A tunnel wave velocity model establishment method and system based on preliminary geological data

Abstract

The application belongs to the field of tunnel advance prediction, and provides a tunnel wave velocity model establishment method and system based on preliminary geological data. The tunnel wave velocity model establishment method comprises the following steps: obtaining geological data of a current tunnel project during tunnel alignment and preliminary geological data of the tunnel, extracting effective geological information along the tunnel and digitizing the same, and constructing a digital information database of field geological data; based on the digital information database of field geological data, counting geological form information and wave velocity related information; based on the counted geological form information, establishing a tunnel geological model structure form, combining the counted wave velocity related information to set corresponding wave velocity values of the tunnel geological model structure form, and constructing a preliminary wave velocity model; using the geological form information in the digital information database of field geological data to correct the preliminary wave velocity model; based on parameters and excavation exposure conditions of a current tunnel project construction process, continuously adjusting and optimizing the corrected wave velocity model.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of tunnel advance prediction, and particularly relates to a tunnel wave velocity model establishment method and system based on preliminary geological data. BACKGROUND

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

[0003] In the process of tunnel engineering construction, adverse geological conditions (such as faults and rock fracture zones, etc.) are often encountered, which can seriously affect the safety and efficiency of tunnel construction. In order to discover the adverse geological structure in front, researchers use the tunnel seismic wave method for advance geological prediction technology. Among them, accurately obtaining the wave velocity distribution in front of the tunnel is the main factor for technicians to judge the integrity of the rock mass in front, and also determines the accuracy of the geological structure imaging in front.

[0004] In the process of researchers' research on tunnel wave velocity inversion methods, testing the effect of wave velocity inversion methods using typical geological models is an important part of the research. For the current popular deep learning wave velocity inversion method, the wave velocity geological model construction is used to establish the network training data set, so it is more important. This is because the essence of the deep learning wave velocity inversion method is to obtain the inversion result of the data distribution solved by the training data set, so as to alleviate the multi-solution of the traditional inversion method. Therefore, how to propose a geological model close to the actual situation is an important part of the wave velocity inversion research.

[0005] The inventors found that current researches mostly use conventional faults, fracture zones, etc. as typical models for testing, and do not design wave velocity models combined with engineering characteristics, and the existing methods have the following problems:

[0006] (1) The existing methods do not make full use of the information obtained in advance, for example, before the tunnel site selection, preliminary geological survey is usually carried out, such as using electromagnetic, drilling, etc. to obtain preliminary geological information along the tunnel, and further organizing into surrounding rock classification, whether there is disaster, geological structure, etc. The above data is displayed in the form of preliminary survey report, and is not in the form of digitization and numerical statistics, which leads to the fact that it cannot be clearly referenced numerically, and it cannot be effectively used to realize the modeling of the geological conditions in front of the tunnel.

[0007] (2) There is no wave velocity modeling method based on preliminary survey data, and the relationship between preliminary survey data and wave velocity modeling is not clear. Researchers usually ignore the modeling method that is closer to the real geological conditions, resulting in the fact that the model obtained by the existing method is relatively simple and does not conform to the site conditions, which affects the effective application of the wave velocity inversion method, especially the deep learning wave velocity inversion method on the measured data. SUMMARY

[0008] To solve the technical problems in the background art, the present application provides a tunnel wave velocity model establishment method and system based on preliminary geological data, which can generate a wave velocity model closer to the actual engineering situation, effectively improve the effectiveness of wave velocity inversion methods represented by deep learning wave velocity inversion in practical data application, and verify the effectiveness of the proposed wave velocity inversion method.

[0009] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0010] The first aspect of the present application provides a tunnel wave velocity model establishment method based on preliminary geological data.

[0011] A tunnel wave velocity model establishment method based on preliminary geological data, comprising:

[0012] Obtaining the geological data of the current tunnel project during tunnel route selection and the preliminary geological data of the tunnel, extracting the effective geological information along the tunnel line and digitizing, and constructing a digital information base of the field geological data;

[0013] Based on the digital information base of the field geological data, the geological form information and wave velocity related information are counted;

[0014] Based on the counted geological form information, the tunnel geological model structure form is established, and the corresponding wave velocity value of the tunnel geological model structure form is set in combination with the counted wave velocity related information, and a preliminary wave velocity model is constructed;

[0015] The preliminary wave velocity model is corrected by using the geological form information in the digital information base of the field geological data;

[0016] Based on the parameters and excavation exposure conditions of the current tunnel engineering construction process, the corrected wave velocity model is continuously adjusted and optimized.

