Tunnel water control structure based on hydrochemistry spatio-temporal evolution and water burst risk prediction method

Abstract

The invention discloses a tunnel water control structure based on hydrochemistry spatio-temporal evolution and a water burst risk prediction method, and relates to the technical field of tunnel geology advanced prediction and disaster prevention and reduction control. The tunnel water control structure and water gushing risk prediction method based on hydrochemistry spatio-temporal evolution mainly comprises the following steps: a system collects, monitors, tests and analyzes the hydrochemistry characteristics of underground water samples at different parts such as a tunnel face and the periphery in a tunnel excavation process, and constructs a hydrochemistry parameter spatio-temporal evolution model; and judging whether the runoff intensity in front of the tunnel face is communicated with the water control structure in advance, and evaluating the water burst risk level. By implementing the tunnel water control structure and water gushing risk prediction method based on hydrochemistry spatio-temporal evolution provided by the invention, the accuracy of water control structure identification and the scientificity of water gushing risk prediction can be improved.

Description

Technical Field

[0001] This invention relates to the field of tunnel geological advance prediction and disaster prevention and mitigation control technology, and more specifically, to a tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry. Background Technology

[0002] In deep-buried mountain tunnels, high-pressure, sudden-rush groundwater disasters are the primary geological risks restricting safety, construction period, and cost. These disasters are mostly controlled by water-conducting structures such as faults and densely fractured zones, and are characterized by their suddenness, large water volume, and difficulty in handling. Currently, the prediction of water-rich structures ahead during tunnel construction mainly relies on geophysical advance forecasting and advance drilling. However, geophysical advance forecasting suffers from significant ambiguity under complex hydrogeological conditions, making it difficult to distinguish between water-rich faults or fracture zones and water-free weak rock zones, and it cannot determine the dynamic state of groundwater. It can only qualitatively delineate suspected water-bearing anomalies and cannot support differentiated prevention and control decisions. Although advance drilling is a direct verification method, it has blind spots due to limited observation, is prone to false alarms and missed alarms, and can only obtain instantaneous water outflow data, unable to determine the source of water bodies and runoff patterns, leading to frequent deviations in disaster risk assessment.

[0003] Hydrogeochemical studies have shown that the chemical composition of groundwater is a comprehensive reflection of its formation environment and transport history. Groundwater in active runoff zones has a short contact time with the surrounding rocks, a weak water-rock interaction, and insufficient hydrochemical evolution, typically exhibiting low total dissolved solids (TDS) and simple ionic composition. Conversely, stagnant water bodies in closed- to semi-closed fracture systems, having undergone a long water-rock interaction process, have undergone more thorough hydrochemical evolution, often exhibiting complex hydrochemical types with high mineralization and enrichment of specific ions.

[0004] How to utilize the chemical composition information of groundwater to predict whether the water-controlling structure maintains a safe distance from the tunnel face, and to identify the source of water and the hydrodynamic characteristics of the water-controlling structure, thereby enabling the identification and early warning of sudden water inrush in tunnels, is an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting tunnel water control structures and water inrush risks based on the spatiotemporal evolution of hydrochemistry, which can improve the accuracy of water control structure identification and the scientific nature of water inrush risk prediction.

[0006] This invention provides a method for predicting tunnel water control structures and water inrush risks based on the spatiotemporal evolution of hydrochemistry, comprising the following steps:

[0007] S1: Obtain multi-source geophysical exploration data for the current area, and obtain a water chemical characteristic monitoring network based on the multi-source geophysical exploration data; S2: Use the aforementioned water chemistry characteristic monitoring network to collect groundwater samples and conduct in-situ tests to obtain in-situ test data of groundwater; S3: Conduct indoor water chemical composition tests on the collected groundwater samples to obtain total dissolved solids data and main anion and cation concentration data; S4: Based on the in-situ test data and the main anion and cation concentration data, construct an index system to characterize the hydrochemical characteristics of groundwater; S5: Based on the lithology of the surrounding rock, construct a combination of sensitive identification indicators according to the aforementioned indicator system; S6: Calculate the average value of the water chemical test results of multiple water samples initially obtained from deep monitoring wells in the water chemical characteristic monitoring network, calculate the corresponding sensitive identification index using the sensitive identification index combination, and obtain the background value of water chemical characteristics of stagnant fracture water in the current area; calculate the sensitive identification index of the maximum water outlet point in the tunnel in the water chemical characteristic monitoring network. S7: Determine that the sensitive identification index of the maximum water outlet point of the tunnel is consistent with the background value of the water chemical characteristics, and construct the spatiotemporal evolution sequence of the water chemical characteristics; S8: Based on the spatiotemporal evolution sequence of the hydrochemical characteristics, the background value of the hydrochemical characteristics, and the sensitive identification index of the maximum water outlet point of the tunnel, the water-controlling structural boundary is obtained using the determination criteria of the water-controlling structural boundary. S9: Based on the most sensitive indicator in the sensitive identification indicator combination, the water flow from the fracture, and the size parameters of the geophysical anomaly, the water inrush risk is predicted, and the water inrush risk level is obtained.

[0008] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry.

