.

[0117] When defining the verification index of the preliminary density model in the corresponding rock mass area, the verification index is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the microcrack group.

[0118] The calculation method of the absolute value of the relative error is as follows: Subtract the spatial density of the microcrack group from the predicted density of the model, take the absolute value, and then divide by the spatial density of the microcrack group to convert it into a percentage error. For example, if the predicted density of the model is 0.30 cracks per cubic meter and the spatial density of the microcrack group is 0.25 cracks per cubic meter, the absolute value of the relative error is |0.30 - 0.25| / 0.25 = 20%. The preset error threshold is determined according to the statistical distribution of the model prediction errors in the historical data of the mining area. For example, take the 95th percentile value of the historical error data as the threshold. If 95% of the errors in the historical data are lower than 25%, then the threshold is set to 25%.

[0119] When screening the areas where the verification index exceeds the preset error threshold and the direction of the microcrack group does not match the acoustic wave propagation path, the determination criterion for the direction mismatch is that the angle between the average extension direction of the microcrack group and the acoustic wave propagation path direction exceeds the preset angle. For example, if the model prediction error in a certain area is 30% (exceeding the threshold of 25%), and the angle between the average extension direction of the microcrack group and the acoustic wave path is 40 degrees (exceeding the preset angle of 30 degrees), then it is determined as a misjudged area. If the error in a certain area is 20% (not exceeding the threshold) but the direction angle is 35 degrees (exceeding the angle), or the error is 30% (exceeding the threshold) but the direction angle is 25 degrees (not exceeding the angle), neither is marked as a misjudged area.

[0120] When removing the density parameter data of the misjudged area from the preliminary density model, the removal method is to set the density parameter corresponding to the misjudged area to zero or replace it with the interpolation result of the adjacent area. For example, if the density parameter of a misjudged area is 2.0 g / cm³, and the density parameters of its adjacent areas are 1.5 g / cm³, 1.8 g / cm³, and 1.6 g / cm³, then after setting it to zero, this area does not participate in the subsequent analysis in the corrected model, or is replaced with the adjacent area average value of 1.63 g / cm³.

[0121] When retaining the corrected density model, the corrected model only includes the density parameters and interpolated and completed data of the regions not marked as misjudged regions, which are used to generate the final three-dimensional rock mass deterioration risk map.

[0122] S7. Generate a three-dimensional rock mass deterioration risk map based on the verified micro-fracture chain density and micro-fracture group distribution, including:

[0123] When performing weighted fusion of the verified micro-fracture chain density parameters and the spatial density of micro-fracture groups according to spatial coordinates, the weight coefficient of the weighted fusion is determined according to the historical data correlation between the micro-fracture chain density parameters and the spatial density of micro-fracture groups.

[0124] For example, in the historical data of a certain iron ore mining area, the contribution ratio of the micro-fracture chain density parameters to the rock mass deterioration is 70%, and the spatial density of micro-fracture groups accounts for 30%. Then the calculation formula for the fusion density parameter is fusion density = 0.7×micro-fracture chain density + 0.3×micro-fracture spatial density; if in a certain copper ore mining area, due to the micro-fracture groups being more sensitive to seepage, the weights are adjusted to 60% for the micro-fracture chain density and 40% for the micro-fracture spatial density. The fusion density parameters after weighted fusion are stored in association with spatial coordinates. For example, the center point coordinates of a certain grid cell are (X = 1000.5 m, Y = 2000.3 m, Z = -50.0 m), and the fusion density is 1.8 g / cm³.

[0125] When dividing three-dimensional grid cells based on the borehole positions and acoustic wave propagation paths in the rock mass area, the grid cell size is determined according to the borehole spacing and the preset accuracy requirements; for example, when the borehole spacing is 20 m and the preset accuracy requirement is a 5 m resolution, the grid cell size is set to 5 m×5 m×5 m; if the borehole spacing is 50 m and the accuracy requirement is 10 m, the grid size is set to 10 m×10 m×10 m.

