Earthquake wave tunnel advanced forecasting method and system of acceleration probe

Abstract

The invention relates to the technical field of tunnel earthquake forecasting, and discloses a seismic wave tunnel advanced forecasting method and system of an acceleration probe, and the method comprises the steps: collecting X, Y and Z three-component data of tunnel seismic waves through a piezoelectric acceleration three-component sensor, and recording direct wave, reflected wave and sound wave signals; de-noising processing is carried out on the X, Y and Z three-component data; establishing a karst tunnel velocity model including longitudinal wave velocity and transverse wave velocity according to tunnel geological data, and performing cross-correlation operation on a forward continuation wave field and a reverse continuation wave field to generate PP wave and PS wave migration profiles; performing point sum and dot product operation on the offset data of the PP wave and the PS wave to generate a point sum offset profile and a dot product offset profile; and integrating the low-frequency contour information, the high-frequency detail information and the comprehensive interpretation result to generate a tunnel advanced forecasting report. The system comprises a seismic wave acquisition module, a model establishment module and an advanced forecasting module. According to the method, a more reliable interpretation result can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel seismic prediction, and in particular to a method and system for advanced tunnel seismic prediction using an acceleration probe for seismic waves. Background Art

[0002] Advanced tunnel seismic prediction is a frontier topic in the field of geophysical research, and this direction has always been the focus of attention in the academic and engineering circles. At present, geophysicists at home and abroad have done a lot of work in the research and development of advanced tunnel detection equipment and theoretical research, and successful cases have been achieved in tunnels in various regions of the world. In addition, transient electromagnetic method, geological radar method, resistivity method, induced polarization method, and nuclear magnetic resonance method are currently in the active research stage. Although the seismic method is not very sensitive to the prediction of water-bearing bodies, compared with other methods, it has obvious advantages in the ability to identify layered interfaces, can comprehensively predict the lithological characteristics and spatial distribution of geological bodies, and has a long detection distance. During the tunnel detection process, it is not easily affected by external factors, and combined with geological and hydrological conditions, it can also make a qualitative inference on the water-rich situation of surrounding rocks. This method has always been the most commonly used geophysical method for advanced prediction at home and abroad. At present, there is a general consensus in the engineering circle in China on the implementation of seismic advanced prediction for under-construction tunnels, and there are roughly the following problems:

[0003] 1: Problems existing in the probe in the advanced seismic wave prediction:

[0004] In the process of advanced seismic wave prediction, due to the existence of the tunnel cavity, sound waves oscillate back and forth in the tunnel, seriously suppressing the effective signal. In addition, since the acquisition site coincides with the tunnel construction site, there will inevitably be various noise interferences such as fan or other mechanical vibrations and workers' drilling. Limited by the tunnel construction site conditions, it is difficult to adopt the method of multi-shot stacking in oil seismology to suppress interference. Although techniques such as hole sealing can improve the signal-to-noise ratio of seismic data to a certain extent during the acquisition process, the interference of various noises on seismic data is still significant. Therefore, the electromagnetic velocity sensors used in oil seismology are difficult to meet the requirements of tunnel seismic detection, and high-sensitivity sensors must be used to ensure that effective signals can be collected under various noise interferences. In addition, the main frequency of oil seismic sensors is relatively low because oil seismology mainly focuses on seismic signals reflected within a few kilometers underground. After the seismic source is filtered by the earth, the main frequency is about dozens of Hz. In advanced tunnel detection, the reflection wave frequency band within 150m ranges from a few Hz to thousands of Hz, which requires the sensor to have a wider frequency band width to ensure that richer seismic signals can be collected.

[0005] At present, the TGP system still uses electromagnetic moving coil velocity geophones with a relatively narrow bandwidth and low sensitivity. Most of the TRT and TST systems still use single-component acceleration geophones. The TSP uses three-component acceleration geophones. Through comparison with existing sensors, it is found that at present, the sensors for seismic wave method advanced prediction are uneven. Single-component and three-component, velocity-type sensors and acceleration-type sensors are flooding in engineering applications, and there are generally problems such as low sensitivity (≤1V / g), narrow bandwidth, poor coupling effect, and low signal-to-noise ratio of the collected signals.

[0006] 2: Problems existing in the data processing migration imaging method in the tunnel advanced prediction by seismic wave method

[0007] Tunnel seismic advanced prediction technology is the main technology for identifying abnormal geological bodies along the tunnel. Due to the limited tunnel observation space and the small physical differences between the fracture structure and the surrounding rock, the investigation accuracy of hidden karst caves is still not ideal. Tunnel seismic data are full-wavefield data, including P-waves and S-waves. Compared with P-waves, S-waves have a slower propagation speed, shorter wavelength, and better resolution. Currently, the F-K method, τ-p transformation, and polarization filtering are usually used to separate the P-waves and S-waves of tunnel seismic data first, and then migration imaging is carried out. It is challenging to interpret only relying on the migration imaging results of P-waves and S-waves in actual data. Due to the errors in separating P-waves and S-waves, the accuracy of migration imaging will be reduced, resulting in more missed reports and false alarms in actual prediction. Generally speaking, the accuracy of migration imaging still needs to be improved in the processing of tunnel seismic data.

