Fusion intelligent analysis method for long-distance advanced geological prediction based on target point difference

Abstract

The present invention belongs to the technical field of advanced geological prediction, and specifically relates to a long-distance advanced geological prediction fusion intelligent analysis method based on target point difference. The method comprises the following steps: 1. performing target point sketch collection on the tunnel face, and judging the surrounding rock integrity, surrounding rock grade and groundwater development level based on the collection results; 2. performing long-distance detection in front of the tunnel face, extracting target point physical property parameters and TSP detection physical property parameters in front of the detection body; 3. performing difference analysis between the target point and the front of the detection body, determining the numerical jitter deviation, determining the surrounding rock grade change based on the jitter deviation, and adjusting the surrounding rock grade in combination with the "Code for Railway Engineering Geological Survey" TB10012-2019, thereby achieving comprehensive surrounding rock judgment; and 4. performing comprehensive geological risk judgment based on the groundwater judgment result obtained in step 1 and the surrounding rock comprehensive judgment result obtained in step 3.

Description

Technical Field

[0001] The present invention belongs to the technical field of advanced geological prediction, and in particular relates to a long-distance advanced geological prediction fusion intelligent analysis method based on target point difference. Background Art

[0002] At present, domestic advanced geological forecasting is mostly implemented using a single method. However, relying solely on a single method for judgment will often mislead professional and technical personnel, leading to misjudgment and even seriously affecting tunnel construction safety and causing engineering accidents.

[0003] When using multiple means for detection, geological sketching is usually carried out first, and TSP or geological radar is used for detection. Comprehensive analysis relies on experience. The results of long-distance TSP detection mainly rely on the absolute rock mass longitudinal wave velocity V p , rock mass shear wave V s However, the physical parameters obtained in the actual implementation of geophysical advanced geological prediction are affected by factors such as rock density, rock integrity, and rock hardness. Often, there are significant differences among the same rock masses. Ultimately, under the same level of surrounding rock conditions, the longitudinal wave velocity and Poisson's ratio of the rock mass will also vary significantly, leading to large errors in the integrity judgment of the same type of rock mass. Summary of the Invention

[0004] The purpose of this invention is to provide a long-distance advanced geological prediction fusion intelligent analysis method based on target point difference, which is used to solve the problem that the integrity of the surrounding rock is completely dependent on the longitudinal wave velocity V of the rock mass. P , rock mass shear wave V s and the problem of errors in surrounding rock judgment caused by the absolute value of the Poisson's ratio of the rock mass.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A long-distance advanced geological prediction fusion intelligent analysis method based on target point difference includes the following steps: Step 1. Target point sketch collection is performed on the tunnel face, and the surrounding rock integrity, surrounding rock grade, and groundwater development level are determined based on the collection results. According to the "Railway Tunnel Design Code TB1003-2016", tunnel surrounding rock is classified into six surrounding rock categories: I, II, III, IV, V, and VI;

[0007] Step 2. Perform long-distance detection in front of the tunnel face to extract the target point physical property parameters and the TSP detection physical property parameters in front of the detection body;

[0008] Step 3. Perform a difference analysis between the target point and the front of the detection volume to determine the numerical jitter deviation. Based on the jitter deviation, determine the change in surrounding rock grade. Adjust the surrounding rock grade in accordance with the "Code for Geological Survey for Railway Engineering" TB10012-2019 to achieve a comprehensive assessment of the surrounding rock.

[0009] Step 4. Based on the groundwater judgment results obtained in step 1 and the surrounding rock comprehensive judgment results obtained in step 3, make a comprehensive judgment on geological risks.

[0010] Furthermore, the range of long-distance detection in step 2 is taken as the target point to 100m in front of the detection body.

[0011] Furthermore, the surrounding rock grade adjustment algorithm in step 3 is as follows:

[0012] When Rc>60MPa, 500≤V P变化量 <1000,500≤V s变化量 <1000,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level;

[0013] When Rc>60MPa, 1000≤V P变化量 0, 1000 ≤ V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 , if three of the four conditions are met, the surrounding rock grade is adjusted by 2 levels;

[0014] When Rc≤60MPa, 300≤V P变化量 <600,300≤V s变化量 <600,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level;

[0015] When Rc≤60MPa, 600≤V P变化量 0,600≤V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 If three of the four conditions are met, the surrounding rock grade will be adjusted by 2 levels.

