Comprehensive advanced geological forecasting method based on geological analysis
By combining geological analysis and geophysical exploration, a three-dimensional geological model is constructed and unstable block analysis is performed. Long- and short-range forecasts are made using the TGS method and ground-penetrating radar method, which solves the problem of insufficient forecast accuracy in tunnel construction and achieves more efficient and safer advanced geological forecasting for tunnels.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-31
AI Technical Summary
Existing single advanced geological prediction methods in tunnel construction suffer from problems such as long time, high labor cost, poor safety, numerous interference waves, low signal-to-noise ratio, and insufficient prediction accuracy, especially under complex geological conditions where prediction deviations are large.
By combining geological analysis and geophysical exploration methods, an unstable block analysis is conducted by constructing a three-dimensional geological model. Long- and short-range forecasts are then made using the TGS method and ground-penetrating radar method to comprehensively determine the risk sections of the tunnel surrounding rock and improve the accuracy of the forecasts.
It has improved the accuracy of tunnel geological forecasting, reduced the impact of construction, enhanced safety and data reliability, and enabled accurate forecasting under complex geological conditions.
Smart Images
Figure CN121763443A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a comprehensive advanced geological prediction method based on geological analysis, belonging to the field of advanced geological prediction technology. Background Technology
[0002] Through long-term research and practice both domestically and internationally, advanced geological prediction technology has developed into various methods, which can be mainly summarized as geological prediction, geophysical prediction, and comprehensive prediction. Geological prediction involves systematically collecting and analyzing geological data, applying geological theories and methods to compare, demonstrate, and infer the engineering geological conditions ahead of the tunnel to achieve advanced prediction. Specific methods include surface geological surveys, tunnel face geological logging, and advanced drilling. Common methods include geological survey and analysis, advanced exploration, and comprehensive geological analysis. Geophysical methods are mainly divided into seismic wave methods and electromagnetic wave methods. Seismic wave methods currently mainly include TSP, TRT, TST, TGP, and TGS methods; electromagnetic wave methods include ground-penetrating radar, transient electromagnetic methods, induced polarization methods, and focused current methods.
[0003] However, existing seismic wave methods have the following problems in tunnel advanced geological prediction: ① They take a long time, affecting construction, and require drilling multiple deep blasting holes and receiving holes, which is time-consuming and labor-intensive; ② They have poor safety and are highly dependent on the environment. Generally, blasting is used as the seismic source, which requires professional personnel to operate and approval from the safety department. They are only applicable to sites where blasting is permitted; ③ They have many interference waves and low signal-to-noise ratio. The signals received by the detector are mixed with various interferences such as surface waves, sound waves, and lateral reflection waves, which leads to signal distortion, makes filtering difficult, and affects the accuracy of prediction.
[0004] At the same time, the existing method of using a single approach for advanced geological forecasting has certain limitations. Coupled with the complex and ever-changing geological conditions, this can lead to problems such as extremely large forecast deviations. Summary of the Invention
[0005] This invention provides a comprehensive advanced geological forecasting method based on geological analysis, which can solve the problem that existing single forecasting methods have certain limitations when performing advanced geological forecasting, resulting in poor forecasting accuracy.
[0006] This invention provides a comprehensive advanced geological prediction method based on geological analysis, the method comprising: S1. Conduct surface geological analysis on the target tunnel to obtain surface geological survey data of the target tunnel, and analyze the tunnel face of the predicted tunnel section to obtain tunnel face geological data. S2. Construct a three-dimensional geological model of the predicted tunnel segment based on the surface geological survey data and the tunnel face geological data, and determine the risk tunnel segment data of the predicted tunnel segment based on the three-dimensional geological model; S3. Use geophysical exploration methods to make advance predictions on the predicted tunnel section and obtain geological prediction data; S4. Determine the comprehensive forecast result of the target tunnel based on the surface geological survey data, the risk tunnel section data, and the geological forecast data.
[0007] Optionally, the geological forecast data includes long-range forecast data and short-range forecast data; S3 specifically includes: The target tunnel is predicted using the TGS method to obtain long-range prediction data, and potential dangerous tunnel sections are identified based on the long-range prediction data and the surface geological survey data. The potential dangerous tunnel section was predicted using ground-penetrating radar, and short-range prediction data was obtained.
