Method for estimating three-dimensional structure of tunnel face rock mass

Abstract

The invention discloses a tunnel face rock mass three-dimensional structure estimation method, and the method comprises the steps: obtaining point cloud data describing tunnel face space information, recognizing rock mass structural surface occurrence and two-dimensional space distribution information based on the point cloud data, calculating the inter-group spacing of each dominant group of the structural surface, and estimating the three-dimensional structure of the tunnel face rock mass. Based on the geometric center coordinate, the occurrence and the average elevation of the structural plane, calculating the inter-group spacing of each dominant group; calculating the joint fissure diameter and the joint fissure center coordinate; a joint fissure three-dimensional model is established by combining the diameter, occurrence and center coordinates of the joint fissure, and the representation form is disc-shaped fissures distributed in the rock mass; building a rock stratum layer three-dimensional model and a fault layer three-dimensional model by combining the two-dimensional trace position and occurrence, wherein the expression form is fracture of transverse rock mass; and coupling the joint fissure three-dimensional model, the rock stratum surface three-dimensional model, the fault model three-dimensional model and a bedrock model based on actual working conditions to obtain a discrete fissure network model, namely a rock mass three-dimensional structure.

Description

Technical Field

[0001] This application belongs to the technical field of on-site geological logging, and specifically relates to a method for estimating the three-dimensional structure of rock masses in tunnel faces. Background Technique

[0002] The three-dimensional structure characteristics of rock masses are important indicators for characterizing the mechanical properties of rock masses and are also important reference indicators for engineering geology and tunnel surrounding rock classification. The characterization of traditional rock mass three-dimensional structure characteristics is based on the measurement and statistics of rock mass discontinuity planes in local sample windows, and then the Monte Carlo method is used based on statistical indicators to randomly simulate the actual three-dimensional structure of rock masses. Generally, the specific steps mainly include: (1) With the help of outcrop sample windows or in-situ borehole measurements, the occurrence, trace length, aperture, dip direction, and distribution pattern of the two-dimensional planar traces of discontinuity planes are measured; (2) Select an appropriate probability density function to statistically analyze the in-situ measurement results; (3) Based on the statistical probability density function, use the Monte Carlo method to randomly simulate the three-dimensional structure characteristics of rock masses; (4) Use the chi-square test to verify the probabilistic consistency between the randomly simulated results and the actual observation results.

[0003] Chinese Patent CN112131642A collects two-dimensional trace information of outcrop surfaces through digital photography technology, statistically analyzes the distribution characteristics of two-dimensional traces, and uses the Monte Carlo method to randomly generate a discrete fracture network model based on the characteristics of two-dimensional traces.

[0004] Chinese Patent CN106570287A collects and interprets the data of the tunnel face, and after coordinate transformation, uses the Monte Carlo method to simulate a discrete fracture network model. During the simulation process, it is assumed that the three-dimensional shape of fractures is a disk model, and the distribution characteristics such as fracture diameter and spacing are deduced, and finally a discrete fracture network model is established.

[0005] The three-dimensional models of rock mass structures established by the above-mentioned existing technologies can only restore the real situation at the probability and statistics level, but the methods used are all to simply statistically analyze the distribution characteristics of two-dimensional planar traces and directly use them to replace the actual distribution characteristics of three-dimensional fractures for simulation modeling. In addition, the actual shape of three-dimensional fractures in rock masses is not considered during the modeling process, and different fracture types are represented by a unified model. Summary of the Invention

[0006] Objective: In view of at least one of the above technical problems, this application provides a method for estimating the three-dimensional structure of rock masses in tunnel faces. By using the laser point cloud data quickly identified by ground three-dimensional laser scanning technology, grouping and extracting two-dimensional geological information, starting from the two-dimensional geological information, estimating three-dimensional geological information, and finally building a model with deterministic three-dimensional geological information, instead of directly using two-dimensional geological information to simulate and build a three-dimensional model.