[0008] Existing technology one, a Chinese patent with the application number 202411542644.3 discloses a method for predicting the deformation of a submarine tunnel under seismic action, which specifically includes: constructing a fluid-structure-soil coupling model based on the finite element simulation method, simulating the influence of earthquakes on the soil and the tunnel by applying seismic waves, and recording the dynamic parameters of the model; collecting the environmental and material parameters of the actual tunnel, and then performing dimensionless processing on the parameters to calculate the first influence index and the second influence index; comprehensively analyzing the above indexes, calculating the comprehensive deformation index, and monitoring the environmental temperature in real time, incorporating the temperature compensation term, and obtaining the corrected tunnel deformation amount. Although by constructing a fluid-structure-soil coupling model, the deficiencies of traditional prediction methods in complex seismic environments can be effectively solved, providing more accurate deformation prediction, and comprehensively analyzing the first influence index and the second influence index can comprehensively reflect the actual deformation of the tunnel under seismic action; however, simplified calculations may lead to the incomplete elimination of the influence of boundary reflected waves, thus affecting the accuracy of the results.

[0009] Prior Art II: A Chinese patent with application number 202110635486.6 discloses a method for predicting tunnel seismic vulnerability based on stochastic IDA and machine learning, including: establishing a tunnel seismic response model; using the stochastic IDA method to conduct stochastic dynamic response analysis; calculating the damage index of the lining according to the results of the stochastic dynamic response analysis; selecting eigenvalue to form a data set and determining a machine learning algorithm model; optimizing hyperparameters to establish a machine learning prediction model for the damage index; collecting samples to verify the effect of the prediction model and completing the prediction of the damage index; selecting ground motion intensity indices, conducting logarithmic regression analysis on the ground motion intensity indices of the samples and the predicted damage index, determining the total logarithmic standard deviation corresponding to different damage states, and establishing a tunnel seismic vulnerability curve based on the seismic demand probability model and the determined total logarithmic standard deviation. Although it can evaluate the seismic damage risk and seismic performance of tunnel structures under various conditions, it is mainly applicable to representative tunnel structures, and for different tunnel structures, model parameters need to be adjusted or the model needs to be rebuilt.

[0010] Prior Art III: A Chinese patent with application number 202211629044.1 discloses a method and system for tunnel risk decision-making in earthquake-prone areas based on deep learning. The method includes: establishing a sample data set of tunnels in earthquake-prone areas; constructing and training a neural network model to obtain a trained neural network model; inputting the sample data set into the trained neural network model and outputting prediction results, where the prediction results include: circumferential peak stress of the tunnel and surrounding rock deformation; based on the prediction results, obtaining the support form of the tunnel surrounding rock according to the tunnel risk level; the system includes: a data acquisition module, a model training module, a prediction output module and a risk decision-making module; where the data acquisition module, the model training module, the prediction output module and the risk decision-making module are connected in sequence. Although it can provide exclusive intelligent decision-making for different risk levels, effectively guide the support of the surrounding rock and ensure construction safety, the prediction results of the circumferential peak stress of the tunnel and the surrounding rock deformation are affected by various factors.

[0011] Currently, Prior Art I, Prior Art II and Prior Art III have problems such as low probe sensitivity, narrow frequency band range, poor coupling effect, and low signal-to-noise ratio of the collected signals; and the errors in separating P-waves and S-waves reduce the accuracy of migration imaging, resulting in many missed alarms and false alarms in actual forecasting. Therefore, the present invention provides a method and system for seismic wave tunnel advanced prediction using an acceleration probe. Summary of the Invention

[0012] The main object of the present invention is to provide a method and system for earthquake wave tunnel advanced prediction using an acceleration probe, so as to solve the problems existing in the prior art, such as low sensitivity of the probe, narrow frequency band range, poor coupling effect, low signal-to-noise ratio of the collected signal; and the error in separating P waves and S waves, reducing the accuracy of migration imaging, resulting in many missed alarms and false alarms in actual prediction.

[0013] To achieve the above object, the present invention provides the following technical solutions:

[0014] A method for earthquake wave tunnel advanced prediction using an acceleration probe, the method for earthquake wave tunnel advanced prediction using the acceleration probe includes:

[0015] Arrange piezoelectric acceleration three-component sensors near the tunnel working face, use the piezoelectric acceleration three-component sensors to collect the X, Y, and Z three-component data of the tunnel seismic waves, and record the direct wave, reflected wave, and acoustic wave signals; perform denoising processing on the X, Y, and Z three-component data.