[0017] As an implementation mode, the digitization operation of the effective geological information along the tunnel line comprises:

[0018] Constructing the matrix of the tunnel line model;

[0019] Counting the possible lithology and sub-classification in the current tunnel project;

[0020] Counting the rock mass classification of the tunnel lithology;

[0021] Marking the adverse geological structure.

[0022] As an implementation mode, the counted geological form information includes adverse geological structure, surrounding rock change trend, fault number and fault form.

[0023] As an implementation form, the statistical wave velocity related information comprises a wave velocity distribution range and a distribution number of wave velocities at each level of surrounding rock.

[0024] As an implementation form, the tunnel geological model construction form comprises an interface of the tunnel geological model, an abnormal body form and an abnormal body number.

[0025] As an implementation form, in the process of establishing the tunnel geological model construction form, further comprising:

[0026] For the key detection target area, the uncertain parameters are arranged and combined to construct a plurality of groups of adverse geological form models.

[0027] As an implementation form, the operation of continuously adjusting and optimizing the corrected wave velocity model comprises:

[0028] The rock mass level or wave velocity value obtained by tunnel advanced drilling is used to adjust the initial exploration data value, and the similar area in the digital information base of the field geological data is adjusted;

[0029] The parameters in the tunnel construction process are used to analyze the quality of the rock mass in front, and the similar area in the digital information base of the field geological data is adjusted;

[0030] Based on the parameters of the rock mass collected on site, the numerical relationship of the wave velocity parameters is analyzed, and the different parameter keys in the digital information base of the field geological data are adjusted.

[0031] The second aspect of the present application provides a tunnel wave velocity model establishment method based on initial exploration geological data.

[0032] A tunnel wave velocity model establishment method based on initial exploration geological data comprises:

[0033] A digital information base construction module is used to acquire geological data of a current tunnel project during tunnel alignment and initial exploration geological data of the tunnel, extract effective geological information along the tunnel and perform digitization, and construct a digital information base of field geological data;

[0034] A geological wave velocity information statistics module is used to statistically acquire geological form information and wave velocity related information based on the digital information base of field geological data;

[0035] A preliminary wave velocity model construction module is used to establish a tunnel geological model construction form based on the statistical geological form information, set corresponding wave velocity values of the tunnel geological model construction form in combination with the statistical wave velocity related information, and construct a preliminary wave velocity model;

[0036] a preliminary wave velocity model correction module, configured to correct the preliminary wave velocity model by using geological formation information in a digital information base of the field geological data;

[0037] a wave velocity model continuous optimization module, configured to continuously adjust and optimize the corrected wave velocity model based on parameters and excavation exposure conditions of a current tunnel engineering construction process.

[0038] A third aspect of the present application provides a computer readable storage medium.

[0039] A computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps in the tunnel wave velocity model establishment method based on preliminary geological data as described above.

[0040] A fourth aspect of the present application provides an electronic device.

[0041] An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the tunnel wave velocity model establishment method based on preliminary geological data as described above when executing the program.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] (1) The present application fills the gap in the current lack of reasonable wave velocity modeling by digitizing and information statistics of the geological data during the tunnel route selection and the tunnel preliminary geological data, establishing the tunnel geological model structure based on the statistical geological formation information, and setting the corresponding wave velocity value of the tunnel geological model structure by combining the statistical wave velocity related information, and constructing the preliminary wave velocity model.

[0044] (2) The present application continuously adjusts and optimizes the corrected wave velocity model by using the parameters and excavation exposure conditions of the current tunnel engineering construction process, generates a velocity model closer to a specific engineering and region, and effectively improves the effect of the wave velocity inversion method represented by deep learning wave velocity inversion in the application of actual data and the effectiveness of the proposed construction method verification by constructing a wave velocity model closer to the actual engineering conditions.