[0009] Implementing the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry provided by this invention has the following beneficial effects: This invention addresses water-controlling structures such as fault zones and densely fractured zones where deep-buried tunnels pass through. By integrating multi-source geophysical exploration data to construct a hydrochemical characteristic monitoring network, and through standardized on-site water sample collection, in-situ testing, and indoor hydrochemical component analysis, a sensitive identification index system adapted to different surrounding rock lithologies is established. The background values ​​of hydrochemical characteristics are calibrated, and a hydrochemical spatiotemporal evolution sequence is constructed. Based on the combination criteria of the most sensitive indicator mutation, strong corroborating indicators, and confirming indicators synergistic response, the groundwater runoff intensity is accurately predicted and the water-controlling structural boundary is determined. By coupling the percentage of sensitive indicator mutation, fissure water output, and geophysical anomaly scale, quantitative prediction of water inrush risk by lithology and level is achieved.

[0010] In summary, this invention systematically collects, monitors, tests, and analyzes the hydrochemical characteristics of groundwater samples from different locations, including the tunnel face and surrounding areas, during tunnel excavation. It then constructs a spatiotemporal evolution model of hydrochemical parameters, thereby proactively determining the runoff intensity ahead of the tunnel face, whether it is connected to water-controlling structures, and assessing the risk level of water inrush. This effectively improves the accuracy of water-controlling structure identification and the scientific nature of water inrush risk prediction, providing key target area guidance for grouting and water plugging design, and offering reliable technical support for tunnel construction safety control. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of water chemistry provided by the present invention; Figure 2 This is a flowchart of the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry provided by the present invention. Figure 3 This is a schematic diagram of the comprehensive geophysical exploration layout for the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry provided by the present invention. Figure 4 This is a schematic diagram of the tunnel water chemistry monitoring network layout based on the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of water chemistry provided by the present invention. Among them, 1. Fault, 2. Fault-affected fracture zone, 3. Surrounding rock, 4. Deep monitoring hole, 5. Water-emerging fracture, 6. Tunnel, 7. L1 deepened blast hole, 8. L2 deepened blast hole; Figure 5 This invention provides a spatial evolution sequence of hydrochemical parameters for tunnel water control structures and water inrush risk prediction methods based on the spatiotemporal evolution of hydrochemistry. Figure 6 This invention provides a time evolution sequence of hydrochemical parameters for tunnel water control structures and water inrush risk prediction methods based on the spatiotemporal evolution of hydrochemistry. Detailed Implementation

[0012] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0013] Figure 1 This diagram illustrates a method for predicting tunnel water control structures and inrush risks based on the spatiotemporal evolution of hydrochemistry, as described in this embodiment. In this embodiment, the method for predicting tunnel water control structures and inrush risks based on the spatiotemporal evolution of hydrochemistry includes the following steps: S1: Obtain multi-source geophysical exploration data for the current region, and obtain a water chemical characteristic monitoring network based on the multi-source geophysical exploration data.

[0014] In one exemplary embodiment, the multi-source geophysical exploration data includes macroscopic geometric parameters and occurrence environment characteristics of geological anomalies of water-bearing faults; the macroscopic geometric parameters include the scale and spatial distribution of the geological anomalies; the occurrence environment characteristics include water abundance and degree of fragmentation.

[0015] In one exemplary embodiment, the method for acquiring multi-source geophysical exploration data includes: using the tunnel seismic wave advance prediction method to delineate anomaly areas through long-distance exploration as target areas, using the transient electromagnetic method to conduct medium-range exploration of the target areas and delineate key sections in front of water-controlling structures, and using the ground-penetrating radar method to conduct short-range exploration of the key sections in front of the water-controlling structures, thereby obtaining multi-source geophysical exploration data.

[0016] In one exemplary embodiment, the hydrochemical characteristic monitoring network includes groundwater hydrochemical characteristic background monitoring points, groundwater deepening borehole monitoring points, and groundwater dynamic monitoring points on the tunnel surface.

[0017] In one exemplary embodiment, the method for determining the background monitoring points for groundwater hydrochemical characteristics includes: targeting large-scale... Large, intersects with the tunnel at a large angle, and has moderate water content. Strong Strong, moderate degree of fragmentation For high geological anomalies, deep monitoring boreholes with an oblique downward orientation are set up at the arch foot of the section where the maximum water outlet of the tunnel is located within 10-30m behind the key section detected by ground-penetrating radar as background monitoring points for groundwater hydrochemical characteristics. The opening of the deep monitoring borehole is not less than 0.5m higher than the lowest point of the excavated tunnel, and the bottom of the deep monitoring borehole is not less than 1m higher than the lowest point of the excavated tunnel.

[0018] In one exemplary embodiment, the method for determining the monitoring points inside the groundwater deepening borehole includes: starting from the key cross-section detected by ground-penetrating radar, setting up monitoring points on each excavation face. Each deepened blast hole serves as a monitoring point for groundwater infiltration. The deepened blast holes are aligned with the tunnel excavation direction, and their depth exceeds the excavation advance by 3-6 meters.

[0019] In one exemplary embodiment, the method for determining the groundwater dynamic monitoring points on the tunnel free face includes: for the largest water-producing fissure in the deepened blast hole, tracing the development location of the largest water-producing fissure on the tunnel free face and setting up monitoring points as groundwater dynamic monitoring points on the tunnel free face.

[0020] In one exemplary embodiment, the spacing and sampling frequency of the monitoring points are determined according to the classification of fissure water outflow.

