A method and system for identifying unstable structural surfaces that integrates source inversion and cluster reconstruction.

Abstract

This invention discloses a method and system for identifying unstable structural surfaces that integrates source inversion and cluster reconstruction. The identification method includes the following steps: obtaining raw acoustic data; performing location calculations and source mechanism inversion on the raw acoustic data to obtain core parameters of coal and rock fracture sources directly related to the fracture; calculating the fracture penetration probability index (BCI) by combining standardized fracture volume, source radius, and spatial distance; calculating the similarity between fractures based on their multidimensional characteristics and performing density clustering of fractures using the DBSCAN clustering algorithm; identifying potential unstable surfaces by calculating the fracture penetration probability index value on each fracture cluster and selecting fractures with higher fracture penetration probability indices; generating macroscopic unstable surfaces using a B-spline surface fitting algorithm; determining the main fracture direction by selecting control points using principal component analysis, constructing B-spline curves based on fracture distribution to generate boundary contours, and accurately fitting the unstable structural surface using the B-spline algorithm.

Description

Technical Field

[0001] This invention relates to the field of microseismic monitoring technology, and in particular to a method and system for identifying unstable structural surfaces that integrates source inversion and cluster reconstruction. Background Technology

[0002] The formation of unstable structural surfaces is a typical multi-scale dynamic evolution phenomenon in mine rock masses under complex stress fields, where microscopic cracks expand into macroscopic structural instability. Their continuity directly affects the bearing capacity of the rock mass, the formation of seepage channels, and the triggering of disaster chains. Failure to identify and characterize these unstable structural surfaces in a timely manner may lead to secondary disasters such as gas outbursts, instability in coal and gas co-mining areas, and roadway collapses, further exacerbating the chain reaction of mine disasters and causing severe economic losses and personnel safety hazards. Therefore, the significance of accurate identification and visualization of unstable structural surfaces lies not only in revealing the evolution mechanism of rock mass instability but also in providing a scientific basis for mine disaster prediction, helping to formulate more reliable disaster prevention and control plans and mine engineering reinforcement measures. Simultaneously, by identifying unstable structural surfaces, deep mining design can be optimized, reducing the risk of sudden accidents during engineering construction, thereby improving the long-term stability and safety of mine operations. Taking into account the impact of rock mass instability on disasters, studying the dynamic evolution characteristics and formation mechanism of instability structural surfaces has important theoretical value and practical significance for improving the stability of deep mining and ensuring the efficient mining and safe management of coal resources.

[0003] The formation and connection of unstable surfaces is a highly complex and dynamically evolving "black box" process, and traditional methods struggle to reveal its underlying mechanisms. Currently, advanced imaging technologies such as CT scans, X-ray imaging, and magnetic resonance imaging, as well as numerical simulation methods for discrete fracture networks, have made some progress in characterizing fractures in coal and rock masses. For example, patent CN118172406B provides a method and system for extracting discontinuities within rock masses based on borehole fracture images; patent CN117195447A discloses a method for generating two-dimensional rough discrete fracture networks in rock masses based on self-affine features; and patent CN117150842A proposes a method for analyzing rock fracture surface behavior based on near-field dynamics. While these technologies provide effective means for fracture characterization, they still have certain limitations in practical applications, often relying on high-resolution equipment and complex model reconstruction methods, thus restricting their widespread application in actual engineering. Microseismic monitoring, as a non-destructive detection technology, provides an effective means to open this "black box" by capturing the elastic wave signals associated with coal and rock under load. Microseismic inversion can not only accurately locate the spatial position of the fracture source, but also quantitatively deduce the spatiotemporal dynamic process of crack propagation and the fracture mechanism, providing scientific support for studying the dynamic evolution law, penetration characteristics and induced disaster mechanism of cracks inside rock masses. For example, patent CN114966849A provides a rock mass fracture characterization method based on microseismic or acoustic emission and source mechanism constraints, and patent CN115077437A discloses a rock hydraulic fracturing fracture morphology characterization method based on acoustic emission location constraints. However, these methods mainly focus on the spatial distribution of fractures and emphasize the analysis of discrete fracture location points, lacking a comprehensive analysis of the multidimensional characteristics of fractures, especially the in-depth exploration of the penetration mechanism between fractures. This limits the accurate understanding and characterization of the global characteristics of the fracture network, and it still remains at the stage of describing fracture location points only superficially, failing to effectively reveal the geometric structure and spatial orientation of the fractures. Therefore, there is an urgent need for a method and system for identifying unstable structural surfaces that integrates source inversion and cluster reconstruction. By combining source mechanism inversion, BCI quantization, multidimensional clustering, and surface fitting techniques, the analysis can be shifted from point analysis to fracture surface reconstruction. This allows for a comprehensive characterization of fracture penetration mechanisms, geometric morphology, and spatial orientation at multiple scales, revealing the complex dynamic behavior and global evolution characteristics of fracture networks. This enables accurate identification and visualization of macroscopic unstable structural surfaces in rock masses, and promotes the advancement of microseismic monitoring and characterization of rock mass fractures from "point" to "surface". Summary of the Invention

[0004] This solution addresses the problems and needs raised above by proposing a method and system for identifying unstable structural surfaces that integrates seismic source inversion and cluster reconstruction. It achieves the aforementioned technical objectives and brings about several other technical benefits by adopting the following technical features.

[0005] One objective of this invention is to propose a method for identifying unstable structural surfaces that integrates seismic source inversion and cluster reconstruction, comprising the following steps:

[0006] S10: Raw acoustic wave data is acquired through a microseismic monitoring system. The raw acoustic wave data is used for location calculation and focal mechanism inversion to obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0007] S20: Determine the critical distance for rupture penetration by the focal radius, standardize the fracture volume using Z-score, and calculate the rupture penetration probability index (BCI) by combining the standardized fracture volume, focal radius, and spatial distance.

[0008] S30: Based on the multidimensional characteristics of fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, the similarity between fractures is calculated, and the DBSCAN clustering algorithm is used to perform density clustering of fractures; similar fractures are grouped into the same cluster, and each fracture cluster corresponds to a potential fracture surface;

[0009] S40: By calculating the fracture penetration probability index (BCI) on each fracture cluster, fractures with a high fracture penetration probability index are screened out, thereby identifying potential instability surfaces.

[0010] S50: The macroscopic unstable structural surface is reconstructed using the B-spline surface fitting algorithm to generate a smooth and continuous macroscopic unstable surface; the main direction of rupture is determined by selecting control points through principal component analysis; the boundary contour is generated by constructing B-spline curves based on the crack distribution; the outer contour line is triangulated and meshed; and the unstable structural surface is accurately fitted using the B-spline algorithm by combining the crack points and the triangular mesh.

[0011] In addition, the method for identifying unstable structural surfaces based on source inversion and cluster reconstruction according to the present invention may also have the following technical features:

[0012] In one example of the present invention, in step S10, the original acoustic data is used to perform localization calculations and focal mechanism inversion to obtain core parameters of the focal mechanism directly related to the rupture, including the following steps:

[0013] S11: Post-process the raw acoustic data acquired by the microseismic monitoring system, and use the red-delay information criterion method to extract the arrival time and amplitude information of the first wave;

[0014] S12: Using the arrival time data of the first wave of acoustic waves at different locations of sensors in the microseismic monitoring system, the spatial coordinates of the fracture are obtained by spatially locating the acoustic emission source using a robust simplex localization algorithm.