[0045] The advantages of the additional aspects of the present application will be partially given in the following description, partially will become apparent from the following description, or will be understood by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0046] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the description of the exemplary embodiments of the present application and their description serve to explain the present application, and do not constitute an improper limitation of the present application.

[0047] Figure 1 is a flow chart of a tunnel wave velocity model establishment method based on preliminary geological data of the present embodiment;

[0048] Figure 2 is a data diagram of preliminary data of the present embodiment;

[0049] Figure 3 is a flow chart of adjusting modeling parameters with construction parameters in the present embodiment;

[0050] Fig. 4(a) is a representative model one established in the present embodiment;

[0051] Fig. 4(b) is a representative model two established in the present embodiment;

[0052] Fig. 4(c) is a representative model three established in the present embodiment. DETAILED DESCRIPTION

[0053] The present application will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0055] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a presence of a feature, step, operation, device, component and / or combinations thereof.

[0056] In the tunnel seismic wave method advanced geological prediction technology, when the excited seismic wave encounters changes in the front rock stratum, such as encountering a fault or fracture zone, a reflected signal will be generated and received by the geophone on the tunnel wall. Through data processing, inversion and imaging of the collected seismic data, information about the structure of the front rock stratum can be obtained, which can be used by construction personnel to timely adjust construction parameters to avoid disasters such as collapse. This method has many advantages, including a detection distance of up to 100 meters and high sensitivity to wave impedance changes. Therefore, it has been widely used in many tunnel projects, effectively ensuring the safe construction and efficient construction of tunnels.

[0057] To solve the problems of inaccurate data distribution of deep learning inversion network and insufficient verification of traditional inversion method caused by not considering actual geological conditions in the existing tunnel earthquake advanced prediction wave velocity model mentioned in the background art, the application provides a tunnel wave velocity model establishment method and system based on preliminary geological data, which digitizes preliminary geological structure, establishes a wave velocity model based on reference to specific project preliminary data, and can fill the gap in the current lack of reasonable wave velocity modeling. Moreover, by constructing a wave velocity model closer to the actual engineering conditions, the effect of wave velocity inversion methods represented by deep learning wave velocity inversion in practical data application can be effectively improved.

[0058] Embodiment one

[0059] According to Figure 1 , the embodiment provides a tunnel wave velocity model establishment method based on preliminary geological data, which specifically includes the following steps:

[0060] Step 1: Obtain the geological data and tunnel preliminary geological data of the current tunnel project during tunnel alignment, extract the effective geological information along the tunnel and digitize it, and construct a digital information database of field geological data, as shown in Figure 2

[0061] In the specific implementation process, the digitization operation of the effective geological information along the tunnel includes but is not limited to:

[0062] (1) Construct a matrix of the tunnel line model;

[0063] According to the grid size selected according to the detection resolution requirement, the geological model is converted into a multi-channel numerical matrix, and each channel respectively represents rock mass lithology, lithology classification, and poor geological structure annotation data.

[0064] For example, according to the detection meter-level resolution requirement, the grid size is selected as 1m x 1m, the width of the tunnel line is set to 100m, and the geological model is converted into a model with a size of 3 x 100 x n, where n is the number of grids corresponding to the length of the tunnel line, and 3 represents the number of channels of the matrix, which respectively represent rock mass lithology parameters, lithology classification indexes, and poor geological structure annotation data.

[0065] (2) Rock mass lithology: count the possible rock types and sub-classifications in the project, set two-level rock type annotations, and count them as channels of the geological model matrix;

[0066] For example, according to the possible rock types and sub-classifications in the project, two-level rock type annotations are set, and the first channel is annotated with rock mass lithology values.

[0067] ​(3) Rock mass classification: Statistics of the rock mass classification of the tunnel rock, reference to the existing tunnel construction standard, involving 5 levels of surrounding rock parameters, as a channel for the statistics of the geological model matrix;

[0068] For example, according to the existing tunnel construction standard, marked with numbers 1-5, the rock mass lithology value is marked in the second channel;

[0069] (4) Marking of adverse geological structures.

[0070] For example, according to the common adverse geological structures and development conditions, the type and development condition are represented by numbers 1-i and numbers 1-j respectively, for example, a serious fault can be marked as 1-5, and the adverse geological structure information is marked in the third channel.