[0021] In one exemplary embodiment, when the groundwater discharge state of the monitoring point is wet or dripping and the discharge volume Q 25 The monitoring points are spaced 15-20m apart, and the sampling frequency is once every 3 days. If there are no fluctuations in the data for 3 consecutive times, the frequency is extended to once every 5 days. When the groundwater discharge from the monitoring points is in the form of rain or linear flow and the discharge volume is 25... Q 125 The monitoring points are spaced 10-15m apart, and the sampling frequency is once a day. If the water discharge Q fluctuates by more than 20%, the frequency is increased to twice a day. When the groundwater discharge state of the monitoring points is a gushing flow and the water discharge Q is... 125 The monitoring points are set up at intervals of 5-10m, and the sampling frequency is twice a day. If the outflow exceeds 30% per day, the frequency is increased to once every 3 hours.

[0022] S2: Use the aforementioned hydrochemical characteristic monitoring network to collect groundwater samples and conduct in-situ tests to obtain in-situ test data of groundwater.

[0023] In one exemplary embodiment, the in-situ test data includes water temperature, pH value, conductivity, and redox potential.

[0024] As an exemplary embodiment, the on-site groundwater sampling specifically includes: sampling points including the maximum water outlet point of the tunnel and the bottom of the borehole in the deep monitoring well, the maximum water outlet fissure in the deepened blast hole, and the groundwater monitoring point on the tunnel free face; water samples are collected using 500mL high-density polyethylene bottles that have been washed three times with deionized water and dried to constant weight at 105℃, with two bottles collected for each sample, one for cation testing (acidified with nitric acid to pH < 2) and the other for anion testing (preserving the original water state). After collection, the samples are stored in a 0-4℃ low-temperature insulated box to avoid light and for shockproof transportation; the sampling points at the maximum water outlet point in the tunnel, the bottom of the borehole, and the deepened blast hole are sampled only once immediately after being set up, and the monitoring points on the tunnel free face are sampled for the first time immediately after excavation, with subsequent sampling frequencies implemented according to the grading standard; the in-situ testing includes: using a pH meter, conductivity EC meter, redox potentiometer, and thermometer to perform in-situ testing on the water samples, with the accuracy requirement being: water temperature... 0.1℃, pH value 0.01, conductivity is Oxidation-reduction potential is 5mV.

[0025] S3: Conduct indoor water chemical composition tests on the collected groundwater samples to obtain total dissolved solids data and main anion and cation concentration data.

[0026] In one exemplary embodiment, the main anion and cation concentration data includes , , , , , , , Concentration data.

[0027] S4: Based on the in-situ test data and the main anion and cation concentration data, construct an index system to characterize the hydrochemical characteristics of groundwater.

[0028] In one exemplary embodiment, the indicator system includes stagnation degree indicator ratio, runoff intensity indicator ratio, sodium chloride coefficient, desulfurization coefficient, calcium magnesium coefficient, and total dissolved solids.

[0029] S5: Based on the lithology of the surrounding rock, construct a combination of sensitive identification indicators according to the aforementioned indicator system.

[0030] In one exemplary embodiment, the surrounding rock lithology includes carbonate rocks, clastic rocks, igneous rocks, and metamorphic rocks.

[0031] In one exemplary embodiment, the sensitive identification index combination includes the most sensitive index, strong corroborating index, and confirming index corresponding to each type of surrounding rock lithology.

[0032] As an exemplary embodiment, the combination of sensitive identification indicators based on the lithology of the surrounding rock in step S5 is as follows: For carbonate rocks, the most sensitive indicator is the calcium-magnesium coefficient (RCM), the strongly corroborating indicator is the stagnation index (SIR), and the confirming indicator is the total dissolved solids (TDS), combined with electrical conductivity (EC); for clastic rocks, the most sensitive indicator is the sodium-chlorine coefficient (RNC), the strongly corroborating indicator is the desulfurization coefficient (RSC), and the confirming indicator is the runoff intensity index (FIR), combined with redox potential (Eh); for igneous and metamorphic rocks, the most sensitive indicator is the stagnation index (SIR), the strongly corroborating indicator is the total dissolved solids (TDS), and the confirming indicator is the total potassium and sodium content (…). ).

[0033] S6: Calculate the average value of the water chemical test results of multiple water samples initially obtained from deep monitoring wells in the water chemical characteristic monitoring network, calculate the corresponding sensitive identification index using the sensitive identification index combination, and obtain the background value of water chemical characteristics of stagnant fracture water in the current area; calculate the sensitive identification index of the maximum water outlet point in the tunnel in the water chemical characteristic monitoring network.

[0034] S7: Determine that the sensitive identification index of the maximum water outlet point of the tunnel is consistent with the background value of the water chemical characteristics, and construct the spatiotemporal evolution sequence of the water chemical characteristics.

[0035] In one exemplary embodiment, the spatiotemporal evolution sequence of water chemical characteristics includes a spatial evolution sequence along the tunnel mileage and a temporal evolution sequence of a single monitoring point over time, the temporal evolution sequence being associated with effluent status data.

[0036] In one exemplary embodiment, the process of constructing the spatial evolution sequence includes: starting from the background value of water chemical characteristics, plotting the evolution curve of the sensitive identification index of the monitoring point inside the blast hole along the tunnel mileage, thereby obtaining the spatial evolution sequence along the tunnel mileage.

[0037] In one exemplary embodiment, the process of constructing the time evolution sequence includes: starting with the sensitive identification index of the monitoring point inside the blast hole, plotting the evolution curve of the sensitive identification index of the tunnel water outlet fracture monitoring point over time, and obtaining the time evolution sequence of a single monitoring point over time.

[0038] S8: Based on the spatiotemporal evolution sequence of the hydrochemical characteristics, the background value of the hydrochemical characteristics, and the sensitive identification index of the maximum water outlet point of the tunnel, the water-controlling structural boundary is obtained using the determination criteria of the water-controlling structural boundary.