[0007] The technical solution adopted in this application is as follows:

[0008] In a first aspect, the present application provides a method for estimating the three-dimensional structure of a tunnel face rock mass, including:

[0009] Obtain point cloud data describing the spatial information of the tunnel face, and perform triangulation on the point cloud data to obtain DEM data of TIN data;

[0010] Identify rock mass structural planes based on the DEM data of TIN data;

[0011] Convert the TIN data corresponding to the rock mass structural planes into point cloud data, calculate the attitude, two-dimensional trace length, and number of traces of each rock mass structural plane, and extract plane features. The attitude includes dip and dip angle; extract the dominant grouping of rock mass structural planes according to the plane features. The dominant grouping includes joint fissures, rock layer bedding planes, and fault bedding planes;

[0012] Calculate the distance between groups of each dominant grouping based on the geometric center coordinates, attitude, and average elevation of the structural planes; calculate the diameter of the joint fissures and the center coordinates of the joint fissures;

[0013] Establish a three-dimensional model of joint fissures by combining the diameter, attitude, and center coordinates of the joint fissures, and the form of expression is disc-shaped fissures distributed inside the rock mass; establish a three-dimensional model of the rock layer bedding plane and a three-dimensional model of the fault bedding plane by combining the two-dimensional trace position and attitude, and the form of expression is fissures cutting across the rock mass;

[0014] Couple the three-dimensional model of joint fissures, the three-dimensional model of the rock layer bedding plane, and the three-dimensional model of the fault model with the bedrock model based on the actual working conditions to obtain a discrete fracture network model, that is, the three-dimensional structure of the rock mass.

[0015] In a second aspect, the present application provides a device for estimating the three-dimensional structure of a tunnel face rock mass, including a processor and a storage medium;

[0016] The storage medium is used to store instructions;

[0017] The processor is used to operate according to the instructions to execute the method according to the first aspect.

[0018] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method according to the first aspect is implemented.

[0019] In a fourth aspect, the present application provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method according to the first aspect is implemented.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method according to the first aspect is implemented.

[0021] Beneficial effects: The three-dimensional structure estimation method for tunnel face rock mass provided by this application has the following advantages: On the basis of obtaining point cloud data with rock mass geometric features using a terrestrial laser scanner, the DEM data is obtained after patching and denoising, and ISODATA clustering is performed on the dip angle, dip direction, and elevation standard deviation of all grids. After filtering out smaller clusters using the moving window method, potential structural planes are obtained; then, by determining whether the DEV value is within the range of -1 to +1, the planar features of the potential structural planes are identified, and three major types of fractures, namely bedding planes, joints, and faults, are obtained. The DEM data of the three major types of fractures is extracted, and each discontinuous set in the major type is separated and numbered through the segmentation method. Two-dimensional geological information such as the occurrence, elevation value, central point position of the trace, two-dimensional coordinates of the trace, and areal density of the trace is statistically analyzed and corresponding to the numbers one by one; the spacing is calculated through the central position of the trace, the elevation value, and the occurrence of the discontinuous set to estimate the three-dimensional spatial distribution of the bedding plane and the fault. The relationship between the fracture volume density and the fracture diameter is deduced through the trace areal density formula, and the size of the joint fractures is estimated according to the numerical relationship table of the diameter, volume density, and spacing, and the central coordinates of the joint fractures are calculated using geometric methods. Considering the size of the rock mass in the experimental area, the rock stratum is represented by a plane that cuts across the rock mass along the dip direction, the fault is represented by a plane that penetrates the rock mass along the dip direction, and the joint is represented by a disc with occurrence, diameter size, and central coordinates; finally, a four-in-one three-dimensional structure model of the bedding plane, joint, fault, and experimental area rock mass is established based on the estimated three-dimensional geological information, and its degree of blockification is analyzed and compared with the actual exposure results. Description of the drawings

[0022] Figure 1 FIG. is a schematic flow chart of a three-dimensional structure estimation method for tunnel face rock mass according to an embodiment of the present application;

[0023] Figure 2 FIG. is a schematic diagram of a three-dimensional model of joint fractures according to an embodiment of the present application;

[0024] Figure 3 FIG. is a schematic diagram of a three-dimensional model of a rock stratum bedding plane according to an embodiment of the present application;

[0025] Figure 4 FIG. is a schematic diagram of a three-dimensional model of a fault bedding plane according to an embodiment of the present application;

[0026] Figure 5 FIG. is a schematic diagram of a three-dimensional model of a rock mass structure according to an embodiment of the present application. Detailed implementation manners

[0027] The present application will be further described below in conjunction with the drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application and cannot be used to limit the protection scope of the present application.

[0028] In the description of this application, the meaning of "several" is more than one, the meaning of "multiple" is more than two, and understandings such as "greater than", "less than", and "exceeding" do not include the corresponding number, while understandings such as "above", "below", and "within" include the corresponding number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features or implicitly specifying the sequence relationship of the indicated technical features.