[0016] According to the tunnel geological data, establish a karst tunnel velocity model including the longitudinal wave velocity and the transverse wave velocity, use the finite difference method to perform forward simulation on the elastic wave equation to generate theoretical wave field data; use the forward simulated wave field data for forward continuation to generate a forward propagating wave field; perform backward continuation on the collected actual wave field data to generate a backward propagating wave field; perform cross-correlation operation on the forward continuation wave field and the backward continuation wave field to generate PP wave and PS wave migration profiles.

[0017] Perform dot sum and dot product operations on the migration data of the PP wave and the PS wave to generate a dot sum migration profile and a dot product migration profile; use the dot sum migration profile to identify the low-frequency contour information of the abnormal body and determine the location of the karst area; use the dot product migration profile to extract the high-frequency detail information of the abnormal body; fuse the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information, and comprehensive interpretation result to generate a tunnel advanced prediction report.

[0018] As a further improvement of the present invention, the process of collecting the X, Y, and Z three-component data of the tunnel seismic waves includes the following steps:

[0019] Use four three-component sensors at the target tunnel working face to collect seismic data, place the sensors in the boreholes, and collect the X, Y, and Z three-component data of the seismic waves.

[0020] Check the noise of the sensors and their environment, and use the piezoelectric acceleration three-component sensors to collect seismic wave data.

[0021] Perform denoising processing on the collected seismic wave data, and after denoising, perform further correction and analysis on the data.

[0022] As a further improvement of the present invention, the process of forward modeling of the elastic wave equation includes the following steps:

[0023] According to the tunnel geological data, conduct a detailed analysis of the tunnel rock type, geological structure, and groundwater distribution information. Simulate the two-dimensional full-space wave field through the finite difference method, and establish a karst tunnel velocity model including the longitudinal wave velocity and the transverse wave velocity;

[0024] Design 2 boreholes and two geophones; use the two-dimensional elastic decoupled wave equation with finite difference approximation to conduct forward modeling of the karst tunnel velocity model to generate theoretical wave field data;

[0025] Correlate the forward continuous longitudinal wave with the backward continuous longitudinal wave to achieve the migration imaging of the PP wave; correlate the forward continuation of the longitudinal wave with the backward continuation of the transverse wave to obtain the migration imaging result of the PS wave.

[0026] As a further improvement of the present invention, RTM imaging is based on the forward model, and the first-order stress-velocity elastic decoupled equation is:

[0027]

[0028] The elastic wave decoupled equation is:

[0029] u = u p + u s

[0030] w = w p + w s

[0031]

[0032] Among them, λ and μ are the Lame constants of the medium, t is the time variable, (τ yy , τ xx , τ yx ) are all stress tensors, (u, w) are the vibration velocities in the horizontal and vertical directions; V p and V s are the longitudinal wave velocity and the transverse wave velocity respectively; the partial differential equation is transformed into a second-order equation in time and a twelfth-order equation in space; the perfectly matched layer is used as the boundary condition; u p , w p represent the horizontal and vertical vibration velocity components of the longitudinal wave; u s , w s represent the horizontal and vertical vibration velocity components of the transverse wave;

[0033] The formula for generating the migration imaging results of the PP wave and the PS wave through the cross-correlation operation of the forward and backward wave fields:

[0034] PP wave migration imaging formula:

[0035]

[0036] PS wave migration imaging formula:

[0037]

[0038] In the formula, I PP (x, z) represents the migration imaging result of the PP wave; I PS (x, z) represents the migration imaging result of the PS wave; u p (x, z, t) represents the longitudinal wave vibration velocity field; w s (x, z, t) represents the shear wave vibration velocity field; t 1 , t 2 represents the time integration range.

[0039] As a further improvement of the present invention, the process of establishing a karst tunnel velocity model including the longitudinal wave velocity and the shear wave velocity includes the following steps:

[0040] Obtain the geological background information of the tunnel area, and based on the tunnel geological data, conduct a detailed analysis of the tunnel rock type, geological structure, and groundwater distribution information;

[0041] Use the finite difference method to simulate the two-dimensional full-space wave field, establish an initial condition model including the longitudinal wave velocity and the shear wave velocity, and calibrate the numerical model through on-site monitoring data;

[0042] Adopt the finite difference method to simulate the two-dimensional full-space wave field. Through the wave field simulation results, generate a karst tunnel velocity model including the longitudinal wave velocity and the shear wave velocity that reflects the physical properties of the medium around the tunnel, and conduct data verification on the generated velocity model to compare and evaluate the accuracy of the model prediction results.