[0015] S13: Solving for the six components M of the source rupture moment tensor based on the acoustic first-wave amplitude data collected by sensors at different locations under the constraint of the tension-shear rupture source model. pq ;

[0016] S14: Based on the calculated moment tensor components, quantitatively invert and calculate the core parameters of the coal and rock fracture source. Among them, the core parameters of the coal and rock fracture source include fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0017] In one example of the present invention, in step S14, the specific calculation formula for the core parameters of the coal and rock fracturing source is as follows:

[0018] Crack volume:

[0019]

[0020] In the formula, ΔV is the fracture volume at the fracture point, and M1 and M3 are the moment tensors M. pq eigenvalues, where μ is the Lamé constant;

[0021] Spatial orientation of the fracture:

[0022]

[0023] n=(cosα,0,±sinα)

[0024]

[0025] In the formula, α is the angle between the direction of motion of the earthquake source rupture surface and the normal direction, and M1, M2, and M3 are the moment tensors M. pq The eigenvalues ​​are μ and λ, which are Lamé constants, n is the spatial orientation, and b is the direction of motion.

[0026] fracture energy:

[0027]

[0028] In the formula: ΔA is the fracture area at the crack point, σ is the material strength, where σ t For tensile strength, σ s This refers to shear strength.

[0029] In one example of the present invention, the calculation of the fracture penetration probability index (BCI) in step S20, combining the standardized fracture volume, source radius, and spatial distance, includes the following steps:

[0030] S21: Determine the critical distance for rupture penetration based on the source radius of the fracture point in order to determine whether there is a possible rupture penetration relationship between fracture points;

[0031] S22: The Z-score standardization method is used to transform the data into a standard normal distribution with zero mean and unit variance in order to standardize the volume of the fracture points;

[0032] Wherein, for each crack point, the volume V i The formula for calculating its standardized volume is as follows:

[0033]

[0034] In the formula: Vi is the volume of the i-th fracture point, μ V σ is the mean volume of all fracture points. V Z(V) represents the standard deviation of the volume of all fracture points. i ) represents the standardized value of the volume of the i-th fracture point;

[0035] S23: When two adjacent fracture points i and j satisfy the fracture penetration condition, calculate the fracture penetration probability index (BCI) between the two adjacent fracture points. ij And thus calculate the total fracture penetration index (BCI) at fracture point i. i .

[0036] In one example of the present invention, in step S21, determining the critical distance for rupture penetration based on the source radius of the fracture point to determine whether there is a possible rupture penetration relationship between the fracture points specifically includes:

[0037] S211: Assume the source radius of the fracture point i is R. i Then the critical distance D for fracture penetration at that fracture point i Defined as twice the radius of the seismic source at that point:

[0038]

[0039] In the formula, R i V is the source radius of the fracture point i. i Let D be the volume of crack point i. i Let be the critical distance for fracture penetration at fracture point i;

[0040] S212: Calculate the Euclidean distance d between fracture point i and other fracture points j. ij :

[0041]

[0042] In the formula: (x i ,y i ,z i ), (x j ,y j ,z j ) are the spatial coordinates of the fracture points i and j, respectively;

[0043] S213: For each fracture point j, determine its distance d from i. ij Critical distance D from the fracture penetration i Relationship:

[0044] If d ij ≤D i Therefore, it is considered that a fracture connection may occur between fracture points i and j. ij =1;

[0045] If d ij >D i Therefore, it is assumed that a fracture cannot occur between fracture points i and j. ij =0.

[0046] In one example of the present invention, in step S23, the total fracture penetration index (BCI) of fracture point i is calculated. i The specific process is as follows:

[0047] The rupture penetration probability index (BCI) is calculated by comprehensively considering the focal radius and the volume of the fracture points to quantify the probability of rupture penetration between fracture points. Specifically, for fracture points i and j, the formula for calculating the BCI when the rupture penetration condition is met is as follows:

[0048]

[0049] In the formula: Z(V) i ) and Z(V j ) represent the standardized volume values ​​of the i-th and j-th fracture points, respectively; d ij Let be the Euclidean distance between the i-th fracture point and the j-th fracture point;

[0050] Total fracture penetration index (BCI) at fracture point i i The total fracture penetration index (BCI) is the sum of the fracture penetration indices of the i fracture point and all other remaining fracture points. Here, assuming there are n fracture points surrounding fracture point i, the total fracture penetration index (BCI) is... i The calculation formula is as follows:

[0051]

[0052] In the formula: I ij As an indicator function, when d ij ≤D i A time value of 1 indicates that the rupture and penetration conditions are met between the two points.

[0053] In one example of the present invention, step S30, using the DBSCAN clustering algorithm to perform density clustering of the fractures, includes the following steps:

[0054] S31: Multidimensional Feature Vector Reading and Euclidean Distance Calculation: Read the multidimensional feature vector of each fracture point obtained from the focal mechanism inversion, and calculate the Euclidean distance between fracture points; where, for each pair of fracture points P i =(x i ,y i ,z i ,nx i ,ny i ,nz i V i E i ) and P j =(x j ,y j ,z j ,nx j ,ny j ,nz j V j E j The Euclidean distance is calculated using the following formula:

[0055]

[0056] In the formula, n is the dimension of the feature vector, and P i,k and P i,k These are the values ​​of the k-th dimension in the multidimensional feature vectors of the two fracture points, w. k Weights for each feature;

[0057] S32: Determine the neighborhood distance and minimum number of points: Select an appropriate neighborhood distance ∈ and minimum number of points MinPts. Based on the Euclidean distance and neighborhood distance ∈, calculate the neighborhood of each fracture point. If the number of points contained in the neighborhood of a fracture point is greater than or equal to MinPts, then the fracture point is a core point; otherwise, it is a boundary point or a noise point.

[0058] S33: Cluster Expansion and Core Point Processing; Select an initial point from the unlabeled core points, mark it as the starting point of a new cluster, and assign a cluster number to the point; Expand the neighborhood of the core point, grouping all points within the neighborhood into the same cluster; For newly added points in the expanded cluster, check if they are core points. If they are core points, continue expanding the cluster; if they are boundary points, keep their cluster labels unchanged; Repeat the above steps to continue selecting new points from the unlabeled core points for cluster expansion until all core points have been processed.

[0059] S34: Cluster Numbering and Clustering Result Check: Based on the DBSCAN clustering results in step S33, assign a unique crack surface number to each cluster, with each cluster representing an independent crack surface. If a crack point belongs to the neighborhood of multiple core points, that point will be assigned the crack surface number of the first discovered cluster. Then check the clustering results to ensure that each cluster is assigned a unique crack surface number, with noise points numbered as -1. If any errors or omissions are found in the cluster numbering, make necessary adjustments to ensure that the final crack surface numbering is accurate.

[0060] In one example of the present invention, in step S40, by calculating the fracture penetration probability index value on each fracture cluster, fractures with a high fracture penetration probability index are screened out, thereby identifying potential instability surfaces, including the following steps:

[0061] S41: Calculate the BCI value for each fracture: For each fracture cluster obtained by DBSCAN clustering, calculate the fracture penetration probability index for each fracture within it.

[0062] S42: Crack screening based on BCI value: By visualizing the distribution of BCI values ​​of cracks, setting an appropriate threshold, and selecting crack points with higher BCI values ​​as potential instability surfaces;

[0063] S43: Visualization of optimized clustering results: The optimized clustering results are presented through three-dimensional visualization. The primary and secondary fracture surfaces can be distinguished by different colors or point sizes, thereby intuitively displaying the spatial characteristics of fracture propagation and further analyzing the evolution and distribution patterns of fractures.

