Attribute modeling method and system of tunnel appearance-hole periphery multi-source information joint constraint

Abstract

The invention discloses a tunnel appearance-hole periphery multi-source information joint constraint attribute modeling method and system, and relates to the technical field of underground engineering geologic modeling, and the method comprises the steps: obtaining the spatial information and geological attribute information of a tunnel geologic body region, employing a local feature-based adaptive network division method, and obtaining the spatial information and geological attribute information of the tunnel geologic body region; dividing the region into a plurality of grids, and constructing a grid model; for each grid region, performing data preprocessing on the acquired geological attribute data, preliminarily endowing a grid model with geological attributes according to the preprocessed data, and modeling spatial distribution, porosity, water-containing body and elastic models of fractures in the model so as to construct an initial attribute model; and calculating the correlation between the new and old drilling data according to the latest drilling data, fitting the drilling data according to the correlation, and fusing the latest drilling data into the initial attribute model through fitting to obtain an updated tunnel geological attribute model. According to the method, refined modeling of the geological attributes of the tunnel region can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of underground engineering geological modeling, and in particular to an attribute modeling method and system for joint constraints of tunnel surface-hole perimeter multi-source information. Background Art

[0002] As an important part of underground engineering, the safety, stability and long-term performance of tunnels are directly related to construction, operation and later maintenance. Especially under complex geological conditions, tunnel geological body attribute modeling is particularly critical. The rock and soil properties around the tunnel, such as cracks, porosity, water content, elastic modulus, etc., have a profound impact on key indicators such as tunnel permeability and stability. In order to accurately assess the construction risks, long-term stability and possible operational problems of the tunnel, it is necessary to accurately model and dynamically predict the geological characteristics of the tunnel area. With the continuous advancement of exploration technology, existing detection methods such as drilling detection, seismic waves, geological radar, and acoustic exploration are gradually applied to tunnel geological modeling. These exploration technologies can provide richer and more comprehensive geological information, which helps to more accurately describe the complex geological characteristics around the tunnel. However, the data obtained by the above exploration technologies are often heterogeneous and diverse. How to integrate these data from different sources and effectively integrate them into a unified model is still a major challenge facing the current field of tunnel geological modeling.

[0003] Traditional tunnel geological modeling methods mainly rely on borehole exploration data, field tests and empirical knowledge, and are usually constructed using two-dimensional or three-dimensional geological models. Although these methods have achieved certain success in practical applications, these models are difficult to fully and accurately reflect the complex geological conditions of the tunnel area because they rely on limited exploration data. In particular, when modeling complex geological bodies (such as fracture zones, gravel layers, water content, etc.), the processing capacity and accuracy of traditional methods are limited, resulting in the accuracy and reliability of modeling results being restricted. In addition, based on limited borehole data, spatial interpolation modeling using geostatistical methods can provide certain modeling accuracy in some cases, but due to the limitations of the number of boreholes and spatial positions, it is often difficult to fully reflect the complex underground geological bodies. For example, in the modeling process of complex geological bodies such as fractures, water content and gravel layers, the interpolation results of traditional methods often have accuracy deviations or incompleteness, resulting in poor accuracy and reliability of tunnel geological models. Summary of the invention

[0004] In order to solve the shortcomings of the above-mentioned prior art, the present invention provides a tunnel surface-hole perimeter multi-source information joint constraint attribute modeling method and system, based on the information such as cracks, porosity, water content revealed by drilling data, combined with the spatial information provided by means of geological radar and acoustic exploration, by integrating different exploration data and combining adaptive grid division technology, to achieve refined modeling of the geological attributes of the tunnel area, improve the accuracy and reliability of the tunnel geological model, and enhance the tunnel safety assessment and construction risk prediction capabilities.

[0005] In a first aspect, the present invention provides an attribute modeling method for joint constraints of tunnel surface-hole perimeter multi-source information.

[0006] A tunnel surface-hole perimeter multi-source information joint constraint attribute modeling method, comprising:

[0007] The spatial information and geological attribute information of the tunnel geological body area are obtained, and the area is divided into several grids using an adaptive network partitioning method based on local features to construct a grid model; the geological attribute information includes the location and distribution of cracks, porosity, water seepage information, and elastic modulus;

[0008] For each grid area, the acquired geological attribute data is preprocessed, and the grid model is initially assigned geological attributes based on the preprocessed data. The spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models are modeled in the model to construct an initial attribute model.