[0043] As a further improvement of the present invention, the process of generating dot and offset profiles and dot product offset profiles includes the following steps:

[0044] Perform dot sum and dot product operations on the offset data of the PP wave and the PS wave;

[0045] Use the karst tunnel velocity model for RTM imaging, use the dot sum offset profile to identify the low-frequency contour information of the anomaly, and determine the location of the karst area;

[0046] Using the information after data processing, implement RTM imaging using the longitudinal wave and shear wave velocity models, identify the contour line of the anomaly, and extract pure longitudinal waves and pure shear waves from the full-wave data of the longitudinal wave and shear wave data migration imaging.

[0047] As a further improvement of the present invention, two-dimensional elastic wave P x Px , P y P y , P x S x , P y S y Each corresponding component can achieve cross - correlation migration imaging. PP is P y P y , PS is P y P y ; When adding the PP and PS migration data, the I sum , I product is the result of the dot product of the PP and PS migration data points, and its calculation process is as follows:

[0048]

[0049] I product =(∑ t P forward ·P reverse )·(∑ t P forward ·S reverse ).

[0050] As a further improvement of the present invention, the process of determining the location of the karst area includes the following steps:

[0051] Complete the RTM imaging of all single - shot data, use the Laplace filter to remove low - frequency noise, reconstruct the reflection wave field of the underground structure, and obtain the migration imaging result;

[0052] Use the point and migration profile to identify the low - frequency contour information of the abnormal body, and identify the low - resistivity abnormal area by analyzing the propagation characteristics of seismic waves at different depths and positions;

[0053] After identifying the low - frequency contour information, use the high - density resistivity method to delineate the location and floor depth of karst development through the apparent resistivity section, and further determine the specific location of the karst area.

[0054] As a further improvement of the present invention, the process of extracting pure P - wave and pure S - wave includes the following steps:

[0055] Use the karst tunnel velocity model for reverse - time migration imaging, reconstruct the underground structure by combining the information of the reflection wave field and the converted wave field; separate the P - wave and S - wave signals;

[0056] On the basis of RTM imaging with the karst tunnel velocity model, identify the contour lines of underground faults and cavity abnormal bodies by analyzing the differences between the reflection wave field and the converted wave field;

[0057] Extract pure P-waves and pure S-waves from full-wave data, compare and verify the extracted pure P-waves and pure S-waves with geological data, and adjust the parameters of the karst tunnel velocity model if they do not match.

[0058] To achieve the above object, the present invention also provides the following technical solutions:

[0059] A seismic wave tunnel advanced prediction system for an acceleration probe, which is applied to the seismic wave tunnel advanced prediction method for the acceleration probe. The seismic wave tunnel advanced prediction system for the acceleration probe includes:

[0060] A seismic wave acquisition module, which is used to arrange piezoelectric acceleration three-component sensors near the tunnel working face, collect the X, Y, and Z three-component data of the tunnel seismic waves by using the piezoelectric acceleration three-component sensors, and record the direct wave, reflected wave, and acoustic wave signals; perform denoising processing on the X, Y, and Z three-component data;

[0061] A model establishment module, which is used to establish a karst tunnel velocity model including P-wave velocity and S-wave velocity according to the tunnel geological data, perform forward simulation on the elastic wave equation by using the finite difference method, and generate theoretical wave field data; perform forward continuation on the wave field data obtained by the forward simulation to generate a forward propagation wave field; perform backward continuation on the actually collected wave field data to generate a backward propagation wave field; perform cross-correlation operation on the forward continuation wave field and the backward continuation wave field to generate PP-wave and PS-wave migration profiles;

[0062] An advanced prediction module, which is used to perform dot sum and dot product operations on the migration data of PP-waves and PS-waves to generate a dot sum migration profile and a dot product migration profile; use the dot sum migration profile to identify the low-frequency contour information of the abnormal body and determine the location of the karst area; use the dot product migration profile to extract the high-frequency detail information of the abnormal body; fuse the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information, and comprehensive interpretation result to generate a tunnel advanced prediction report.

