Intelligent tunnel three-dimensional geological modeling method and system under complex geological conditions

Abstract

The invention belongs to the technical field of three-dimensional geological modeling, and provides an intelligent tunnel three-dimensional geological modeling method and system under a complex geological condition, and the technical scheme is as follows: carrying out data reconstruction by combining three-dimensional tunnel profile data, tunnel site area inversion gravity data and magnetic susceptibility, and obtaining a reconstructed tunnel data set; training a geological attribute classifier based on the reconstructed tunnel data set to obtain a trained geological attribute classifier; predicting the geological attribute of each point in the space in the tunnel site modeling area according to the trained geological attribute classifier, and constructing a three-dimensional geological model containing the surface undulation form and structure in combination with the surface elevation data; and performing Boolean operation on the three-dimensional geologic model containing the surface undulation form and structure and a tunnel three-dimensional body model obtained by sweeping along the tunnel three-dimensional path to obtain a tunnel-containing three-dimensional geologic model. According to the invention, three-dimensional model construction can be carried out on the tunnel under extremely complex conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional geological modeling, and in particular relates to an intelligent three-dimensional geological modeling method and system for tunnels under complex geological conditions. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Tunnel engineering has been widely used in the fields of highways, railways, urban rail transit, etc. However, tunnel engineering often faces complex geological conditions, such as faults, fracture zones, karst caves, etc. These geological conditions bring great challenges to the design, construction, excavation and operation safety of tunnels. Three-dimensional geological modeling of tunnels under complex geological conditions is a key technology to ensure engineering safety. By constructing an accurate three-dimensional geological model, reliable geological information can be provided for engineering design, construction and operation, which helps to identify potential risk sources and provide a basis for formulating reasonable construction plans and safety measures.

[0004] Faced with tunnel construction under complex geological conditions, traditional three-dimensional geological modeling does not take into account the problems of multi-source heterogeneity of geological survey data and the intricate geological structure and spatial relationship. It only uses simple stratigraphic data for modeling. The resulting three-dimensional geological model cannot be applied to tunnel modeling under more complex geological conditions. At the same time, traditional three-dimensional geological models have long modeling cycles, difficulty in model updating, and little information contained in the model. Therefore, they cannot provide high guidance for actual tunnel construction. Summary of the invention

[0005] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides an intelligent tunnel three-dimensional geological modeling method and system under complex geological conditions. The method introduces multi-source heterogeneous data into the stacking ensemble learning algorithm in machine learning, and the obtained three-dimensional geological model is suitable for tunnel modeling under more complex geological conditions. At the same time, update information can be obtained in real time to update the model, which can highly guide the actual construction of the tunnel and other problems.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides an intelligent three-dimensional geological modeling method for tunnels under complex geological conditions, comprising the following steps:

[0008] Convert the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data;

[0009] Combining the acquired 3D tunnel profile data, the inverted gravity data and magnetic susceptibility of the tunnel site, data reconstruction is performed to obtain a reconstructed tunnel data set;

[0010] The geological attribute classifier is trained based on the reconstructed tunnel data set to obtain a trained geological attribute classifier;

[0011] The geological attributes of each point in the space of the tunnel site modeling area are predicted based on the trained geological attribute classifier, and a three-dimensional geological model including the surface undulations and structures is constructed in combination with the surface elevation data;

[0012] The three-dimensional geological model including the surface undulations and structures and the three-dimensional tunnel model obtained by sweeping along the three-dimensional path of the tunnel are subjected to Boolean operation to obtain the three-dimensional geological model including the tunnel.

[0013] Furthermore, the three-dimensional tunnel profile data acquired, the gravity data and magnetic susceptibility inverted from the tunnel site area are combined to perform data reconstruction to obtain a reconstructed tunnel data set, including:

[0014] Based on the acquired 3D tunnel profile data, virtual boreholes are added to generate new virtual borehole data at complex locations where profile data is sparse or contains faults;

[0015] The original borehole data, virtual borehole data and three-dimensional tunnel profile data are resampled and combined with the gravity data and magnetic susceptibility inverted in the tunnel site area to obtain a data set with three-dimensional geographic coordinates, gravity data and magnetic susceptibility as feature engineering and stratigraphic categories as labels.

