Drilling and blasting method tunnel face surrounding rock three-dimensional refined grading method based on seismic wave reflection method

Abstract

The invention relates to the technical field of tunnel face surrounding rock grading in railway construction, and provides a drilling and blasting method tunnel face surrounding rock three-dimensional refined grading method based on a seismic wave reflection method, which comprises the following steps: collecting three-dimensional inversion dot matrix data of an area in front of a tunnel face; the data coordinate system is converted into a tunnel design coordinate system, the surrounding rock basic quality index BQ value of each point is calculated according to the wave velocity, and a space BQ value dot matrix database is generated; performing spatial region division on the BQ value dot matrix by adopting a three-dimensional region self-growth method to obtain a connected region with similar BQ value characteristics; after the representative value of the region boundary is determined, using a Marking Cubes algorithm to extract contour surfaces to construct a surrounding rock three-dimensional grid model; and finally, mapping the model vertex BQ value with a preset grading standard, and outputting a three-dimensional refined grading model with a surrounding rock grading label. Automatic processing and three-dimensional visualization of surrounding rock classification are achieved, and classification accuracy and construction decision reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of surrounding rock classification technology in railway construction tunnels, and particularly to a three-dimensional refined classification method for surrounding rock in tunnels using the drill-and-blast method based on seismic wave reflection. Background Technology

[0002] Seismic wave reflection is a geophysical exploration method that infers underground geological structures by artificially generating seismic waves and receiving the signals reflected at rock interfaces. In drill-and-blast tunnel construction, the tunnel face serves as the working face at the excavation front, and the rock mass around it that is disturbed by the project is called the surrounding rock. Combining the results of seismic wave reflection detection, the surrounding rock in front of and around the tunnel face can be classified in three dimensions. That is, based on data such as wave velocity and reflection characteristics, the geological conditions and stability categories of the rock mass are classified in detail in three-dimensional space, thus providing a reliable geological basis for tunnel design and construction.

[0003] Existing surrounding rock classification technologies suffer from the following technical challenges: the evaluation process heavily relies on manual experience and lacks automation and 3D data processing capabilities, resulting in highly subjective and inaccurate classification results. In the application scenario of 3D refined classification technology for surrounding rock at the tunnel face during railway drill-and-blast tunnel construction, manual methods struggle to effectively integrate geophysical data such as seismic wave reflection data. For instance, when the rock mass ahead of the tunnel face exhibits joint development or uneven hardness, empirical classification may overlook 3D spatial variation characteristics, leading to misjudgments of the surrounding rock level and consequently causing unreasonable support design or construction safety hazards. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a three-dimensional refined classification method for the surrounding rock of tunnel face in the drill-and-blast method based on seismic wave reflection, which solves the technical problem of inaccurate classification of surrounding rock caused by reliance on manual experience in evaluating the quality of surrounding rock.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: This invention provides a three-dimensional refined classification method for the surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection, comprising: Three-dimensional inversion point data of the area in front of the tunnel face acquired by seismic wave reflection method is obtained. The three-dimensional inversion point data includes the coordinate information of multiple spatial points, P-wave velocity and S-wave velocity. The coordinate system of the three-dimensional inversion matrix data is transformed to the tunnel design coordinate system, and the basic quality index (BQ) value of the surrounding rock at each spatial point is calculated based on the longitudinal wave velocity and the transverse wave velocity, thereby generating a spatial BQ value matrix database. Extract BQ value lattice points for a specified mileage range from the spatial BQ value lattice database, and use the three-dimensional region self-growth method to divide the BQ value lattice into spatial regions to obtain multiple spatially connected regions with similar BQ value characteristics. For each spatially connected region, the boundary representative value between the spatially connected region and the adjacent region is determined, and the Marching Cubes algorithm is used to extract the isosurface of each spatially connected region to construct a three-dimensional mesh model of the surrounding rock. The BQ value associated with each vertex in the surrounding rock 3D mesh model is mapped to a preset surrounding rock grading standard to output a refined 3D grading model with surrounding rock grading labels.

[0006] Furthermore, the aforementioned three-dimensional refined classification method for the surrounding rock of tunnel faces in the drill-and-blast method based on seismic wave reflection includes, in the step of acquiring three-dimensional inversion matrix data of the area in front of the tunnel face collected by the seismic wave reflection method, the following steps: Three-component detectors are arranged on the rear sidewall of the tunnel face according to a preset three-dimensional grid pattern to form a sensor array. At the same time, micro explosive sources are drilled and buried at the node positions of the grid pattern. The miniature explosive source is excited to generate seismic wave signals, and the original reflected waveform, including amplitude and phase information, is received by the sensor array. The original reflected waveform is subjected to preprocessing operations of bad segment removal, gain restoration and bandpass filtering in sequence to obtain standardized waveform data; The standardized waveform data is input into a wave equation tomography inversion algorithm based on the finite difference method to calculate and output the discretized three-dimensional inversion lattice data.

[0007] Furthermore, the three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection, wherein the calculation of the basic quality index (BQ) value of the surrounding rock at each spatial point based on the longitudinal wave velocity and the transverse wave velocity includes: Calculate the ratio of P-wave velocity to S-wave velocity at each spatial point, and map the ratio of P-wave velocity to S-wave velocity to the rock mass integrity coefficient of the corresponding spatial point using a preset first empirical formula. Based on the longitudinal wave velocity and the preset rock mass density, the reference value of the uniaxial saturated compressive strength at each spatial point is calculated using the second empirical relationship between dynamic Poisson's ratio and dynamic Young's modulus. The rock mass integrity coefficient and the uniaxial saturated compressive strength reference value are substituted into the preset industry standard BQ calculation formula for solution.

[0008] Furthermore, the three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection includes the following: The spatial division of the BQ value lattice using a three-dimensional region self-growing method includes: Construct an octree spatial index for the BQ value matrix of the specified mileage interval; Select the unvisited point with the smallest curvature as the initial seed point, mark it as visited, and use it as the starting point of the new region; Starting from the initial seed point, query all neighboring points within the preset radius neighborhood of the initial seed point, and calculate the absolute difference between the BQ value of the initial seed point and the BQ value of each neighboring point; If the absolute difference is less than a preset point difference threshold and the neighboring point has not been visited, then the neighboring point is included in the current region, marked as visited, and added to the seed point queue to be expanded. The next seed point is taken out from the seed point queue in turn, and the query and inclusion process is recursively executed until the seed point queue is empty, thus completing the growth of the current region; Traverse all points, repeat the selection and growth process until there are no unvisited points, and output the spatially connected region.