[0016] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0017] Effectively eliminate the same type of surrounding rock mass longitudinal wave velocity Vp, rock mass shear wave V sIt can eliminate the problem of judgment errors caused by large changes in absolute parameters such as the Poisson's ratio of the rock mass, reduce the risk of errors caused by relying on experience, and improve the accuracy of long-distance advanced geological forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is the core flow chart of the method of the present invention;

[0019] Figure 2 This is a curve chart of target point differential longitudinal and shear wave data analysis in Example 1;

[0020] Figure 3 This is a data analysis curve diagram of the target point difference Poisson's ratio and dynamic Young's modulus in Example 1;

[0021] Figure 4 This is a curve chart of target point differential longitudinal and shear wave data analysis in Example 2;

[0022] Figure 5 This is a data analysis graph of the target point difference Poisson's ratio and dynamic Young's modulus in Example 2. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to clearly understand and reproduce the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0024] A long-distance advanced geological prediction fusion intelligent analysis method based on target point difference includes the following steps: Step 1. Target point sketch collection is performed on the tunnel face, and the surrounding rock integrity, surrounding rock grade, and groundwater development level are determined based on the collection results. According to the "Railway Tunnel Design Code TB1003-2016", tunnel surrounding rock is classified into six surrounding rock categories: I, II, III, IV, V, and VI;

[0025] Step 2. Perform long-distance detection in front of the tunnel face to extract the physical properties of the target point and the physical properties of the TSP detection in front of the detection body. The range of long-distance detection is from the target point to 100m in front of the detection body.

[0026] Step 3. Perform a difference analysis between the target point and the front of the detection volume to determine the numerical jitter deviation. Based on the jitter deviation, determine the change in surrounding rock grade. Adjust the surrounding rock grade in accordance with the "Code for Geological Survey for Railway Engineering" TB10012-2019 to achieve a comprehensive assessment of the surrounding rock.

[0027] When Rc>60MPa, 500≤V P变化量 <1000,500≤V s变化量 <1000,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level;

[0028] When Rc>60MPa, 1000≤V P变化量 0, 1000 ≤ V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 , if three of the four conditions are met, the surrounding rock grade is adjusted by 2 levels;

[0029] When Rc≤60MPa, 300≤V P变化量 <600,300≤V s变化量 <600,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level;

[0030] When Rc≤60MPa, 600≤V P变化量 0,600≤V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 If three of the four conditions are met, the surrounding rock grade will be adjusted by 2 levels.

[0031] Step 4. Based on the groundwater judgment results obtained in step 1 and the surrounding rock comprehensive judgment results obtained in step 3, make a comprehensive judgment on geological risks.

[0032] like Figure 1 As shown, the present invention first performs a geological sketch of the tunnel face, completes data collection, determines the target point base data, judges the integrity of the tunnel face surrounding rock, the degree of groundwater development, and formulates a comprehensive target point judgment result; then performs long-distance data collection and utilization, combines TSP and geological radar results for difference analysis, and finally performs long-distance surrounding rock integrity judgment based on the size of the difference jitter data. This can effectively eliminate the judgment error caused by the detection results of the absolute rock mass longitudinal wave velocity Vp, rock mass shear wave Vs, Poisson's ratio and dynamic Young's modulus, and improve the accuracy of the comprehensive advanced geological forecast.

[0033] Embodiment 1:

[0034] Implementation step 1: Conduct target sketch collection on the tunnel face, and determine the grade of the tunnel face surrounding rock based on the target sketch results. Comprehensive analysis shows that the tunnel face rock mass is gneiss interbedded with quartz schist, the surrounding rock is broken, the tunnel face rock mass compressive strength is 60-80 MPa, the longitudinal wave velocity is 4314 m / s, and the transverse wave velocity of the tunnel face rock mass is 2435 m / s. Comprehensively judged to be Grade IV surrounding rock, the target surrounding rock is determined to be Grade IV surrounding rock based on the tunnel face sketch.

[0035] Implementation Step 2: Generate a histogram of the changes in Vs and Vp. Perform a difference curve analysis of the long-distance P- and S-wave data, and generate a difference histogram to analyze the difference between the difference and the judgment standard.

[0036] Implementation step 3: Form a histogram of the change in Poisson's ratio and dynamic Young's modulus. To analyze the long-distance Poisson's ratio and dynamic Young's modulus, first form a curve chart of the data change value.

[0037] Implementation Step 4: Perform a data difference analysis, then classify the surrounding rock mass based on the difference results to form the final predicted surrounding rock mass grade. Data analysis indicates that the dynamic Young's modulus and longitudinal and transverse wave changes increase 10-15m and 20-25m ahead of the tunnel face. These increases align with the assessment criteria. Therefore, based on these analysis results, the surrounding rock mass of this section of the tunnel was adjusted from Grade IV to Grade III.

[0038] Final construction verification: The geological conditions ahead of the tunnel face are predicted based on the difference, which can effectively eliminate the error caused by the absolute parameter value of the TSP prediction. During the construction process, the tunnel face geological sketch is used to realistically depict the tunnel surrounding rock to further verify the accuracy of the algorithm. Through the results of the tunnel face geological sketch and the statistical analysis of the geological sketch results, such as Figure 2 , Figure 3 As shown, the surrounding rock grade statistics and predicted values ​​were compared with the predicted values, which are shown in Table 1. The excavation revealed that this method is accurate in predicting the surrounding rock.