[0071] In some other optional embodiments, the picture recognition method or deep learning image processing method can also be used to realize the above-mentioned preliminary exploration data information recognition and digitization.

[0072] Step 2: Based on the digital information base of the field geological data, the geological form information and the wave velocity related information are counted.

[0073] In the specific implementation process, the counted geological form information includes adverse geological structures, surrounding rock change trend, fault number and fault form.

[0074] In the specific implementation process, the counted wave velocity related information includes wave velocity distribution range and distribution number of wave velocity of each level of surrounding rock.

[0075] Step 3: Based on the counted geological form information, the tunnel geological model structure form is established, the corresponding wave velocity value of the tunnel geological model structure form is set in combination with the counted wave velocity related information, and a preliminary wave velocity model is constructed.

[0076] In the specific implementation process, the tunnel geological model structure form includes the interface of the tunnel geological model, the abnormal body form and the abnormal body number.

[0077] In this embodiment, the established geological model structure mainly includes the following two parts:

[0078] (1) According to the tunnel structure change trend along the line, the interface common dip angle, the tunnel geological model structure form is constructed, which is used for the subsequent setting of wave velocity, density and other parameters;

[0079] (2) For the key detection target area, taking the fault structure as an example, according to the width, lithology, dip angle and other parameters in the preliminary exploration data, a plurality of adverse geological form models are constructed.

[0080] For example, for the more developed fault structure proved in the preliminary exploration, according to the width, lithology, dip angle and other parameters in the preliminary exploration data, the uncertain parameters are arranged and combined to construct a plurality of groups of adverse geological form models for setting the subsequent wave velocity, density and other parameters.

[0081] In the process of setting the corresponding wave velocity value of the tunnel geological model structure form in combination with the statistical wave velocity related information, the specific operation process includes but is not limited to:

[0082] (1) According to the tunnel depth, rock mass lithology, and classification, the wave velocity range is set, for example, the lower wave velocity is set for the V-class surrounding rock or mudstone rock mass, about 1000-2000 m / s P-wave velocity, the higher wave velocity is set for the II-class surrounding rock or hard rock mass, about 4000 m / s or more P-wave velocity, etc.

[0083] (2) According to the statistical tunnel wave velocity range proportion, the wave velocity value distribution is well done, in the preliminary exploration data engineering geological situation shown in the embodiment, the III-class surrounding rock is mainly displayed, and the corresponding wave velocity value appears more times in the wave velocity range;

[0084] (3) A plurality of rock mass parameters are set for the key detection target area, for the more developed fault structure proved in the preliminary exploration, according to the lithology and drilling obtained wave velocity value in the preliminary exploration data, the wave velocity and density are set, and for the area lacking drilling wave velocity verification, the reasonable range is set and the random parameter adjustment is carried out, so as to strengthen the attention to the key detection area.

[0085] Step 4: The preliminary wave velocity model is corrected by using the geological form information in the digital information base of the field geological data.

[0086] For example, other effective geological information in the digital information base of the field geological data includes but is not limited to adjusting the wave velocity range, constructing the S-wave velocity, density and other data.

[0087] Step 5: Based on the parameters and excavation exposure in the current tunnel engineering construction process, the corrected wave velocity model is continuously adjusted and optimized. In this way, the wave velocity model can be closer to the actual engineering geological condition, and the process is as shown in Figure 3 .

[0088] In this embodiment, the established wave velocity model parameters are continuously adjusted and optimized according to the parameters and excavation exposure in the construction process, including the following three parts:

[0089] The rock mass grade or wave velocity value obtained by the tunnel advanced drilling is used to adjust the preliminary exploration data value, and the similar area in the digital information base of the field geological data is adjusted;

[0090] Utilize the parameters in the tunnel construction process, such as the tunnel boring machine's tunneling speed, etc., to analyze the quality of the rock mass in front, and adjust the similar areas in the digital information base of the field geological data;

[0091] Based on the parameters of the rock mass collected on site, analyze the numerical relationship of the wave velocity parameters, such as the numerical relationship of the P-wave, S-wave, density, etc. parameters, and adjust the different parameter keys in the digital information base of the field geological data.