[0064] In one example of the present invention, in step S50, the cracks on the potential unstable surface are reconstructed using a B-spline surface fitting algorithm to generate a smooth and continuous macroscopic unstable structural surface, including the following steps:

[0065] S51: From the results of cluster analysis, read all valid fractures that have been classified into the same fracture surface; where each fracture includes its fracture spatial coordinates, fracture volume, fracture energy BCI value and fracture spatial orientation;

[0066] S52: Combining BCI and normal direction to comprehensively describe the spatial characteristics and fracture effects of fractures:

[0067] Suppose the normal direction vector of a certain crack is n = (n x ,n y ,n z The normal vector is normalized to ensure that the normalized normal vector only represents direction; the normalization process is calculated using the following formula:

[0068]

[0069] For each fracture, a comprehensive feature weight is calculated based on its BCI value and normal direction. Let W be the feature weight of fracture i. i Since it is a combination of BCI value and normal direction, the formula for calculating the comprehensive feature weight of each fracture point is as follows:

[0070]

[0071] In the formula, It is the normalized normal magnitude, usually 1; BCI i Let be the fracture penetration index of the i-th fracture; α and β are weighting coefficients;

[0072] S53: Based on the feature weights and three-dimensional spatial location information of the same cluster of cracks, the cracks with higher feature weights are extracted by principal component analysis as key control points for the reconstruction of the unstable surface, thereby clarifying the main direction of the unstable surface; S54: After determining the main direction of the unstable surface, the boundary of the same cluster of cracks is fitted by the B-spline curve fitting method to form a closed-loop curve as the outer contour line of the final unstable surface.

[0073] S55: Triangulate the outer contour of the formed crack surface and divide the interior of the final unstable surface into triangular meshes.

[0074] S56: Read all the cracks in the same cluster, combine them with the established triangular mesh to perform the final fitting of the crack surface to be generated, and form the final macroscopic instability structure surface of the crack in the same cluster.

[0075] Another objective of this invention is to propose an unstable structural surface identification system that integrates source inversion and cluster reconstruction, comprising:

[0076] The parameter acquisition module is configured to acquire raw acoustic wave data through a microseismic monitoring system, perform location calculations and source mechanism inversion on the raw acoustic wave data, and obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0077] The penetration index calculation module is configured to determine the critical distance of rupture penetration through the source radius, and to standardize the fracture volume using Z-score. The rupture penetration probability index (BCI) is then calculated by combining the standardized fracture volume, source radius, and spatial distance.

[0078] The fracture surface clustering module is configured to calculate the similarity between fractures based on multidimensional features such as fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, and to perform density clustering of fractures using the DBSCAN clustering algorithm; wherein similar fractures are grouped into the same cluster, and each fracture cluster corresponds to a potential fracture surface;

[0079] The potential instability surface identification module is configured to identify potential instability surfaces by calculating the fracture penetration probability index (BCI) value on each fracture cluster and filtering out fractures with a high fracture penetration probability index.

[0080] The unstable structural surface reconstruction module is configured to reconstruct the macroscopic unstable structural surface using a B-spline surface fitting algorithm, generating a smooth and continuous macroscopic unstable surface. The module selects control points to determine the main direction of rupture using principal component analysis, constructs B-spline curves based on the crack distribution to generate the boundary contour, triangulates the outer contour line and divides it into meshes, and combines the crack points and triangular meshes to accurately fit the unstable structural surface using the B-spline algorithm.

[0081] Compared with the prior art, the present invention has the following advantages:

[0082] 1. This invention obtains core parameters of the focal mechanism through microseismic monitoring and inversion. It goes beyond simply describing fracture location points, further revealing the geometric characteristics and spatial orientation of the surfaces formed by fracture points, achieving a leap from discrete point analysis to a comprehensive characterization of fracture surfaces. Secondly, by utilizing focal inversion technology, it can accurately capture the source characteristics of fractures, overcoming the limitations of traditional methods in terms of resolution and assumed models. It enables real-time monitoring and inversion of fracture occurrence, propagation, and penetration processes, thus providing more accurate input data for instability surface identification.

[0083] 2. This invention is the first to propose calculating the fracture penetration probability index using fracture volume and focal radius. The critical distance for fracture penetration is determined by the focal radius, and the fracture volume is standardized using Z-score, making fractures of different sizes comparable. This method allows the measurement of fracture penetration to consider not only spatial distance but also the geometric characteristics and energy properties of the fracture, thus enabling a more accurate quantification of the degree of penetration between fractures and providing a new approach to fracture evolution analysis.

[0084] 3. This invention combines multi-dimensional features such as spatial distance, orientation, volume, and energy of fractures, and employs the DBSCAN clustering algorithm to perform density clustering of fractures. By setting a reasonable density threshold, the clustering results can identify fracture clusters with actual connectivity and expansion potential. Secondly, the DBSCAN algorithm is used to construct crack surfaces from fracture clusters. This eliminates the need for pre-setting the number of clusters, can adaptively handle complex distributions, and exhibits strong density sensitivity and robustness. It can also automatically identify and eliminate noise points, avoiding interference with the analysis. After clustering, the results are combined with the fracture penetration index optimization to identify potential instability surfaces with significant expansion potential, improving the clustering accuracy and the accuracy of instability surface identification.

[0085] 4. This invention breaks through the limitations of traditional analysis methods by combining source mechanism inversion, fracture penetration index quantification, DBSCAN clustering and B-spline fitting technology. It achieves accurate identification and visualization of macroscopic unstable structural surfaces of rock masses, promotes the microseismic monitoring and characterization of rock mass fractures from "point" to "surface", and provides strong technical support for in-depth analysis of fracture evolution mechanisms.

[0086] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0087] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0088] Figure 1 This is a flowchart of a method for identifying unstable structural surfaces that combines seismic source inversion and cluster reconstruction according to an embodiment of the present invention;

[0089] Figure 2 This is a flowchart illustrating the calculation of the fracture penetration probability index according to an embodiment of the present invention.

[0090] Figure 3 This is a flowchart illustrating the density clustering of fractures using the DBSCAN clustering algorithm according to an embodiment of the present invention.

[0091] Figure 4 This is a flowchart of the B-spline surface fitting instability structure according to an embodiment of the present invention;

[0092] Figure 5 This is a schematic diagram of a simulated loading experiment according to an embodiment of the present invention;

[0093] Figure 6 This is a diagram illustrating the identification effect of unstable structural surfaces according to an embodiment of the present invention. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0095] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0096] According to a first aspect of the present invention, a method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction is provided, such as... Figure 1 As shown, it includes the following steps:

[0097] S10: Raw acoustic wave data is acquired through a microseismic monitoring system. The raw acoustic wave data is used for location calculation and focal mechanism inversion to obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0098] S20: The BCI (Breakthrough Probability Index) is calculated based on the fracture volume and focal radius to quantify the degree of connection between fractures. The critical distance for fracture connection is determined by the focal radius, and the fracture volume is standardized using the Z-score. The BCI is then calculated by combining the standardized fracture volume, focal radius, and spatial distance.