[0039] As an exemplary embodiment, in step S8, the water-controlling structural boundary is determined based on a combination criterion of the most sensitive indicator mutation, the synergistic response of strong corroborating indicators, and the slow change of confirming indicators.

[0040] In an exemplary embodiment, the criteria for determining the water-controlling structural boundary are as follows: the evolution sequence curve of the most sensitive indicator shows a sudden change, and the indicator value stabilizes in the new range after the change; the evolution sequence curve of the strong corroborating indicator shows a corresponding trend change synchronously with the most sensitive indicator, but the change amplitude is small and the duration is long, and the change index values ​​of multiple continuous water outlets or newly formed water outlets gradually tend to stabilize; the corroborating indicator shows a slow change in the corresponding trend with the most sensitive indicator and the strong corroborating indicator, with a smaller initial change amplitude and a longer duration; when all three conditions are met, it is determined that there is a water-controlling structural boundary in the target area, and the station number corresponding to the sudden change point is the approximate location of the structural boundary.

[0041] S9: Based on the most sensitive indicator in the sensitive identification indicator combination, the water flow from the fracture, and the size parameters of the geophysical anomaly, the water inrush risk is predicted, and the water inrush risk level is obtained.

[0042] As an exemplary embodiment, in step S9, the most sensitive indicator mutation percentage, fracture water output, and geophysical anomaly size parameters are coupled to achieve quantitative prediction of water inrush risk based on lithology and level.

[0043] As an exemplary embodiment, the core rule for water inrush risk classification is: the percentage change in the most sensitive indicator is used as the core criterion for judgment; the risk level is not upgraded if the corresponding level indicator requirements are not met; a high-risk determination requires the geophysical anomaly to be of size L. When the depth is 20m and there are no large water-rich anomalies, the highest risk level is considered to be medium.

[0044] As an exemplary embodiment, the flood risk classification standard is as follows: Level I (High Risk): Percentage change in the most sensitive indicator. 50%, fissure water discharge Q 125 Geophysical anomaly size L 20 Level II (Medium Risk): The most sensitive indicator is a sudden change in percentage of 10%-50%, and the flow rate from the fissures is [missing information]. Q 125 The geophysical anomaly is 10 L 20m; Level III (Low Risk): Percentage of mutations in the most sensitive indicator 10%, fissure water discharge Q 25 Geophysical anomaly size L 10m; where the size L of the geophysical anomaly is calculated by fusing TSP+TEM+GPR multi-source geophysical data.

[0045] As an exemplary embodiment, the risk assessment of water inrush in carbonate rocks also needs to meet the following requirements: Level I (High Risk): Additionally, the electrical conductivity EC must be satisfied. 1500 S / m; Level II (medium risk), Level III (low risk): No additional conditions, as long as the core classification criteria are met.

[0046] The risk assessment of water inrush in clastic rocks must also meet the following criteria: Level I (High Risk): Additionally, the desulfurization coefficient RSC must be met. 20. Redox potential Eh 100mV; Level II (Medium Risk): Auxiliary condition is a desulfurization coefficient of 20. RSC 80; Level III (Low Risk): The auxiliary condition is the desulfurization coefficient RSC. 80.

[0047] The assessment of water inrush risk in igneous and metamorphic rocks must also meet the following criteria: Level I (High Risk): At least one additional criterion must be met: Electrical conductivity EC 800 S / cm, TDS mutation rate 50%; Level II (Medium Risk): The auxiliary condition is a conductivity of 500. S / cm EC 800 S / cm, TDS mutation rate 10%-50%; Grade III (low risk): auxiliary condition is conductivity EC 500 S / cm, TDS mutation rate 10%.

[0048] In some embodiments, the above-described method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry can also be implemented in the following ways.

[0049] like Figure 2 As shown in this embodiment, the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry includes: S1: Deployment of a water chemical characteristic monitoring network integrating multi-source geophysical exploration data; S11: Comprehensive geophysical delineation of geological anomalies: such as Figure 3 As shown, the target area is the anomalous zone delineated by long-range detection using the Tunnel Seismic Prediction (TSP) method. Then, a medium-range detection using the Transient Electromagnetic Method (TEM) is conducted on this target area. Finally, short-range detection using Ground Penetrating Radar (GPR) is carried out on key sections in front of the water-controlling structures delineated by the long-range and medium-range detections. These multi-source geophysical data are then integrated to generate macroscopic geometric parameters such as the scale and spatial distribution of the geological anomaly, as well as environmental characteristics such as water abundance and fragmentation.

[0050] like Figure 4 The diagram shows the layout of the tunnel water chemistry monitoring network; where 1 is the fault, 2 is the fault-affected fracture zone, 3 is the surrounding rock, 4 is the deep monitoring hole, 5 is the water-emitting fracture, 6 is the tunnel, 7 is the L1 deepened blast hole, and 8 is the L2 deepened blast hole. S12: Groundwater hydrochemical characteristics background monitoring point: For geological anomalies that are large to large in scale, intersect the tunnel at a large angle, have moderate to strong water-bearing capacity and moderate to high degree of fragmentation, select the maximum water outlet point of the tunnel within 10-30m behind the key section where ground-penetrating radar is carried out. At the arch foot of the section where the maximum water outlet point is located, set up deep monitoring holes that slope downwards. The hole opening should be no less than 0.5m higher than the lowest point of the excavated tunnel, and the hole bottom should be no less than 1m higher than the lowest point of the excavated tunnel.