[0009] Furthermore, in the aforementioned three-dimensional refined classification method for the surrounding rock of a drill-and-blast tunnel face based on seismic wave reflection, determining the representative boundary value between the spatially connected region and adjacent regions includes: After completing the spatial region division, traverse each spatial connected region and retrieve all data points of the spatial connected region; For each data point, all neighboring points within a preset first distance threshold are found using the octree spatial index; Determine whether the neighboring point belongs to another different spatially connected region; if so, mark the data point as a boundary candidate point. All the boundary candidate points of each spatially connected region are collected to form the boundary candidate point set of the spatially connected region; Calculate the arithmetic mean of the BQ values ​​of all boundary candidate points in the boundary candidate point set of each spatial connected region, and define the arithmetic mean as the representative boundary value of the spatial connected region to the outside.

[0010] Furthermore, the three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection, wherein the method uses the Marching Cubes algorithm to extract the isosurfaces of each spatially connected region and constructs a three-dimensional mesh model of the surrounding rock, includes: Calculate the axially aligned bounding box for each of the spatially connected regions, and use the boundary representative value as the scalar threshold for isosurface extraction; The internal space of the bounding box is divided into a cubic voxel grid with a side length of a preset resolution value; For each vertex of the voxel mesh, if its spatial location exists in the point set of the spatially connected region, the vertex is directly assigned a BQ value; if it does not exist, the vertex is interpolated using trilinear interpolation based on the BQ values ​​of the eight nearest neighbor data points in the spatially connected region. Traverse each voxel unit, compare the BQ values ​​of the eight vertices of the voxel unit with the scalar threshold, and generate an integer index value between 0 and 255 based on the comparison result; Based on the integer index value, query the preset Marching Cubes triangulation table to determine the edges where the isosurface intersects with the current voxel unit; On each intersecting edge, the precise three-dimensional coordinates of the intersection point of the isosurface are calculated by linear interpolation based on the BQ values ​​of the two vertices and the scalar threshold. According to the connection order specified in the triangle partitioning table, the intersection points within the same voxel unit are connected to generate one or more triangular facets; By aggregating the triangular patches generated from all voxel units within a single spatially connected region, an isosurface triangular mesh of the spatially connected region is obtained; by merging the triangular meshes of all spatially connected regions, a complete three-dimensional mesh model of the surrounding rock is generated.

[0011] Furthermore, the three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection includes mapping the BQ value associated with each vertex in the three-dimensional mesh model of the surrounding rock to a preset surrounding rock classification standard, comprising: Read the pre-stored classification mapping table, which defines a continuous range of BQ values, a basic classification code of the surrounding rock that uniquely corresponds to the range of values, and a sub-classification modifier associated with the basic classification code. Traverse each vertex of the surrounding rock 3D mesh model and read the BQ value associated with that vertex; The BQ value is compared one by one with all the numerical intervals in the hierarchical mapping table until the target numerical interval into which the BQ value falls is found. The basic classification code of the surrounding rock corresponding to the target numerical range is combined with the sub-classification modifier to generate a structured classification label string; The hierarchical label string is used as attribute data and associated with the vertex.

[0012] Furthermore, the three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection further includes, after outputting the three-dimensional refined classification model with surrounding rock classification labels: Input the surrounding rock 3D mesh model and the hierarchical label string associated with the vertices into the graphics rendering module; In the graphics rendering module, a color mapping rule is predefined. The rule maps different basic grade codes to different primary colors and different sub-grade modifiers to different brightness or saturation under the same primary color. Based on the hierarchical label string associated with each vertex, the final display color is assigned to the vertex according to the color mapping rules; Coloring and lighting calculations are performed on all triangular facets, and three-dimensional geological bodies are rendered in a three-dimensional view to distinguish the surrounding rock levels by color. In response to user interaction commands on the 3D window, the display view of the 3D geological body is dynamically updated or a cross-sectional view at a specified location is generated.

[0013] Furthermore, the aforementioned three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection also includes: The complete three-dimensional mesh model of the surrounding rock is exported in STL file format, and the vertex attribute file is also exported. The vertex attribute file records the coordinates of each vertex and its associated hierarchical label string. Import the 3D model of the tunnel lining design profile into the computer-aided design platform; The exported STL format surrounding rock 3D mesh model and the design outline 3D model are spatially registered and superimposed with the tunnel design axis as the reference. In the superimposed composite model, spatial Boolean operations are performed to calculate the interface between the surface of the three-dimensional model of the design contour and the surrounding rock areas identified by different graded label strings; The geometric information of the interface is correlated with the corresponding surrounding rock classification information to generate an interface report for guiding the differentiated design of support structures.

[0014] Furthermore, the aforementioned three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection also includes: The output, a refined 3D grading model with surrounding rock classification labels, is used for construction decision-making. A construction early warning rule base is established, which includes a first early warning rule and a second early warning rule. The first early warning rule sets a lower limit threshold for the BQ value, and the second early warning rule sets a maximum rate of change threshold for the BQ value within a unit mileage. Traverse all vertices in the three-dimensional refined grading model, filter out vertices with BQ values ​​lower than the lower threshold, mark the spatial region including the vertices as a potential weak surrounding rock area, and trigger the first-level construction early warning signal. The recommended measures associated with the first-level construction early warning signal are to strengthen the strength and density of the advanced support in the mileage section. The serialized profiles of the three-dimensional refined classification model along the tunnel axis are traversed synchronously. The average rate of change of BQ values ​​between adjacent profiles is calculated. Profile intervals with average rate of change exceeding the maximum rate of change threshold are selected. The corresponding intervals are marked as lithological abrupt change zones and a second-level construction early warning signal is triggered. The recommended measures associated with the second-level construction early warning signal are to adjust blasting parameters and set up more dense monitoring sections. Based on the spatial distribution of surrounding rock levels displayed by the three-dimensional refined grading model, the system is divided into segments at preset mileage intervals. For each segment, the system automatically matches the corresponding initial support type, support parameters, and secondary lining thickness from the preset support parameter library to generate segment-specific support design parameter tables.