[0063] The present invention arranges piezoelectric acceleration three-component sensors near the tunnel working face, enabling precise acquisition of the X, Y, and Z three-component data of seismic waves; collecting direct waves, reflected waves, and acoustic wave signals, providing a basis for seismic wave analysis; performing denoising processing on the collected three-component data, improving the signal-to-noise ratio of the data and making the analysis more accurate; establishing a karst tunnel velocity model based on tunnel geological data, providing necessary parameters for forward modeling by the finite difference method; using the finite difference method to perform forward modeling on the elastic wave equation to generate theoretical wave field data, which can be used for comparison and analysis with actual wave field data; through forward continuation and backward continuation of the wave field and performing cross-correlation operations, generating PP-wave and PS-wave migration profiles, which can reflect the geological structure information in front of the tunnel; performing dot sum and dot product operations on the migration data of PP waves and PS waves to generate dot sum migration profiles and dot product migration profiles, which can reflect different characteristic information of abnormal bodies; using the dot sum migration profile to identify the low-frequency contour information of abnormal bodies and determine the location of the karst area; using the dot product migration profile to extract the high-frequency detail information of abnormal bodies, which helps to more finely depict the shape and characteristics of abnormal bodies; fusing the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result, improving the accuracy and reliability of abnormal body identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic flow chart of the steps of an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0065] Figure 2 It is a schematic flow chart of the steps of collecting the X, Y, and Z three-component data of tunnel seismic waves in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0066] Figure 3 It is a schematic flow chart of the steps of performing forward modeling on the elastic wave equation in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0067] Figure 4 It is a schematic flow chart of the steps of establishing a karst tunnel velocity model including longitudinal wave velocity and transverse wave velocity in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0068] Figure 5 It is a schematic flow chart of the steps of generating dot sum migration profiles and dot product migration profiles in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0069] Figure 6 It is a schematic principle diagram of generating dot sum migration profiles and dot product migration profiles in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0070] Figure 7Schematic diagram of the steps for determining the location of a karst area in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0071] Figure 8 Schematic diagram of the steps for extracting pure longitudinal waves and pure transverse waves in an embodiment of the seismic wave tunnel advanced prediction method using the acceleration probe of the present invention;

[0072] Figure 9 Schematic diagram of the structure of an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0073] Figure 10 Schematic diagram of the original X, Y, and Z component data of an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0074] Figure 11 Schematic diagram of the original spectrum of the piezoelectric acceleration sensor in an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0075] Figure 12 Schematic diagram of the original X, Y, and Z component data of the electromagnetic moving coil velocity sensor in an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0076] Figure 13 Schematic diagram of the original spectrum of the electromagnetic moving coil velocity sensor in an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0077] Figure 14 Schematic diagram of the single-shot data of the sensor in an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention; (a) Single-shot data of the acceleration sensor; (b) Single-shot data of the velocity sensor;

[0078] Figure 15 Schematic diagram of the comparison of the frequency band ranges of the acceleration sensor and the velocity sensor in an embodiment of the high-sensitivity piezoelectric three-component sensor of the present invention;

[0079] Figure 16 Schematic diagram of the functional modules of an embodiment of the seismic wave tunnel advanced prediction system using the acceleration probe of the present invention;

[0080] Figure 17 Schematic diagram of the observation system of an embodiment of the seismic wave tunnel advanced prediction experiment using the acceleration probe of the present invention;

[0081] Figure 18 Schematic diagram of the original seismic data of an embodiment of the seismic wave tunnel advanced prediction system using the acceleration probe of the present invention;

[0082] Figure 19Schematic diagram of P-wave and S-wave RTM imaging results of an embodiment of the seismic wave tunnel advanced prediction system with the acceleration probe of the present invention;

[0083] Figure 20 Schematic diagram of the structure of an embodiment of the electronic device of the present invention;

[0084] Figure 21 Schematic diagram of the structure of an embodiment of the storage medium of the present invention;

[0085] Figure 22 Schematic diagram of the mechanism of the tension section of an embodiment of the seismic wave tunnel advanced prediction system with the acceleration probe of the present invention. Detailed implementation manners

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0087] The terms "first", "second", and "third" in the present invention are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0088] Referring to "embodiment" in this article means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0089] As shown Figure 1 in the figure, this embodiment provides an embodiment of the seismic wave tunnel advanced prediction method for an acceleration probe. In this embodiment, the seismic wave tunnel advanced prediction method for the acceleration probe includes:

[0090] Step S100: Arrange a piezoelectric acceleration three-component sensor near the tunnel working face, use the piezoelectric acceleration three-component sensor to collect the X, Y, and Z three-component data of the tunnel seismic wave, and record the direct wave, reflected wave, and acoustic wave signals; perform denoising processing on the X, Y, and Z three-component data;

[0091] Step S200: According to the tunnel geological data, establish a karst tunnel velocity model including the longitudinal wave velocity and the transverse wave velocity, perform forward modeling of the elastic wave equation using the finite difference method, and generate theoretical wave field data; use the wave field data of the forward modeling for forward continuation to generate a forward propagating wave field; perform backward continuation on the collected actual wave field data to generate a backward propagating wave field; perform cross-correlation operation on the forward continuation wave field and the backward continuation wave field to generate PP wave and PS wave migration profiles;

[0092] Step S300: Perform dot sum and dot product operations on the migration data of the PP wave and the PS wave to generate a dot sum migration profile and a dot product migration profile; use the dot sum migration profile to identify the low-frequency contour information of the abnormal body and determine the location of the karst area; use the dot product migration profile to extract the high-frequency detail information of the abnormal body; fuse the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information, and comprehensive interpretation result to generate a tunnel advanced prediction report.