[0009] According to the latest drilling data, the correlation between the new and old drilling data is calculated, and the drilling data is fitted according to the correlation. After fitting, the latest drilling data is integrated into the initial attribute model to obtain an updated tunnel geological attribute model.

[0010] In a second aspect, the present invention provides an attribute modeling system with joint constraints of tunnel surface-hole perimeter multi-source information.

[0011] A tunnel surface-hole perimeter multi-source information joint constraint attribute modeling system, comprising:

[0012] The grid division module is used to obtain the spatial information and geological attribute information of the tunnel geological body area. The area is divided into several grids and a grid model is constructed by using an adaptive network division method based on local characteristics. The geological attribute information includes the location and distribution of cracks, porosity, water seepage information, and elastic modulus.

[0013] The initial attribute model modeling module is used to preprocess the acquired geological attribute data for each grid area, preliminarily assign geological attributes to the grid model based on the preprocessed data, and model the spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models in the model to construct the initial attribute model;

[0014] The tunnel geological body attribute modeling module is used to calculate the correlation between the new and old drilling data based on the latest drilling data, and to fit the drilling data based on the correlation. After fitting, the latest drilling data is integrated into the initial attribute model to obtain an updated tunnel geological attribute model.

[0015] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned attribute modeling method of the joint constraints of tunnel surface-hole perimeter multi-source information when executing the executable instructions stored in the memory.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned tunnel surface-hole perimeter multi-source information joint constraint attribute modeling method.

[0017] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned attribute modeling method of the joint constraints of tunnel surface-hole perimeter multi-source information is implemented.

[0018] One or more of the above technical solutions have the following beneficial effects:

[0019] 1. The present invention provides a tunnel surface-hole-surrounding multi-source information joint constraint attribute modeling method and system, which obtains data on the complex geology of the tunnel and its surroundings through different exploration means (such as drilling, seismic waves, geological radar, etc.), and on the basis of the information such as cracks, pores, water content, etc. revealed by the drilling data, combined with the spatial information provided by means of geological radar and acoustic wave exploration, the above-mentioned different exploration data are integrated, combined with adaptive grid division technology, to achieve refined modeling of the geological attributes of the tunnel area, improve the accuracy and reliability of the tunnel geological model, and enhance the safety assessment and construction risk prediction capabilities of the tunnel.

[0020] 2. Based on the joint constraint modeling of the above-mentioned multi-source information data, the present invention also proposes a model update mechanism. With the continuous supplementation and update of drilling data, the tunnel geological model can be dynamically iterated and optimized to achieve a more accurate prediction of the geological characteristics of the tunnel area. Compared with traditional methods, the modeling method proposed by the present invention has higher accuracy and stronger adaptability, can effectively meet the evaluation needs of tunnel safety and stability under complex geological conditions, and has important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 The present invention is a flowchart of the attribute modeling method of the tunnel surface-hole perimeter multi-source information joint constraint. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0024] Embodiment 1

[0025] This embodiment provides a tunnel surface-hole perimeter multi-source information joint constraint attribute modeling method, such as Figure 1 As shown, the specific steps include:

[0026] Step S1, obtaining spatial information and geological attribute information of the tunnel geological body area, using an adaptive network partitioning method based on local features to divide the area into several grids, and constructing a grid model; wherein the geological attribute information includes crack location and its distribution, porosity, water seepage information, elastic modulus, etc.

[0027] Specifically, through geological radar, sonic exploration, drilling and other means, spatial information such as the stratigraphic structure of the tunnel geological body area (hereinafter referred to as the tunnel area) and geological attribute information such as the location and distribution of cracks, porosity, water seepage information, elastic modulus, etc., where the water seepage information usually includes the characteristics of the water body near the cracks, such as the permeability of the aquifer, the water seepage rate, etc. On this basis, in order to efficiently describe the complex geological structure of the tunnel and its surroundings, this embodiment adopts an adaptive grid division method to divide the tunnel area into several grids, thereby constructing a grid model, including:

[0028] First, a rough initial grid is randomly defined;