[0008] Optionally, the comprehensive forecast results include information on the predicted dangerous tunnel sections and their corresponding surrounding rock categories; S4 specifically includes: Overlapping tunnel segments appearing in the surface geological survey data, the risk tunnel segment data, the long-distance forecast data, and the short-distance forecast data are identified as predicted dangerous tunnel segments. The location and number of the predicted dangerous tunnel segments are combined to form predicted dangerous tunnel segment information, and the surrounding rock category corresponding to the predicted dangerous tunnel segment is downgraded by one level.
[0009] Optionally, S1 specifically includes: The surface geology of the target tunnel was analyzed using geological analysis methods to obtain surface geological survey data of the target tunnel. The tunnel face of the predicted tunnel section was also analyzed using geological analysis methods to obtain tunnel face geological data.
[0010] Optionally, the construction of a three-dimensional geological model of the predicted tunnel segment based on the surface geological survey data and the tunnel face geological data in step S2 specifically includes: A geological information database is constructed based on the surface geological survey data and the working face geological data; The structural surface data from the geological information database are input into the modeling software to generate a three-dimensional geological model of the predicted tunnel segment.
[0011] Optionally, the step S2, determining the risk segment data of the predicted cave segment based on the three-dimensional geological model, specifically includes: Unstable block analysis was performed on the three-dimensional geological model to obtain risk segment data for the predicted tunnel segment.
[0012] Optionally, unstable block analysis is performed on the three-dimensional geological model to obtain risk segment data for the predicted cave segment, specifically including: Unstable blocks were calculated using the three-dimensional geological model to obtain the development characteristics of the unstable blocks; The surrounding rock hazard level of the predicted tunnel segment is determined based on the development characteristics of the unstable block, and the development characteristics of the unstable block and the surrounding rock hazard level are combined to form the risk segment data of the predicted tunnel segment.
[0013] Optionally, prior to S4, the method further includes: The three-dimensional distribution map of groundwater in the target tunnel is determined based on the long-distance forecast data.
[0014] The beneficial effects that this invention can produce include: The comprehensive advanced geological prediction method based on geological analysis provided by this invention combines geological analysis and geophysical methods when making advanced geological predictions of target tunnels. During the prediction process, a three-dimensional geological model is used to analyze unstable blocks, identify risky sections of the surrounding rock of the target tunnel, and compare and verify the predictions with geophysical methods (TGS method and ground-penetrating radar method). Specifically, this invention employs geological analysis to conduct surface geological surveys of the target tunnel, understanding the geological structural characteristics and existing adverse geological phenomena of the tunnel's location, and identifying key sections of the target tunnel requiring special attention. When this section is predicted, geological analysis is again used to comprehensively assess the surrounding rock conditions at the tunnel face (such as stratigraphic attitude, degree of development and combination of structural planes, groundwater conditions, etc.). By combining surface geological survey data with tunnel face geological data, a three-dimensional geological model is constructed to analyze unstable blocks, identify potential risk sections, and then compare and verify the results using geophysical methods. This significantly improves the accuracy of the prediction, forming a comprehensive and multi-layered advanced geological prediction and analysis method that mutually corroborates each other from regional to local, from surface to underground perspectives.
[0015] The comprehensive advanced geological prediction method based on geological analysis provided by this invention differs from single geophysical prediction methods. It combines the TGS method with ground-penetrating radar (GPR) for integrated long- and short-range predictions, which are then cross-verified. Existing technologies primarily employ TST, TSP, and TRT methods in seismic wave prediction, with limited integration with TGS360 Pro. This new technology offers advantages such as multiple operating modes, short data acquisition time, enhanced security, and 3D visualization. Specific engineering examples demonstrate the wide applicability and high accuracy of TGS360 Pro prediction, providing new ideas and methods for advanced geological prediction of water diversion tunnels and other underground caverns. Attached Figure Description
[0016] Figure 1 A flowchart of a comprehensive advanced geological prediction method based on geological analysis provided in an embodiment of the present invention; Figure 2A schematic diagram of a three-dimensional geological model of the predicted tunnel segment in the target tunnel provided in this embodiment of the invention; Figure 3 This is a schematic diagram of unstable block analysis using a three-dimensional geological model provided in an embodiment of the present invention. Detailed Implementation
[0017] The present invention will now be described in detail with reference to the embodiments, but the present invention is not limited to these embodiments.
[0018] This invention provides a comprehensive advanced geological prediction method based on geological analysis, such as... Figure 1 As shown, the method includes: S1. Conduct surface geological analysis on the target tunnel to obtain surface geological survey data of the target tunnel, and analyze the tunnel face of the predicted tunnel section to obtain tunnel face geological data.