[0029] In the description of this application, descriptions with reference terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0030] The term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0031] Embodiment 1: This embodiment provides a method for estimating the three-dimensional structure of the rock mass at the tunnel face, as Figure 1 shown, including:

[0032] Obtain the point cloud data describing the spatial information of the tunnel face, perform triangulation on the point cloud data to obtain the DEM data of the TIN data;

[0033] Identify the rock mass structural planes based on the DEM data of the TIN data;

[0034] Convert the TIN data corresponding to the rock mass structural planes into point cloud data, calculate the attitude, two-dimensional trace length, and number of traces of each rock mass structural plane, and extract the plane features. The attitude includes dip direction and dip angle; extract the dominant grouping of the rock mass structural planes according to the plane features. The dominant grouping includes joint fissures, rock strata bedding planes, and fault planes;

[0035] Calculate the inter-group spacing of each dominant grouping based on the geometric center coordinates, attitude, and average elevation of the structural planes; calculate the diameter of the joint fissures and the central coordinates of the joint fissures;

[0036] A three-dimensional joint fissure model is established by combining the joint fissure diameter, attitude, and central coordinates, and its manifestation is disc-shaped fissures distributed inside the rock mass. A three-dimensional model of the rock stratum surface and a three-dimensional model of the fault surface are established by combining the two-dimensional trace position and attitude, and their manifestation is fissures cutting across the rock mass.

[0037] The three-dimensional joint fissure model, the three-dimensional rock stratum surface model, and the three-dimensional fault model are coupled with the bedrock model based on the actual working conditions to obtain a discrete fracture network model, that is, the three-dimensional structure of the rock mass.

[0038] In some embodiments, a method for estimating the three-dimensional structure of the rock mass at the tunnel face specifically includes:

[0039] S1: Point cloud data acquisition. At a distance of 5 m in front of the selected tunnel face, a three-dimensional laser scanner is installed, and three-dimensional laser scanning technology is used to obtain point cloud data describing the geometric shape of the rock mass surface at the tunnel face.

[0040] S2: Data denoising and correction. Remove the noise generated in the point cloud data caused by engineering interference at the tunnel face; repair the local voids in the point cloud data after denoising, and fill the point cloud data according to the trend distribution of the point cloud data in the void neighborhood; thinning. The uneven distribution density of the point cloud data causes local data redundancy, and the density of the over-dense area of the data distribution is thinned according to the trend distribution of the point cloud data; correction and matching. According to three calibration points and using compass measurement, the point cloud data is corrected to the geological coordinate system. The specific implementation steps are as follows:

[0041] (1) Import the point cloud data into cloudcompare to fill the point cloud voids and thin.

[0042] (2) Import the processed point cloud data into GIS software, and perform three-dimensional coordinate correction during the import process. The horizontal coordinate is the Y coordinate, the vertical coordinate is the X coordinate, and the Z coordinate is the elevation coordinate.

[0043] S3: DEM data conversion. According to the point cloud data, triangulation is performed to obtain the TIN data format of DEM. The specific implementation steps are as follows:

[0044] (1) Generate TIN data using the triangular network growth method. The steps are as follows:

[0045] Step 1: Select any point in the point cloud as the initial point.

[0046] Step 2: Detect the point closest to the initial point and connect them as a reference edge of the triangle. Determine the third endpoint according to the triangular network discrimination rule.

[0047] Step 3: Connect the three endpoints in Step 2 to form two new reference edges.

[0048] Step 4: Iterate Step 2 and Step 3 until all reference edges are processed.

[0049] (2) Generate DEM data using the point - by - point interpolation method based on TIN data. The steps of the point - by - point interpolation method are as follows:

[0050] Step 1: Determine the domain size of the interpolation point;

[0051] Step 2: Select the sampling points included in the neighborhood;

[0052] Step 3: Select the interpolation method model;

[0053] Step 4: Solve the elevation of the interpolation point using the interpolation method model based on the sampling points in the neighborhood.