[0051] S13: Groundwater Deepening Borehole Monitoring Points: Starting from the key section where geological radar detection is carried out, 3-5 deepening boreholes are set up on each excavation face. The deepening boreholes are in the same direction as the tunnel excavation and their depth exceeds the excavation advance by 3-6m.

[0052] S14: Groundwater Dynamic Monitoring Points on the Tunnel Free Face: For the largest water-producing fractures within the deepened blast holes, after tunnel excavation, the development location of these fractures on the tunnel free face is traced based on their orientation, and groundwater monitoring points are deployed at these locations. The interval between monitoring point deployments and subsequent sampling frequencies are determined according to the fracture water production levels, as shown in Table 1. Table 1: Standards for Monitoring Point Layout Intervals and Sampling Frequency

[0053] S2: On-site groundwater sampling and in-situ testing S21: Groundwater sampling points: Groundwater samples will be collected at the maximum water outflow point in the tunnel and at the bottom of the borehole in step S12. In the deepened blast hole in step S13, the maximum water-producing fracture will be identified using an endoscope, and groundwater samples will be collected at the maximum water outflow point using a segmented sealed sampler. Groundwater samples will be collected at the groundwater monitoring point on the tunnel free face in step S14.

[0054] S22: Groundwater Sampling: Water samples are collected in high-density polyethylene bottles (500 mL / bottle). Before use, the bottles are rinsed three times with deionized water, dried to constant weight in a 105℃ oven, cooled, and sealed for later use, ensuring no ion residue remains in the containers. Two bottles are collected for each sample; one bottle is used for cation testing (acidified to pH by adding nitric acid). 2) Another bottle is kept in its original water state for anion testing (keeping the original water state). The bottle is marked with the sampling time, station number, sampling point type, and processing method. After collection, the sample is immediately placed in a 0-4℃ low-temperature insulated box and stored away from light. During transportation, shockproof measures are taken to prevent bottle breakage or sample mixing. All samples must be delivered to the laboratory in a timely manner.

[0055] S23: In-situ testing: During the sampling process, in-situ tests are conducted on the groundwater using pH meters, conductivity (EC) meters, oxidation-reduction potential (Eh) meters, thermometers, etc. Before testing, the relevant instruments are calibrated to ensure measurement accuracy. The sensors are directly immersed in the water sample, and data is recorded after the readings stabilize for 30 seconds. Each indicator is measured three times, and the average value is taken as the final result. Recording accuracy requirements: water temperature ( 0.1℃), pH ( 0.01), EC ( 1 S / cm), Eh ( 5mV).

[0056] S24: Groundwater sampling frequency: Groundwater samples were collected only once at the maximum water outlet point in the tunnel (step S12), the bottom sampling point of the borehole, and the groundwater sampling point inside the deepened blast hole (step S13), all immediately after the groundwater sampling points were set up. For the groundwater monitoring point on the free face of the tunnel (step S14), the first groundwater sampling was conducted immediately after excavation. Subsequent groundwater sampling frequencies were determined based on the fissure water output; specific sampling frequencies are shown in Table 1.

[0057] S3: Indoor water chemical composition testing S31: Groundwater TDS determination: The gravimetric method with drying at 105℃ was used. A 50 mL sample of raw water was subjected to a 0.45... After filtration through an m-filter membrane, the sample is transferred to a constant-weight weighing bottle and dried in an oven at 105°C for 24 hours. After drying, it is placed in a desiccator to cool to room temperature and weighed using an electronic balance (accuracy 0.0001 g). This drying-cooling-weighing process is repeated until a constant weight is achieved (the difference between two weighings is considered the final weight). 0.0005g), according to the formula calculate( For the mass of the weighing bottle, For the weight of the weighing bottle and residue, (Water sample volume); S32: Groundwater major anion and cation concentration test: Cation testing was performed using inductively coupled plasma optical emission spectrometry (ICP-OES). , , , Anion test: , The determination was performed using ion chromatography (IC). , A dual-indicator acid-base titration method was employed. During the testing process, quality control measures such as anion-cation balance verification and parallel determination of monitoring samples were simultaneously used to ensure the accuracy and reliability of the test results.

[0058] S4: Index system characterizing groundwater hydrochemical properties: The applications of in-situ testing indicators are as follows: Water temperature: Used to correct ion concentration test results. Based on the influence of temperature on mineral solubility and dissolution rate, the measured ion concentration is calibrated to the equivalent concentration at a standard temperature of 25℃. pH: Optimize the accuracy of judgment of sensitive indicators such as calcium-magnesium coefficient (RCM), and adjust the threshold range of sensitive indicators according to lithological characteristics; Electrical conductivity (EC): determined by the empirical formula (TDS) 0.55 EC) Rapidly verify laboratory TDS test results, and complement transient electromagnetic resistivity data to correct the water-bearing level of geological anomalies. Redox potential (Eh): This helps interpret the causes of changes in the desulfurization coefficient (RSC), verify the redox environment of the water body, and avoid misjudgment of water-controlling tectonic boundaries. Based on the fundamental principle that the intensity of water-rock interaction is positively correlated with time, strong runoff zones (rapidly flowing water) and stagnant zones (still / slowly flowing water) will exhibit systematic differences in the following indicators.

[0059] S41: Stagnation Indication Ratio (SIR): Defined as... concentration / Concentration, a high SIR value usually indicates a longer water-rock interaction time and a more stagnant water body.

[0060] S42: Runoff Intensity Indication Ratio (FIR): Defined as... A high FIR value for concentration / TDS may indicate the presence of fresher, faster-moving water mixed in.