[0126] For the areas where the acoustic wave propagation paths cross or overlap, the grid cell size can be locally encrypted. For example, within a range of 1 m around the path crossing point, the grid size is reduced to 1 m×1 m×1 m. The boundaries of the grid cells are aligned with the borehole positions. For example, if borehole A is located within the grid cell (1000 - 1005 m, 2000 - 2005 m, -50 to -45 m), the grid division expands with this borehole as the center.

[0127] When normalizing the fusion density parameters in each grid cell, the normalization range is from 0 to 1, where 0 indicates that there is no deterioration risk in this cell (fusion density ≤ 1.0 g / cm³), and 1 indicates the highest risk (fusion density ≥ 3.0 g / cm³).

[0128] The normalization method is as follows: subtract the minimum value of 1.0 g / cm³ from the grid cell fusion density, and then divide by the difference between the maximum value of 3.0 g / cm³ and the minimum value of 1.0 g / cm³; for example, if the fusion density of a certain grid cell is 2.5 g / cm³, the normalized value is (2.5 - 1.0) / (3.0 - 1.0) = 0.75. If there is an extremely high-density area in the mining area (such as the fusion density > 3.0 g / cm³), the normalized value is forced to be set to 1.0.

[0129] When setting the risk level threshold according to the normalization result, the risk level threshold is determined based on the statistical data of historical disasters in the mining area; for example, in the historical data of a certain iron ore mining area, there are no disaster records in the areas where the normalized value < 0.3, local rock mass spalling occurs in the areas with 0.3 - 0.6, and roof caving accidents occur in the areas > 0.6. Therefore, the low-risk threshold is set to 0.3, the medium-risk to 0.6, and the high-risk to 1.0. If the threshold is adjusted due to lithological differences in a certain copper mining area, the low-risk is set to 0.4, the medium-risk to 0.7, and the high-risk to 1.0.

[0130] After the grid cells are divided according to the threshold intervals, the low-risk areas are marked green, the medium-risk are yellow, and the high-risk are red, forming a color coding rule.

[0131] When mapping the risk level to a color gradient through 3D visualization technology, the color gradient uses transparency superposition to enhance spatial recognition; for example, the transparency of the low-risk green area is set to 50%, the medium-risk yellow is 30%, and the high-risk red is 0% (completely opaque). The generated 3D map contains the spatial coordinates, risk level, and color coding of the grid cells, and supports rotation, scaling, and cross-section viewing.

[0132] For example, users can view the color distribution of the horizontal slice at Z = -50 meters, or analyze the risk gradient change along the cross-section view from borehole A to borehole B; the output format of the 3D map is general point cloud data or grid model, which is compatible with the import of mainstream geological software (such as Surpac, Micromine).

[0133] Embodiment 2: Figure 2 The structural schematic diagram of an identification system for the deterioration of the rock mass structure of a mined slope in the present invention is given. An identification system for the deterioration of the rock mass structure of a mined slope includes:

[0134] Acoustic time window module: collect the acoustic signals between boreholes in the target rock mass area, and adjust the signal acquisition time window according to the adjacent borehole spacing;

[0135] Frequency band screening module: perform multi-scale wavelet decomposition on the acoustic signals to extract the energy distribution of sub-signals in each frequency band, and screen the characteristic frequency bands corresponding to the micro-fracture chain response based on the energy gradient;

[0136] Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the characteristic frequency band sub-signal to generate a statistical map, and perform time-domain sliding comparison with the complete rock mass reference map after segmenting according to the acoustic wave propagation path, and mark the abnormal path segments;

[0137] Density modeling module: Fit the microcrack chain density parameter according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model;

[0138] Fracture screening module: Extract the coordinates of the microfractures that do not penetrate the borehole wall in the borehole television observation data, and screen the microfracture groups that match the acoustic wave propagation path direction;

[0139] False judgment elimination module: Compare and verify the spatial density of the microfracture group with the preliminary density model, and eliminate the false judgment data in the area with mismatched directions;

[0140] Three-dimensional map module: Generate a three-dimensional rock mass deterioration risk map according to the verified microcrack chain density and the distribution of microfracture groups.

