Surrounding rock grade discrimination method based on elastic wave advanced geological forecast

Abstract

The invention belongs to the field of advanced geological forecast, and discloses a surrounding rock grade discrimination method based on elastic wave advanced geological forecast, which comprises the following steps: acquiring elastic wave advanced geological forecast data of a certain tunnel face and performing tunnel face geological sketch; processing elastic wave advanced geological forecast data; carrying out prediction parameter characteristic discretization; establishing a corresponding relation between the forecast parameters and the geological features; determining the surrounding rock grade of each mileage point; forecasting mileage segmentation; and comprehensively analyzing and outputting the primary forecast mileage section surrounding rock grade. Therefore, by utilizing the characteristics of the elastic wave advanced geological forecast parameters, the relationship between each elastic wave advanced geological forecast parameter and the surrounding rock geological condition is determined; the surrounding rock geological condition represented by the mileage section is quantitatively forecasted through the threshold values of the elastic wave parameters and the tunnel face parameters; and calculating a result of the relative tunnel face surrounding rock grade of the predicted mileage section by utilizing surrounding rock characteristics represented by each parameter of the elastic wave, and rapidly realizing judgment of the surrounding rock grade of the predicted mileage section.

Description

Technical Field

[0001] The present invention relates to the technical field of geological advance prediction, and in particular to a method for distinguishing surrounding rock grades based on elastic wave advanced geological prediction. Background Art

[0002] During tunnel excavation, the surrounding rock grade is an important reference indicator for guiding the progress of construction and excavation. However, the technical specifications for geophysical advanced geological prediction do not clearly stipulate that the advanced geological prediction method can be used to determine the surrounding rock grade. In actual construction, the predictions are mostly based on subjective experience, and the accuracy of the judgment results needs to be improved. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for distinguishing the surrounding rock grade based on elastic wave advanced geological prediction. The method establishes the relationship between the prediction parameters and the geological characteristics, determines the correction method of the surrounding rock grade by single parameters and comprehensive parameters, and forms a method for efficiently and quickly distinguishing the surrounding rock grade, which effectively guides tunnel excavation construction.

[0004] The object of the present invention is achieved through the following technical solutions: A method for identifying surrounding rock grade based on elastic wave advanced geological prediction, characterized by comprising: Collect the advanced geological prediction data of elastic waves at the tunnel face, make a geological sketch of the tunnel face, constrain the initial model parameters required for elastic wave inversion according to the surrounding rock grade of the tunnel face and based on the geological sketch, so as to obtain the initial model selection of water content probability, longitudinal wave velocity, shear wave velocity, and Poisson's ratio parameters; discretize the predicted mileage segment K into K i The mileage of each mileage point K i The parameter characteristics within the mileage point K i The parameter comprehensive characteristic value of the observation center point is replaced; the judgment threshold of stress gradient value and surrounding rock integrity is established to determine the surrounding rock integrity of the rock mass according to the stress gradient value, and the structural characteristics of the rock mass and the relative relationship between the hardness and the tunnel face are predicted according to the longitudinal wave velocity ratio, transverse wave velocity ratio and longitudinal wave velocity change rate, and Poisson's ratio, so as to determine the surrounding rock grade of each mileage point; the surrounding rock grade change characteristics and duration of the predicted tunnel are determined according to the lithologic characteristics, and the segmented mileage position is determined based on the lithologic change characteristics and the spatial distribution characteristics of each parameter; the surrounding rock grade of each segmented mileage is analyzed section by section until the surrounding rock grade judgment of the segmented mileage of the entire predicted tunnel is completed, and the surrounding rock grade of the entire segmented mileage is output and predicted.

[0005] Furthermore, constraining the initial model parameters required for elastic wave inversion according to the surrounding rock grade of the tunnel face and based on the geological sketch includes: setting initial X, Y, and Z three-dimensional P-wave velocity values ​​within the P-wave velocity range given by the surrounding rock grade specification, and constructing a three-dimensional initial P-wave velocity model as the starting point for elastic wave inversion calculation; constructing an initial water content probability model and an initial Poisson's ratio model based on the acquired water content, and calculating a S-wave velocity model based on the initial Poisson's ratio model and the P-wave velocity model: in, is the initial Poisson's ratio model, is the initial P-wave velocity model, is a shear wave velocity model; the inversion obtains stress gradient, water probability, longitudinal wave velocity, shear wave velocity, and Poisson's ratio parameters.