[0029] Secondly, the grid is refined according to the information such as the distribution of cracks, changes in porosity, and distribution of aquifers obtained by drilling, radar, and sound waves. The grid division is dynamically adjusted according to the complexity of the geological area around the tunnel. For example, for areas near the tunnel surface and where cracks are concentrated, the use of finer grids can ensure accurate capture of changes in these key areas. Therefore, by analyzing the characteristics of the crack distribution and porosity in the area around the tunnel, the grid is refined using an adaptive grid division method based on local features, which can be expressed as:

[0030]

[0031] Where Δx is the size of the grid, L is the length of the tunnel region, and N is the number of nodes in the grid.

[0032] Based on the above division method, for complex areas (such as crack-concentrated areas), Δx takes a smaller value and N takes a larger value to ensure that details can be better captured; while in relatively simple areas, Δx is appropriately increased and N takes a smaller value.

[0033] Furthermore, based on the local geological attribute information, the degree of mesh refinement can be dynamically adjusted by adopting finer meshes in areas with dense fractures or areas with drastic porosity changes to ensure that these important physical properties can be fully reflected. That is, based on the geological characteristics of the target area, different adaptive algorithms are used for meshing, and the control criterion for mesh refinement can be expressed as:

[0034]

[0035] Among them, G(x,y,z) represents the fineness of the grid division at the point (x,y,z), and f(x,y,z) is the attribute function value at this point (such as fracture density, porosity, aquifer permeability, etc.). In this way, in areas with dense fractures or aquifers, the grid will be refined to obtain more accurate modeling results.

[0036] Preferably, local error estimation is used to optimize grid division, and the grid is increased in places where regional characteristics change greatly, and the number of grids is reduced in places where the change is small. Specifically, the following adaptive grid refinement criteria can be used for local error control, which is:

[0037]

[0038] Among them, E local (x,y,z) represents the error estimate of the local area, Indicates the optimization, reduction or minimization of the current quantity or target. is the gradient of the point, Ω is the spatial region where G continues to generate, and G is a control parameter related to grid division, which represents the fineness or density of the grid and is used to guide the generation and adaptive division of the grid.

[0039] Step S2: For each grid area, the acquired geological attribute data is preprocessed, and geological attributes are preliminarily assigned to the grid model based on the preprocessed data. The spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models are modeled in the model to construct an initial attribute model.

[0040] Step S2.1, data preprocessing. Considering that the data of different attributes (such as cracks, water seepage information, porosity, etc.) obtained by drilling may have different dimensions and spatial distributions, it is necessary to ensure that all data are compared under the same standard. In this embodiment, the obtained attribute data is first standardized, including: standardizing the data such as crack direction, crack inclination, permeability, etc. to have the same dimensions to avoid data inconsistency caused by different dimensions. The standardization formula is:

[0041]

[0042] Among them, X is the original data, μ is the mean, σ is the standard deviation, X std Represents the normalized data.

[0043] In addition, all the data obtained from the drilling are mapped into a unified three-dimensional spatial coordinate system, and spatial unification is performed to ensure that different data can be directly compared and interpolated in space.

[0044] Step S2.2: Based on the preliminary exploration data (such as the fracture information and porosity data revealed by the drilling), the grid model is assigned preliminary physical attributes, including the distribution of fractures, porosity, water content, permeability, elastic modulus, etc. Through attribute assignment, the fractures and their spatial distribution, porosity, water content and elastic model are modeled, including:

[0045] Step S2.2.1. Considering that the fracture information revealed by drilling often includes the geometric shape of the fracture (such as strike, dip, size, etc.) and its distribution underground, in this embodiment, an interpolation method is used to assign fracture attributes to the grid model, model the fractures and their spatial distribution, fit the geometric model of the fractures, and identify coplanar fractures.

[0046] Specifically, assuming that the crack information is f(x), the interpolation function of the crack can be expressed by the interpolation formula:

[0047]

[0048] Among them, α iis the calculation coefficient, which is the initial setting value; φ(||·||) is the radial basis function; ||xx i || is the position x to the crack node x i distance; N represents the total number of crack nodes.

[0049] In this way, the cracks and their spatial distribution are modeled, and then the cracks are fitted. The geometric shape of the cracks can be identified by principal component analysis (PCA) or least squares fitting to identify the main direction of the cracks, and the crack plane in three-dimensional space is generated based on this information.