[0141] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0142] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0143] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0144] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0145] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0146] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, in each embodiment of the present application, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0148] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0149] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0150] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying the deterioration of the rock mass structure in a mined slope, characterized in that It includes the following steps: S1. Collect the acoustic wave signals between boreholes in the target rock mass area, and adjust the signal acquisition time window according to the adjacent borehole spacing; S2. Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency bands corresponding to the micro-fracture chain response based on the energy gradient; S3. Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the sub-signals in the characteristic frequency band to generate a statistical map, and perform time-domain sliding comparison with the complete rock mass reference map after segmenting according to the acoustic wave propagation path, and mark the abnormal path segments; S4. Fit the micro-fracture chain density parameter according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model; S5. Extract the coordinates of the micro-fractures that do not penetrate the borehole wall in the borehole television observation data, and screen the micro-fracture groups that match the acoustic wave propagation path direction; S6. Compare and verify the spatial density of the micro-fracture group with the preliminary density model, and eliminate the misjudged data in the regions with unmatched directions; S7. Generate a three-dimensional rock mass deterioration risk map according to the verified micro-fracture chain density and the distribution of the micro-fracture group.

2. The identification method for the deterioration of the rock mass structure of a mined slope according to claim 1, characterized in that, S1 includes: Determine the spacing between adjacent boreholes, and calculate the theoretical propagation time of the acoustic wave between adjacent boreholes according to the preset average sound velocity of the rock mass; Determine the total duration of the signal acquisition time window based on the sum of the theoretical propagation time and the preset redundancy time threshold; Divide the start time and end time of the signal acquisition time window according to the total duration of the signal acquisition time window, and control the cross-hole acoustic wave device to collect acoustic wave signals according to the divided time window.

3. The identification method for the deterioration of the rock mass structure of a mined slope according to claim 1, wherein S2 It includes: Calculate the energy proportion of each sub-signal after multi-scale wavelet decomposition to generate an energy distribution sequence; Generate an energy gradient sequence according to the absolute value of the difference between the energy proportions of adjacent frequency bands; Mark the frequency bands with the absolute value of the difference in the energy gradient sequence exceeding the preset gradient threshold as candidate frequency bands; Extract the target frequency bands that match the frequency band characteristics of the micro-fracture chain response in the candidate frequency bands as the final characteristic frequency bands.

4. The identification method for the deterioration of the rock mass structure in a mined slope according to claim 3, characterized in that, The final characteristic frequency bands are determined by the preset frequency band range of the micro-fracture chain response or the frequency band characteristic library statistically obtained from historical data.

5. The identification method for the deterioration of the rock mass structure in a mined slope according to claim 1, characterized in that, S3 includes: Divide the acoustic wave propagation path into multiple continuous sub-path segments according to the adjacent borehole spacing and the preset segmentation rule, and the length of each sub-path segment is dynamically adjusted based on the homogeneity of the rock mass structure; Perform time-domain window segmentation on the acoustic wave travel time statistical map within each sub-path segment, and the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment; Extract the reference data window corresponding to the current sub-path segment from the complete rock mass reference map, and calculate the combined difference value of the skewness and kurtosis of the statistical map within the current window and the reference data window; According to the comparison result of the combined difference value and the preset difference threshold, mark the sub-path segments with the combined difference value exceeding the preset difference threshold as abnormal path segments.

6. The identification method for the deterioration of the rock mass structure of a mined slope according to claim 1, characterized in that S4 It includes: Extract the skewness and kurtosis data of the acoustic wave travel time sequence corresponding to the abnormal path segment to generate a statistical feature vector of the abnormal path segment; Based on the mapping relationship between the acoustic wave data and the density parameter in the historical micro-fracture chain development area, construct a regression model of skewness-kurtosis and density parameter; Input the statistical feature vector of the abnormal path segment into the regression model and output the micro-fracture chain density parameter. According to the spatial distribution of density parameters, a preliminary density model is generated through the Kriging interpolation algorithm, and the acoustic wave propagation path of the abnormal path segment is used as the spatial constraint condition during the interpolation process.