[0006] Furthermore, the predicted mileage segment K is discretized into K i The mileage of each mileage point K i The parameter characteristics within the mileage point K i The parameter comprehensive characteristic value of the observation center point is replaced by the characteristic parameters, and the characteristic parameters include: A series of stress gradient values ​​at each mileage point within the segmented mileage: A series of water content probabilities at each mileage point within the segmented mileage: A series of longitudinal wave velocities at each mileage point within the segmented mileage: A series of shear wave velocities at each mileage point within the segmented mileage: A series of Poisson's ratios for each mileage point within the segmented mileage: .

[0007] Furthermore, the longitudinal wave velocity ratio, shear wave velocity ratio, longitudinal wave velocity change rate, and Poisson's ratio are calculated by vp for: in is the longitudinal wave velocity in the first 3 m, , i is an intermediate variable, i∈(1,100);Calculate the shear wave velocity ratio K vs for: in is the shear wave velocity in the first 3 m, , i is an intermediate variable, i∈(1,100);the change rate of the longitudinal wave velocity is calculated as follows: Where j is the distance between each mileage point, i is the intermediate variable, i∈(1,100).

[0008] Furthermore, the value predicts the relative relationship between the structural characteristics of the rock mass and the hardness and the tunnel face, including: using the wave velocity characteristics to determine the structural characteristics and hardness, forming the upper and lower thresholds K1', K1 of the longitudinal wave velocity ratio of the predicted tunnel, the upper and lower thresholds K2', K2 of the transverse wave velocity ratio, and the threshold K3 of the longitudinal wave velocity change rate; the judgment results include: Case 1: When >K1, >K2, or ≥K3, <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; Case 2: When >K1, >K2, and <K3, <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the degree of development of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 3: When >K1,K2'< ≤K2, and <K3, <0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the development degree of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 4: When ≤K1',K2'< ≤K2, or When ≥K3, When ≥0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 5: When ≤K1', ≤K2', or ≥K3, When ≥0.35, the hardness of the surrounding rock and the hardness of the tunnel face are reduced by one level, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 6: When ≤K1', ≤K2', and <K3, When ≥0.35, the surrounding rock hardness is reduced by one level compared to the surrounding rock hardness of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; in other cases, the surrounding rock strength is judged to be consistent with the degree of development of joints and fissures on the tunnel face.

[0009] Furthermore, the relationship between the water content probability value and the groundwater development situation is established, and the spatial distribution characteristics and threshold values ​​of the water content probability are used to determine the water content probability. The water-rich conditions of the surrounding rocks are divided into dry or wet, dripping water, linear flowing water, and gushing water.

[0010] Furthermore, the determination of the surrounding rock grade at each mileage point includes comprehensively determining the surrounding rock grade at each point by determining the hardness, integrity, structural characteristics, and water-richness of the surrounding rock; wherein, the comparison results of the surrounding rock integrity obtained based on the stress gradient and the tunnel face, and the changes in the surrounding rock structural characteristics and hardness relative to the tunnel face determined based on the longitudinal and transverse wave velocity ratio, the longitudinal wave velocity change rate, and the Poisson's ratio are used to comprehensively determine the changes in the surrounding rock grade of the mileage point relative to the surrounding rock grade of the tunnel face.