[0054] S4: Structural plane identification. Based on the three vertex coordinates of each triangular mesh in the DEM data of TIN data, calculate three indicators: the trend, dip angle of the normal vector of the triangular mesh plane, and the elevation standard deviation of the three vertex coordinates; use cluster analysis to perform ISODATA cluster analysis on the trend, dip angle, and elevation standard deviation of the normal vectors of all triangular mesh elements; use the moving window method to filter out smaller cluster groups, and the remaining cluster groups are potential rock mass structural planes; calculate the elevation variation standard deviation of the vertex coordinates of the triangular meshes composed of the potential rock mass structural planes to check whether the potential rock mass structural planes are real rock mass structural planes. The specific implementation steps are as follows:

[0055] (1) Statistically analyze the dip angle, trend, and elevation standard deviation data based on the DEM data. The dip angle and trend respectively represent the dip angle and trend of the normal vector of the grid plane, and the elevation standard deviation represents the undulation degree of the surface.

[0056] Trend and dip angle are expressed as the elevation change rates in the east - west and north - south directions of the surface function. The calculation formulas are as follows:

[0057]

[0058] Elevation standard deviation is used to reflect the undulation degree of the rock mass structural plane and is related to the elevation value. The formula is as follows:

[0059]

[0060]

[0061]

[0062] where is the elevation value, is the average elevation, is the elevation variance, and m and n are the grid numbers of the calculation window.

[0063] (2) Use the dip, dip angle, and elevation standard deviation as clustering indicators for ISODATA clustering. The specific steps of ISODATA clustering are as follows:

[0064] Step 1: Select initial parameters.

[0065] Step 2: Calculate the distance metric function for each cluster.

[0066] Step 3: Merge or split clusters according to the given requirements.

[0067] Step 4: Repeat the iteration. Calculate the new metrics and determine whether the results meet the clustering requirements;

[0068] Calculate the planar characteristics of the ISODATA clustering results (potential rock mass structural planes) based on DEM data and average elevation deviation;

[0069] (3) Use the moving window method to group the clustering results spatially again. According to the area of the on-site rock mass structural plane and the density of the point cloud data, set an appropriate number of grouped triangular grids as the threshold to filter out the clustering groups smaller than the threshold.

[0070] (4) Check whether the elevation variation standard deviation is between +1 and -1. If so, it is determined that the point cloud data formed by this cluster presents planar characteristics. If not, this cluster is a general rock exposure surface. If so, it is determined that the potential rock mass structural plane is a real rock mass structural plane. The calculation formula for the elevation variation standard deviation DEV is as follows:

[0071]

[0072] The rock mass structural plane position index (TPI) represents the difference between the elevation z0 of the center point and the average elevation within a predetermined radius R around it SD represents the standard deviation of the elevation, is the number of point clouds within the predetermined radius R, is the elevation of the

[0073] S5: Two-dimensional geological information extraction. Convert the TIN data corresponding to the rock mass structural plane into point cloud data, and calculate the attitude of each structural plane, including dip, dip angle, two-dimensional trace length, and quantity. Cluster and group the dip angles and dips of the rock mass structural planes to obtain the dominant grouping of the rock mass structural planes, and classify them into joint fissures, bedding planes, and fault planes according to the dominant grouping. The specific steps are as follows:

[0074] (1) Based on the extracted real structural plane TIN data, convert it into point cloud data, use the least squares method to calculate the attitude, trace length of each structural plane point cloud set, and count the number of trace lines. The dip and dip angle are calculated as follows:

[0075]

[0076]

[0077] where and are obtained from the direction vector of the plane fitted by the least squares method.

[0078] (2) According to the plane characteristics of each structural plane, the dominant grouping of rock mass structural planes is extracted: three categories, namely joint fissures, rock bedding planes, and fault planes.

[0079] (3) Sort each type of structural plane according to the area size, eliminate the structural planes with smaller areas through the area screening method, and retain the larger structural planes. To ensure that the structural planes have a certain representativeness, retain the top 20 structural planes in terms of area as the 20 major structural areas.

[0080] S6: Three-dimensional geological information estimation, calculate the inter-group spacing of each dominant grouping, and combine the number of fissures per unit volume to calculate the three-dimensional fissure diameter and the center point position of the structural plane.

[0081] (1) Use GIS software to count the geometric center coordinates of each structural plane in each structural plane grouping and the average elevation of the DEM data corresponding to each structural plane. Based on the average elevation, the geometric center coordinates of the structural plane, and the attitude corresponding to each structural plane, calculate the structural plane spacing in ascending order of the horizontal coordinates. The formula for the spacing is as follows:

[0082] The first major category, when and the dip is between 0 degrees and 180 degrees:

[0083]

[0084]

[0085] The second major category, when and the dip is between 180 degrees and 360 degrees:

[0086]

[0087] The third major category, when and the dip When it belongs to the range of 0 degrees to 180 degrees:

[0088]

[0089] The fourth major category, when and the tendency When it belongs to the range of 180 degrees to 360 degrees:

[0090]

[0091]

[0092] Among them, represents the spacing between joint sets; represents the dip angle; represents the average elevation of the left side of the adjacent two structural planes for determination; represents the average elevation of the right side; is expressed as the absolute value of the difference in the Y coordinates of the center points of the two structural planes.