[0015] The beneficial effects of this invention are: This invention effectively solves the problems of high subjectivity and low accuracy caused by the reliance on manual experience in existing rock grading by integrating seismic wave reflection data acquisition and three-dimensional automated processing technology. It generates a spatial BQ value lattice database using three-dimensional inversion lattice data, and achieves spatial consistency between the data and the design model through coordinate system transformation and calculation of basic rock quality indicators. It uses the three-dimensional region self-growth method to divide the BQ value lattice into spatial regions to reveal the spatial clustering characteristics of rock quality. Combined with the Marching Cubes algorithm, it extracts isosurfaces to construct a refined three-dimensional mesh model. Finally, it outputs labeled three-dimensional grading results through grading mapping, which significantly improves the objectivity and visualization capability of rock grading and provides reliable data support for dynamic design and safety control in tunnel construction. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the three-dimensional refined classification method for the surrounding rock of tunnel face in the drilling and blasting method based on seismic wave reflection, as described in this invention.

[0018] Figure 2 This is a basic topological model diagram of the three-dimensional fine classification method for the surrounding rock of the tunnel face in the drilling and blasting method based on the seismic wave reflection method of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0020] To better understand the purpose of this invention, the invention will now be described in further detail.

[0021] Please see Figures 1 to 2 This invention provides a three-dimensional refined classification method for the surrounding rock of the tunnel face in the drill-and-blast method based on seismic wave reflection, comprising: Step 1: Obtain three-dimensional inversion matrix data of the area in front of the tunnel face acquired by seismic wave reflection method. The three-dimensional inversion matrix data includes coordinate information of multiple spatial points, P-wave velocity and S-wave velocity. Step 2: Transform the coordinate system of the three-dimensional inversion matrix data to the tunnel design coordinate system, and calculate the basic quality index (BQ) of the surrounding rock at each spatial point based on the longitudinal wave velocity and transverse wave velocity, thereby generating a spatial BQ value matrix database. Step 3: Extract the BQ value matrix of the specified mileage interval from the spatial BQ value matrix database, and use the three-dimensional region self-growth method to divide the BQ value matrix into spatial regions to obtain multiple spatially connected regions with similar BQ value characteristics. Step 4: For each spatially connected region, determine the boundary representative value between the spatially connected region and the adjacent region, and use the Marching Cubes algorithm to extract the isosurface of each spatially connected region to construct a three-dimensional mesh model of the surrounding rock. Step 5: Map the BQ value associated with each vertex in the surrounding rock 3D mesh model to the preset surrounding rock grading standard, and output a 3D refined grading model with surrounding rock grading labels.

[0022] This invention relates to a three-dimensional refined classification scheme for the surrounding rock of tunnel faces using the drill-and-blast method based on seismic wave reflection. The aim is to improve the accuracy of surrounding rock classification and the three-dimensional visualization capability through automated data processing. The method begins by acquiring three-dimensional inversion matrix data of the area in front of the tunnel face. This data is acquired using the seismic wave reflection method and includes coordinate information of multiple spatial points, P-wave velocity, and S-wave velocity. In practice, a sensor array of three-component geophones is arranged on the rear sidewall of the tunnel face according to a preset three-dimensional grid pattern. Miniature explosive seismic sources are drilled and buried at the grid node locations. After the seismic sources generate seismic wave signals, the sensor array receives the original reflected waveform, including amplitude and phase information. The waveform undergoes preprocessing, including bad path removal, gain recovery, and bandpass filtering, to obtain standardized waveform data. Subsequently, the standardized data is input into a wave equation tomography inversion algorithm based on the finite difference method, which outputs discretized three-dimensional inversion matrix data, providing a foundation for subsequent processing.

[0023] Next, the coordinate system of the 3D inversion matrix data is transformed to the tunnel design coordinate system, and the basic rock quality index (BQ value) of each spatial point is calculated based on the P-wave velocity and S-wave velocity, generating a spatial BQ value matrix database. The coordinate system transformation uses the tunnel design axis as a reference, employing an arbitrary spatial line mileage and 3D frame conversion algorithm to achieve coordinate registration of the inversion data. The BQ value calculation involves mapping the rock mass integrity coefficient through the ratio of P-wave velocity to S-wave velocity, and simultaneously calculating the uniaxial saturated compressive strength reference value based on the empirical relationship between P-wave velocity and rock mass density using dynamic elastic parameters. Finally, these values ​​are substituted into the industry standard BQ calculation formula for solution. This step ensures spatial consistency between the data and the design model, laying the foundation for surrounding rock quality assessment.

[0024] After extracting BQ value lattice data for a specified mileage range from the spatial BQ value lattice database, a three-dimensional region self-growth method is used to spatially divide the lattice into multiple spatially connected regions with similar BQ value characteristics. The region division process first constructs an octree spatial index for the lattice to improve query efficiency; the unvisited point with the smallest curvature is used as the initial seed point, marked as visited, and serves as the starting point for the new region; from the seed point, neighboring points within a preset radius are queried, and the absolute difference in BQ values ​​is calculated. If the difference is less than the point difference threshold and the neighboring point has not been visited, it is included in the current region and added to the seed point queue; the expansion process is recursively executed until the queue is empty, completing the region growth; after traversing all points, a set of spatially connected regions is output, revealing the spatial clustering characteristics of the surrounding rock quality.

[0025] For each spatially connected region, the boundary representative value between the region and its adjacent regions is determined, and the Marching Cubes algorithm is used to extract the isosurfaces of each region to construct a 3D mesh model of the surrounding rock. The boundary representative value is obtained by searching all data points in the region and finding neighboring points within a preset distance threshold. If neighboring points belong to different regions, they are marked as boundary candidate points, and the arithmetic mean of the BQ values ​​of the candidate points is calculated as the boundary representative value. The isosurface extraction is used to calculate the axially aligned bounding box for the region. Using the boundary representative value as a scalar threshold, the bounding box is divided into a cubic voxel mesh. The vertex BQ values ​​are assigned by trilinear interpolation. The voxel cells are traversed to compare the vertex BQ values ​​with the threshold, and integer index values ​​are generated to query the Marching Cubes triangulation table to determine the intersecting edges of the isosurfaces. The coordinates of the intersection points are calculated by linear interpolation. After connecting the triangular faces, the regional triangular mesh is aggregated. Finally, all regional meshes are merged to generate a complete 3D mesh of the surrounding rock.

[0026] Finally, the BQ value associated with each vertex in the 3D surrounding rock mesh model is mapped to a preset surrounding rock grading standard, outputting a refined 3D grading model with surrounding rock grading labels. The mapping process reads a pre-stored grading mapping table, which defines the correspondence between BQ value ranges and the basic grading code and sub-grading modifiers of the surrounding rock. Each vertex of the mesh is traversed, and the BQ value is compared to the target value range. The basic grading code and sub-grading modifier are combined to generate a structured grading label string, which is used as the vertex attribute association. After the model is output, it can be input into the graphics rendering module. Based on the color mapping rules, display colors are assigned to the vertices, and shading and lighting calculations are performed. A 3D geological body, distinguishing surrounding rock levels by color, is rendered in a 3D viewport, supporting user interactive operations such as viewpoint updates or profile generation, assisting in construction decisions and support design.