[0099] S30: Based on the multidimensional characteristics of fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, the similarity between fractures is calculated, and the DBSCAN clustering algorithm is used to perform density clustering of the fractures. Specifically, similar fractures are grouped into the same cluster, and each cluster corresponds to a potential fracture surface. By setting an appropriate density threshold, the DBSCAN algorithm clusters similar fractures into the same cluster based on the spatial relationship and characteristics (such as orientation, volume, energy, etc.) between points, with each cluster corresponding to a potential fracture surface. Simultaneously, noise points are effectively eliminated, thereby reducing the interference of irrelevant data on the clustering analysis and improving the accuracy of the clustering results.

[0100] S40: Based on DBSCAN clustering, the clustering results are further optimized by incorporating the fracture continuity probability index (BCI). By calculating the BCI value for each fracture cluster, fractures with high BCI values ​​are screened out, thereby identifying potential instability surfaces. Generally, the fracture clusters corresponding to these points are considered to have high continuity and propagation potential, thus identifying potential instability surfaces to ensure that the selected instability surfaces have strong physical connectivity and fracture propagation capabilities.

[0101] S50: The macroscopic unstable structural surface is reconstructed using the B-spline surface fitting algorithm to generate a smooth and continuous macroscopic unstable surface; the main direction of rupture is determined by selecting control points through principal component analysis; the boundary contour is generated by constructing B-spline curves based on the crack distribution; the outer contour line is triangulated and meshed; and the unstable structural surface is accurately fitted using the B-spline algorithm by combining the crack points and the triangular mesh.

[0102] This identification method obtains core parameters of the focal mechanism through microseismic monitoring and inversion. It goes beyond simply describing fracture location points, further revealing the geometric characteristics and spatial orientation of the surfaces formed by fracture points, achieving a leap from discrete point analysis to a comprehensive characterization of fracture surfaces. Secondly, by leveraging focal inversion technology, it can accurately capture the source characteristics of fractures, overcoming the limitations of traditional methods in terms of resolution and assumed models. It can monitor and invert the occurrence, propagation, and penetration processes of fractures in real time, thus providing more accurate input data for instability surface identification.

[0103] This identification method is the first to propose calculating the rupture penetration probability index using fracture volume and focal radius. The critical distance for rupture penetration is determined by the focal radius, and the fracture volume is standardized using Z-scores, making fractures of different sizes comparable. This method allows the measurement of rupture penetration to consider not only spatial distance but also the geometric and energy characteristics of the fracture, thus enabling a more accurate quantification of the degree of penetration between fractures and providing a new approach to fracture evolution analysis.

[0104] This identification method combines multi-dimensional features such as spatial distance, orientation, volume, and energy of fractures. It employs the DBSCAN clustering algorithm to perform density clustering of fractures, and by setting a reasonable density threshold, the clustering results can identify fracture clusters with actual connectivity and expansion potential. Secondly, the DBSCAN algorithm is used to construct crack surfaces from fracture clusters. This method does not require pre-setting the number of clusters, can adaptively handle complex distributions, and has strong density sensitivity and robustness. It can also automatically identify and eliminate noise points to avoid interference with the analysis. After clustering, the results are combined with the fracture penetration index optimization to identify potential instability surfaces with high expansion potential, improving the clustering accuracy and the accuracy of instability surface identification.

[0105] This identification method, through the combination of source mechanism inversion, fracture penetration index quantification, DBSCAN clustering and B-spline fitting technology, breaks through the limitations of traditional analysis methods, realizes the accurate identification and visualization of macroscopic unstable structural surfaces of rock masses, promotes the characterization of rock mass fracture microseismic monitoring from "point" to "surface", and provides strong technical support for in-depth analysis of fracture evolution mechanism.

[0106] In one example of the present invention, in step S10, the original acoustic data is used to perform localization calculations and focal mechanism inversion to obtain core parameters of the focal mechanism directly related to the rupture, including the following steps:

[0107] S11: Post-process the raw acoustic data acquired by the microseismic monitoring system, and use the red-delay information criterion method to extract the arrival time and amplitude information of the first wave;

[0108] S12: Using the arrival time data of the first wave of acoustic waves at different sensor locations of the microseismic monitoring system, a robust simplex localization algorithm is employed to calculate the spatial coordinates of the fracture by spatially locating the acoustic emission source; the localization control equations are as follows:

[0109]

[0110] In the formula, x(x,y,z) and x i (x i ,y i ,z i ) represent the spatial coordinates of the acoustic emission source and the i-th sensor, respectively, and c p For P-wave velocity, t i When the P-wave arrives at the i-th sensor, t is the moment when the acoustic emission source emits vibrations;

[0111] S13: Solving for the six components M of the source rupture moment tensor based on the acoustic first-wave amplitude data collected by sensors at different locations under the constraint of the tension-shear rupture source model. pq ;

[0112] S14: Based on the calculated moment tensor components, quantitatively invert and calculate the core parameters of the coal and rock fracture source. Among them, the core parameters of the coal and rock fracture source include fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0113] In one example of the present invention, in step S14, the specific calculation formula for the core parameters of the coal and rock fracturing source is as follows:

[0114] Crack volume:

[0115]

[0116] In the formula, ΔV is the fracture volume at the fracture point, and M1 and M3 are the moment tensors M. pq eigenvalues, where μ is the Lamé constant;

[0117] Spatial orientation of the fracture:

[0118]

[0119] n=(cosα,0,±sinα)

[0120]

[0121] In the formula, α is the angle between the direction of motion of the earthquake source rupture surface and the normal direction, and M1, M2, and M3 are the moment tensors M. pq The eigenvalues ​​are μ and λ, which are Lamé constants, n is the spatial orientation, and b is the direction of motion.

[0122] fracture energy:

[0123]

[0124] In the formula: ΔA is the fracture area at the crack point, σ is the material strength, where σ t For tensile strength, σ s This refers to shear strength.

[0125] In one example of the present invention, in step S20, as Figure 2 As shown, the calculation of the fracture penetration probability index (BCI) by combining standardized fracture volume, focal radius, and spatial distance includes the following steps:

[0126] S21: Determine the critical distance for rupture penetration based on the source radius of the fracture point in order to determine whether there is a possible rupture penetration relationship between fracture points;

[0127] S22: To eliminate the scale differences in the volume of different fracture points, the Z-score standardization method is used to transform the data into a standard normal distribution with zero mean and unit variance, so as to standardize the volume of fracture points;

[0128] Wherein, for each crack point, the volume V i The formula for calculating its standardized volume is as follows:

[0129]

[0130]

[0131] In the formula: Vi is the volume of the i-th fracture point, μ V σ is the mean volume of all fracture points. V Z(V) represents the standard deviation of the volume of all fracture points. i ) represents the standardized value of the volume of the i-th fracture point;

[0132] S23: When two adjacent fracture points i and j satisfy the fracture penetration condition, calculate the fracture penetration probability index (BCI) between the two adjacent fracture points. ij And thus calculate the total fracture penetration index (BCI) at fracture point i. i .

[0133] In one example of the present invention, in step S21, determining the critical distance for rupture penetration based on the source radius of the fracture point to determine whether there is a possible rupture penetration relationship between the fracture points specifically includes:

[0134] S211: Assume the source radius of the fracture point i is R. i Then the critical distance D for fracture penetration at that fracture point i Defined as twice the radius of the seismic source at that point:

[0135]

[0136] In the formula, R i V is the source radius of the fracture point i. i Let D be the volume of crack point i. i Let be the critical distance for fracture penetration at fracture point i;

[0137] S212: Calculate the Euclidean distance d between fracture point i and other fracture points j. ij :

[0138]

[0139] In the formula: (x i ,y i ,z i ), (x j ,y j ,z j ) are the spatial coordinates of the fracture points i and j, respectively;

[0140] S213: For each fracture point j, determine its distance d from i. ij Critical distance D from the fracture penetration i Relationship:

[0141] If d ij ≤D i Therefore, it is considered that a fracture connection may occur between fracture points i and j. ij =1;

[0142] If d ij >D i Therefore, it is assumed that a fracture cannot occur between fracture points i and j. ij =0.