[0061] S43: Sodium Chloride Coefficient (RNC): Defined as... concentration / Concentration indicates the "maturity" of a water body and its transport pathway. Stable properties Easily adsorbed; long stagnant water time. Relative losses.

[0062] S44: Desulfurization coefficient (RSC): defined as... concentration / Concentration indicates the redox environment. Areas with high runoff are rich in oxygen. High content; sulfate reduction occurs in the closed stagnant zone under anaerobic conditions. reduce.

[0063] S45: Calcium-Magnesium Coefficient (RCM): Defined as... concentration Concentration reflects the dissolution sequence of water on surrounding rocks (such as limestone and dolomite), and its long-term effects lead to... The dissolution rate increases.

[0064] S46: Total Dissolved Solids (TDS): Directly reflects the time of interaction between water and rock. Faster flow indicates a shorter interaction time and fewer dissolved minerals.

[0065] S5: Sensitive Identification Index Combination Based on Surrounding Rock Lithology The hydrochemical characteristics of groundwater are highly dependent on the rock mass in which it is contained, and are the product of the long-term interaction between groundwater and the rock mass. The dominant water-rock interaction differs under different lithological backgrounds, resulting in varying effective sensitive identification indicators.

[0066] S51: Carbonate rocks such as limestone and dolomite: The effective sensitive identification indicators are as follows: ① The most sensitive indicator is the calcium-magnesium coefficient (RCM). In areas with strong runoff, water preferentially dissolves calcite. High proportion, ratio 2. The dolomite in the stagnant zone continues to dissolve. The proportion is rising, and the ratio is approaching 1 or even higher. ② A strong corroborating indicator is the stagnation index ratio (SIR), in In the waters of the Lord, As a "conservative ion", its relative increase is a strong signal of water retention; ③ The corroborating indicator is total dissolved solids (TDS). Active runoff has lower TDS, while stagnant runoff has higher TDS; at the same time, the results of laboratory TDS tests are quickly verified by combining conductivity (EC).

[0067] S52: Clastic rocks such as sandstone, shale, and mudstone: The effective sensitive identification indicators are as follows: ① The most sensitive indicator is the sodium chloride coefficient (RNC). Easily adsorbed by clay minerals, and prolonged action in stagnant zones leads to... relatively Loss, sodium chloride coefficient 0.5, short water-rock interaction time in areas with strong runoff, sodium-chlorine coefficient 0.85 or higher; ② A strong supporting indicator is the desulfurization coefficient (RSC). Sluggish, enclosed environments are prone to sulfate reduction, consuming... ,generate and This significantly reduces the ratio, indicating that areas with high runoff are rich in oxygen. The ratio is relatively high; ③ The corroborating indicator is the runoff intensity indicator ratio (FIR), which can be used to assist in judgment, especially in areas with strong runoff. Relatively low; simultaneously, the change in RSC is verified by combining the Eh value, Eh 100mV (anaerobic) and RSC 20 can confirm a stagnant, enclosed environment.

[0068] S53: Granite, gneiss, and other igneous and metamorphic rocks: The effective sensitive identification indicators are as follows: ① The most sensitive indicator is the stagnation index ratio (SIR). Silicate minerals dissolve slowly, and the chemical evolution of groundwater is mainly controlled by time and pH. As an inert tracer, an increase in its relative concentration is direct evidence that the water has undergone long-term retention and dissolution; ② A strong corroborating indicator is total dissolved solids (TDS), which is used to distinguish "fresh makeup water" (with extremely low TDS). The most direct indicators are 50 mg / L and "ancient fissure water" (TDS increases due to concentration); ③ Confirmatory indicator: total potassium and sodium ( Accumulated in stagnant water due to long-term water-rock interaction, it can serve as an auxiliary confirmatory indicator.

[0069] S6: Background values ​​of groundwater hydrochemical characteristics S61: Calculate the average value of the hydrochemical test results of multiple water samples initially obtained from the deep monitoring well in step S12. Calculate the corresponding sensitive identification index based on the surrounding rock lithology category in step S5. Use these sensitive identification indices as the background values ​​of hydrochemical characteristics of stagnant fracture water in the area, and use them as the benchmark for subsequent anomaly judgment.

[0070] S62: Calculate the sensitive identification index of the maximum water outlet point in the tunnel obtained in step S12, and compare these sensitive identification indices with the background values ​​of hydrochemical characteristics obtained in step S61. When the two sensitive identification indices are consistent, the tunnel continues to be excavated forward and in-hole monitoring and groundwater dynamic monitoring on the tunnel free face are carried out; when the two sensitive identification indices are inconsistent, step S8 is executed directly.

[0071] S7: Construction of the Spatiotemporal Evolution Sequence of Hydrochemical Characteristics S71: Construction of the spatial evolution sequence of hydrochemical features: Starting from the background value of hydrochemical features obtained in step S61, the evolution curve of the sensitive identification index corresponding to the water sample collected from the monitoring point inside the blast hole during the tunnel excavation process is plotted along the tunnel mileage.

[0072] like Figure 5 The figure shows the spatial evolution sequence of water chemical parameters.

[0073] S72: Construction of the time evolution sequence of hydrochemical characteristics: Starting with the sensitive identification index corresponding to the water sample collected from the monitoring point inside the blast hole during the tunnel excavation process, the evolution curve of the sensitive identification index corresponding to the water sample collected at different times at each groundwater dynamic monitoring point on the tunnel free surface is plotted over time, and the water outflow status (pressure, flow rate) data at different time nodes are associated.