[0092] Among them, the established tunnel wave velocity model geological modeling database is as shown in Figures 4(a)-4(c) , for subsequent deep learning wave velocity inversion network training or traditional wave velocity inversion method result verification.

[0093] In one or more embodiments, a deep learning wave velocity inversion method is used to train the wave velocity inversion network on the established wave velocity model library, to provide reasonable wave number result distribution for the wave velocity inversion network; or a deep neural network is used to pre-train the constructed data set, and then the network parameter method is used in specific field data to carry out the full waveform inversion method based on network reparameterization.

[0094] Embodiment two

[0095] The embodiment provides a tunnel wave velocity model establishment system based on preliminary geological data, which specifically comprises the following modules:

[0096] (1) A digital information base construction module is used to obtain the geological data of the current tunnel project during tunnel alignment and the preliminary geological data of the tunnel, extract effective geological information along the tunnel and carry out digitization, and construct a digital information base of field geological data.

[0097] In the specific implementation process, the digitization operation of the effective geological information along the tunnel includes but is not limited to:

[0098] (1.1) Matrix construction of the tunnel line model;

[0099] According to the grid size selected according to the detection resolution requirement, the geological model is converted into a multi-channel numerical matrix, and each channel respectively represents rock mass lithology, lithology classification, and poor geological structure annotation data.

[0100] For example, according to the detection meter-level resolution requirement, the grid size is selected as 1m*1m, the width of the tunnel line is set to 100m, and the geological model is converted into a model with a size of 3*100*n, wherein n is the number of grids corresponding to the length of the tunnel line, and 3 represents the number of channels of the matrix, which respectively represent rock mass lithology parameters, lithology classification indexes, and poor geological structure annotation data.

[0101] (1.2) Rock mass lithology: Statistics of possible rock mass lithology and sub-classification in the project, set two levels of rock mass classification mark, as the channel of the geological model matrix for statistics;

[0102] For example, according to the rock mass lithology and sub-classification that may appear in the project, set two levels of rock mass classification mark, mark the rock mass lithology value in the first channel;

[0103] (1.3) Rock mass classification: Statistics of rock mass classification of tunnel rock mass, refer to existing tunnel construction standards, involving 5 levels of surrounding rock parameters, as the channel of the geological model matrix for statistics;

[0104] For example, according to the existing tunnel construction standard, mark with numbers 1-5, mark the rock mass lithology value in the second channel;

[0105] (1.4) Marking adverse geological structure.

[0106] For example, according to the common adverse geological structure and development, mark with numbers 1-i and numbers 1-j to represent the type and development, for example, a serious fault can be marked as 1-5, mark the adverse geological structure information in the third channel.

[0107] In some other optional embodiments, the picture recognition method or deep learning image processing method can also be used to realize the above-mentioned preliminary exploration information recognition and digitization.

[0108] (2) The geological wave velocity information statistics module is used to statistically obtain geological form information and wave velocity related information based on the digital information base of the field geological data.

[0109] In the specific implementation process, the statistical geological form information includes adverse geological structure, surrounding rock change trend, fault number and fault form.

[0110] In the specific implementation process, the statistical wave velocity related information includes wave velocity distribution range and distribution number of estimated wave velocity of each level of surrounding rock.

[0111] (3) The preliminary wave velocity model construction module is used to establish the tunnel geological model structure based on the statistical geological form information, set the corresponding wave velocity value of the tunnel geological model structure combining with the statistical wave velocity related information, and construct the preliminary wave velocity model.

[0112] In the specific implementation process, the tunnel geological model structure includes the interface of the tunnel geological model, the abnormal body form and the abnormal body number.

[0113] In this embodiment, the established geological model structure mainly includes the following two parts:

[0114] (a1) According to the tunnel along the line of the change trend, the interface is common to the angle, the construction of the tunnel geological model structure, for subsequent wave velocity, density and other parameters setting;

[0115] (a2) For key detection target area, for example, according to the fault structure, according to the width, lithology, dip angle and other parameters in the preliminary exploration data, a plurality of groups of adverse geological model are constructed.