[0019] In this embodiment of the invention, the surface geology of the target tunnel can be analyzed using geological analysis methods to obtain surface geology survey data of the target tunnel, and the tunnel face of the predicted tunnel section can be analyzed using geological analysis methods to obtain tunnel face geological data.
[0020] In practical applications, geological engineers review and analyze the preliminary geological survey data and bidding design blueprints for the project. They then use geological analysis methods to conduct a surface geological survey of the entire target tunnel. This involves further data collection and analysis of the topographical conditions, lithology, geological structural characteristics (location and scale of fault or fracture zone outcrops, degree of joint and fissure development), and the classification of the surrounding rock in the design drawings. This process identifies tunnel sections with adverse geological phenomena and yields surface geological survey data. When conducting advanced geological forecasting for predicted tunnel sections, geological analysis methods are again used to comprehensively assess the surrounding rock conditions at the tunnel excavation face (lithology, integrity, degree and combination of structural planes, groundwater conditions, etc.). This determines the surrounding rock category parameters and the distribution and extension of structural planes in unexcavated sections, resulting in geological data for the tunnel face.
[0021] S2. Construct a three-dimensional geological model of the predicted tunnel segment based on surface geological survey data and tunnel face geological data, and determine the risk segment data of the predicted tunnel segment based on the three-dimensional geological model.
[0022] The above-mentioned construction of a three-dimensional geological model of the predicted tunnel segment based on surface geological survey data and tunnel face geological data specifically includes: firstly, constructing a geological information database based on surface geological survey data and tunnel face geological data; then, inputting the structural surface data in the geological information database into the modeling software to generate a three-dimensional geological model of the predicted tunnel segment.
[0023] The above-mentioned risk segment data for predicting cave sections is determined based on the three-dimensional geological model. Specifically, unstable block analysis is performed on the three-dimensional geological model to obtain the risk segment data for the predicted cave sections.
[0024] Specifically, unstable block calculations are performed on the three-dimensional geological model to obtain the development characteristics of the unstable blocks; then, the surrounding rock hazard level of the predicted tunnel section is determined based on the development characteristics of the unstable blocks, and the development characteristics of the unstable blocks and the surrounding rock hazard level are combined to form the risk tunnel section data of the predicted tunnel section.
[0025] In practical applications, surface geological survey data and tunnel face geological data obtained based on geological analysis are jointly compiled to establish a geological information database. The structural surface data in this database are then imported into 3D geological modeling software to form a 3D geological model of the target tunnel segment for prediction. Figure 2 As shown, this three-dimensional geological model can intuitively display the distribution and extension of structural surfaces and their combined cutting relationships in the unexcavated tunnel section. Furthermore, by using the software's built-in program to calculate and analyze unstable blocks, it can quantitatively predict the distribution of unfavorable geological bodies and the development characteristics of unstable blocks in the unexcavated tunnel section, such as... Figure 3 As shown, data such as volume, weight, surface area, and embedment depth of each unstable block can be obtained. This is a dynamic adjustment analysis process. When a new structural surface is encountered during excavation, the geological information database is updated in a timely manner, and the data is imported into the three-dimensional geological model for secondary calculations. This ensures that the analysis and calculations conform to the actual excavation and exposure conditions, and also improves the accuracy of unstable block analysis.
[0026] S3. Use geophysical methods to make advance predictions on the predicted tunnel sections and obtain geological prediction data.
[0027] Geological forecast data includes long-range forecast data and short-range forecast data.
[0028] Specifically, it includes: First, the target tunnel is predicted long distance using the TGS method to obtain long distance prediction data. Then, potential dangerous tunnel sections are identified based on the long distance prediction data and surface geological survey data. Then, ground-penetrating radar is used to make short-range predictions of potentially dangerous tunnel sections, and short-range prediction data is obtained.
[0029] In this invention, the TGS360 PRO can be used for long-distance prediction. The TGS360 Pro prediction system is based on the prediction method of seismic reflection waves with different polarizations. The system mainly consists of a control unit, a receiving unit, and related accessories. Under suitable geological conditions, it can achieve an effective detection distance of 100m to 150m. During prediction, seismic waves propagate in the surrounding rock of the tunnel in the form of spherical waves. When the wave impedance of the surrounding rock changes (such as encountering karst caves, faults or rock strata interfaces, weak interlayers or joint and fracture zones, groundwater, etc.), part of the seismic waves will be reflected back, and the other part will continue to propagate forward. The partially reflected seismic waves are received by a high-precision receiver and transmitted to the host to form a seismic wave record. After the prediction test is completed, professional software is used for data processing to obtain intermediate data such as seismic wave velocity, water content probability, Poisson's ratio, Young's modulus, and hazard level in front of the tunnel face, forming long-distance prediction data.