[0050] For each crack data point P i =(x i ,y i ,z i ), a fracture plane model is obtained by fitting, which can be expressed as:

[0051] ax+by+cz+d=0;

[0052] Where a, b, c are the normal vectors of the fracture plane, and d is the plane intercept. The geometry of fractures can help understand the spatial distribution of fractures and their impact on the surrounding medium (such as aquifers).

[0053] Furthermore, multiple cracks often present coplanarity, and the angle between the normal vectors of each crack plane can be calculated to determine whether two cracks belong to the same crack plane. The calculation formula is:

[0054]

[0055] If the angle cosθ of the normal vectors is less than a certain set threshold, the two cracks can be considered to be coplanar cracks. In this embodiment, when cosθ is close to 1, it means that the normal vectors of the two cracks are almost parallel, indicating that the two cracks are coplanar.

[0056] By analyzing whether the cracks are coplanar, the extension of the cracks can be determined. When they are judged to be coplanar, the points through which the crack surfaces pass can be recorded as having cracks, which can be used to further analyze the crack conditions.

[0057] Step S2.2.2: Use the interpolation method to assign porosity attributes to the grid model and model the porosity.

[0058] Specifically, porosity is an important physical quantity that describes the size and distribution of voids inside a geological body. It has an important influence on permeability, elastic modulus, etc. The correlation assessment between porosity and information such as fractures and aquifers is a key step in fusing different data sources. Further, when modeling porosity, the distribution of porosity can be obtained by spatial interpolation of porosity data. Commonly used interpolation methods include Kriging interpolation and inverse distance weighted method. In this embodiment, when obtaining the borehole position (x i ,y i ,z i ) on the porosity data φ i Based on this, the interpolation method is used to generate the porosity distribution of the entire grid area space, which can be expressed as:

[0059]

[0060] Among them, φ i is the porosity data of each drilling point i, w i is the corresponding weight coefficient.

[0061] The porosity distribution obtained by the above interpolation can reflect the porosity differences between different geological bodies.

[0062] Step S2.2.3: Based on the seepage information, the interpolation method is used to assign water-bearing attributes to the grid model to obtain the distribution of groundwater information, and then the fitting variation function is used in combination with spatial correlation to model the water-bearing body.

[0063] Specifically, water seepage information usually includes the characteristics of the water body near the crack, such as permeability, water seepage rate, etc., which is crucial for groundwater flow and water flow control during tunnel construction. For the water seepage information revealed by the drilling, the distribution of the aquifer (i.e., the water body) can be fitted in three-dimensional space to complete the modeling of the water body. In this embodiment, according to the obtained water seepage information k i , the spatial distribution of the water-bearing attribute, that is, the distribution of groundwater information k(x,y,z), is obtained by interpolation method, which can be expressed as:

[0064]

[0065] Among them, w i is the weight of each drilling point i, k i It is the water content data or seepage data corresponding to the drilling point, which can help identify the path of groundwater flow, etc.

[0066] Furthermore, the groundwater distribution k(x, y, z) revealed by the borehole is used to fit the water-bearing body, and the water-bearing area revealed by the borehole is marked to model the water-bearing body.

[0067] Specifically, according to the water-bearing body model constructed above, the water-bearing body is fitted by using a fitting variogram. Based on the spatial correlation modeling, the following formula is used to fit the spatial variogram:

[0068]

[0069] Among them, γ(h) is the variogram, which represents the spatial difference that changes with distance h; z(x) represents the attribute value at a certain spatial position x of the water body; h represents the distance between two spatial positions, which affects the calculation of the spatial variogram; Var is the variance.

[0070] Through the above fitting variation function, the interpolation results of water body information at different locations can be obtained.

[0071] Step S2.2.4: Use the interpolation method to assign elastic properties to the grid model and build an elastic model.

[0072] Specifically, the obtained formation elastic modulus is interpolated using finite element analysis (FEM), where the elastic modulus F(x, y, z) can be obtained by solving the following finite element formula:

[0073] [K]{u}={F};

[0074] In the above formula, [K] is the stiffness matrix, {u} is the node displacement vector, and {F} is the external force vector.