[0027] The method for acquiring 3D inversion matrix data involves arranging three-component geophones on the rear sidewall of the tunnel face using a pre-designed 3D grid pattern. The grid node spacing is set according to the detection accuracy requirements, and the geophone installation must ensure good coupling with the rock mass to reduce signal attenuation. A miniature explosive source is buried in the borehole, with the borehole depth adjusted according to the surrounding rock characteristics. The seismic wave signal generated after the source excitation propagates in the rock mass, and the raw reflected waveform received by the sensor array includes amplitude and phase information. In the waveform preprocessing stage, bad channels are eliminated by identifying channels with abnormal amplitudes or noise interference. Gain recovery uses time-varying gain control to compensate for energy loss during wave propagation, and bandpass filtering selects a specific frequency range to retain effective waveform components. The standardized waveform data is input using a wave equation tomography inversion algorithm based on the finite difference method. This algorithm solves the velocity field by discretizing the wave equation. The inversion process uses iterative optimization to match the measured and simulated waveforms, ultimately outputting discrete 3D inversion matrix data, where each point includes spatial coordinates and P-wave and S-wave velocity information.

[0028] When calculating the basic quality index (BQ) of surrounding rock, the rock mass integrity coefficient is first derived using an empirical formula based on the ratio of P-wave velocity to S-wave velocity at each spatial point. This coefficient reflects the degree of rock mass fracture development. The P-wave velocity is then combined with a preset rock mass density, and a dynamic elastic parameter empirical model is used to calculate the reference value of uniaxial saturated compressive strength. This model is based on statistical laws of rock mechanics. Subsequently, the rock mass integrity coefficient and the uniaxial saturated compressive strength reference value are substituted into the industry standard BQ calculation formula. This formula considers the interaction between rock hardness and integrity, and calculates the BQ value for each spatial point, thus constructing a spatial BQ value lattice database.

[0029] When dividing spatial regions using the 3D region self-growth method, an octree spatial index is constructed for the BQ value lattice of a specified mileage interval. This index structure accelerates the lookup of neighboring points. The initial seed point is selected based on point cloud curvature calculations; the point with the minimum curvature represents the smoothing starting position of the region and is marked as visited, serving as the base point for the new region. Starting from the seed point, all neighboring points within a preset radius are queried. The absolute difference between the seed point and its neighboring points' BQ values ​​is calculated. If the difference is less than a point difference threshold and the neighboring point is unvisited, it is added to the current region and placed in the seed point queue. The expansion process is recursively executed, processing the seed points in the queue sequentially until the queue is empty, completing the current region growth. This process is repeated for all unvisited points, ultimately outputting multiple spatially connected regions, each with similar BQ value characteristics.

[0030] The method for determining the boundary representative value involves traversing all data points in each spatially connected region after spatial region division. Using an octree spatial index, neighboring points within a preset distance threshold are found for each data point. If neighboring points belong to different regions, the data point is marked as a boundary candidate point. All boundary candidate points are aggregated to form a region boundary candidate point set. The arithmetic mean of the BQ values ​​of all candidate points in the set is calculated as the boundary representative value for that region. This representative value is used for subsequent isosurface extraction to characterize the transition characteristics between regions.

[0031] The steps for extracting isosurfaces and constructing a 3D mesh model of the surrounding rock using the Marching Cubes algorithm are supplemented as follows: After dividing the spatially connected regions, an axially aligned bounding box is calculated for each region. This bounding box is determined based on the coordinate extrema of the region's data points and is used to define the operational space for isosurface extraction. The interior of the bounding box is then divided into a cubic voxel mesh. The voxel side length is set according to a preset resolution, which needs to balance model accuracy and computational efficiency, and is usually adjusted based on the density of the probed data and engineering requirements. For each vertex of the voxel mesh, if its coordinates coincide with a data point in the spatially connected region, a BQ value is directly assigned to that point; for non-coincident vertices, trilinear interpolation is used, assigning values ​​based on the BQ values ​​of the eight nearest neighbor regions, thus ensuring the continuity of the scalar field within the voxel mesh. When traversing each voxel cell, the BQ values ​​of its eight vertices are compared with the boundary representative values ​​to generate an integer index value from 0 to 255. This index corresponds to the configuration state of the voxel cell in the Marching Cubes algorithm. Next, a predefined triangular meshing table is consulted to determine the intersection relationship between the isosurfaces and the edges of the voxel elements. The 3D coordinates of the intersection points are calculated using linear interpolation on each intersecting edge, with the interpolation weight determined by the ratio of the difference between the vertex BQ value and the boundary representative value. Finally, the intersection points are combined into triangular patches according to the connection order specified in the meshing table. These triangular patches from all voxel elements are aggregated to form an isosurface mesh of spatially connected regions, and the meshes from all regions are merged to generate a complete 3D model of the surrounding rock. This process transforms the discrete BQ value lattice into a smooth 3D surface, visually displaying the spatial distribution characteristics of the surrounding rock quality and providing a geometric basis for subsequent graded mapping.

[0032] The following supplementary technical solution details the steps for mapping BQ values ​​to surrounding rock grading standards: The system pre-stores a grading mapping table defined in structured data format, including continuous numerical ranges of BQ values, corresponding basic surrounding rock grading codes (e.g., I to V), and sub-grading modifiers (e.g., III1 or IV2). When traversing each vertex of the 3D surrounding rock mesh model, the associated BQ value is read, and the target numerical range is determined through a segment-by-segment comparison algorithm. The comparison process uses binary search to optimize efficiency and ensure rapid location of the interval range corresponding to the BQ value. Subsequently, the basic grading code and sub-grading modifier of the target interval are concatenated to generate a standardized grading label string, such as "III1" or "V2," and this string is associated with the vertex as attribute data. This mapping operation achieves semantic conversion of surrounding rock quality parameters, enabling the 3D model to not only visualize geometric morphology but also carry engineering grading information, supporting differentiated support design.