[0030] This invention employs ground-penetrating radar (GPR) for short-range forecasting. Specifically, the CrossOver pulse radar system is used for short-range advanced geological exploration during tunnel forecasting. This electromagnetic technology uses radio waves to detect the distribution of underground media and scan invisible targets or underground interfaces to determine their internal structure or location. Its effective detection range is generally 30-40 meters. When high-frequency electromagnetic waves are emitted in broadband pulse form, their path, electromagnetic field strength, and waveform change with the electrical properties and geometry of the medium they pass through as they propagate. By acquiring, processing, and analyzing the time-domain waveform, the presence of cavities ahead of the tunnel, the integrity of the rock mass, and the distribution of groundwater can be determined. This invention utilizes GPR to conduct short-range forecasting of potentially hazardous tunnel sections identified based on geological analysis and long-range forecasting using the TGS360 PRO, thereby obtaining short-range forecasting data.
[0031] S4. Determine the comprehensive forecast results of the target tunnel based on surface geological survey data, risk tunnel section data, and geological forecast data.
[0032] The comprehensive forecast results include information on dangerous tunnel sections and their corresponding surrounding rock categories.
[0033] Specifically, this includes: identifying overlapping cave segments in surface geological survey data, risk cave segment data, long-range forecast data, and short-range forecast data as predicted dangerous cave segments; compiling the location and number of predicted dangerous cave segments into predicted dangerous cave segment information; and downgrading the surrounding rock category corresponding to the predicted dangerous cave segments by one level.
[0034] This invention summarizes and establishes a database of various forecast results obtained from geological analysis, unstable block analysis using a three-dimensional geological model, long-range forecasting using TGS360 PRO, and short-range forecasting using ground-penetrating radar. This database forms a database of surrounding rock classification and risk level assessment results for adverse geological bodies, specifically including the following data: (1) The surface geological survey data obtained from the geological analysis (including the strata lithology changes of the tunnel section, the existing cross-ditch section, the tunnel section affected by the fault fracture zone, etc.) are recorded as a, and the corresponding data are a1, a2, a3, ......; (2) The risk cave segment data of potential unstable block distribution obtained from the three-dimensional geological model is denoted as b, and the corresponding data are b1, b2, b3, ...; (3) The long-range forecast data obtained by TGS360 PRO is denoted as c, and the corresponding data are denoted as c1, c2, c3, ...; (4) Based on the geological analysis and the long-range forecast results of TGS360 PRO, the short-range forecast data obtained by the ground-penetrating radar is denoted as d, and the corresponding data are denoted as d1, d2, d3, ...
[0035] Then, a model for classifying the surrounding rock of the target tunnel and evaluating the risk level of adverse geological bodies is constructed. Specifically, by comparing and analyzing the forecast data of a, b, c, and d, tunnel segments with overlapping results are obtained. Tunnel segments with high overlap (in practical applications, this can be tunnel segments with overlapping characteristics) are classified as predicted dangerous tunnel segments, and the corresponding surrounding rock category is downgraded by one level. When excavating to this tunnel segment, special attention needs to be paid to prevention. In this way, accurate classification of surrounding rock level and identification of dangerous tunnel segments where adverse geological bodies are located can be carried out.
[0036] Furthermore, prior to S4, the method also includes: determining a three-dimensional distribution map of groundwater in the target tunnel based on long-distance forecast data.
[0037] After obtaining long-range forecast data, the data can be imported into the supporting software to obtain three-dimensional views of groundwater bodies, allowing technicians to have a clearer and more intuitive understanding of the distribution of groundwater bodies.
[0038] Compared with the prior art, the present invention has the following beneficial effects: (1) Three-dimensional visualization of adverse geological body prediction. Based on geological analysis, this invention establishes a three-dimensional geological model of the predicted tunnel section of the target tunnel, which can intuitively and three-dimensionally display the distribution and extension of the structural surface of the unexcavated tunnel section and the combination and cutting relationship. Then, through quantitative analysis and calculation, the development characteristics such as the distribution location, size and volume of adverse geological bodies are obtained. Correspondingly, the risk level of the surrounding rock of this tunnel section increases accordingly. When excavating to these dangerous tunnel sections, collapse and rockfall are very likely to occur. Therefore, it can also serve as the basis for adjusting the blasting construction technology and support measures.