[0093] Preferably, in step S100 of this embodiment, by arranging three-component piezoelectric accelerometers near the tunnel working face, the X, Y, and Z three-component data of seismic waves can be accurately collected; the direct waves, reflected waves, and acoustic wave signals are collected, providing a basis for seismic wave analysis; the collected three-component data is denoised, improving the signal-to-noise ratio of the data and making the analysis more accurate; in step S200, a karst tunnel velocity model is established based on the tunnel geological data, providing necessary parameters for the forward modeling of the finite difference method; the elastic wave equation is forward modeled using the finite difference method to generate theoretical wavefield data, which can be used for comparison and analysis with the actual wavefield data; by forward and backward extrapolating the wavefield and performing cross-correlation operations, PP-wave and PS-wave migration profiles are generated, which can reflect the geological structure information in front of the tunnel; in step S300, dot sum and dot product operations are performed on the migration data of PP waves and PS waves to generate dot sum migration profiles and dot product migration profiles, which can reflect different characteristic information of the abnormal body; the low-frequency contour information of the abnormal body is identified using the dot sum migration profile to determine the location of the karst area; the high-frequency detailed information of the abnormal body is extracted using the dot product migration profile, which helps to more finely depict the shape and characteristics of the abnormal body; the dot sum migration data and the dot product migration data are fused to generate a comprehensive interpretation result, improving the accuracy and reliability of abnormal body identification.

[0094] In summary, the high-sensitivity three-component piezoelectric accelerometer of this embodiment is beneficial to improving the consistency of seismic data in the X, Y, and Z components, has a wider frequency spectrum range, clear direct wave arrivals and acoustic waves, and is rich in low-frequency, medium-frequency, and high-frequency information, which can improve the signal-to-noise ratio of seismic data; DE-RTM can prevent errors in separating wavefields and improve the signal fidelity; DE-RTM effectively utilizes P-wave and S-wave data to achieve high-resolution imaging of abnormal bodies in karst areas and improve the accuracy of karst detection; the low-frequency contour information of the abnormal body is identified using the dot sum migration data, and the high-frequency detailed information of the abnormal body is identified using the dot product migration data. By combining the dot sum and dot product migration data, a more reliable interpretation result can be obtained.

[0095] Furthermore, as Figure 2 shown, the process of collecting the X, Y, and Z three-component data of tunnel seismic waves in step S100 specifically includes the following steps:

[0096] Step S101: Use four three-component sensors to collect seismic data at the target tunnel working face. The sensors are placed in boreholes with a depth of 30 cm to collect the X, Y, and Z three-component data of seismic waves;

[0097] Step S102: Conduct noise inspection on the sensors and their environment, and use three-component piezoelectric accelerometers to collect seismic wave data;

[0098] Step S103: Denoise the collected seismic wave data to improve the signal-to-noise ratio of the reflected wave; after denoising, further correct and analyze the data.

[0099] Preferably, in step S101 of this embodiment, four three-component sensors are used to collect seismic data at the target tunnel working face, and the sensors are placed in boreholes with a depth of 30 cm; this ensures that the X, Y, and Z three-component data of the seismic wave can be comprehensively and accurately collected; the three-component data can provide more comprehensive seismic wave information, which is helpful for analysis and processing; in step S102, the sensors and their environment are checked for noise, and piezoelectric acceleration three-component sensors are used to collect seismic wave data; the noise check can identify and eliminate external interferences that may affect the data quality, and the piezoelectric acceleration sensor can capture the seismic wave signal more accurately due to its high precision and sensitivity; in step S103, the collected seismic wave data is denoised to improve the signal-to-noise ratio of the reflected wave; after denoising, the data is further corrected and analyzed; the denoising process can significantly reduce the noise interference in the data and improve the clarity and readability of the data.

[0100] In summary, this embodiment provides a reliable data basis for geological structure analysis and tunnel stability assessment through precise seismic data collection; through noise inspection and the use of high-quality sensors, the accuracy and reliability of the collected seismic wave data are ensured; it helps to improve the accuracy of the entire seismic exploration; through denoising processing and correction analysis, the reflected wave information in the seismic wave can be more accurately identified and analyzed, which is of great significance for the identification of geological structures and the risk assessment of tunnel construction; clear and accurate data helps decision-makers make more informed and safe decisions.

[0101] Further, as Figure 3 shown, the process of forward modeling of the elastic wave equation in step S200 specifically includes the following steps:

[0102] Step S201: According to the tunnel geological data, analyze in detail information such as the tunnel rock type, geological structure, and groundwater distribution, simulate the two-dimensional full-space wave field by the finite difference method, and establish a karst tunnel velocity model including the longitudinal wave velocity and the transverse wave velocity.

[0103] Step S202: Design 24 blast holes and two geophones, with a distance of 1.5 meters between adjacent blast holes; the geophones are located 20 meters away from the first blast point; use the two-dimensional elastic decoupled wave equation approximated by the finite difference to perform forward modeling on the karst tunnel velocity model to generate theoretical wave field data.