[0074] like Figure 6 The figure shows the time evolution sequence of water chemical parameters.

[0075] S8: Criteria for Determining the Boundary of Water-Controlling Structures Real-time analysis of the evolution sequence curve of hydrochemical characteristics and comparison with hydrochemical background values ​​indicate that a change in hydrodynamic state has occurred ahead, indicating entry into the boundary of a water-controlling structure, i.e., transition from the retention zone to the runoff zone: S81: Most sensitive indicator: The evolution sequence curve of the most sensitive indicator mentioned in step S5 shows a sudden change, and the parameter value stabilizes in the new range after the change, that is, multiple consecutive water outlets or newly formed water outlets all show consistent change characteristics.

[0076] S82: Strong corroborating indicator: The evolution sequence curve of the strong corroborating indicator mentioned in step S5 and the evolution sequence curve of the most sensitive indicator in S81 show corresponding trend changes synchronously. The change amplitude is relatively small and the change duration is relatively long. The change index values ​​of multiple continuous water outlets or newly formed water outlets on the strong corroborating indicator evolution sequence curve gradually tend to stabilize.

[0077] S83: Confirmation Indicators: The confirmation indicators in step S5, along with the most sensitive indicators described in S81 and the strong corroborating indicators described in S82, show a slow change in the corresponding trend. In the early stages of the change, the change in the relevant indicator values ​​is relatively smaller and the duration of the change is relatively longer.

[0078] S84: Judgment result: When all three of the above situations occur, there is a water-controlling structural boundary in the target area. The station number corresponding to the most sensitive index mutation point is the approximate location of the structural boundary. It is necessary to pay close attention to changes in hydrodynamic conditions.

[0079] S9: Risk Prediction of Water Inrush By combining the percentage change of the most sensitive indicator, the water flow from the fractures revealed by the deeper boreholes, and the scale of the geological anomaly in front of the tunnel face determined by geophysical exploration, a comprehensive prediction of water inrush risk can be achieved.

[0080] Core rules: The percentage of mutations in the most sensitive indicators (RCM / RNC / SIR) is the core basis for risk assessment. If the corresponding level indicator requirements are not met, the risk level will not be upgraded even if other conditions are met. A high-risk (Level I) determination must include "anomaly size L". When there is no large water-rich anomaly, the highest risk level is only Class II (medium risk), which is consistent with the occurrence pattern of water-rich disasters in engineering projects.

[0081] The anomaly size L was calculated by fusing TSP+TEM+GPR multi-source geophysical data.

[0082] The specific risk level determination criteria are shown in Table 2.

[0083] Table 2: Risk Level Determination Criteria

[0084] In some embodiments, the above-described method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry can also be implemented in the following ways.

[0085] The surrounding rock of a deep-buried mountain tunnel is mainly metamorphic rock, belonging to the igneous and metamorphic rock categories. Through the fusion of multi-source geophysical data from long-range TSP (Transient Electromagnetic Spinning) detection, medium-range TEM (Transient Electromagnetic Transmission) detection, and short-range Ground Penetrating Radar (GPR) detection, a water-bearing fault anomaly was delineated. When the tunnel was excavated to ZK20+420 (30m from the pre-defined fault boundary), a rain-like water outflow (25 cubic meters per second) appeared at the tunnel face and sidewalls. Q 125 Water chemistry monitoring and analysis will be carried out according to the following plan: Deep monitoring boreholes (background monitoring points): Within 22m behind the key cross section (ZK20+420) detected by ground-penetrating radar, two deep monitoring boreholes are set up obliquely downwards at the arch foot of the cross section where the maximum water outlet of the tunnel is located. The borehole openings are 0.5m higher than the lowest point of the tunnel, and the bottom of the boreholes is 1.2m higher than the lowest point of the excavated tunnel. Water samples collected after the monitoring boreholes are set up are used as the basis for the background values ​​of the hydrochemical characteristics of the regional stagnant fracture water.

[0086] Deepening the monitoring points of the blast holes: Starting from ZK20+420, four deeper blast holes (in the same direction as the tunneling) are set up on the face of the excavation in each cycle. The depth of the blast holes is 6m. Water samples are collected from each section using a segmented closed sampler.

[0087] Dynamic monitoring points on the tunnel's exposed surface: Monitoring points were set up in the section from ZK20+420 to ZK20+440. Initially, the sampling frequency was once a day, and the outflow rate fluctuated. It was later adjusted to twice a day.

[0088] Determination of background values ​​of water chemical characteristics Using the water sample from the ZK20+420 deep monitoring well as a benchmark, the calibrated background values ​​for sensitive indicators are: Stagnation Indication Ratio (SIR) = 2.6, Total Dissolved Solids (TDS) = 1200 mg / L, and Total Potassium and Sodium (TDS) = 1200 mg / L. + =150mg / L (characteristic value of ion accumulation in stagnant water under long-term water-rock interaction, consistent with the hydrochemical laws of igneous and metamorphic rock areas) Construction of spatiotemporal evolution sequence of hydrochemistry and determination of water-controlling structures (a) Spatial Evolution Sequence From ZK20+420 to ZK20+440, the most sensitive indicator, SIR, continuously decreased from 2.6 (the mutation percentage gradually increased to -38.5%). The strongly corroborating indicator, TDS, simultaneously decreased from 1200 mg / L to 700 mg / L, and total potassium and sodium levels plummeted from 125 mg / L to 110 mg / L. All three indicators showed a synergistic decreasing trend, indicating that ZK20+440 was the most likely candidate. The ZK20+440 section is the boundary of the water-controlling structure (the transition zone between the water-conducting zone and the stagnant zone).