[0039] (2) High forecast accuracy. This invention combines three-dimensional geological modeling of geological analysis results with quantitative calculation and geophysical exploration methods, and compares and analyzes multiple sets of results to verify the forecast results, making the forecast results more realistic, reliable, and consistent with the actual geological conditions on site. At the same time, according to the statistical analysis of advanced geological forecast data carried out in the 5km long water diversion tunnel of the Horgut Hydropower Station project, the overall accuracy of forecasting using the method of this invention is over 72%, and the accuracy of most tunnel sections can reach 100%.
[0040] (3) The method of the present invention can solve the problems of excessive interference waves, difficult filtering technology, serious data distortion, time and effort and safety when using the seismic wave method for advanced geological prediction of water diversion tunnels in the prior art.
[0041] The above description is merely a few embodiments of this application and is not intended to limit this application in any way. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any changes or modifications made by those skilled in the art without departing from the scope of the technical solution of this application using the disclosed technical content are equivalent to equivalent implementation cases and fall within the scope of the technical solution.
Claims
1. A comprehensive advanced geological prediction method based on geological analysis, characterized in that, The method includes: S1. Conduct surface geological analysis on the target tunnel to obtain surface geological survey data of the target tunnel, and analyze the tunnel face of the predicted tunnel section to obtain tunnel face geological data. S2. Construct a three-dimensional geological model of the predicted tunnel segment based on the surface geological survey data and the tunnel face geological data, and determine the risk tunnel segment data of the predicted tunnel segment based on the three-dimensional geological model; S3. Use geophysical exploration methods to make advance predictions on the predicted tunnel section and obtain geological prediction data; S4. Determine the comprehensive forecast result of the target tunnel based on the surface geological survey data, the risk tunnel section data, and the geological forecast data.
2. The method according to claim 1, characterized in that, The geological forecast data includes long-range forecast data and short-range forecast data; S3 specifically includes: The target tunnel is predicted using the TGS method to obtain long-range prediction data, and potential dangerous tunnel sections are identified based on the long-range prediction data and the surface geological survey data. The potential dangerous tunnel section was predicted using ground-penetrating radar, and short-range prediction data was obtained.
3. The method according to claim 2, characterized in that, The comprehensive forecast results include information on predicted dangerous tunnel sections and their corresponding surrounding rock categories; S4 specifically includes: Overlapping tunnel segments appearing in the surface geological survey data, the risk tunnel segment data, the long-distance forecast data, and the short-distance forecast data are identified as predicted dangerous tunnel segments. The location and number of the predicted dangerous tunnel segments are combined to form predicted dangerous tunnel segment information, and the surrounding rock category corresponding to the predicted dangerous tunnel segment is downgraded by one level.
4. The method according to claim 1, characterized in that, S1 specifically includes: The surface geology of the target tunnel was analyzed using geological analysis methods to obtain surface geological survey data of the target tunnel. The tunnel face of the predicted tunnel section was also analyzed using geological analysis methods to obtain tunnel face geological data.
5. The method according to claim 1, characterized in that, The three-dimensional geological model of the predicted tunnel segment constructed in S2 based on the surface geological survey data and the tunnel face geological data specifically includes: A geological information database is constructed based on the surface geological survey data and the working face geological data; The structural surface data from the geological information database are input into the modeling software to generate a three-dimensional geological model of the predicted tunnel segment.
6. The method according to claim 1, characterized in that, The risk segment data for the predicted tunnel segment determined based on the three-dimensional geological model in S2 specifically includes: Unstable block analysis was performed on the three-dimensional geological model to obtain risk segment data for the predicted tunnel segment.
7. The method according to claim 6, characterized in that, Unstable block analysis was performed on the three-dimensional geological model to obtain risk segment data for the predicted cave segment, specifically including: Unstable blocks were calculated using the three-dimensional geological model to obtain the development characteristics of the unstable blocks; The surrounding rock hazard level of the predicted tunnel segment is determined based on the development characteristics of the unstable block, and the development characteristics of the unstable block and the surrounding rock hazard level are combined to form the risk segment data of the predicted tunnel segment.
8. The method according to claim 2, characterized in that, Prior to S4, the method further includes: The three-dimensional distribution map of groundwater in the target tunnel is determined based on the long-distance forecast data.