[0143] In one example of the present invention, in step S23, the total fracture penetration index (BCI) of fracture point i is calculated. i The specific process is as follows:

[0144] The fracture penetration probability index (BCI) is calculated by comprehensively considering the focal radius and fracture point volume to quantitatively assess the possibility of fracture penetration between fracture points. Since the energy dissipation of a fracture point is proportional to its volume, its energy is characterized by its volume to reflect the fracture penetration potential. Specifically, for fracture points i and j, the fracture penetration probability index is calculated using the following formula when the fracture penetration condition is met:

[0145]

[0146] In the formula: Z(V) i ) and Z(V j ) represent the standardized volume values ​​of the i-th and j-th fracture points, respectively; d ij Let be the Euclidean distance between the i-th fracture point and the j-th fracture point;

[0147] Total fracture penetration index (BCI) at fracture point i i The total fracture penetration index (BCI) is the sum of the fracture penetration indices of the i fracture point and all other remaining fracture points. Here, assuming there are n fracture points surrounding fracture point i, the total fracture penetration index (BCI) is... i The calculation formula is as follows:

[0148]

[0149] In the formula: I ij As an indicator function, when d ij ≤D i A time value of 1 indicates that the rupture and penetration conditions are met between the two points.

[0150] In one example of the present invention, in step S30, as Figure 3As shown, the density clustering of fractures using the DBSCAN clustering algorithm includes the following steps:

[0151] S31: Multidimensional Feature Vector Reading and Euclidean Distance Calculation: Read the multidimensional feature vector (spatial coordinates, spatial orientation, volume, and energy) of each fracture point obtained from the focal mechanism inversion, and calculate the Euclidean distance between fracture points to measure their similarity; where, for each pair of fracture points P i =(x i ,y i ,z i ,nx i ,ny i ,nz i V i E i ) and P j =(x j ,y j ,z j ,nx j ,ny j ,nz j V j E j The Euclidean distance is calculated using the following formula:

[0152]

[0153] In the formula, n is the dimension of the feature vector, and P i,k and P i,k These are the values ​​of the k-th dimension in the multidimensional feature vectors of the two fracture points, w. k The weights for each feature (which can be adjusted based on the importance of different features);

[0154] S32: Determine the neighborhood distance and minimum number of points: Select an appropriate neighborhood distance ∈ and a minimum number of points MinPts, where ∈ determines the neighborhood radius of a certain fracture point in Euclidean space, and MinPts is the minimum number of points that the neighborhood needs to contain, often used to determine whether a point is a core point; Based on the Euclidean distance and the neighborhood distance ∈, calculate the neighborhood of each fracture point. If the number of points contained in the neighborhood of a fracture point is greater than or equal to MinPts, then the fracture point is a core point; otherwise, it is a boundary point or a noise point;

[0155] S33: Cluster Expansion and Core Point Processing; Select an initial point from the unlabeled core points, mark it as the starting point of a new cluster, and assign a cluster number to the point; then expand the neighborhood of the core point, grouping all points in the neighborhood (including the core point and density-reachable boundary points) into the same cluster; for newly added points in the expanded cluster, check if they are core points. If they are core points, continue expanding the cluster; if they are boundary points, keep their cluster labels unchanged; repeat the above steps to continue selecting new points from the unlabeled core points for cluster expansion until all core points have been processed;

[0156] In other words, such as Figure 3 As shown, for each core point, it is necessary to simultaneously determine whether each core point has a label and whether all core points have been read. If a core point has no label, its neighborhood needs to be expanded and a label set; otherwise, the next core point is read. If it is determined that there are unread points among all core points, it is necessary to continue to determine whether the core point has a label; otherwise, all crack clusters are generated. After the core point expands its neighborhood and sets a label, it is also necessary to determine whether the newly added points in the neighborhood are core points. If they are core points, it is necessary to continue to expand and set the same label as the initial core point, while continuing to determine whether the newly added points in the neighborhood are core points; otherwise, the label remains unchanged, and new crack clusters are generated to form all crack clusters.

[0157] S34: Cluster Numbering and Clustering Result Check: Based on the DBSCAN clustering results in step S33, assign a unique crack surface number to each cluster, with each cluster representing an independent crack surface. If a crack point belongs to the neighborhood of multiple core points (i.e., the point is a density-reachable point), then the point will be assigned the crack surface number of the first discovered cluster. Next, check the clustering results to ensure that each cluster is assigned a unique crack surface number, with noise points numbered as -1. If any errors or omissions are found in the cluster numbering, make necessary adjustments to ensure that the final crack surface numbering is accurate.

[0158] In one example of the present invention, in step S40, by calculating the fracture penetration probability index value on each fracture cluster, fractures with a high fracture penetration probability index are screened out, thereby identifying potential instability surfaces, including the following steps:

[0159] S41: Calculate the BCI value for each fracture: For each fracture cluster obtained by DBSCAN clustering, calculate the fracture penetration probability index for each fracture within it; the BCI value reflects the physical connectivity between fracture points, taking into account the influence of volume, distance, and source radius. The BCI calculation formula described in S23 is as follows:

[0160]

[0161] Where: BCI i Z(V) is the fracture penetration probability index at fracture point i. i ) represents the standardized value of the volume of the i-th fracture point, d ij I is the distance between fracture point i and other fracture points j. ij This is an indicator function.

[0162] S42: Fracture Screening Based on BCI Values: By visualizing the distribution of BCI values ​​in fractures, an appropriate threshold is set, and fracture points with higher BCI values ​​are selected as potential instability surfaces. Specifically, firstly, based on the distribution characteristics of BCI values, a reasonable threshold is determined, and fractures with BCI values ​​greater than this threshold are screened out. These fractures typically have strong physical connectivity and propagation potential, and are used as primary fracture surfaces to represent potential instability surfaces. Subsequently, the fracture surfaces where these high BCI value fracture points are located are marked as primary fracture surfaces, while the surfaces corresponding to fracture points with lower BCI values ​​can be classified as secondary fracture surfaces or crack surfaces with lower propagation potential, thereby effectively distinguishing between primary and secondary crack surfaces.

[0163] S43: Visualization of optimized clustering results: The optimized clustering results are presented through three-dimensional visualization. The primary and secondary fracture surfaces can be distinguished by different colors or point sizes, thereby intuitively displaying the spatial characteristics of fracture propagation and further analyzing the evolution and distribution patterns of fractures.

[0164] In one example of the present invention, in step S50, as Figure 4 As shown, the B-spline surface fitting algorithm is used to reconstruct the cracks in the potential unstable surface, generating a smooth and continuous macroscopic unstable structural surface, including the following steps:

[0165] S51: From the results of cluster analysis, read all valid fractures that have been classified into the same fracture surface; where each fracture includes its fracture spatial coordinates, fracture volume, fracture energy BCI value and fracture spatial orientation;

[0166] S52: Since the fracture continuity index (BCI) reflects the probability of fracture continuity between fractures, and a higher BCI value indicates a more significant contribution of the fracture to the propagation of the unstable surface, the BCI value as a feature weight can more effectively guide the unstable surface reconstruction process. Simultaneously, the normal direction of the fracture provides spatial orientation information of the unstable surface; therefore, combining BCI and normal direction comprehensively describes the spatial characteristics and fracture effects of the fracture.