[0039] Table 1. Comparison of geological conditions revealed by target point difference method prediction results during construction of Example 1

[0040]

[0041] Example 2:

[0042] Implementation step 1: Conduct target sketch collection on the tunnel face, and determine the grade of the tunnel face surrounding rock based on the target sketch results. Comprehensive analysis shows that the tunnel face rock mass is Proterozoic gneiss, the surrounding rock is relatively intact, the tunnel face rock mass has a compressive strength of 70-95 MPa, a longitudinal wave velocity of 4922 m / s, and a transverse wave velocity of 2798 m / s. Comprehensively judged to be Grade III surrounding rock, combined with the tunnel face sketch, the target surrounding rock is determined to be Grade III surrounding rock.

[0043] Implementation step 2: Form a histogram of the changes in Vs and Vp (see Figure 4 ). Perform difference curve analysis of long-distance longitudinal and shear wave data, and generate a difference bar chart to analyze the difference between the difference and the judgment standard.

[0044] Implementation step 3: Form a histogram of the Poisson's ratio and dynamic Young's modulus changes. To analyze the Poisson's ratio and dynamic Young's modulus over a long distance, first form a curve of the data change value (see Figure 5 ).

[0045] Implementation Step 4: Conduct a difference analysis of various data types. Based on these differences, the surrounding rock grade is then classified to form the final predicted surrounding rock grade. Data chart analysis shows an increase in the longitudinal and transverse wave variation values ​​at 26.6m, 58.8m, and 64.1m ahead of the tunnel face, ranging from 500 to 1000. The Poisson's ratio at 58.2 and 62.31m reaches 0.05, and the dynamic Young's modulus at 58.2 and 62.31m varies between 5 and 10. These data analysis indicates that the surrounding rock in this area requires an adjustment of one level, and the numerical increase aligns with the judgment criteria. Therefore, based on these analysis results, the surrounding rock of the 50-70m section of the tunnel face is adjusted from Grade III to Grade IV.

[0046] Final construction verification: The geological conditions ahead of the tunnel face are predicted based on the difference, which can effectively eliminate the error caused by the absolute parameter value of the TSP prediction. During the construction process, the tunnel face geological sketch is used to realistically depict the tunnel surrounding rock to further verify the accuracy of the algorithm. Through the results of the tunnel face geological sketch and the statistical analysis of the geological sketch results, such as Figure 4 and Figure 5 As shown in the figure, the surrounding rock grade statistics and predicted values ​​were compared with the predicted values, which are shown in Table 2. The excavation revealed that the method was accurate in predicting the surrounding rock.

[0047] Table 2. Comparison of geological conditions revealed by target point difference method prediction results during construction of Example 2

[0048]

[0049] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A long-distance advanced geological prediction fusion intelligent analysis method based on target point difference, characterized by: The steps include: Step 1. Conduct target point sketching on the tunnel face and use the results to determine the surrounding rock integrity, rock grade, and groundwater development. According to the Railway Tunnel Design Code TB1003-2016, tunnel surrounding rock is classified into six categories: I, II, III, IV, V, and VI. Step 2. Perform long-distance detection in front of the tunnel face to extract the target point physical property parameters and the TSP detection physical property parameters in front of the detection body; Step 3. Perform a difference analysis between the target point and the front of the detection volume to determine the numerical jitter deviation. Based on the jitter deviation, determine the change in surrounding rock grade. Adjust the surrounding rock grade in accordance with the "Code for Geological Investigation for Railway Engineering" TB 10012-2019 to achieve a comprehensive assessment of the surrounding rock. Step 4. Based on the groundwater judgment results obtained in step 1 and the surrounding rock comprehensive judgment results obtained in step 3, make a comprehensive judgment on geological risks.

2. The long-distance advanced geological prediction fusion intelligent analysis method based on target point difference according to claim 1 is characterized by: In step 2, the range of long-distance detection is from the target point to 100m in front of the detection object.

3. The long-distance advanced geological prediction fusion intelligent analysis method based on target point difference according to claim 1 or 2 is characterized in that: The surrounding rock grade adjustment algorithm in step 3 is as follows: When Rc>60MPa, 500≤V P变化量 <1000,500≤V s变化量 <1000,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level; When Rc>60MPa, 1000≤V P变化量 0, 1000 ≤ V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 , if three of the four conditions are met, the surrounding rock grade is adjusted by 2 levels; When Rc≤60MPa, 300≤V P变化量 <600,300≤V s变化量 <600,0.05≤μ 变化量 <0.1,5≤E 变化量 <10, if three of the four conditions are met, the surrounding rock grade is adjusted by 1 level; When Rc≤60MPa, 600≤V P变化量 0,600≤V s变化量 ,0.1≤μ 变化量 , 1-≤E 变化量 If three of the four conditions are met, the surrounding rock grade will be adjusted by 2 levels.

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