[0075] The elastic modulus distribution in different areas can be obtained by interpolating the elastic properties of the strata around the tunnel using the finite element method.

[0076] Preferably, correlation analysis is performed on the multi-attribute data, and the above modeling results are corrected based on the correlation analysis results, including:

[0077] (1) The relationship between aquifers and fractures is usually expressed as a coupling between fracture density and permeability. The relationship between fracture permeability and the permeability of the surrounding aquifers can be estimated using the following formula:

[0078] k 裂隙 =f(k 含水 ,ρ 裂隙 );

[0079] Among them, k 裂隙 is the permeability of the fracture, k 含水 is the permeability of the water-containing body, ρ 裂隙 is the density of the cracks, and f() is a nonlinear function. The permeability of the cracks is affected by the geometric characteristics of the crack direction and inclination, so it is necessary to comprehensively consider the coupling relationship between the crack geometry and the properties of the seepage body.

[0080] (2) Considering the correlation between porosity, permeability and fracture information, porosity directly affects permeability. Therefore, the relationship between porosity and permeability needs to be considered when fusion model is used. The relationship between porosity and permeability can be described by the following empirical formula:

[0081] k=k0·φ m ;

[0082] Where k is the permeability, k0 is the benchmark permeability, φ is the porosity, and m is the empirical exponent.

[0083] As porosity increases, permeability also increases. During the model fusion process, changes in porosity can directly affect the prediction of permeability, so the relationship between porosity and permeability needs to be calibrated.

[0084] Based on the above analysis results, the initial attribute model is corrected using the above relationship formula to ensure the accuracy of the relationship between the attributes and the accuracy of the final modeling.

[0085] Through the above step-by-step analysis of the distribution and correlation of various geological attributes, the final analysis results are combined and integrated into the grid model to build the initial attribute model.

[0086] Step S3: Calculate the correlation between the new and old drilling data based on the latest drilling data, and fit the drilling data based on the correlation. After fitting, integrate the latest drilling data into the initial attribute model to obtain an updated tunnel geological attribute model.

[0087] Step S3.1: Analyze and match the correlation between new and old drilling data. The data revealed by the new drilling usually has a certain spatial relationship with the previous drilling data. However, how to reasonably evaluate the correlation between new and old data is a complex task. Based on the physical consistency of the data, this embodiment proposes the following standardized evaluation method:

[0088] (1) For the new borehole data, the spatial distance between it and all historical boreholes is calculated, and different weights are assigned according to the spatial distance; among them, the boreholes with closer distance are given a larger weight, and vice versa.

[0089] (2) Based on the above weights, the correlation between new and old drilling data can be evaluated by the following weighted formula:

[0090]

[0091] Among them, C new,old represents the correlation between new drilling data and historical drilling data, w i is the weight calculated based on the spatial distance, Cor(X new ,Xold,i ) is the correlation between the new drilling data and the i-th historical drilling data. In this embodiment, the correlation is calculated using a measurement method such as the Pearson correlation coefficient.

[0092] Step S3.2: spatial matching of crack information, and updating the initial attribute model according to the matching results.

[0093] For a newly drilled fracture, the relevance can be evaluated by comparing its geometric matching with the existing fractures in the drilled hole. In terms of fracture geometry, if the newly revealed fracture is similar in geometry to the fracture in the historical data and the angle of the normal vector is less than a certain threshold, then these fractures can be considered to belong to the same fracture surface. For example, the criterion for the angle θ of the fracture normal vector is:

[0094]

[0095] When cosθ>0.95, the two cracks can be considered as coplanar cracks; otherwise, the cracks are considered as non-coplanar cracks.

[0096] Step S3.3: Based on the correlation between the new and old borehole data, the porosity, water-bearing body, and elastic modulus are fitted and updated to update the initial attribute model and obtain an updated tunnel geological attribute model.

[0097] Combined with the correlation between new and old borehole data, the following method can be used to jointly fit the data to ensure that the newly revealed information can be effectively integrated into the initial attribute model and have a reasonable impact on the geological model. That is, the new and old borehole data are fitted by nonlinear least squares method (such as Levenberg-Marquardt algorithm) to extract the geometric characteristics of the fracture, water seepage information (i.e. permeability), porosity and other physical properties. Take the permeability model of the fracture as an example:

[0098]

[0099] Among them, k 裂隙 is the permeability of the crack, L is the length of the crack, L0 is the reference length, and n is the fitting index, which is calculated based on the above correlation coefficient.