[0167] Suppose the normal direction vector of a certain crack is n = (n x ,n y ,n z The magnitude of this vector is calculated using the following formula:

[0168]

[0169] Since the magnitude of the normal vector may be affected by factors such as the location and size of the crack, the normal vector is normalized to ensure that the normalized normal vector only represents the direction. The normalization process is calculated using the following formula:

[0170]

[0171] For each fracture, a comprehensive feature weight is calculated based on its BCI value and normal direction. Let W be the feature weight of fracture i. i It is a combination of BCI value and normal direction, and the comprehensive characteristic weight of each fracture is calculated using the following weighted formula:

[0172]

[0173] In the formula, It is the normalized normal magnitude, usually 1; BCI i α is the fracture penetration index of the i-th fracture; α and β are weighting coefficients used to adjust the influence of the normal direction and BCI value on the final feature weights.

[0174] S53: Based on the feature weights and three-dimensional spatial location information of the same cluster of cracks, the cracks with high feature weights can be extracted by principal component analysis (PCA) as key control points for the reconstruction of the unstable surface, thereby clarifying the main direction of the unstable surface; specifically, the two cracks with the largest feature weights are selected as the initial control points of PCA, and the main direction of the final unstable structural surface is defined and spanned by these points, providing the basic vector for subsequent fitting and morphology construction.

[0175] S54: After determining the main direction of the unstable surface, the boundary of the same cluster of cracks is fitted using the B-spline curve fitting method to form a closed-loop curve as the outer contour line of the final unstable surface.

[0176] S55: Triangulate the outer contour of the formed crack surface and divide the interior of the final unstable surface into triangular meshes.

[0177] S56: Read all the cracks in the same cluster, combine them with the established triangular mesh to perform the final fitting of the crack surface to be generated, and form the final macroscopic instability structure surface of the crack in the same cluster.

[0178] According to a second aspect of the present invention, an unstable structural surface identification system integrating seismic source inversion and cluster reconstruction includes:

[0179] The parameter acquisition module is configured to acquire raw acoustic wave data through a microseismic monitoring system, perform location calculations and source mechanism inversion on the raw acoustic wave data, and obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation.

[0180] The penetration index calculation module is configured to determine the critical distance of rupture penetration through the source radius, and to standardize the fracture volume using Z-score. The rupture penetration probability index (BCI) is then calculated by combining the standardized fracture volume, source radius, and spatial distance.

[0181] The fracture surface clustering module is configured to calculate the similarity between fractures based on multidimensional features such as fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, and to perform density clustering of fractures using the DBSCAN clustering algorithm; wherein similar fractures are grouped into the same cluster, and each fracture cluster corresponds to a potential fracture surface;

[0182] By setting an appropriate density threshold, the DBSCAN algorithm clusters similar fractures into clusters based on the spatial relationships and characteristics (such as orientation, volume, and energy) between points, with each cluster corresponding to a potential fracture surface. Simultaneously, noise points are effectively eliminated, thereby reducing the interference of irrelevant data on clustering analysis and improving the accuracy of the clustering results.

[0183] The potential instability surface identification module is configured to further optimize the clustering results by combining the fracture continuity probability index (BCI) with DBSCAN clustering. By calculating the fracture continuity probability index value (BCI) on each fracture cluster, fractures with a high fracture continuity probability index are screened out, thereby identifying potential instability surfaces to ensure that the selected instability surfaces have strong physical connectivity and fracture propagation capabilities.

[0184] The unstable structural surface reconstruction module is configured to reconstruct the macroscopic unstable structural surface using a B-spline surface fitting algorithm, generating a smooth and continuous macroscopic unstable surface. Control points are selected using principal component analysis to determine the main fracture direction. B-spline curves are constructed based on the fracture distribution to generate the boundary contour. The outer contour lines are triangulated and meshed. Combining the fracture points and the triangular mesh, the B-spline algorithm is used to accurately fit the unstable structural surface. The optimal embodiments of the present invention will be described in more detail below with reference to the accompanying drawings to facilitate a better understanding of the features and advantages of the invention.

[0185] This identification system obtains core parameters of the focal mechanism through microseismic monitoring and inversion. It goes beyond simply describing fracture location points; it further reveals the geometric characteristics and spatial orientation of the surfaces formed by fracture points, achieving a leap from discrete point analysis to a comprehensive characterization of fracture surfaces. Secondly, by leveraging focal inversion technology, it can accurately capture the source characteristics of fractures, overcoming the limitations of traditional methods in terms of resolution and assumed models. It can monitor and invert the occurrence, propagation, and penetration processes of fractures in real time, thus providing more accurate input data for instability surface identification.

[0186] This identification system is the first to propose calculating the rupture penetration probability index using fracture volume and focal radius. The critical distance for rupture penetration is determined by the focal radius, and the fracture volume is standardized using Z-scores, making fractures of different sizes comparable. This method allows the measurement of rupture penetration to consider not only spatial distance but also the geometric features and energy characteristics of the fracture, thus enabling a more accurate quantification of the degree of penetration between fractures and providing a new approach to fracture evolution analysis.

[0187] This identification system combines multi-dimensional features of fractures, such as spatial distance, orientation, volume, and energy, and employs the DBSCAN clustering algorithm to perform density clustering of fractures. By setting a reasonable density threshold, the clustering results can identify fracture clusters with actual connectivity and expansion potential. Secondly, the DBSCAN algorithm is used to construct crack surfaces from fracture clusters. This eliminates the need for pre-setting the number of clusters, adaptively handles complex distributions, and exhibits strong density sensitivity and robustness. It can also automatically identify and eliminate noise points, avoiding interference with the analysis. After clustering, the system combines the results of fracture penetration index optimization to identify potential instability surfaces with significant expansion potential, improving the clustering accuracy and the accuracy of instability surface identification.

[0188] This identification system, through the combination of source mechanism inversion, fracture penetration index quantification, DBSCAN clustering and B-spline fitting technology, breaks through the limitations of traditional analysis methods, realizes the accurate identification and visualization of macroscopic unstable structural surfaces of rock masses, promotes the microseismic monitoring and characterization of rock mass fractures from "point" to "surface", and provides strong technical support for in-depth analysis of fracture evolution mechanisms.

[0189] Specific Cases

[0190] like Figure 5As shown, a three-point bending loading simulation experiment of coal and rock fracture was conducted. The experimental system included a loading system, a digital image acquisition (DIC) system, and an acoustic emission acquisition system. The loading system used an MTS810 servo press with an effective range of 22 kN for its pressure sensor and a loading rate of 0.05 μm / s. The digital image acquisition system consisted of a Unibrain Fire-I810 IEEE-1394 CCD camera, image acquisition software, and an LED cold light source. During the experiment, the DIC image acquisition rate was set to 1 frame per second. The acoustic emission monitoring system mainly consisted of an Agilent L4534A high-speed data acquisition instrument, an S1220C preamplifier, and an S9225 miniature acoustic emission sensor. Before the experiment, four acoustic emission sensors were placed at different positions on the front and back surfaces of the sample to ensure that acoustic emission events fell within the sensor array as much as possible. During the loading process, the mechanical (load, displacement, etc.) and acoustic emission waveform data of the quartzite sample with pre-cut notches were acquired in real time and synchronously, and the crack propagation process on the sample surface was recorded simultaneously.