[0011] Furthermore, the comprehensive determination of the change in the surrounding rock grade at the milestone point relative to the surrounding rock grade of the tunnel face includes the following conditions: the condition for increasing the grade by one level is: the surrounding rock integrity degree obtained based on the stress gradient is consistent with that of the tunnel face, and the wave velocity parameter determination result falls into Case 2; or the surrounding rock integrity degree obtained based on the stress gradient is better than that of the tunnel face, and the wave velocity parameter determination result falls into Case 1, Case 2, or Case 3 accordingly. The condition for decreasing the grade by one level is: the surrounding rock integrity degree obtained based on the stress gradient is consistent with that of the tunnel face, and the wave velocity parameter determination result falls into Case 5. Alternatively, if the degree of surrounding rock integrity obtained based on the stress gradient is worse than that of the tunnel face, the corresponding wave velocity parameter determination result belongs to Case 4, Case 5, or Case 6. The condition for the grade to remain unchanged is as follows: if the determination at a certain mileage point does not meet any of the above conditions, the surrounding rock grade at that location is determined to be consistent with the surrounding rock grade of the tunnel face and no adjustment is required. After determining the surrounding rock grade corresponding to each mileage point, the surrounding rock grade is corrected based on the water-rich condition: if the water-rich condition is linear flow or gushing water, the surrounding rock grade deteriorates by one level and the surrounding rock grade is already the worst surrounding rock grade, then the surrounding rock grade does not change; if the water-rich condition is dry, wet, or seepage water, then the surrounding rock grade does not change.

[0012] Furthermore, the method of determining whether the surrounding rock grade of the predicted tunnel changes frequently according to the tunnel lithologic characteristics, and determining the characteristics and duration of the surrounding rock grade change of the predicted tunnel according to the lithologic characteristics, and determining the segment mileage position based on the lithologic change characteristics and the spatial distribution characteristics of each parameter includes: dividing the total length of a forecast into multiple segment mileages of the forecast ; Among them, the location of the segmented mileage does not separate the continuous surrounding rock stress characteristics, the location of the segmented mileage does not separate a certain continuous water-bearing body distribution characteristics, and the location of the segmented mileage does not separate the high-speed closed zone or the low-speed closed zone.

[0013] Furthermore, the rock mass grade of each segment is analyzed section by section until the rock mass grade of the entire segment is determined and the output of the predicted rock mass grade of the entire segment is output. Y i Determine the surrounding rock grade and calculate the segment mileage Y i Length N, calculate the proportion of various surrounding rock grades within the segment mileage, take the maximum surrounding rock grade proportion value as the surrounding rock grade of the segment mileage; and, if the surrounding rock grade distribution with the lowest proportion is continuous, and the continuous data exceeds 6, then the output at the forecast mileage includes the surrounding rock with the largest proportion and the continuous surrounding rock grade; according to the segment mileage , analyze the surrounding rock grade section by section until the surrounding rock grade determination of the entire segment mileage is completed, and output a predicted surrounding rock grade of the entire segment mileage.

[0014] The beneficial effects of the present invention are: The method of the embodiment of the present application overcomes the shortcoming of requiring a large amount of field test data and manual experience in the identification of surrounding rock grades. It realizes efficient, fast and intelligent identification of surrounding rock grades by relying only on the elastic wave advanced geological prediction parameters, saving construction costs and improving construction efficiency. The present invention uses the characteristics of the elastic wave advanced geological prediction parameters to establish the relationship between the various parameters of the elastic wave advanced geological prediction and the surrounding rock geological conditions; quantifies the surrounding rock geological conditions represented by the segmented mileage through the thresholds of the various elastic wave parameters and the tunnel face parameters; uses the surrounding rock characteristics represented by the various elastic wave parameters to calculate the results of the segmented mileage relative to the tunnel face surrounding rock grade, and quickly realizes the determination of the segmented mileage surrounding rock grade. The prediction effect is more efficient and fast. It provides an efficient and convenient method for the rapid identification of surrounding rock grades, improving the speed and efficiency of surrounding rock grade identification.