[0089] (ii) Time evolution sequence The maximum outflow fracture point in section L2 (ZK20+435) was sampled twice daily, and the hydrochemical time series was monitored for 7 days. Within 3 days, the SIR dropped sharply from 2.3 to 1.2 (mutation percentage -56.5%), and the TDS simultaneously decreased from 1000 mg / L to 800 mg / L; after 3 days, the SIR stabilized at 1.1. 1.3, TDS stabilized at 600 The concentration of 700 mg / L meets the criteria for water-controlling structures that stabilize after a sudden change in the most sensitive indicator and that strongly corroborating indicators show a synergistic response. This confirms that the total potassium and sodium concentration dropped sharply from 145 mg / L to 115 mg / L and eventually stabilized at 88 mg / L. The concentration in the 115 mg / L range further confirms that ZK20+435 is the tectonic boundary location.

[0090] Based on the core parameters shown in the diagram (SIR mutation percentage -38.5%, geophysical anomaly size 18m, water output 94), The risk level of water inrush was determined to be Level II (medium risk), indicating that the area ahead is an intersection of a semi-closed fracture network and the main channel. The water inrush pressure may be high, but the total amount is controllable. It is recommended to carry out pre-grouting reinforcement of the working face before the ZK20+440 section.

[0091] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry.

[0092] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for predicting tunnel water control structures and inrush risk based on the spatiotemporal evolution of hydrochemistry, characterized in that, Includes the following steps: S1: Obtain multi-source geophysical exploration data for the current area, and obtain a water chemical characteristic monitoring network based on the multi-source geophysical exploration data; S2: Use the aforementioned water chemistry characteristic monitoring network to collect groundwater samples and conduct in-situ tests to obtain in-situ test data of groundwater; S3: Conduct indoor water chemical composition tests on the collected groundwater samples to obtain total dissolved solids data and main anion and cation concentration data; S4: Based on the in-situ test data and the main anion and cation concentration data, construct an index system to characterize the hydrochemical characteristics of groundwater; S5: Based on the lithology of the surrounding rock, construct a combination of sensitive identification indicators according to the aforementioned indicator system; S6: Calculate the average value of the water chemical test results of multiple water samples initially obtained from the deep monitoring wells in the water chemical characteristic monitoring network, and use the sensitive identification index combination to calculate the corresponding sensitive identification index to obtain the background value of water chemical characteristics of stagnant fissure water in the current area. Calculate the sensitive identification index of the maximum water outlet point in the tunnel in the water chemistry characteristic monitoring network; S7: Determine that the sensitive identification index of the maximum water outlet point of the tunnel is consistent with the background value of the water chemical characteristics, and construct the spatiotemporal evolution sequence of the water chemical characteristics; S8: Based on the spatiotemporal evolution sequence of the hydrochemical characteristics, the background value of the hydrochemical characteristics, and the sensitive identification index of the maximum water outlet point of the tunnel, the water-controlling structural boundary is obtained using the determination criteria of the water-controlling structural boundary. S9: Based on the most sensitive indicator in the sensitive identification indicator combination, the water flow from the fracture, and the size parameters of the geophysical anomaly, the water inrush risk is predicted, and the water inrush risk level is obtained.

2. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The hydrochemical characteristic monitoring network includes background monitoring points for groundwater hydrochemical characteristics, monitoring points inside groundwater deepening boreholes, and dynamic monitoring points for groundwater on the tunnel surface.

3. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The in-situ test data includes water temperature, pH value, conductivity, and redox potential.

4. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The main anion and cation concentration data include , , , , , , , Concentration data.

5. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The indicator system includes the stagnation degree indicator ratio, the runoff intensity indicator ratio, the sodium-chlorine coefficient, the desulfurization coefficient, the calcium-magnesium coefficient, and the total dissolved solids.

6. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The surrounding rock lithology includes carbonate rocks, clastic rocks, igneous rocks, and metamorphic rocks.

7. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The sensitive identification index combination includes the most sensitive index, strong corroborating index, and confirming index corresponding to each type of surrounding rock lithology.

8. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The spatiotemporal evolution sequence of the hydrochemical characteristics includes a spatial evolution sequence along the tunnel mileage and a temporal evolution sequence of a single monitoring point over time, wherein the temporal evolution sequence is associated with the effluent status data.

9. The method for tunnel water control structure and water inrush risk prediction based on the spatiotemporal evolution of hydrochemistry according to claim 1, characterized in that, The criteria for determining the water-controlling structural boundary are as follows: the evolution sequence curve of the most sensitive indicator shows a sudden change, and the indicator value stabilizes in the new range after the change; the evolution sequence curve of the strong corroborating indicator shows a corresponding trend change synchronously with the most sensitive indicator, but the change amplitude is small and the duration is long, and the change index values ​​of multiple continuous water outlets or newly formed water outlets gradually tend to stabilize; the corroborating indicator shows a slow change in the corresponding trend with the most sensitive indicator and the strong corroborating indicator, with a smaller initial change amplitude and a longer duration; when all three conditions are met, it is determined that there is a water-controlling structural boundary in the target area, and the station number corresponding to the sudden change point is the approximate location of the structural boundary.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the tunnel water control structure and water inrush risk prediction method based on the spatiotemporal evolution of hydrochemistry as described in any one of claims 1-9.

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