[0033] The graphic rendering and interactive operation steps, supplemented by the following sub-technical solution: After the 3D mesh model of the surrounding rock and vertex classification labels are input into the graphic rendering module, the module's built-in color mapping rules map different basic classification codes to distinct primary colors. For example, Class I corresponds to dark green, and Class V corresponds to red. Sub-class modifiers are distinguished by adjusting the brightness or saturation of the same primary color, such as using light green for Class III-1 and dark green for Class III-2. The rendering engine dynamically assigns color values ​​based on the vertex classification labels and performs Phong lighting model calculations on all triangular faces to enhance the three-dimensionality of the 3D geological body. In the 3D viewport, users can drag and rotate the viewpoint, zoom the model using the scroll wheel, or click the profile tool to generate a cut view at any position. The profile operation calculates the intersection line between the mesh and the cutting plane in real time and highlights the distribution of different classifications of surrounding rock. This interactive visualization function facilitates engineers to dynamically analyze the stability of the surrounding rock and optimize construction plans.

[0034] The model export and CAD integration steps are supplemented by the following technical solutions: A complete 3D surrounding rock mesh model is exported in STL file format, which widely supports 3D printing and CAD software. A vertex attribute file is also generated, recording the 3D coordinates and classification label strings of each vertex. In the computer-aided design platform, the 3D model of the tunnel lining design profile is first imported, typically as a parametric surface or solid model. Next, spatial registration is performed using the tunnel design axes to align the surrounding rock mesh model with the design model. The registration method is based on least squares fitting or feature point matching. In the superimposed composite model, spatial Boolean operations are performed to calculate the interfaces between the design profile surface and different graded surrounding rock areas. Boolean operations use boundary representation or voxelization methods to handle mesh intersections. Finally, the geometric parameters of the interfaces, such as area and curvature, are extracted and correlated with the surrounding rock classification information to generate a report guiding the differentiated configuration of the support structure, such as increasing the anchor density in Grade IV surrounding rock areas.

[0035] The construction decision-making application steps, supplemented by the following sub-technical solutions, are as follows: In the construction early warning rule library, the first early warning rule sets a lower limit threshold for the BQ value to identify potentially weak surrounding rock areas. The second early warning rule sets a threshold for the maximum rate of change of the BQ value within a unit mileage to detect lithological abrupt change zones. The system traverses all vertices of the three-dimensional refined classification model, filters points with BQ values ​​below the lower limit threshold, clusters them, marks them as weak surrounding rock areas, triggers the first-level early warning, and suggests strengthening the advanced support for that section. Simultaneously, sequential profiles are extracted along the tunnel axis, the average rate of change of the BQ value between adjacent profiles is calculated, the data is smoothed using the sliding window method, and intervals with rate of change exceeding the threshold are identified as lithological abrupt change zones, triggering the second-level early warning and suggesting adjustments to blasting parameters. In addition, based on the spatial distribution of surrounding rock levels, the system is automatically segmented according to preset mileage intervals, queries the support parameter library to match corresponding initial support types, steel frame spacing, shotcrete thickness, and other parameters, and generates a segmented differentiated support design parameter table to improve construction safety and economy.

[0036] This invention addresses the technical problem of low accuracy and reliance on manual experience in rock classification during railway drill-and-blast tunnel construction by providing a three-dimensional refined classification method based on seismic wave reflection. Through automated data processing and three-dimensional modeling, it achieves objective and accurate classification of rock levels.

[0037] A sensor array of three-component geophones is arranged on the rear sidewall of the tunnel face according to a preset three-dimensional grid pattern. Simultaneously, miniature explosive seismic sources are buried in boreholes drilled at the grid nodes. After the seismic sources generate seismic wave signals, the sensor array receives the raw reflected waveforms, including amplitude and phase information. Waveform preprocessing includes bad channel removal, gain restoration, and bandpass filtering. Bad channel removal is achieved by identifying abnormal amplitude channels, gain restoration uses time-varying gain control to compensate for energy attenuation, and bandpass filtering retains the effective frequency band waveform components. Standardized waveform data is input using a wave equation tomography inversion algorithm based on the finite difference method. The velocity field is iteratively solved by discretizing the wave equation, outputting discrete three-dimensional inversion point data. Each point includes spatial coordinates, P-wave velocity, and S-wave velocity.

[0038] The coordinate system of the 3D inversion matrix data was transformed to the tunnel design coordinate system. Using an arbitrary mileage spatial line and 3D frame conversion algorithm, coordinate registration was performed with the tunnel design axis as the reference. The basic rock mass quality index (BQ value) for each spatial point was calculated based on the P-wave velocity and S-wave velocity. The rock mass integrity coefficient was mapped using the ratio of P-wave velocity to S-wave velocity. Combined with the rock mass density, a dynamic elastic parameter empirical model was used to calculate the reference value of uniaxial saturated compressive strength. Finally, the BQ value was substituted into the industry standard BQ formula for solution. The generated spatial BQ value matrix database was aligned with the design model space, providing a foundation for subsequent analysis.

[0039] Extract BQ value lattice data for a specified mileage range from the database and use a 3D region self-growth method for spatial region division. An octree spatial index is constructed for the lattice to improve query efficiency. The unvisited point with the smallest curvature is used as the initial seed point, marked as visited, and then used as the starting point for a new region. Starting from the seed point, query neighboring points within a preset radius neighborhood, calculate the absolute difference in BQ values. If the difference is less than the point difference threshold and the neighboring point is unvisited, it is added to the current region and the seed point queue is entered. The expansion process is recursively executed until the queue is empty. After region growth is complete, all points are traversed, and the above operations are repeated, outputting a set of spatially connected regions with similar BQ value characteristics.

[0040] For each spatially connected region, a representative boundary value is determined for its proximity to adjacent regions. All data points within the region are traversed, and neighboring points within a preset distance threshold are found using an octree index. If neighboring points belong to different regions, they are marked as candidate boundary points. The arithmetic mean of the BQ values ​​of the candidate points is calculated as the representative boundary value for isosurface extraction. A 3D mesh model of the surrounding rock is constructed using the Marching Cubes algorithm. An axially aligned bounding box is calculated for each region, and a cubic voxel mesh is created using the representative boundary value as a scalar threshold. BQ values ​​are assigned to voxel vertices or trilinear interpolation is performed. The voxel cells are traversed, and the vertex BQ values ​​are compared with the threshold to generate integer indices. The triangulation table is consulted to determine the intersection points of isosurfaces and edges. The coordinates of the intersection points are calculated using linear interpolation and connected to form triangular patches. The complete 3D model is generated by aggregating the triangular meshes of the region.

[0041] The BQ values ​​associated with the vertices of the 3D mesh model of the surrounding rock are mapped to a preset grading standard. The BQ value range, basic grading code, and sub-grading modifiers defined in the grading mapping table are read. Each vertex's BQ value is traversed, and the target range is located using binary search, generating a grading label string as a vertex attribute. After the model is input into the graphics rendering module, a primary color tone is assigned to the basic grading code according to the color mapping rules, and the brightness and saturation are adjusted for the sub-grading modifiers, performing shading and lighting calculations. User interactive operations, such as rotation, zoom, and profile cutting, are supported in the 3D viewport, displaying the distribution of surrounding rock grades in real time.