[0015] In other words, this application utilizes the geological sketch results of the tunnel face during elastic wave advanced geological prediction and the characteristics of elastic wave data inversion parameters, and through multiple excavation verifications, forms a threshold judgment condition process to streamline the surrounding rock grade determination process. This overcomes the technical difficulties that elastic wave geological prediction data mainly relies on the personal experience of technicians, and the surrounding rock grade determination is highly subjective and requires high experience. Therefore, this method obtains forecast data based on elastic wave advanced geological prediction to reveal the surrounding rock geological conditions, and then further obtains the changes in the surrounding rock geological structure characteristics to form a threshold model for each tunnel. Finally, the surrounding rock grade is determined, achieving rapid and intelligent determination of the surrounding rock grade, and improving the accuracy and efficiency of the surrounding rock grade determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of a method for determining surrounding rock grade based on elastic wave advanced geological prediction according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0018] See Figure 1 As shown in the figure, the present invention provides a method for distinguishing surrounding rock grade based on elastic wave advanced geological prediction. The method uses parameters such as stress gradient, water content probability, longitudinal and transverse wave velocity, and Poisson's ratio obtained by inversion of elastic wave data to determine the relative quality of the geological conditions at the prediction point and the geological conditions at the tunnel face. The surrounding rock grade of each mileage point is determined, and the mileage segmentation of the prediction result is determined by the spatial distribution characteristics of the parameters. A comprehensive analysis is performed to determine the surrounding rock grade of the segmented mileage. The method specifically includes the following steps: S1. Use instruments (such as seismographs) to collect advanced geological prediction data of elastic waves at the tunnel face and draw a geological sketch of the tunnel face. The geological sketch includes geological information such as lithology, degree of weathering, joint development (such as number and spacing of joints), and water content.

[0019] In some examples, based on the observation mode of the elastic wave advanced geological prediction system, advanced geological prediction data with a good signal-to-noise ratio can be collected, and a detailed geological sketch can be made of the tunnel face where the data was collected, including geological conditions such as lithology, weathering degree, joint development, number of joints, and water content.

[0020] S2. Process the elastic wave advanced geological prediction data. According to the surrounding rock grade of the tunnel face and based on the geological sketch, constrain the initial model parameters (such as P-wave velocity) required for elastic wave inversion, so as to obtain the initial model selection of parameters such as water content probability, P-wave velocity, S-wave velocity, Poisson's ratio, and obtain initial model values ​​that are more consistent with the geological conditions.

[0021] Specifically, an initial tunnel face surrounding rock grade is inferred based on the tunnel face geological sketch (such as the number of joints, spacing, lithology, and water content mentioned above). The surrounding rock grade inferred from the geological sketch is used to constrain the initial model parameters required for elastic wave inversion (mainly the longitudinal wave velocity). This constraint process is reflected as follows: Based on the surrounding rock grade inferred from the geological sketch, the initial X, Y, and Z three-dimensional P-wave velocity values ​​are set within the P-wave velocity range specified in the corresponding surrounding rock grade specification to construct a three-dimensional initial P-wave velocity model. This model will serve as the starting point for the elastic wave inversion calculation.

[0022] Based on the water content obtained in S1, an initial water content probability model and an initial Poisson's ratio model are constructed. For example, when the water content of the tunnel face is dry or wet, the initial water content probability value is 30% and the Poisson's ratio value is 0.27-0.28; when the water content of the tunnel face is drip water or seepage water, the initial water content probability value is 50% and the Poisson's ratio value is 0.29-0.30.

[0023] Then, according to the initial Poisson's ratio model and the initial P-wave velocity model , according to the calculation formula , the initial shear wave velocity model is obtained .

[0024] Based on the above information, the P-wave velocity, S-wave velocity, water probability, Poisson's ratio, and initial velocity model are input into the elastic wave geological prediction data processing module, where the stress gradient, water probability, wave velocity, and Poisson's ratio parameters are inverted. For example, the elastic wave geological prediction data processing module can utilize professional elastic wave advanced geological prediction data processing software to obtain the stress gradient, water probability, P-wave velocity, and S-wave velocity parameters.

[0025] S3. Discretize the forecast parameter characteristics and discretize the forecast mileage segment K into K i The mileage of each mileage point K i The parameter characteristics within the mileage point K i The parameter comprehensive characteristic value of the observation center point is replaced by the parameter comprehensive characteristic value; wherein the parameter characteristics include stress gradient value, water content probability, longitudinal wave velocity, shear wave velocity, and Poisson's ratio.