[0116] For example, for the more developed fault structure in the preliminary exploration, according to the width, lithology, dip angle and other parameters in the preliminary exploration data, a plurality of groups of adverse geological model are constructed, which are used for subsequent wave velocity, density and other parameter setting.

[0117] In the process of setting the corresponding wave velocity value of the tunnel geological model structure in combination with the statistical wave velocity related information, the specific operation process includes but is not limited to:

[0118] (b1) According to the tunnel depth, rock mass lithology, wave velocity range setting, for example, V type surrounding rock or mudstone rock mass setting lower wave velocity, about 1000-2000m / s P wave velocity, II type surrounding rock or hard rock rock mass setting higher wave velocity, about 4000m / s or more P wave velocity, etc.

[0119] (b2) According to the statistical tunnel wave velocity range proportion, the wave velocity value distribution is good, the preliminary exploration data engineering geological situation shown in the embodiment shows that the III type surrounding rock is mainly used, and the corresponding wave velocity value appears more frequently in the wave velocity range.

[0120] (b3) For key detection target area, set multiple rock mass parameters, for the more developed fault structure in the preliminary exploration, according to the lithology and drilling wave velocity value in the preliminary exploration data, set its wave velocity and density, for the area lacking drilling wave velocity verification, set reasonable range and adjust random parameters, pay attention to key detection area.

[0121] (4) Preliminary wave velocity model correction module, which is used for correcting the preliminary wave velocity model by using the geological form information in the digital information base of the field geological data.

[0122] For example, other effective geological information in the digital information base of the field geological data includes but is not limited to adjusting wave velocity range, constructing S wave velocity, density and other data.

[0123] (5) Wave velocity model continuous optimization module, which is used for continuously adjusting and optimizing the corrected wave velocity model based on the parameters and excavation exposure in the current tunnel engineering construction process.

[0124] In the embodiment, the established wave velocity model parameters are continuously adjusted and optimized according to the parameters and excavation exposure in the construction process, including the following three parts:

[0125] Using the rock mass level or wave velocity value obtained by tunnel advanced drilling, adjusting the initial exploration data value, adjusting the similar area in the digital information base of the field geological data;

[0126] Using the parameters in the tunnel construction process, such as the tunneling speed of the tunnel boring machine, analyzing the quality of the rock mass in front, and adjusting the similar area in the digital information base of the field geological data;

[0127] Based on the parameters of the rock mass collected on site, the numerical relationship of the wave velocity parameters is analyzed, for example, the numerical relationship of the parameters such as P-wave, S-wave, and density is analyzed, and the different parameter keys in the digital information base of the field geological data are adjusted.

[0128] Among them, the established tunnel wave velocity model geological modeling database is used for subsequent deep learning wave velocity inversion network training or traditional wave velocity inversion method result verification.

[0129] In one or more embodiments, a deep learning wave velocity inversion method is used to train the wave velocity inversion network on the established wave velocity model library to provide reasonable wave number result distribution for the wave velocity inversion network; or a deep neural network is used to pre-train the constructed data set, and then the network parameter method is used in specific field data to perform a full waveform inversion method based on network reparameterization.

[0130] It should be noted that each module in the present embodiment corresponds to each step in Embodiment One, and the specific implementation process is the same, which will not be repeated here.

[0131] Embodiment Three

[0132] The present embodiment provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the tunnel wave velocity model establishment method based on initial exploration geological data as described above.

[0133] Embodiment Four

[0134] The present embodiment provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps in the tunnel wave velocity model establishment method based on initial exploration geological data as described above.

[0135] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present application. In this regard, each flowchart block and / or block in the Figures can represent a module, segment, or portion of code, which comprises one or more executable Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present application. In this regard, each flowchart block and / or block in the Figures can represent a module, segment, or portion of code, which comprises one or more executable