[0042] The complete 3D mesh model is exported in STL format, and vertex attribute files are output to record coordinates and classification labels. The tunnel lining design model is imported into the CAD platform and spatially registered using the design axis as a reference. Boolean operations are performed to calculate the interface between the design surface and the surrounding rock area, extracting geometric parameters and associating them with classification information to generate a support design report. During construction decision-making, an early warning rule base is established, setting lower thresholds and rate-of-change thresholds for the BQ value. The model vertices are traversed to filter weak surrounding rock areas and lithological abrupt change zones, triggering classified early warnings and generating a differentiated support parameter table to guide adjustments to advanced support and blasting parameters.

[0043] This method integrates seismic wave reflection data with three-dimensional processing technology to replace manual experience-based judgment, thereby improving the accuracy and efficiency of surrounding rock classification and providing a reliable basis for safe tunnel construction.

[0044] After generating the spatial BQ value lattice database, this database stores the 3D coordinates and corresponding BQ values ​​of each spatial point in a structured array format, facilitating rapid retrieval and data extraction based on mileage intervals. The point difference threshold used in spatial region division is preset based on the accuracy of exploration data and the stability requirements of the surrounding rock, determined through trial calculations or optimization using historical data to ensure the continuity of BQ values ​​within the region. When calculating the boundary representative value, the preset distance threshold is set based on the lattice density, typically a multiple of the average point spacing, ensuring the rationality of boundary point retrieval. The scalar threshold extracted from the isosurface directly uses the boundary representative value without additional parameter adjustments, simplifying the operation process. The surrounding rock classification mapping table is pre-entered according to industry standards and specifications, including the correspondence between continuous BQ value intervals and classification codes, ensuring mapping consistency. After the 3D model is output, it can be directly imported into mainstream tunnel design software to support 3D visualization and interactive analysis. The early warning threshold in construction decision-making is dynamically adjusted based on geological conditions, optimized through training on historical data using machine learning algorithms to improve adaptability.

[0045] Embodiment 1 of this invention: In the construction of a drill-and-blast tunnel on a plateau railway, the method of this invention is applied to perform three-dimensional fine-grained classification of the surrounding rock at the tunnel face. The geological conditions of the area traversed by the tunnel are complex, and the rock mass joints are unevenly developed, making it difficult for existing manual classification methods to accurately capture the spatial variation characteristics of the surrounding rock. The construction team arranged a three-component geophone array on the rear sidewall of the tunnel face according to a preset three-dimensional grid pattern. The grid node spacing was set to 0.5 meters to balance detection accuracy and efficiency. At the same time, miniature explosive seismic sources were buried in the node boreholes. After the seismic sources were excited to generate seismic wave signals, the geophone array received the original reflected waveforms and performed bad path removal, gain restoration, and bandpass filtering preprocessing on the waveforms. The bandpass filtering range was set to 100Hz to 1000Hz to retain the effective frequency band. The standardized waveform data was input into a wave equation tomography inversion algorithm based on the finite difference method. The inversion output included three-dimensional inversion point data including coordinates, P-wave velocity, and S-wave velocity. The data coordinate system was then transformed to the tunnel design coordinate system. The BQ value of each point was calculated using P-wave and S-wave velocities. The rock mass integrity coefficient was mapped using the wave velocity ratio, and the uniaxial saturated compressive strength reference value was derived based on an empirical model of dynamic elastic parameters. After generating a spatial BQ value lattice database, points within a specified mileage interval (e.g., DK100+50 to DK100+200) were extracted. A three-dimensional region self-growing method was used for region division, with a point difference threshold set to 30 to aggregate points with similar BQ values. After region division, the Marching Cubes algorithm was used to extract isosurfaces, constructing a three-dimensional mesh model of the surrounding rock. Finally, a labeled three-dimensional model was output through hierarchical mapping. Field verification showed that this method automatically identified a weak interlayer in a section of Class IV surrounding rock ahead of the tunnel face, whereas manual experience had previously misclassified it as Class III. The construction team adjusted the support parameters for this section based on the model results, avoiding potential safety hazards.

[0046] Embodiment 2 of this invention: Another example is for a drill-and-blast tunnel in an urban subway system, where the tunnel traverses alternating layers of soft and hard rock, resulting in significant spatial variations in surrounding rock stability. During project implementation, a high-density sensor array was deployed at the tunnel face, with the grid node spacing reduced to 0.3 meters to improve resolution. After seismic excitation, the received reflected waveforms underwent preprocessing to eliminate electromagnetic interference from the urban environment, and the bandpass filter was adjusted to 50Hz to 800Hz to adapt to low-frequency noise environments. The inverted 3D point matrix data, after coordinate transformation, incorporated local rock mass density correction parameters in the BQ value calculation to enhance accuracy. When extracting the mileage interval point matrix from the database, the 3D region self-growing method was used, setting the point difference threshold to 20 and the region difference threshold to 15 to generate a more refined spatially connected region. In the isosurface extraction stage, the Marching Cubes algorithm was set to a resolution of 0.1-meter cube voxels, and the boundary representative value was dynamically calculated based on the difference in BQ values ​​between adjacent regions. After the output 3D hierarchical model was imported into the tunnel design software, color mapping clearly showed that the Class V surrounding rock was distributed in a banded pattern, with a consistency of over 90% with the borehole sampling results. Based on the model's early warning, the construction team increased the monitoring density of the cross-section in the lithological abrupt change zone, dynamically adjusted the blasting parameters, improved construction efficiency, and prevented any surrounding rock instability events.

[0047] In this invention, the three-dimensional region self-growth method is used to spatially divide the BQ value lattice of a specified mileage interval. Its data processing path begins by extracting the lattice data of the target interval from the spatial BQ value lattice database; then, an octree spatial index is constructed for the lattice to improve query efficiency, and the unvisited point with the smallest curvature is selected as the initial seed point and marked as visited; starting from the seed point, all neighboring points within a preset radius neighborhood are queried, and the absolute difference between the BQ values ​​of the seed point and the neighboring points is calculated. If the difference is less than the point difference threshold and the neighboring point has not been visited, the neighboring point is included in the current region and added to the seed point queue; the expansion process is recursively executed until the queue is empty. After the current region growth is completed, all unvisited points are traversed and the above operation is repeated, finally outputting a set of spatially connected regions with similar BQ value characteristics, realizing the spatial clustering feature identification of surrounding rock quality.