[0026] Specifically, the 100m mileage segment is discretized into mileages with a spacing of 1m, and the parameter characteristics of each mileage are replaced by the parameter comprehensive characteristic value of the observation center point; the parameter value of the observation center point is calculated, and the observation center point position of each mileage is taken as the center. The three-dimensional space is used to obtain a stress gradient value stress by taking the weighted average of the stress gradient values ​​of all points in the space. center1 , calculate the stress gradient value of each mileage point in the segment mileage in turn There are 100 in total. Similarly, we can get the following results in the following way: A series of water content probabilities at each mileage point within the segmented mileage: A series of longitudinal wave velocities at each mileage point within the segmented mileage: A series of shear wave velocities at each mileage point within the segmented mileage: A series of Poisson's ratios for each mileage point within the segmented mileage: .

[0027] S4. Construct qualitative and semi-quantitative relationships between common parameters and surrounding rock geological characteristics, including establishing a corresponding relationship between stress gradient values ​​and surrounding rock integrity. Specifically, establish a threshold for determining stress gradient values ​​and surrounding rock integrity (stress gradient is an important parameter indicating the degree of surrounding rock integrity). Based on the stress gradient value, the rock mass can be classified as complete, relatively complete, poorly complete, relatively broken, and broken. In addition, the structural characteristics of the rock mass and the relative relationship between hardness and the tunnel face are predicted based on the longitudinal wave velocity ratio, shear wave velocity ratio, longitudinal wave velocity change rate, and Poisson's ratio.

[0028] Specifically, the P-wave velocity ratio, S-wave velocity ratio, P-wave velocity change rate, and Poisson's ratio are established to predict the relative relationship between the structural characteristics of the rock mass and the hardness and tunnel face, including: Calculate the longitudinal wave velocity ratio K vp : Furthermore, considering that the lithologic characteristics of a general rock mass are almost unchanged within 3 m, the P-wave velocity of the first 3 m is used to replace the P-wave velocity characteristics of the surrounding rock face: .

[0029] Calculate the shear wave velocity ratio K vs : Similarly is the shear wave velocity in the first 3 m, then .

[0030] Calculate the rate of change of longitudinal wave velocity. For example, the rate of change of longitudinal wave velocity at a mileage point interval of 1m is: In other examples, the rate of change of longitudinal wave velocity at a spacing of 2m is: .

[0031] Then, the wave velocity characteristics are used to determine the structural characteristics and hardness, and the upper and lower thresholds of the longitudinal wave velocity ratio K1', K1, the upper and lower thresholds of the shear wave velocity ratio K2', K2, and the longitudinal wave velocity change rate threshold K3 of the predicted tunnel are formed, specifically including: Case 1: When >K1, >K2, or ≥K3, When it is <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; Case 2: When >K1, >K2, and <K3, When it is <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the development degree of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 3: When >K1,K2'< ≤K2, and <K3, When it is <0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the development degree of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 4: When ≤K1',K2'< ≤K2, or When ≥K3, When ≥0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 5: When ≤K1', ≤K2', or ≥K3, When ≥0.35, the hardness of the surrounding rock is reduced by one level compared with the surrounding rock hardness of the tunnel face, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 6: When ≤K1', ≤K2', and <K3, When ≥0.35, the hardness of the surrounding rock is reduced by one level compared with that of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; In other cases, the surrounding rock strength is determined to be consistent with the degree of development of joints and fissures on the tunnel face.

[0032] Establish the relationship between the water content probability value and the groundwater development. The water content probability mainly reflects the water-richness of the surrounding rock. According to the spatial distribution characteristics and threshold of the water content probability, The water-rich conditions of the surrounding rock can be divided into dry or wet, dripping water, linear flowing water, and gushing water.

[0033] In the above judgment process, the threshold values ​​of various parameters of the controlled surrounding rock characteristics are corrected through multiple actual excavation results.

[0034] S5. Determine the surrounding rock grade at each mileage point, including judging the hardness, integrity, structural characteristics, and water-richness of the surrounding rock to comprehensively determine the surrounding rock grade at each point.