[0093] The three-dimensional spatial distribution of joint fissures, rock bedding planes, and fault planes can be estimated through the spacing between joint sets.

[0094] (2)Calculate the fissure surface density based on the number of traces in the tunnel face, deduce its bulk density by combining the surface density and the occurrence of each structural plane, finally deduce the diameter of the nodular joint fissures in space through the relationship between the bulk density, spacing, and diameter, and calculate the central coordinates of the joint fissures.

[0095] Tunnel face trace surface density The calculation formula is as follows:

[0096]

[0097] is the side length of the square tunnel face, represents all the trace numbers;

[0098] The expectation of the cosine of the acute angle γ between the discontinuous normal vector and the sampling window plane is approximately calculated using the direction tendency sample as follows:

[0099]

[0100] Among them represents the expectation formed by the k-th joint grouping, and respectively represent the tendency and dip angle of the i-th sample of the k-th discontinuous set.

[0101] Combining the trace center surface density and the expectation of the cosine of the acute angle γ, the bulk density formula can be obtained :

[0102]

[0103] From this, the relationship formula between the fracture volume density and the fracture diameter can be obtained. Combining with the relationship table of volume density, spacing and diameter, the average size of the diameter can be obtained. The table is as follows:

[0104]

[0105] The calculation formula for the central coordinates of the joint fractures is as follows:

[0106]

[0107]

[0108] Where: R is the radius size of the fracture, L is the length of the trace, (a, b, c) is the direction vector from the fracture center to the trace center, (X1, Y1, Z1) is the coordinate of the trace center, and (X2, Y2, Z2) is the coordinate of the fracture center.

[0109] S7: Three-dimensional rock mass structure modeling. Based on the three-dimensional fracture diameters and the central point positions of the three types of structural planes, namely joint fractures, rock stratum planes and faults, a discrete fracture network model is established. Calculate the volume of the blocks cut by the three types of structural planes in the model, cluster the block volumes according to the center of gravity points of the blocks, and divide the three-dimensional structure partition of the rock mass. The specific steps are as follows:

[0110] (1) Establish a three-dimensional model of joint fractures by combining the fracture diameter, occurrence and central coordinates. Its manifestation is disk-shaped fractures distributed inside the rock mass, such as Figure 2 ; Establish a model of rock stratum planes and faults by combining the two-dimensional trace position and occurrence. Its manifestation is fractures cutting across the rock mass, such as Figure 3 and Figure 4 shown.

[0111] (2) Couple the joint fracture, rock stratum plane, fault model and bedrock model in the GeneralBlock software, as shown in Figure 5 shown. Obtain the volume of the blocks generated by the fractures cutting the rock mass, calculate the degree of blockification, evaluate the surrounding rock classification, and compare with the actual exposure results. The corresponding relationship between the degree of blockification and the surrounding rock classification is as follows in the table.

[0112]

[0113] Embodiment 2: Based on Embodiment 1, this embodiment provides a three-dimensional rock mass structure estimation device for a tunnel face, including a processor and a storage medium;

[0114] The storage medium is used to store instructions;

[0115] The processor is used to operate according to the instructions to execute the method according to Embodiment 1.

[0116] Embodiment 3: Based on Embodiment 1, this embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method according to Embodiment 1 is implemented.

[0117] Embodiment 4: Based on Embodiment 1, this embodiment provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the method according to Embodiment 1 is implemented.

[0118] Embodiment 5: Based on Embodiment 1, this embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, the method according to Embodiment 1 is implemented.

[0119] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0121] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0123] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present application.