[0016] Furthermore, the geological attribute classifier is trained based on the reconstructed tunnel data set to obtain the trained geological attribute classifier, including:

[0017] The tunnel data set is evenly and randomly divided into a training set and a test set according to a set ratio;

[0018] The training set data is divided into n parts, and n-1 parts of the data are selected each time, and the base classifier is used for training. The base classifier to be used is determined according to the performance of different base classifiers with the best hyperparameters in the test set;

[0019] The n prediction results obtained by training the base classifier are used as training data for retraining, and the meta-classifier is trained to obtain a trained geological attribute classifier.

[0020] Furthermore, during the training process, the geological attribute classifier obtains the data added during the tunnel construction and excavation process and adds it to the data set, re-divides the training set and the test set, and re-predicts the attributes of the spatial coordinate points in the modeling area to update the established three-dimensional geological model.

[0021] Furthermore, during the training process, the geological attribute classifier also includes obtaining advanced geological prediction data during tunnel excavation to invert the properties of the tunnel surrounding rock, including stratum occurrence, surrounding rock grade, rock burst grade, compressive strength, elastic modulus, wave velocity, and groundwater, and assigning the properties of the tunnel surrounding rock to the classifier.

[0022] Furthermore, during the training process of the geological attribute classifier, the performance of the geological attribute classifier is evaluated by drawing a confusion matrix diagram and a ROC curve diagram.

[0023] Furthermore, the predicted values ​​of the geological attribute classifier are combined with the surface elevation data, including:

[0024] Import the geological attributes of each point in the space within the modeling area of ​​the tunnel site into the visualization software, create a SGrid model, and assign different colors to different lithologies represented by different predicted values, and finally obtain a three-dimensional geological model of the tunnel with clear geological boundaries, special geological structures and unfavorable geological bodies;

[0025] The surface elevation data is used to constrain the three-dimensional geological model of the tunnel containing special geological structures and unfavorable geological bodies, and a three-dimensional geological model with surface undulations and structures is established.

[0026] A second aspect of the present invention provides an intelligent three-dimensional geological modeling system for tunnels under complex geological conditions, comprising:

[0027] A data conversion module, which is used to convert the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data;

[0028] A data reconstruction module is used to combine the acquired three-dimensional tunnel profile data, the gravity data and magnetic susceptibility inverted from the tunnel site area to perform data reconstruction and obtain a reconstructed tunnel data set;

[0029] A classifier training module, which is used to train a geological attribute classifier based on the reconstructed tunnel data set to obtain a trained geological attribute classifier;

[0030] The three-dimensional geological model construction module is used to predict the geological attributes of each point in the space within the modeling area of ​​the tunnel site based on the trained geological attribute classifier, and to construct a three-dimensional geological model including surface undulations and structures in combination with surface elevation data; the three-dimensional geological model including surface undulations and structures and the three-dimensional tunnel model obtained by sweeping along the three-dimensional path of the tunnel are subjected to Boolean operations to obtain a three-dimensional geological model including the tunnel.

[0031] A third aspect of the present invention provides a computer-readable storage medium.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the intelligent three-dimensional geological modeling method for tunnels under complex geological conditions as described above.

[0033] A fourth aspect of the present invention provides a computer device.

[0034] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the intelligent three-dimensional geological modeling method for tunnels under complex geological conditions as described above are implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention introduces a stacking integrated machine learning method based on three-dimensional coordinate points, gravity, magnetic susceptibility, lithology and other characteristics. It can predict the geological conditions in the modeling area by analyzing the existing sampling data and inversion data. In the case of limited and sparse geological exploration data, it can make full use of the existing data and encrypt the geological data to achieve high-precision three-dimensional geological modeling of strip bodies such as tunnels. It is suitable for the construction of three-dimensional geological models of strip tunnels under various complex terrain conditions.

[0037] 2. The present invention can obtain the data added during the tunnel construction and excavation process and add it to the data set. At the same time, it can obtain the advanced geological prediction data during tunnel excavation to invert the properties of the tunnel surrounding rock and train the classifier. Compared with other modeling methods, this method has the advantages of simple modeling process, comprehensive consideration of multiple factors, low cost, higher modeling accuracy and faster modeling speed under the same available data. The established model can be quickly updated according to the modification or addition of training data, and the geological information contained in the model is richer, which can effectively improve the intelligent level of geological modeling.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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.