7. A method for identifying the deterioration of the rock mass structure in a mined slope according to claim 1, characterized in that S5 It includes: Extract the spatial coordinates of microfractures that do not penetrate the borehole wall from the borehole television observation data to generate a set of microfracture coordinates; Calculate the corresponding extension direction for each microfracture in the set of microfracture coordinates, and the extension direction is determined by fitting the straight line direction of the coordinates of the two endpoints of the microfracture; Calculate the direction of the acoustic wave propagation path in the borehole television observation coordinate system, and the direction of the acoustic wave propagation path is determined by connecting the spatial coordinates of the transmitting probe and the receiving probe; Screen the microfractures with the included angle between the extension direction and the acoustic wave propagation path direction less than the preset angle to generate a group of microfractures with matching directions.

8. The identification method for the deterioration of the rock mass structure in a mined slope according to claim 1, characterized in that S6 It includes: Calculate the spatial density based on the number of microfractures and the distribution volume within the microfracture group, and the distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the microfracture group; Define the verification index for the preliminary density model in the corresponding rock mass area, and the verification index is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the microfracture group; Screen the areas where the verification index exceeds the preset error threshold and the direction of the microfracture group does not match the acoustic wave propagation path, and mark them as misjudged areas; Remove the density parameter data of the misjudged areas from the preliminary density model and retain the corrected density model.

9. The identification method for the deterioration of the rock mass structure of a mining-induced slope according to claim 1, characterized in that, S7 includes: Weightedly fuse the verified microfracture chain density parameters and the spatial density of the microfracture group according to the spatial coordinates to generate fused density parameters; Divide the three-dimensional grid units based on the borehole positions and the acoustic wave propagation path in the rock mass area, and the grid unit size is determined according to the borehole spacing and the preset accuracy requirements; Normalize the fused density parameters within each grid unit, and the normalization range is from 0 to 1, where 0 represents no risk of deterioration and 1 represents the highest risk; Set the risk level threshold according to the normalization result, and divide the grid units into low-risk, medium-risk, and high-risk areas according to the threshold interval; Map the risk level to a color gradient through three-dimensional visualization technology to generate a three-dimensional map containing spatial coordinates and risk levels.

10. An identification system for the deterioration of the rock mass structure in a mined slope, which is used to implement the identification method for the deterioration of the rock mass structure in a mined slope according to any one of claims 1-9, characterized in that, It includes: Acoustic wave time window module: Collect the acoustic wave signals between boreholes in the target rock mass area and adjust the signal acquisition time window according to the adjacent borehole spacing; Frequency band screening module: Perform multi-scale wavelet decomposition on the acoustic wave signals to extract the energy distribution of sub-signals in each frequency band, and screen the characteristic frequency bands corresponding to the microfracture chain response based on the energy gradient; Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the sub-signals in the characteristic frequency band to generate a statistical map, and perform time-domain sliding comparison with the complete rock mass reference map after segmenting according to the acoustic wave propagation path to mark the abnormal path segments; Density modeling module: Fit the microfracture chain density parameters according to the acoustic wave travel time data of the abnormal path segments to generate a preliminary density model; Fracture screening module: Extract the coordinates of microfractures that do not penetrate the borehole wall from the borehole television observation data and screen the microfracture groups that match the acoustic wave propagation path direction; Misjudgment elimination module: Compare and verify the spatial density of the microfracture group with the preliminary density model, and eliminate the misjudged data in the areas with unmatched directions; Three-dimensional map module: Generate a three-dimensional rock mass deterioration risk map based on the verified microfracture chain density and the distribution of the microfracture group.

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