[0191] like Figure 6 As shown in the figure, the comparison between the actual instability structural surface and the instability structural surface identified by the method of this invention in the three-point bending loading simulation experiment of coal and rock fracture is intuitively demonstrated. In the experiment, the actual instability structural surface is based on the macroscopic crack distribution observed on the sample surface and is recorded and generated by digital image correlation (DIC) technology. The identification method of this invention generates the predicted instability structural surface through source mechanism inversion, fracture penetration probability index calculation, density clustering and fitting techniques.

[0192] As can be clearly seen from the renderings, the identified unstable structural surfaces are essentially consistent with the actual unstable structural surfaces in terms of their propagation direction, especially in key features such as crack initiation location, propagation path, and penetration area. This consistency indicates that the method of this invention can accurately capture the geometric morphology and propagation law of internal fractures in coal and rock, while effectively characterizing the spatial characteristics of unstable structural surfaces.

[0193] By comparing and analyzing actual unstable structural surfaces with those identified, the reliability and accuracy of this invention in identifying unstable structural surfaces can be further verified. The comparison results not only demonstrate that the method of this invention can reconstruct the dynamic evolution of internal fractures in coal and rock, but also accurately predict the instability risk of complex coal and rock masses, providing a scientific basis for coal mine strata control and disaster prevention. The intuitive illustrations highlight the wide applicability and practical value of this invention's method in coal and rock fracture analysis, while also providing a feasible reference solution for research and engineering applications in related fields.

[0194] The foregoing description, with reference to preferred embodiments, details the exemplary implementation of the method and system for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention, without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A method for identifying unstable structural surfaces that integrates source inversion and cluster reconstruction, characterized in that, Includes the following steps: S10: Raw acoustic wave data is acquired through a microseismic monitoring system. The raw acoustic wave data is used for location calculation and focal mechanism inversion to obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation. S20: Determine the critical distance for rupture penetration using the focal radius, and standardize the fracture volume using the Z-score. Calculate the rupture penetration probability index (BCI) by combining the standardized fracture volume, focal radius, and spatial distance; where the total rupture penetration index (BCI) for fracture point i is calculated. i The specific process is as follows: The rupture penetration probability index (BCI) is calculated by comprehensively considering the focal radius and the volume of the fracture points to quantify the probability of rupture penetration between fracture points. Specifically, for fracture points i and j, the formula for calculating the BCI when the rupture penetration condition is met is as follows: In the formula: and d represents the normalized volume values ​​of the i-th and j-th fracture points, respectively; ij Let be the Euclidean distance between the i-th fracture point and the j-th fracture point; Total fracture penetration index (BCI) at fracture point i i The total fracture penetration index (BCI) is the sum of the fracture penetration indices of the i fracture point and all other remaining fracture points. Here, assuming there are n fracture points surrounding fracture point i, the total fracture penetration index (BCI) is... i The calculation formula is as follows: In the formula: I ij For indicator functions, when A time value of 1 indicates that the rupture and penetration conditions are met between the two points. S30: Based on the multidimensional characteristics of fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, the similarity between fractures is calculated, and the DBSCAN clustering algorithm is used to perform density clustering of fractures; similar fractures are grouped into the same cluster, and each fracture cluster corresponds to a potential fracture surface; S40: By calculating the fracture penetration probability index (BCI) on each fracture cluster, fractures with a high fracture penetration probability index are screened out, thereby identifying potential instability surfaces. S50: The macroscopic unstable structural surface is reconstructed using the B-spline surface fitting algorithm to generate a smooth and continuous macroscopic unstable surface; the main direction of rupture is determined by selecting control points through principal component analysis; the boundary contour is generated by constructing B-spline curves based on the crack distribution; the outer contour line is triangulated and meshed; and the unstable structural surface is accurately fitted using the B-spline algorithm by combining the crack points and the triangular mesh.

2. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 1, characterized in that, In step S10, the original acoustic wave data is used for location calculation and focal mechanism inversion to obtain the core parameters of the focal mechanism directly related to the rupture, including the following steps: S11: Post-process the raw acoustic data acquired by the microseismic monitoring system, and use the red-delay information criterion method to extract the arrival time and amplitude information of the first wave; S12: Using the arrival time data of the first wave of acoustic waves at different locations of sensors in the microseismic monitoring system, the spatial coordinates of the fracture are obtained by spatially locating the acoustic emission source using a robust simplex localization algorithm. S13: Solving for the six components M of the source rupture moment tensor based on the acoustic first-wave amplitude data collected by sensors at different locations under the constraint of the tension-shear rupture source model. pq ; S14: Based on the calculated moment tensor components, quantitatively invert and calculate the core parameters of the coal and rock fracture source, including fracture volume, fracture energy, and fracture spatial orientation.

3. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 2, characterized in that, In step S14, the specific calculation formula for the core parameters of the coal and rock fracturing source is as follows: Crack volume: In the formula, M1 and M3 are the fracture volume at the fracture point, and M1 and M3 are the moment tensors M. pq eigenvalues, Let Lamé's constant be denoted by . Spatial orientation of the fracture: In the formula, M1, M2, and M3 are the angles between the direction of motion of the rupture surface at the earthquake source and the normal direction, respectively, and the moment tensors M1, M2, and M3 are the angles between the direction of motion of the rupture surface at the earthquake source and the normal direction. pq eigenvalues, and Let n be the Lamé constant, n be the spatial orientation, and b be the direction of motion; fracture energy: In the formula: The fracture area at the crack point. For material strength, where For tensile strength, This refers to shear strength.

4. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 1, characterized in that, In step S20, calculating the fracture penetration probability index (BCI) by combining the standardized fracture volume, focal radius, and spatial distance includes the following steps: S21: Determine the critical distance for rupture penetration based on the source radius of the fracture point in order to determine whether there is a possible rupture penetration relationship between fracture points; S22: The Z-score standardization method is used to transform the data into a standard normal distribution with zero mean and unit variance in order to standardize the volume of the fracture points; Wherein, for each crack point, the volume V i The formula for calculating its standardized volume is as follows: In the formula: Vi is the volume of the i-th fracture point. The average volume of all fracture points. The standard deviation of the volume of all crack points. Let be the standardized value of the volume of the i-th fracture point; S23: When two adjacent fracture points i and j satisfy the fracture penetration condition, calculate the fracture penetration probability index between the two adjacent fracture points. And thus calculate the total fracture penetration index (BCI) at fracture point i. i .

5. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 4, characterized in that, In step S21, determining the critical distance for rupture penetration based on the source radius of the fracture point to determine whether there is a possible rupture penetration relationship between fracture points specifically includes: S211: Assume the source radius of the fracture point i is R. i Then the critical distance D for fracture penetration at that fracture point i Defined as twice the radius of the seismic source at that point: In the formula, R i V is the source radius of the fracture point i. i Let D be the volume of crack point i. i Let be the critical distance for fracture penetration at fracture point i; S212: Calculate the Euclidean distance d between fracture point i and other fracture points j. ij : In the formula: , These are the spatial coordinates of the crack points i and j, respectively. S213: For each fracture point j, determine its distance d from i. ij Critical distance D from the fracture penetration i Relationship: like Therefore, it is considered that a fracture connection may occur between fracture points i and j. ij =1; like Therefore, it is assumed that a fracture cannot occur between fracture points i and j. ij =0.

6. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 1, characterized in that, In step S30, the density clustering of fractures using the DBSCAN clustering algorithm includes the following steps: S31: Multidimensional Feature Vector Reading and Euclidean Distance Calculation: Read the multidimensional feature vector of each fracture point obtained from the focal mechanism inversion, and calculate the Euclidean distance between fracture points; where, for each pair of fracture points... and The Euclidean distance is calculated using the following formula: In the formula, n is the dimension of the feature vector, and P i,k and P i,k These are the values ​​of the k-th dimension in the multidimensional feature vectors of the two fracture points, w. k Weights for each feature; S32: Determine the neighborhood distance and minimum number of points: Select an appropriate neighborhood distance ϵ and minimum number of points MinPts. Based on the Euclidean distance and the neighborhood distance ϵ, calculate the neighborhood of each fracture point. If the number of points contained in the neighborhood of a fracture point is greater than or equal to MinPts, then the fracture point is a core point; otherwise, it is a boundary point or a noise point. S33: Cluster Expansion and Core Point Processing; Select an initial point from the unlabeled core points, mark it as the starting point of a new cluster, and assign a cluster number to the point; Expand the neighborhood of the core point, grouping all points within the neighborhood into the same cluster; For newly added points in the expanded cluster, check if they are core points. If they are core points, continue expanding the cluster; if they are boundary points, keep their cluster labels unchanged; Repeat the above steps to continue selecting new points from the unlabeled core points for cluster expansion until all core points have been processed. S34: Cluster Numbering and Clustering Result Check: Based on the DBSCAN clustering results in step S33, assign a unique crack surface number to each cluster, with each cluster representing an independent crack surface. If a crack point belongs to the neighborhood of multiple core points, that point will be assigned the crack surface number of the first discovered cluster. Then check the clustering results to ensure that each cluster is assigned a unique crack surface number, with noise points numbered as -1. If any errors or omissions are found in the cluster numbering, make necessary adjustments to ensure that the final crack surface numbering is accurate.

7. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 1, characterized in that, In step S40, by calculating the fracture penetration probability index value on each fracture cluster, fractures with higher fracture penetration probability indices are screened out, thereby identifying potential instability surfaces, including the following steps: S41: Calculate the BCI value for each fracture: For each fracture cluster obtained by DBSCAN clustering, calculate the fracture penetration probability index for each fracture within it. S42: Crack screening based on BCI value: By visualizing the distribution of BCI values ​​of cracks, setting an appropriate threshold, and selecting crack points with higher BCI values ​​as potential instability surfaces; S43: Visualization of optimized clustering results: The optimized clustering results are presented through three-dimensional visualization. The primary and secondary fracture surfaces can be distinguished by different colors or point sizes, thereby intuitively displaying the spatial characteristics of fracture propagation and further analyzing the evolution and distribution patterns of fractures.

8. The method for identifying unstable structural surfaces by combining seismic source inversion and cluster reconstruction according to claim 1, characterized in that, In step S50, the cracks on the potential unstable surface are reconstructed using a B-spline surface fitting algorithm to generate a smooth and continuous macroscopic unstable structural surface, including the following steps: S51: From the results of cluster analysis, read all valid fractures that have been classified into the same fracture surface; where each fracture includes its fracture spatial coordinates, fracture volume, fracture energy BCI value and fracture spatial orientation; S52: Combining BCI and normal direction to comprehensively describe the spatial characteristics and fracture effects of fractures: Suppose the normal direction vector of a certain crack is The normal vector is normalized to ensure that the normalized normal vector only represents direction; the normalization process is calculated using the following formula: For each fracture, a comprehensive feature weight is calculated based on its BCI value and normal direction. Let W be the feature weight of fracture i. i Since it is a combination of BCI value and normal direction, the formula for calculating the comprehensive feature weight of each fracture point is as follows: In the formula, It is the normalized normal magnitude, usually 1; BCI i Let be the fracture penetration index of the i-th fracture. and These are weighting coefficients; S53: Based on the feature weights and three-dimensional spatial location information of the same cluster of cracks, the cracks with higher feature weights are extracted by principal component analysis as key control points for the reconstruction of the unstable surface, thereby clarifying the main direction of the unstable surface; S54: After determining the main direction of the unstable surface, the boundary of the same cluster of cracks is fitted using the B-spline curve fitting method to form a closed-loop curve as the outer contour line of the final unstable surface. S55: Triangulate the outer contour of the formed crack surface and divide the interior of the final unstable surface into triangular meshes. S56: Read all the cracks in the same cluster, combine them with the established triangular mesh to perform the final fitting of the crack surface to be generated, and form the final macroscopic instability structure surface of the crack in the same cluster.

9. A system for identifying unstable structural surfaces that integrates seismic source inversion and cluster reconstruction, characterized in that, include: The parameter acquisition module is configured to acquire raw acoustic wave data through a microseismic monitoring system, perform location calculations and source mechanism inversion on the raw acoustic wave data, and obtain the core parameters of the coal and rock fracture source that are directly related to the fracture. Among them, the core parameters of the coal and rock fracture source include: fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation. The penetration index calculation module is configured to determine the critical distance for rupture penetration based on the source radius, and to perform Z-score normalization on the fracture volume. It then calculates the rupture penetration probability index (BCI) by combining the normalized fracture volume, source radius, and spatial distance. Specifically, it calculates the total rupture penetration index (BCI) for fracture point i. i The specific process is as follows: The rupture penetration probability index (BCI) is calculated by comprehensively considering the focal radius and the volume of the fracture points to quantify the probability of rupture penetration between fracture points. Specifically, for fracture points i and j, the formula for calculating the rupture penetration probability index when the rupture penetration condition is met is as follows: In the formula: and d represents the normalized volume values ​​of the i-th and j-th fracture points, respectively; ij Let be the Euclidean distance between the i-th fracture point and the j-th fracture point; Total fracture penetration index (BCI) at fracture point i i The total fracture penetration index (BCI) is the sum of the fracture penetration indices of the i fracture point and all other remaining fracture points. Here, assuming there are n fracture points surrounding fracture point i, the total fracture penetration index (BCI) is... i The calculation formula is as follows: In the formula: I ij For indicator functions, when A time value of 1 indicates that the rupture and penetration conditions are met between the two points. The fracture surface clustering module is configured to calculate the similarity between fractures based on multidimensional features such as fracture spatial coordinates, fracture volume, fracture energy, and fracture spatial orientation, and to perform density clustering of fractures using the DBSCAN clustering algorithm; wherein similar fractures are grouped into the same cluster, and each fracture cluster corresponds to a potential fracture surface; The potential instability surface identification module is configured to identify potential instability surfaces by calculating the fracture penetration probability index (BCI) value on each fracture cluster and filtering out fractures with a high fracture penetration probability index. The unstable structural surface reconstruction module is configured to reconstruct the macroscopic unstable structural surface using a B-spline surface fitting algorithm, generating a smooth and continuous macroscopic unstable surface. The module selects control points to determine the main direction of rupture using principal component analysis, constructs B-spline curves based on the crack distribution to generate the boundary contour, triangulates the outer contour line and divides it into meshes, and combines the crack points and triangular meshes to accurately fit the unstable structural surface using the B-spline algorithm.

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