[0040] Figure 1 It is the overall technical roadmap of the intelligent tunnel 3D geological modeling method under complex geological conditions provided by the embodiment of the present invention;

[0041] Figure 2 is a schematic diagram of comprehensive data processing provided by an embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of a stacking algorithm training process provided by an embodiment of the present invention;

[0043] Figure 4 It is a three-dimensional geological model constructed by the intelligent tunnel three-dimensional geological modeling method under complex geological conditions provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should 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.

[0047] In view of the characteristics of traditional 3D geological modeling mentioned in the background technology, such as long cycle, much manual interaction, and strong professionalism, the present invention

[0048] Embodiment 1

[0049] like Figure 1 As shown, this embodiment provides an intelligent tunnel 3D geological modeling method under complex geological conditions, including the following steps:

[0050] Step 1: Obtain the two-dimensional profile data of the tunnel and use the simulation method to obtain the inverted gravity data and magnetic susceptibility of the tunnel site area;

[0051] Step 2: converting the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data;

[0052] Calculate the distance D based on the actual coordinates of two drill holes on the cross-section diagram 1 , the formula is:

[0053] D 1 =sqart((x 2 -x 1 ) 2 +(y 2 -y 1 ) 2 ),

[0054] Where (x1, y1) is the coordinate of hole 1, and (x2, y2) is the coordinate of hole 2.

[0055] Measure the distance between the two holes in CAD as D 2 , obtain the scaling ratio a through the distance on the map and the actual distance, convert the distance on the map into the size of the actual distance, determine the actual coordinate position, and complete the coordinate orientation of the map.

[0056] The actual three-dimensional coordinates on the section are obtained through mathematical calculation. The x coordinate in the two-dimensional coordinate system is multiplied by the Cos(θ) value and Sin(θ) value of the angle between the section and the x-axis of the actual longitude and latitude coordinates for calculation. The depth Z value is the y coordinate of any point on the map:

[0057]

[0058] Actual 3D coordinates of the section:

[0059]

[0060] Step 3: Reconstruct the data by combining the acquired 3D tunnel profile data, the inverted gravity data and magnetic susceptibility of the tunnel site area to obtain a reconstructed data set;

[0061] like Figure 2 As shown, in order to effectively apply the data to machine learning algorithm training, the previously acquired data needs to be reconstructed.

[0062] On the basis of 3D tunnel profile data, virtual boreholes are added to generate new data at complex and special locations where profile data is sparse or contains faults. Then, all acquired data are resampled and combined with the inverted gravity data model and magnetic data model, the acquired data are discretized into a series of spatial points with attributes. The attribute features of the data can be selected from 3D geographic coordinates, density and magnetic susceptibility, and stratigraphic attributes. The data are resampled at equal intervals at certain intervals and converted into stratigraphic sequences with spatial coordinates and stratigraphic numbers. The data set includes data sets with 3D geographic coordinates (x, y, z), gravity data, magnetic susceptibility as feature engineering, and stratigraphic categories (A, B, C, D...) as labels.

[0063] In this embodiment, the drilling data is resampled at a certain distance, and the sampling distance is determined according to the size of the model, and is generally selected to be 0.5-5m.

[0064] In particular, geological structures such as faults can be regarded as a special stratigraphic lithology (such as fault rocks, karst caves, etc.).

[0065] Step 4: Train the geological attribute classifier based on the data set reconstructed in step 3, obtain the trained geological attribute classifier and verify it, such as Figure 3As shown, the following steps are included:

[0066] Step 401: Divide the data set uniformly and randomly into a training set and a test set according to a set ratio.

[0067] Step 402: Divide the training set data into n parts, select (n-1) parts of the data each time, use the base classifier for training, and judge the adaptability of each type of base classifier according to the performance of different base classifiers with optimal hyperparameters in the test set. If a base classifier performs too poorly, the base classifier can be replaced until it is satisfactory.

[0068] The base classifiers can be the same or different. Due to the high-dimensional data, the base classifier can choose a complex classifier, such as KNN, DT, RF, SVM, BP and other classifiers.

[0069] Step 403: The n prediction results obtained by training the base classifier are used as training data for retraining. Since the retrained meta-classifier is oriented to low-dimensional data, a simple classifier can be selected for training, such as LR, LDA, XGBoost, etc.