[0048] In this invention, the Marching Cubes algorithm is used to extract isosurfaces from each spatially connected region to construct a 3D mesh model of the surrounding rock. The data processing path uses the region boundary representative value as a scalar threshold. First, the axially aligned bounding box of the spatially connected region is calculated, and the interior of the bounding box is divided into a cubic voxel mesh. The voxel side length is set according to a preset resolution. For voxel mesh vertices, if their coordinates exist in the region point set, a BQ value is directly assigned; otherwise, a value is assigned based on the BQ values ​​of the eight surrounding neighboring points using trilinear interpolation. Each voxel cell is traversed, and the BQ values ​​of the eight vertices are compared with the threshold to generate an integer index value. The intersection point of the isosurface and the edge is determined by querying the triangular partitioning table. The intersection point coordinates are calculated by linear interpolation and connected into triangular patches according to the partitioning table. After aggregating all voxel patches, a regional isosurface mesh is generated. Finally, all regional meshes are merged to form a complete 3D model, realizing the visualization of the surrounding rock structure.

[0049] In this invention, the wave equation tomographic inversion algorithm is used to process seismic wave reflection data to generate a three-dimensional inversion matrix. The data processing path begins with input standardized waveform data. The algorithm discretizes the wave equation based on the finite difference method and performs tomographic inversion calculations by iteratively optimizing the matching of measured and simulated waveforms. The inversion process solves for the velocity field and outputs discrete three-dimensional matrix data, with each point containing spatial coordinates, P-wave velocity, and S-wave velocity. This algorithm transforms the preprocessed waveform signal into quantitative rock mass physical parameters, providing a basic data source for subsequent BQ value calculations and supporting the construction of surrounding rock classification models.

[0050] In this invention, the three-dimensional mesh model of the surrounding rock is constructed by extracting isosurfaces from spatially connected regions using the Marching Cubes algorithm. The data processing path begins by calculating an axially aligned bounding box for each spatially connected region, using the boundary representative value as a scalar threshold for isosurface extraction. Subsequently, the space inside the bounding box is divided into cubic voxel meshes with a preset resolution value on each side. For each vertex of the voxel mesh, if its spatial location exists in the point set of the spatially connected region, its BQ value is directly assigned; otherwise, it is interpolated using trilinear interpolation based on the BQ values ​​of its eight nearest neighbor data points. When traversing each voxel, the BQ values ​​of the eight vertices of the voxel are compared with the scalar threshold. Based on the comparison result, an integer index value from 0 to 255 is generated, and the preset Marching Cubes algorithm is queried based on this index value. The Cubes triangulation table determines the edges where isosurfaces intersect with the current voxel element. On each intersecting edge, the precise 3D coordinates of the isosurface intersection point are calculated using linear interpolation based on the BQ values ​​of the two vertices and a scalar threshold. The intersection points within the same voxel element are connected according to the connection order specified in the triangulation table to generate one or more triangular patches. The triangular patches generated by all voxel elements in a single spatially connected region are aggregated to obtain the isosurface triangular mesh of that region. Finally, the triangular meshes of all spatially connected regions are merged to generate a complete 3D mesh model of the surrounding rock. This model serves as the basis for 3D fine-grained grading and supports the mapping and visualization output of vertex BQ values ​​and surrounding rock grading standards.

Claims

1. A three-dimensional refined classification method for the surrounding rock of a drill-and-blast tunnel face based on seismic wave reflection, characterized in that, include: Three-dimensional inversion point data of the area in front of the tunnel face acquired by seismic wave reflection method is obtained. The three-dimensional inversion point data includes the coordinate information of multiple spatial points, P-wave velocity and S-wave velocity. The coordinate system of the three-dimensional inversion matrix data is transformed to the tunnel design coordinate system, and the basic quality index (BQ) value of the surrounding rock at each spatial point is calculated based on the longitudinal wave velocity and the transverse wave velocity, thereby generating a spatial BQ value matrix database. Extract BQ value lattice points for a specified mileage range from the spatial BQ value lattice database, and use the three-dimensional region self-growth method to divide the BQ value lattice into spatial regions to obtain multiple spatially connected regions with similar BQ value characteristics. For each spatially connected region, the boundary representative value between the spatially connected region and the adjacent region is determined, and the Marching Cubes algorithm is used to extract the isosurface of each spatially connected region to construct a three-dimensional mesh model of the surrounding rock. The BQ value associated with each vertex in the three-dimensional mesh model of the surrounding rock is mapped to a preset surrounding rock classification standard to output a three-dimensional refined classification model with surrounding rock classification labels.

2. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 1, characterized in that, The acquisition of three-dimensional inversion point data of the area in front of the tunnel face acquired by the seismic wave reflection method includes: Three-component detectors are arranged on the rear sidewall of the tunnel face according to a preset three-dimensional grid pattern to form a sensor array. At the same time, micro explosive sources are buried in holes drilled at the node positions of the grid pattern. The miniature explosive source is excited to generate seismic wave signals, and the original reflected waveform, including amplitude and phase information, is received by the sensor array. The original reflected waveform is subjected to preprocessing operations of bad segment removal, gain restoration and bandpass filtering in sequence to obtain standardized waveform data; The standardized waveform data is input into a wave equation tomography inversion algorithm based on the finite difference method to calculate and output the discretized three-dimensional inversion lattice data.

3. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 1, characterized in that, The calculation of the basic quality index (BQ) of the surrounding rock at each spatial point based on the longitudinal wave velocity and the transverse wave velocity includes: Calculate the ratio of P-wave velocity to S-wave velocity at each spatial point, and map the ratio of P-wave velocity to S-wave velocity to the rock mass integrity coefficient of the corresponding spatial point using a preset first empirical formula. Based on the longitudinal wave velocity and the preset rock mass density, the reference value of the uniaxial saturated compressive strength at each spatial point is calculated using the second empirical relationship between dynamic Poisson's ratio and dynamic Young's modulus. The rock mass integrity coefficient and the uniaxial saturated compressive strength reference value are substituted into the preset industry standard BQ calculation formula for solution.

4. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 1, characterized in that, The method of dividing the BQ value lattice into spatial regions using a three-dimensional region self-growing method includes: Construct an octree spatial index for the BQ value matrix of the specified mileage interval; Select the unvisited point with the smallest curvature as the initial seed point, mark it as visited, and use it as the starting point of the new region; Starting from the initial seed point, query all neighboring points within the preset radius neighborhood of the initial seed point, and calculate the absolute difference between the BQ value of the initial seed point and the BQ value of each neighboring point; If the absolute difference is less than a preset point difference threshold and the neighboring point has not been visited, then the neighboring point is included in the current region, marked as visited, and added to the seed point queue to be expanded. The next seed point is taken out from the seed point queue in turn, and the query and inclusion process is recursively executed until the seed point queue is empty, thus completing the growth of the current region; Traverse all points, repeat the selection and growth process until there are no unvisited points, and output the spatially connected region.

5. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 4, characterized in that, Determining the boundary representative value between the spatially connected region and its adjacent regions includes: After completing the spatial region division, traverse each spatial connected region and retrieve all data points of the spatial connected region; For each data point, all neighboring points within a preset first distance threshold are found using the octree spatial index; Determine whether the neighboring point belongs to another different spatially connected region; if so, mark the data point as a boundary candidate point. All the boundary candidate points of each spatially connected region are collected to form the boundary candidate point set of the spatially connected region; Calculate the arithmetic mean of the BQ values ​​of all boundary candidate points in the boundary candidate point set of each spatial connected region, and define the arithmetic mean as the representative value of the boundary of the spatial connected region to the outside.

6. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 5, characterized in that, The step of using the Marching Cubes algorithm to extract isosurfaces of each spatially connected region and constructing a three-dimensional mesh model of the surrounding rock includes: Calculate the axially aligned bounding box for each of the spatially connected regions, and use the boundary representative value as the scalar threshold for isosurface extraction; The internal space of the bounding box is divided into a cubic voxel grid with a side length of a preset resolution value; For each vertex of the voxel mesh, if its spatial location exists in the point set of the spatially connected region, the vertex is directly assigned a BQ value; if it does not exist, the vertex is interpolated using trilinear interpolation based on the BQ values ​​of the eight nearest neighbor data points in the spatially connected region. Traverse each voxel unit, compare the BQ values ​​of the eight vertices of the voxel unit with the scalar threshold, and generate an integer index value between 0 and 255 based on the comparison result; Based on the integer index value, query the preset Marching Cubes triangulation table to determine the edges where the isosurface intersects with the current voxel unit; On each intersecting edge, the precise three-dimensional coordinates of the intersection point of the isosurface are calculated by linear interpolation based on the BQ values ​​of the two vertices and the scalar threshold. According to the connection order specified in the triangle partitioning table, the intersection points within the same voxel unit are connected to generate one or more triangular facets; By aggregating the triangular patches generated from all voxel units within a single spatially connected region, an isosurface triangular mesh of the spatially connected region is obtained; by merging the triangular meshes of all spatially connected regions, a complete three-dimensional mesh model of the surrounding rock is generated.

7. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 1, characterized in that, The step of mapping the BQ value associated with each vertex in the three-dimensional mesh model of the surrounding rock to a preset surrounding rock classification standard includes: Read the pre-stored classification mapping table, which defines a continuous range of BQ values, a basic classification code of the surrounding rock that uniquely corresponds to the range of values, and a sub-classification modifier associated with the basic classification code. Traverse each vertex of the surrounding rock 3D mesh model and read the BQ value associated with that vertex; The BQ value is compared one by one with all the numerical intervals in the hierarchical mapping table until the target numerical interval into which the BQ value falls is found. The basic classification code of the surrounding rock corresponding to the target numerical range is combined with the sub-classification modifier to generate a structured classification label string; The hierarchical label string is used as attribute data and associated with the vertex.

8. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 7, characterized in that, Following the output of the refined 3D grading model with surrounding rock grading labels, the following is also included: Input the surrounding rock 3D mesh model and the hierarchical label string associated with the vertices into the graphics rendering module; In the graphics rendering module, a color mapping rule is predefined. The rule maps different basic grade codes to different primary colors and different sub-grade modifiers to different brightness or saturation under the same primary color. Based on the hierarchical label string associated with each vertex, the final display color is assigned to the vertex according to the color mapping rules; Coloring and lighting calculations are performed on all triangular facets, and three-dimensional geological bodies are rendered in a three-dimensional view to distinguish the surrounding rock levels by color. In response to user interaction commands on the 3D window, the display view of the 3D geological body is dynamically updated or a cross-sectional view at a specified location is generated.

9. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 6, characterized in that, Also includes: The complete three-dimensional mesh model of the surrounding rock is exported in STL file format, and the vertex attribute file is also exported. The vertex attribute file records the coordinates of each vertex and its associated hierarchical label string. Import the 3D model of the tunnel lining design profile into the computer-aided design platform; The exported STL format surrounding rock 3D mesh model and the design outline 3D model are spatially registered and superimposed with the tunnel design axis as the reference. In the superimposed composite model, spatial Boolean operations are performed to calculate the interface between the surface of the three-dimensional model of the design contour and the surrounding rock areas identified by different graded label strings; The geometric information of the interface is correlated with the corresponding surrounding rock classification information to generate an interface report for guiding the differentiated design of support structures.

10. The three-dimensional refined classification method for surrounding rock at the tunnel face in the drill-and-blast method based on seismic wave reflection as described in claim 1, characterized in that, Also includes: The output, a refined 3D grading model with surrounding rock classification labels, is used for construction decision-making. A construction early warning rule base is established, which includes a first early warning rule and a second early warning rule. The first early warning rule sets a lower limit threshold for the BQ value, and the second early warning rule sets a maximum rate of change threshold for the BQ value within a unit mileage. Traverse all vertices in the three-dimensional refined grading model, filter out vertices with BQ values ​​lower than the lower threshold, mark the spatial region including the vertices as a potential weak surrounding rock area, and trigger the first-level construction early warning signal. The recommended measures associated with the first-level construction early warning signal are to strengthen the strength and density of the advanced support in the mileage section. The serialized profiles of the three-dimensional refined classification model along the tunnel axis are traversed synchronously. The average rate of change of BQ values ​​between adjacent profiles is calculated. Profile intervals with average rate of change exceeding the maximum rate of change threshold are selected. The corresponding intervals are marked as lithological abrupt change zones and a second-level construction early warning signal is triggered. The recommended measures associated with the second-level construction early warning signal are to adjust blasting parameters and set up more dense monitoring sections. Based on the spatial distribution of surrounding rock levels displayed by the three-dimensional refined grading model, the rock is divided into segments at preset mileage intervals. For each segment, the corresponding initial support type, support parameters, and secondary lining thickness are automatically matched from the preset support parameter library to generate a segment-specific support design parameter table.

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