[0100] In addition, the water-bearing body information revealed by the new boreholes can be fitted through polynomial regression or support vector machine (SVM) and other methods to determine the spatial distribution of the aquifer; the aquifer information of the new and old borehole data can be evaluated through spatial interpolation combined with relative humidity, porosity, water-bearing body and other characteristics.

[0101] As another implementation method, in order to ensure the physical consistency of the new and old data after merging, consistency verification must be performed. This physical consistency constraint can be implemented in the following ways:

[0102] (1) Consistency between permeability and porosity: According to the empirical formula k = k0·φ m , check whether the relationship between porosity and permeability is consistent;

[0103] (2) Consistency between fractures and water seepage paths: Assess the compatibility between fracture geometry and water seepage paths to ensure that the water flow paths match the fracture distribution in space;

[0104] (3) Comprehensive constraints: Combining information such as porosity, cracks, and water seepage, the fusion and consistency verification of multi-source information are achieved through methods such as weighted average method and constrained optimization algorithm.

[0105] As another implementation method, a dynamic weighting mechanism is applied in the above process. Specifically, the geological characteristics of different regions vary greatly, and the geological conditions at different construction stages will also change. Therefore, it is necessary to dynamically adjust the weights of each attribute according to real-time data in order to more accurately evaluate the grouting needs. In this embodiment, spatial adaptive weighting is used to adjust the weights of different attributes, that is: based on drilling data and real-time monitoring data (such as pressure, flow, temperature, etc.), the weight of each area is dynamically adjusted. For example, in areas with dense fracture zones, the weight of fracture density can be appropriately increased, and in areas with more serious water seepage, the weight of water seepage intensity can be increased.

[0106] Embodiment 2

[0107] This embodiment provides a tunnel surface-hole perimeter multi-source information joint constraint attribute modeling system, including:

[0108] The grid division module is used to obtain the spatial information and geological attribute information of the tunnel geological body area. The area is divided into several grids and a grid model is constructed by using an adaptive network division method based on local characteristics. The geological attribute information includes the location and distribution of cracks, porosity, water seepage information, and elastic modulus.

[0109] The initial attribute model modeling module is used to preprocess the acquired geological attribute data for each grid area, preliminarily assign geological attributes to the grid model based on the preprocessed data, and model the spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models in the model to construct the initial attribute model;

[0110] The tunnel geological body attribute modeling module is used to calculate the correlation between the new and old drilling data based on the latest drilling data, and to fit the drilling data based on the correlation. After fitting, the latest drilling data is integrated into the initial attribute model to obtain an updated tunnel geological attribute model.

[0111] Embodiment 3

[0112] This embodiment provides an electronic device, including: a memory, used to store executable instructions; and a processor, used to implement the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0113] Embodiment 4

[0114] This embodiment also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0115] Embodiment 5

[0116] This embodiment provides a computer program product, which includes an executable instruction, which is a computer instruction; the executable instruction is stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instruction from the computer-readable storage medium and the processor executes the executable instruction, the electronic device executes the above method provided in this embodiment.

[0117] The steps involved in the above embodiments 2 to 5 correspond to the method embodiment 1. For the specific implementation, please refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0118] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0119] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A tunnel surface-hole perimeter multi-source information joint constraint attribute modeling method, characterized in that: include: The spatial information and geological attribute information of the tunnel geological body area are obtained, and the area is divided into several grids using an adaptive network partitioning method based on local features to construct a grid model; the geological attribute information includes the location and distribution of cracks, porosity, water seepage information, and elastic modulus; For each grid area, the acquired geological attribute data is preprocessed, and the grid model is initially assigned geological attributes based on the preprocessed data. The spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models are modeled in the model to construct an initial attribute model. According to the latest drilling data, the correlation between the new and old drilling data is calculated, and the drilling data is fitted according to the correlation. After fitting, the latest drilling data is integrated into the initial attribute model to obtain an updated tunnel geological attribute model.