Claims

1. A three-dimensional structure estimation method for tunnel face rock mass, characterized in that, Including: Obtain point cloud data describing the spatial information of the tunnel face, perform triangulation on the point cloud data to obtain DEM data of TIN data; Identify rock mass structural planes based on the DEM data of TIN data; Convert the TIN data corresponding to the rock mass structural planes into point cloud data, calculate the attitude, two-dimensional trace length, and number of traces of each rock mass structural plane, and extract plane features, where the attitude includes dip and dip angle; extract the dominant grouping of rock mass structural planes according to the plane features, and the dominant grouping includes joint fissures, rock layer planes, and fault planes; Calculate the inter-group spacing of each dominant grouping based on the geometric center coordinates, attitude, and average elevation of the structural planes; calculate the diameter of joint fissures and the center coordinates of joint fissures; Establish a three-dimensional model of joint fissures by combining the diameter of joint fissures, attitude, and center coordinates, and the form of expression is disc-shaped fissures distributed inside the rock mass; establish a three-dimensional model of rock layer planes and a three-dimensional model of fault planes by combining the two-dimensional trace position and attitude, and the form of expression is fissures cutting across the rock mass; Couple the three-dimensional model of joint fissures, the three-dimensional model of rock layer planes, and the three-dimensional model of fault planes with the bedrock model based on the actual working conditions to obtain a discrete fracture network model, that is, the three-dimensional structure of the rock mass.

2. The method according to claim 1, characterized in that, Calculate the inter-group spacing of each dominant grouping based on the geometric center coordinates, attitude, and average elevation of the structural planes, including: The first major category, when and the tendency is between 0 degrees and 180 degrees: ， ， The second major category, when and the tendency is between 180 degrees and 360 degrees: ， The third major category, when and the tendency is between 0 degrees and 180 degrees: ， The fourth major category, when and the tendency is between 180 degrees and 360 degrees: ， ， Among them, represents the spacing of structural planes; represents the dip direction; represents the average elevation of the left side of two adjacent structural planes; represents the average elevation of the right side; is expressed as the absolute value of the difference in the horizontal coordinates of the center points of two structural planes.

3. The method according to claim 1, wherein Calculate the diameter of joint fissures and the center coordinates of joint fissures, including: calculate the joint fissure surface density based on the number of traces inside the tunnel face, deduce the bulk density by combining the surface density and the attitude of each structural plane, deduce the diameter of joint fissures in space through the relationship between the joint fissure bulk density, the joint fissure structural plane spacing, and the diameter of joint fissures, and calculate the center coordinates of joint fissures according to the diameter of joint fissures.

4. The method according to claim 3, wherein Face trace line surface density The calculation formula is as follows: ， is the side length of the square heading face, represents the total number of all traces; The expectation of the cosine of the acute angle γ between the discontinuous normal vector and the sampling window plane is approximately calculated using the direction sample as follows: ， Among them represents the expectation formed by the k-th joint fracture grouping, and represent the dip direction and dip angle of the i-th sample of the k-th discontinuity set, respectively; Combined with the density of the trace center plane and the expectation of the cosine of the acute angle γ to obtain the volume density Formula: ， The relationship between the density of joint fissure bodies and the diameter of joint fissures is thus obtained. Combined with the relationship table of the density of joint fissure bodies, the spacing of structural planes and the diameter of joint fissures, the diameter of joint fissures is obtained.

5. The method according to claim 1 or 3, characterized in that, Calculate the center coordinates of joint fissures according to the diameter of joint fissures, including: ， ， Wherein: is an intermediate parameter; R is the radius of the joint fracture, obtained from the joint fracture diameter; L is the length of the trace, (a, b, c) is the direction vector from the joint fracture center to the trace center, (X1, Y1, Z1) is the coordinate of the trace center, ( ) is the coordinate of the joint fracture center.

6. The method according to claim 1, wherein Identify rock mass structural planes based on the DEM data of TIN data, including: According to the three vertex coordinates of each triangular mesh in the DEM data of TIN data, calculate the dip, dip angle of the triangular mesh plane normal vector, and the elevation standard deviation of the three vertex coordinates; Perform ISODATA clustering analysis on all triangular meshes using the dip, dip angle, and elevation standard deviation as clustering indicators to obtain clustering groups; Filter out the clustering groups with the number of triangular meshes less than the threshold using the moving window method, and the remaining clustering groups are used as potential rock mass structural planes; Calculate the elevation variation standard deviation of the vertex coordinates of the triangular meshes forming the potential rock mass structural planes, and determine the true rock mass structural planes from the potential rock mass structural planes according to the elevation variation standard deviation.

7. The method according to claim 1, characterized in that, In S1, the point cloud data describing the spatial information of the tunnel face is obtained by scanning with a three-dimensional laser scanner.

8. A three-dimensional structure estimation device for tunnel face rock mass, characterized in that, Including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 7.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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