[0035] Specifically, based on the comparison results of the surrounding rock integrity obtained from the stress gradient and the tunnel face, and based on the changes in the surrounding rock structure characteristics and hardness relative to the tunnel face determined by the P-wave and S-wave velocity ratio, the P-wave velocity change rate, and Poisson's ratio, a comprehensive judgment is made on the changes in the surrounding rock grade at the milestone point relative to the surrounding rock grade at the tunnel face, including: The conditions for improving the grade by one level, i.e. the surrounding rock becomes better, include: The degree of surrounding rock integrity obtained based on the stress gradient is consistent with that of the tunnel face, and the wave velocity parameter determination result in step 4 belongs to case 2; or the degree of surrounding rock integrity obtained based on the stress gradient is better than that of the tunnel face, and accordingly, the wave velocity parameter determination result in step 4 belongs to case 1, case 2, or case 3.

[0036] The conditions for a grade reduction of one level, i.e., the surrounding rock becomes worse, include: The degree of surrounding rock integrity obtained based on the stress gradient is consistent with that of the tunnel face, and the wave velocity parameter determination result in step 4 belongs to case 5. Alternatively, the degree of surrounding rock integrity obtained based on the stress gradient is worse than that of the tunnel face, and the wave velocity parameter determination result in step 4 belongs to case 4, case 5, or case 6.

[0037] The conditions for maintaining the same grade include: If the judgment of a certain mileage point does not meet any of the above conditions, it is judged that the surrounding rock grade at this location is consistent with the surrounding rock grade of the tunnel face and no adjustment is required.

[0038] After determining the surrounding rock grade corresponding to each mileage point, the surrounding rock grade is corrected according to the water-rich situation obtained in S4: if the water-rich situation is linear flow or gushing water, the surrounding rock grade deteriorates by one level, and the surrounding rock grade is already the worst surrounding rock grade, then the surrounding rock grade does not change; if the water-rich situation is dry or wet, or seepage water, then the surrounding rock grade does not change.

[0039] S6. Determine whether the surrounding rock grade of the predicted tunnel changes frequently based on the tunnel lithologic characteristics, so as to determine the characteristics and duration of the surrounding rock grade change of the predicted tunnel based on the lithologic characteristics; for example, tunnels made of igneous rock such as granite and other hard rock have a longer duration of surrounding rock grade; tunnels made of sedimentary rock such as muddy siltstone and carbonaceous shale have a shorter duration of surrounding rock grade.

[0040] Based on the above-mentioned lithologic variation characteristics, the segment mileage positions are determined by the spatial distribution characteristics of each parameter. For example, the total length of a forecast is divided into multiple segment mileages. The location of the segmented mileage cannot separate the continuous surrounding rock stress characteristics, the location of the segmented mileage cannot separate the distribution characteristics of a certain continuous water-bearing body, and the location of the segmented mileage cannot separate a certain high-speed closed zone or low-speed closed zone.

[0041] S7, Segment mileage Y i Determine the surrounding rock grade and calculate the segment mileage Y i Length N, calculate the proportion of various surrounding rock grades within the segment mileage ..., take the maximum surrounding rock grade proportion as the surrounding rock grade for the segmented mileage; if the surrounding rock grade with the lowest proportion is distributed continuously and the continuous data exceeds 6, then the output for the predicted mileage includes the surrounding rock with the largest proportion and the continuous surrounding rock grade; similarly, if there are multiple surrounding rock types with more than 6 continuous data, multiple surrounding rock grades will be output.

[0042] Then, based on the segment mileage , analyze the surrounding rock grade section by section until the surrounding rock grade determination of the entire segmented mileage is completed, and output a predicted surrounding rock grade for the entire mileage section.

[0043] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A method for identifying surrounding rock grade based on elastic wave advanced geological prediction, characterized in that: include: Collect advanced geological prediction data of elastic waves at the tunnel face, create a geological sketch of the tunnel face, and constrain the initial model parameters required for elastic wave inversion based on the surrounding rock grade of the tunnel face and the geological sketch, thereby obtaining the initial model selection parameters for water content probability, longitudinal wave velocity, shear wave velocity, and Poisson's ratio; Discretize the predicted mileage segment K into K i The mileage of each mileage point K i The parameter characteristics within the mileage point K i The parameter comprehensive characteristic value of the observation center point is replaced by; Establish thresholds for stress gradient values ​​and surrounding rock integrity to determine the surrounding rock integrity based on stress gradient values. Also, predict the structural characteristics of the rock mass and its relative relationship to the tunnel face based on the P-wave velocity ratio, S-wave velocity ratio, P-wave velocity change rate, and Poisson's ratio to determine the surrounding rock grade at each mileage point. Determine the predicted tunnel surrounding rock grade change characteristics and duration based on lithologic characteristics. Determine the segment mileage location based on lithologic change characteristics and the spatial distribution characteristics of various parameters. The surrounding rock grade of each segmental mileage is analyzed section by section until the surrounding rock grade of the segmental mileage of the entire predicted tunnel is determined, and the surrounding rock grade of the entire segmental mileage is output and predicted.

2. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 1 is characterized in that: The constraints on the initial model parameters required for elastic wave inversion according to the surrounding rock grade of the tunnel face and based on the geological sketch include: Within the P-wave velocity range given by the surrounding rock grade specification, the initial X, Y, and Z three-dimensional P-wave velocity values ​​are set, and a three-dimensional initial P-wave velocity model is constructed as the starting point for elastic wave inversion calculation; According to the acquired water content, the initial water content probability model and the initial Poisson's ratio model are constructed, and the shear wave velocity model is calculated based on the initial Poisson's ratio model and the longitudinal wave velocity model: in, is the initial Poisson's ratio model, is the initial P-wave velocity model, is the shear wave velocity model; The inversion obtains stress gradient, water content probability, longitudinal wave velocity, shear wave velocity, and Poisson's ratio parameters.

3. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 2 is characterized in that: The predicted mileage segment K is discretized into K i The mileage of each mileage point K i The parameter characteristics within the mileage point K i The parameter comprehensive characteristic value of the observation center point is replaced by the parameter comprehensive characteristic value of the observation center point, and the parameter characteristics include: A series of stress gradient values ​​at each mileage point within the segmented mileage: A series of water content probabilities at each mileage point within the segmented mileage: A series of longitudinal wave velocities at each mileage point within the segmented mileage: A series of shear wave velocities at each mileage point within the segmented mileage: A series of Poisson's ratios for each mileage point within the segmented mileage: 。 4. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 3 is characterized in that: The longitudinal wave velocity ratio K is calculated by establishing the longitudinal wave velocity ratio, the transverse wave velocity ratio, the longitudinal wave velocity change rate, and the Poisson's ratio. vp for: in is the longitudinal wave velocity in the first 3 m, , i is the intermediate variable, i∈(1,100); Calculate the shear wave velocity ratio K vs for: in is the shear wave velocity in the first 3 m, , i is the intermediate variable, i∈(1,100); The calculation of the rate of change of longitudinal wave velocity is: Where j is the distance between each mileage point, i is the intermediate variable, i∈(1,100).

5. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 4 is characterized in that: The method of predicting the relative relationship between the structural characteristics of the rock mass and the hardness and the tunnel face based on the longitudinal wave velocity ratio, the transverse wave velocity ratio, the longitudinal wave velocity change rate, and the Poisson's ratio includes: The wave velocity characteristics are used to determine the structural characteristics and hardness, and the upper and lower thresholds of the longitudinal wave velocity ratio K1', K1, the upper and lower thresholds of the transverse wave velocity ratio K2', K2, and the threshold of the longitudinal wave velocity change rate K3 are formed. The judgment results include: Case 1: When >K1, >K2, or ≥K3, When it is <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; Case 2: When >K1, >K2, and <K3, When it is <0.35, the hardness of the surrounding rock is one level higher than that of the tunnel face, and the development degree of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 3: When >K1,K2'< ≤K2, and <K3, When it is <0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the development degree of joints and fissures on the structural surface is weaker than that on the tunnel face; Case 4: When ≤K1',K2'< ≤K2, or When ≥K3, When ≥0.35, the hardness of the surrounding rock is consistent with that of the tunnel face, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 5: When ≤K1', ≤K2', or ≥K3, When ≥0.35, the hardness of the surrounding rock is reduced by one level compared with the surrounding rock hardness of the tunnel face, and the degree of development of joints and fissures on the structural surface is stronger than that on the tunnel face; Case 6: When ≤K1', ≤K2', and <K3, When ≥0.35, the hardness of the surrounding rock is reduced by one level compared with that of the tunnel face, and the degree of development of joints and fissures on the structural surface is consistent with that of the tunnel face; In other cases, the surrounding rock strength is determined to be consistent with the degree of development of joints and fissures on the tunnel face.

6. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 5 is characterized in that: Establish the relationship between the water content probability value and groundwater development, and according to the spatial distribution characteristics and threshold of water content probability The water-rich conditions of the surrounding rocks are divided into dry or wet, dripping water, linear flowing water, and gushing water.

7. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 6, characterized in that: Determining the surrounding rock grade at each mileage point includes comprehensively determining the surrounding rock grade based on its hardness, integrity, structural characteristics, and water-richness. Among them, the comparison results of the surrounding rock integrity and the tunnel face obtained based on the stress gradient, and the surrounding rock structural characteristics and hardness changes relative to the tunnel face determined based on the P-wave and S-wave velocity ratio, P-wave velocity change rate, and Poisson's ratio are used to comprehensively judge the changes in the surrounding rock grade of the mileage point relative to the surrounding rock grade of the tunnel face.

8. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 7 is characterized in that: The comprehensive judgment of the change of the surrounding rock grade of the mileage point relative to the surrounding rock grade of the tunnel face includes: The conditions for upgrading the grade by one level are: the degree of surrounding rock integrity obtained based on stress gradient is consistent with that of the tunnel face, and the wave velocity parameter judgment result belongs to Case 2; or the degree of surrounding rock integrity obtained based on stress gradient is better than that of the tunnel face, and accordingly, the wave velocity parameter judgment result belongs to Case 1, Case 2, or Case 3; The conditions for downgrading the grade by one level are: the degree of surrounding rock integrity obtained based on stress gradient is consistent with that of the tunnel face, and the wave velocity parameter determination result belongs to Case 5; or the degree of surrounding rock integrity obtained based on stress gradient is worse than that of the tunnel face, and accordingly, the wave velocity parameter determination result belongs to Case 4, Case 5, or Case 6; The conditions for grade invariance are: if the judgment of a certain mileage point does not meet any of the above conditions, the surrounding rock grade at that location is determined to be consistent with the surrounding rock grade of the tunnel face; After determining the surrounding rock grade corresponding to each mileage point, the surrounding rock grade is corrected according to the water-rich situation: if the water-rich situation is linear flow or gushing water, the surrounding rock grade deteriorates by one level, and the surrounding rock grade is already the worst surrounding rock grade, then the surrounding rock grade does not change; if the water-rich situation is dry or wet, or seepage water, then the surrounding rock grade does not change.

9. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 1, characterized in that: Determine whether the predicted tunnel's surrounding rock grade changes frequently based on the tunnel's lithologic characteristics, and determine the characteristics and duration of the predicted tunnel's surrounding rock grade changes based on the lithologic characteristics. Determine the segmented mileage positions based on the lithologic change characteristics and the spatial distribution characteristics of various parameters, including: Divide the total length of a forecast into multiple forecast segments ; The location of the segmented mileage does not separate the continuous surrounding rock stress characteristics, the location of the segmented mileage does not separate a certain continuous water-bearing body distribution characteristics, and the location of the segmented mileage does not separate the high-speed closed zone or the low-speed closed zone.

10. The method for identifying surrounding rock grade based on elastic wave advanced geological prediction according to claim 9, characterized in that: The surrounding rock grade of each segment is analyzed section by section until the surrounding rock grade of the entire segment is determined. The output of the predicted surrounding rock grade of the entire segment includes: Segment mileage Y i Determine the surrounding rock grade and calculate the segment mileage Y i Length N, calculate the proportion of various surrounding rock grades within the segment mileage, and take the maximum surrounding rock grade proportion as the surrounding rock grade of the segment mileage; Furthermore, if the distribution of surrounding rock grades with the lowest proportion is continuous and the number of continuous data exceeds 6, the output for the forecast mileage includes the surrounding rock with the largest proportion and the continuous surrounding rock grades; According to segment mileage , analyze the surrounding rock grade section by section until the surrounding rock grade determination of the entire segment mileage is completed, and output a predicted surrounding rock grade of the entire segment mileage.

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