[0070] Step 404: During model training, the relevant data generated due to construction excavation is added to the data set for further training;

[0071] If relevant data is increased due to construction excavation, the newly added data can be incorporated into the data set, and the training set and test set can be re-divided to train the model, and the attributes of the spatial coordinate points in the modeling area can be re-predicted to achieve the purpose of updating the established three-dimensional geological model. Through continuous updating, the established three-dimensional geological model of the tunnel can be continuously approached to the real three-dimensional geological conditions.

[0072] According to the advanced geological prediction data during tunnel excavation, the formation occurrence, surrounding rock grade, strength, elastic modulus, wave velocity, groundwater and other surrounding rock properties of the tunnel surrounding rock are inverted, and the three-dimensional geographic coordinates and the above attributes are exported as (x, y, z, attribute 1, attribute 2...attribute n), the x, y, z coordinates should be consistent with the subsequent SGrid grid coordinates, and the attributes are stored in the grid model to further enrich the model information and provide data support for tunnel excavation and subsequent support design.

[0073] Step 405: Draw a confusion matrix diagram and a ROC curve diagram to evaluate the performance of the geological attribute classifier.

[0074] In the above stacking algorithm, each base learner is trained independently and can use different algorithms or parameters. The different characteristics and advantages of the base classifiers can be fully utilized, thereby improving the performance of the overall model.

[0075] Step 5: A three-dimensional geological model with surface undulations and structures is constructed based on the predicted values ​​of the geological attribute classifier and the surface elevation data, which specifically includes the following steps:

[0076] Step 501: Use the trained classifier to predict the geological attributes of each point in the space within the tunnel site modeling area. The attributes of a point in space can be used as the attributes of a cube centered at the point. To improve the accuracy of the classification results and the model precision, the size of the cube is preferably set within a preset range.

[0077] Step 502: Import the data predicted by the classifier into visualization software such as GOCAD, create a SGrid model, and assign different colors to different lithologies represented by different predicted values, and finally obtain a three-dimensional geological model of the tunnel with clear geological boundaries, special geological structures and unfavorable geological bodies.

[0078] Step 503: Using the surface elevation data to constrain the three-dimensional geological model including the geological structure, a three-dimensional geological model with surface undulations and structures is established.

[0079] In this embodiment, when using elevation data to constrain model cutting, it should be ensured that the model range of the surface elevation data is larger than the modeling range.

[0080] Step 6: Sweep the 2D contour of the tunnel along the 3D path of the tunnel to form a 3D model of the tunnel. Perform Boolean operations on the 3D model of the tunnel and the 3D geological model obtained in step 5 to finally obtain a 3D geological model containing the tunnel, thus realizing intelligent 3D geological modeling of tunnels under complex geological conditions. Figure 4 Shown is the final constructed three-dimensional geological model including the tunnel.

[0081] Embodiment 2

[0082] This embodiment provides an intelligent three-dimensional geological modeling system for tunnels under complex geological conditions, including:

[0083] A data conversion module, which is used to convert the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data;

[0084] A data reconstruction module is used to combine the acquired three-dimensional tunnel profile data, the gravity data and magnetic susceptibility inverted from the tunnel site area to perform data reconstruction and obtain a reconstructed tunnel data set;

[0085] A classifier training module, which is used to train a geological attribute classifier based on the reconstructed tunnel data set to obtain a trained geological attribute classifier;

[0086] The three-dimensional geological model construction module is used to predict the geological attributes of each point in the space within the modeling area of ​​the tunnel site based on the trained geological attribute classifier, and to construct a three-dimensional geological model including surface undulations and structures in combination with surface elevation data; the three-dimensional geological model including surface undulations and structures and the three-dimensional tunnel model obtained by sweeping along the three-dimensional path of the tunnel are subjected to Boolean operations to obtain a three-dimensional geological model including the tunnel.

[0087] Embodiment 3

[0088] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the intelligent tunnel three-dimensional geological modeling method under complex geological conditions as described above are implemented.

[0089] Embodiment 4

[0090] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for intelligent three-dimensional geological modeling of tunnels under complex geological conditions as described above are implemented.

[0091] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0092] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent three-dimensional geological modeling method for tunnels under complex geological conditions, characterized in that: The steps include: Convert the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data; Combining the acquired 3D tunnel profile data, the inverted gravity data and magnetic susceptibility of the tunnel site, data reconstruction is performed to obtain a reconstructed tunnel data set; The geological attribute classifier is trained based on the reconstructed tunnel data set to obtain a trained geological attribute classifier; The geological attributes of each point in the space of the tunnel site modeling area are predicted based on the trained geological attribute classifier, and a three-dimensional geological model including the surface undulations and structures is constructed in combination with the surface elevation data; The three-dimensional geological model including the surface undulations and structures and the three-dimensional tunnel model obtained by sweeping along the three-dimensional path of the tunnel are subjected to Boolean operation to obtain the three-dimensional geological model including the tunnel.

2. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions according to claim 1, characterized in that: The data is reconstructed by combining the acquired three-dimensional tunnel profile data, the gravity data and magnetic susceptibility inverted from the tunnel site area to obtain a reconstructed tunnel data set, including: Based on the acquired 3D tunnel profile data, virtual boreholes are added to generate new virtual borehole data at complex locations where profile data is sparse or contains faults; The original borehole data, virtual borehole data and three-dimensional tunnel profile data are resampled and combined with the gravity data and magnetic susceptibility inverted in the tunnel site area to obtain a data set with three-dimensional geographic coordinates, gravity data and magnetic susceptibility as feature engineering and stratigraphic categories as labels.

3. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions as claimed in claim 1, characterized in that: The step of training the geological attribute classifier based on the reconstructed tunnel data set to obtain the trained geological attribute classifier includes: The tunnel data set is evenly and randomly divided into a training set and a test set according to a set ratio; The training set data is divided into n parts, and n-1 parts of the data are selected each time, and the base classifier is used for training. The base classifier to be used is determined according to the performance of different base classifiers with the best hyperparameters in the test set; The n prediction results obtained by training the base classifier are used as training data for retraining, and the meta-classifier is trained to obtain a trained geological attribute classifier.

4. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions according to claim 1, characterized in that: During the training process of the geological attribute classifier, the data added during the tunnel construction and excavation process is obtained and added to the data set, the training set and the test set are re-divided, and the attributes of the spatial coordinate points in the modeling area are re-predicted to update the established three-dimensional geological model.

5. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions as claimed in claim 1, characterized in that: During the training process, the geological attribute classifier also includes obtaining advanced geological prediction data during tunnel excavation to invert the properties of the tunnel surrounding rock, including stratum occurrence, surrounding rock grade, rock burst grade, compressive strength, elastic modulus, wave velocity, and groundwater, and assigning the properties of the tunnel surrounding rock to the classifier.

6. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions according to claim 1, characterized in that: During the training process of the geological attribute classifier, the performance of the geological attribute classifier is evaluated by drawing a confusion matrix diagram and a ROC curve diagram.

7. The intelligent three-dimensional geological modeling method of tunnels under complex geological conditions according to claim 1, characterized in that: Combine the predictions of the geological attribute classifier with surface elevation data, including: Import the geological attributes of each point in the space within the modeling area of ​​the tunnel site into the visualization software, create a SGrid model, and assign different colors to different lithologies represented by different predicted values, and finally obtain a three-dimensional geological model of the tunnel with clear geological boundaries, special geological structures and unfavorable geological bodies; The surface elevation data is used to constrain the three-dimensional geological model of the tunnel containing special geological structures and unfavorable geological bodies, and a three-dimensional geological model with surface undulations and structures is established.

8. An intelligent three-dimensional geological modeling system for tunnels under complex geological conditions, characterized in that: include: A data conversion module, which is used to convert the acquired two-dimensional tunnel profile data into three-dimensional tunnel profile data; A data reconstruction module is used to combine the acquired three-dimensional tunnel profile data, the gravity data and magnetic susceptibility inverted from the tunnel site area to perform data reconstruction and obtain a reconstructed tunnel data set; A classifier training module, which is used to train a geological attribute classifier based on the reconstructed tunnel data set to obtain a trained geological attribute classifier; The three-dimensional geological model construction module is used to predict the geological attributes of each point in the space within the modeling area of ​​the tunnel site based on the trained geological attribute classifier, and to construct a three-dimensional geological model including surface undulations and structures in combination with surface elevation data; the three-dimensional geological model including surface undulations and structures and the three-dimensional tunnel model obtained by sweeping along the three-dimensional path of the tunnel are subjected to Boolean operations to obtain a three-dimensional geological model including the tunnel.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the intelligent three-dimensional geological modeling method for tunnels under complex geological conditions as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the intelligent tunnel three-dimensional geological modeling method under complex geological conditions as described in any one of claims 1-7 are implemented.

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