[0104] Step S203: Correlate the forward continuous longitudinal wave with the backward continuous longitudinal wave to achieve the migration imaging of PP waves; correlate the forward continuation of the longitudinal wave with the backward continuation of the shear wave to obtain the migration imaging result of PS waves;

[0105] Among them, in step S202, the RTM imaging is based on the forward modeling, and the first-order stress-velocity elastic decoupling equation is:

[0106]

[0107] The elastic wave decoupling equation is:

[0108] u = u p + u s

[0109] w = w p + w s

[0110]

[0111] Among them, λ and μ are the Lame constants of the medium, and t is the time variable. (τ yy , τ xx , τ yx ) are all stress tensors. (u, w) are the vibration velocity components in the horizontal and vertical directions. V p and V s are the longitudinal wave velocity and the shear wave velocity respectively. The partial differential equation can be transformed into a second-order equation in time and a twelfth-order equation in space. The perfectly matched layer is used as the boundary condition; u p , w p represent the horizontal and vertical vibration velocity components of the longitudinal wave; u s , w s represent the horizontal and vertical vibration velocity components of the shear wave;

[0112] The formulas for generating the migration imaging results of PP waves and PS waves through the cross-correlation operation of the forward and backward wave fields in step S203:

[0113] PP wave migration imaging formula:

[0114]

[0115] PS wave migration imaging formula:

[0116]

[0117] In the formula, I PP (x, z) represents the migration imaging result of PP waves; I PS (x, z) represents the migration imaging result of PS waves; u p(x, z, t) represents the vibration velocity field of the longitudinal wave; w s (x, z, t) represents the vibration velocity field of the shear wave; t 1 , t 2 represents the time integration range.

[0118] Preferably, in step S201 of this embodiment, by analyzing in detail the geological data of the tunnel, such as rock type, geological structure, and groundwater distribution, and using the finite difference method to simulate the two-dimensional full-space wave field, a karst tunnel velocity model including longitudinal wave velocity and shear wave velocity is established, providing a basis for the forward modeling of the wave equation; in step S202, a specific layout of blast holes and geophones is designed, and the forward modeling of the karst tunnel velocity model is carried out using the two-dimensional elastic decoupled wave equation approximated by the finite difference method to generate theoretical wave field data; in step S203, the migration imaging of the PP wave is realized by correlating the forward continuous longitudinal wave and the backward continuous longitudinal wave; at the same time, the migration imaging result of the PS wave is obtained by correlating the forward continuation of the longitudinal wave and the backward continuation of the shear wave.

[0119] To sum up, this embodiment ensures the accuracy of the simulation. Accurate geological simulation and wave velocity information are the keys to the reliability of the simulation results; by simulating the actual exploration process, theoretical data for analysis and imaging are generated; it provides a basis for evaluating the effectiveness of exploration methods and optimizing exploration parameters; imaging processing is carried out using the generated theoretical wave field data, so as to visually display the geological structure inside the tunnel; it is crucial for identifying potential geological risks and formulating safe construction plans.

[0120] Furthermore, as Figure 4 shown, the process of establishing a karst tunnel velocity model including longitudinal wave velocity and shear wave velocity in step S201 specifically includes the following steps:

[0121] Step S2011: Obtain the geological background information of the tunnel area, and analyze in detail the information such as the tunnel rock type, geological structure, and groundwater distribution according to the tunnel geological data;

[0122] Step S2012: Use the finite difference method to simulate the two-dimensional full-space wave field, establish an initial condition model including longitudinal wave velocity and shear wave velocity, and calibrate the numerical model through on-site monitoring data;

[0123] Step S2013: Use the finite difference method to simulate the two-dimensional full-space wave field, generate a karst tunnel velocity model including longitudinal wave velocity and shear wave velocity that reflects the physical properties of the medium around the tunnel through the wave field simulation results, and verify the data of the generated velocity model, and compare and evaluate the accuracy of the model prediction results.

[0124] Among them, the relevant formula for establishing the initial condition model through geological data analysis in step S2011:

[0125] The classification formula of rock types is expressed as:

[0126]

[0127] The distribution formula of groundwater is expressed as:

[0128]

[0129] In the formula, C r represents the classification coefficient of rock types; ρ i represents the density of the i-th rock; V pi represents the longitudinal wave velocity of the i-th rock; ρ avg , V pavg represent the average density and longitudinal wave velocity; w i represents the weight coefficient; W d represents the groundwater distribution coefficient; φ(z) represents the porosity at depth z; k(z) represents the permeability at depth z; φ avg , k avg represent the average porosity and permeability;

[0130] The relevant formulas for simulating the two-dimensional full-space wave field by the finite difference method in step S2012:

[0131] Finite difference approximation formula:

[0132]

[0133] Boundary condition formula:

[0134] u(x, z, t) = 0 when x = 0, x = L x , z = 0, z = L z

[0135] In the formula, u(x, z, t) represents the vibration velocity of the wave field; L x , L z represent the length and depth of the simulation area;

[0136] The relevant formulas for calibrating and validating the velocity model through on-site monitoring data in step S2013:

[0137] Model calibration formula:

[0138]

[0139] Model validation formula:

[0140]

[0141] In the formula, E cal represents the model calibration error; E valrepresents the model verification error; represents the longitudinal wave velocity predicted by the model; represents the longitudinal wave velocity monitored on site; represents the PP wave migration imaging predicted by the model; represents the PP wave migration imaging monitored on site.