[0136] The above descriptions are only the preferred embodiment of the application, not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for establishing a tunnel wave velocity model based on preliminary geological data, characterized by, The method comprises the following steps: Obtaining current tunnel engineering geological data during tunnel route selection and tunnel preliminary exploration geological data, extracting effective geological information along the tunnel and digitizing the same to build a digital information database of field geological data; The digitizing operation of the effective geological information along the tunnel comprises the following steps: Matrixing of the tunnel line model; converting the geological model into a multi-channel numerical matrix, each channel representing rock mass lithology parameters, lithology classification indexes and unfavorable geological structure annotation data; Counting possible rock types and sub-classifications in the current tunnel engineering; Counting rock mass classification of the tunnel rock; Annotating unfavorable geological structures; Based on the digital information database of field geological data, counting geological morphology information and wave velocity related information; Based on the counted geological morphology information, establishing a tunnel geological model structure, and combining the counted wave velocity related information to set corresponding wave velocity values of the tunnel geological model structure to build a preliminary wave velocity model; Using the geological morphology information in the digital information database of field geological data to correct the preliminary wave velocity model; Based on parameters and excavation exposure conditions in the current tunnel engineering construction process, continuously adjusting and optimizing the corrected wave velocity model; The operation of continuously adjusting and optimizing the corrected wave velocity model comprises the following steps: Using rock mass levels or wave velocity values obtained by tunnel advanced drilling to adjust preliminary data values and adjust similar areas in the digital information database of field geological data; Using parameters in the tunnel construction process to analyze the quality of the rock mass in front and adjust similar areas in the digital information database of field geological data; Based on parameters of the collected rock mass, analyzing the numerical relationship of wave velocity parameters and adjusting different parameters in the digital information database of field geological data.

2. The method of claim 1, wherein the tunnel wave velocity model is established based on preliminary geological data. The counted geological morphology information comprises unfavorable geological structures, surrounding rock change trends, fault numbers and fault morphologies.

3. The method of claim 1, wherein the tunnel wave velocity model is established based on preliminary geological data. The counted wave velocity related information comprises wave velocity distribution ranges and distribution numbers of estimated wave velocities of surrounding rocks at different levels.

4. The method of claim 1, wherein the tunnel wave velocity model is established based on preliminary geological data. The tunnel geological model structure comprises interfaces of the tunnel geological model, abnormal body morphologies and abnormal body numbers.

5. The method of claim 1, wherein the tunnel wave velocity model is established based on preliminary geological data. In the process of establishing the tunnel geological model structure, the following steps are further included: For key detection target areas, arranging and combining uncertain parameters to build multiple unfavorable geological morphology models.

6. A system for establishing a tunnel wave velocity model based on preliminary geological data, characterized by, The method comprises the following steps: A digital information database building module is configured to obtain current tunnel engineering geological data during tunnel route selection and tunnel preliminary exploration geological data, extract effective geological information along the tunnel and digitize the same to build a digital information database of field geological data; The digitizing operation of the effective geological information along the tunnel comprises the following steps: Matrixing of the tunnel line model; converting the geological model into a multi-channel numerical matrix, each channel representing rock mass lithology parameters, lithology classification indexes and unfavorable geological structure annotation data; Counting possible rock types and sub-classifications in the current tunnel engineering; Counting rock mass classification of the tunnel rock; Annotating unfavorable geological structures; A geological wave velocity information counting module is configured to count geological morphology information and wave velocity related information based on the digital information database of field geological data; a preliminary wave velocity model construction module, configured to establish a tunnel geological model construction form based on statistical geological form information, set corresponding wave velocity values of the tunnel geological model construction form in combination with statistical wave velocity related information, and construct a preliminary wave velocity model; a preliminary wave velocity model correction module, configured to correct the preliminary wave velocity model by using geological form information in a digital information base of the field geological data; a wave velocity model continuous optimization module, configured to continuously adjust and optimize the corrected wave velocity model based on parameters and excavation exposure conditions of a current tunnel engineering construction process; the operation of continuously adjusting and optimizing the corrected wave velocity model includes: adjusting initial survey data values by using rock mass grades or wave velocity values obtained by tunnel advanced drilling, and adjusting similar regions in the digital information base of the field geological data; adjusting similar regions in the digital information base of the field geological data by using parameters in the tunnel construction process to analyze rock mass quality in front; adjusting different parameters in the digital information base of the field geological data based on wave velocity parameter value relations analyzed by using parameters of collected rock mass.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps in the tunnel wave velocity model establishment method based on initial survey geological data in any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the tunnel wave velocity model establishment method based on initial survey geological data in any one of claims 1-5.

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