2. The attribute modeling method of tunnel surface-hole perimeter multi-source information joint constraint as claimed in claim 1, characterized in that: The adaptive network partitioning method based on local features is used to divide the area into several grids, including: According to the length of the tunnel area and the number of grid nodes, the size of each grid is preliminarily determined; According to the geological attribute value of the current position, the size of the grid where the current position is located is adaptively adjusted; wherein the geological attribute value is inversely proportional to the grid size; The grid division is optimized according to the local error of the current regional characteristics, wherein the degree of change of the regional characteristics is proportional to the grid density.

3. The attribute modeling method of tunnel surface-hole perimeter multi-source information joint constraint as claimed in claim 1 is characterized in that: The interpolation method is used to assign fracture properties to the grid model, model fractures and their spatial distribution, fit the fracture geometry model, and identify coplanar fractures, including: The interpolation method is used to assign preliminary fracture attributes, which can be expressed as: Among them, f(x) is the crack information, α i is the initial calculation coefficient, φ(∥·∥) is the radial basis function, ∥xx i ∥ is the position x to the crack node x i distance; For each crack data point, the corresponding crack plane model is fitted, and the coplanarity of two cracks is identified based on the crack plane model.

4. The attribute modeling method of tunnel surface-hole perimeter multi-source information joint constraint as claimed in claim 1 is characterized in that: The interpolation method is used to generate the porosity distribution of the entire grid area space, which is expressed as: Among them, φ i is the porosity data of each drilling point i, w i is the weight coefficient, and N represents the total number of drilling points.

5. The attribute modeling method of tunnel surface-hole perimeter multi-source information joint constraint as claimed in claim 1, characterized in that: Based on the seepage information, the interpolation method is used to assign water-bearing attributes to the grid model to obtain the distribution of groundwater information, which is expressed as: Among them, w i is the weight of each drilling point i, k i It is the water content data corresponding to the drilling point; The fitted variogram is used to model the water body in combination with spatial correlation, which is expressed as: Among them, γ(h) is the variogram, which represents the spatial difference that changes with distance h; z(x) represents the attribute value at a certain spatial position x of the water body; h represents the distance between two spatial positions, which affects the calculation of the spatial variogram; Var is the variance.

6. The attribute modeling method of tunnel surface-hole perimeter multi-source information joint constraint as claimed in claim 1, characterized in that: Conduct correlation analysis on multiple geological attribute data and correct the modeling results based on the correlation analysis results, including: The relationship between the permeability of the fracture and the permeability of the surrounding water-bearing body and the relationship between porosity and permeability are clearly expressed as: k 裂隙 =f(k 含水 ,r 裂隙 ); k=k0·φ m ; Among them, k 裂隙 is the permeability of the fracture, k 含水 is the permeability of the water-containing body, ρ 裂隙 is the density of cracks, f() is a nonlinear function; k is permeability, k0 is the benchmark permeability, φ is the porosity, and m is an empirical index; Based on the relationship formula obtained by analysis, the initial attribute model is corrected to obtain a corrected attribute model.

7. A tunnel surface-hole perimeter multi-source information joint constraint attribute modeling system, characterized in that: include: The grid division module is used to obtain the spatial information and geological attribute information of the tunnel geological body area. The area is divided into several grids and a grid model is constructed by using an adaptive network division method based on local characteristics. The geological attribute information includes the location and distribution of cracks, porosity, water seepage information, and elastic modulus. The initial attribute model modeling module is used to preprocess the acquired geological attribute data for each grid area, preliminarily assign geological attributes to the grid model based on the preprocessed data, and model the spatial distribution of fractures, porosity, water-bearing bodies, and elasticity models in the model to construct the initial attribute model; The tunnel geological body attribute modeling module is used to calculate the correlation between the new and old drilling data based on the latest drilling data, and to fit the drilling data based on the correlation. After fitting, the latest drilling data is integrated into the initial attribute model to obtain an updated tunnel geological attribute model.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the attribute modeling method of the joint constraint of tunnel surface-hole perimeter multi-source information as described in any one of claims 1-6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the attribute modeling method of the tunnel surface-hole perimeter multi-source information joint constraint as described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the attribute modeling method of the tunnel surface-hole perimeter multi-source information joint constraint as described in any one of claims 1 to 6 is implemented.

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