[0142] Preferably, in step S2011 of this embodiment, the geological background information of the tunnel area is obtained and analyzed in detail, including rock types, geological structures, and groundwater distribution, etc., to provide basic data for simulation and modeling; the finite difference method is used to simulate the two-dimensional full-space wave field, an initial condition model including longitudinal wave velocity and shear wave velocity is established, and the numerical model is calibrated through on-site monitoring data to improve the accuracy of the model; in step S2013, the finite difference method is used to simulate the two-dimensional full-space wave field, a karst tunnel velocity model reflecting the physical properties of the medium around the tunnel is generated, and data verification and comparative evaluation are carried out to ensure the accuracy of the model prediction results.

[0143] In summary, the in-depth understanding of the geological background in this embodiment helps to predict potential risks during tunnel construction and ensure the accuracy of simulation and modeling; by simulating the wave field, the propagation characteristics of waves in the medium can be understood, providing a scientific basis for generating the karst tunnel velocity model; the calibration of on-site data ensures the consistency between the model and the actual situation and improves the accuracy of prediction; the generated karst tunnel model can intuitively display the physical properties of the medium around the tunnel, providing an important reference for tunnel design and construction; data verification and comparative evaluation ensure the reliability of the model, help to reduce construction risks, and improve project safety.

[0144] Furthermore, as Figure 5 shown, the process of generating the point and migration profile and the dot product migration profile in step S300 specifically includes the following steps:

[0145] Step S301: Perform point sum and dot product operations on the migration data of PP waves and PS waves. The cross-correlation migration imaging can be realized for each corresponding component of the two-dimensional elastic wave P x P x 、P y P y 、P x S x 、P y S y . PP is P y P y ,PS is P y P y ; when adding the PP and PS migration data, I sum ,I product is the result of the dot product of the PP and PS migration data, and its calculation process is as follows:

[0146]

[0147]

[0148] Step S302: Use the karst tunnel velocity model for RTM imaging, identify the low-frequency contour information of the abnormal body using points and migration profiles, and determine the location of the karst area;

[0149] Step S303: Use the information after data processing, implement RTM imaging using the P-wave and S-wave velocity models, identify the contour line of the abnormal body, and extract pure P-waves and pure S-waves from the full-wave data of P-wave and S-wave data migration imaging.

[0150] Preferably, in step S301 of this embodiment, by performing dot sum and dot product operations on the migration data of PP-waves and PS-waves, cross-correlation migration imaging can be achieved, which helps to enhance the reflection signal of seismic waves and improve the resolution and accuracy of imaging; I sum represents the sum of the PP-wave and PS-wave migration data, while I product represents the result of their dot product; it can reflect the energy distribution and mutual relationship of seismic waves in different directions, which helps to further analyze the underground structure; step S302 can generate a high-resolution underground structure image through reflection wave imaging technology; it is particularly effective for identifying complex geological structures; using points and migration profiles, the low-frequency contour information of the abnormal body can be identified, which is crucial for determining the location of the karst area; step S303 can more accurately describe the physical properties of the underground medium by combining the velocity information of P-waves and S-waves, thereby improving the accuracy of RTM imaging; extracting pure P-wave and pure S-wave information from the full-wave data helps to more clearly identify the contour line of the abnormal body and further confirm the location and shape of the karst area (for the specific schematic diagram, refer to the appendix Figure 6 ).

[0151] In summary, this embodiment provides basic data for imaging and analysis. By integrating the information of PP-waves and PS-waves, a more comprehensive understanding of the underground geological structure can be obtained; I sum , I product The calculation of provides important clues for identifying abnormal bodies because abnormal bodies often lead to abnormal energy distribution of seismic waves; the RTM imaging technology provides an intuitive and accurate method for geological exploration, which helps to quickly locate potential geological risk areas; by identifying the low-frequency contour information of abnormal bodies, it can provide important reference basis for detailed exploration and development; by comprehensively using the information of P-waves and S-waves, the accuracy and reliability of geological exploration are improved; identifying the contour line of the abnormal body provides important geological basis for engineering design and construction, which helps to ensure the safety and stability of the project.

[0152] Furthermore, as Figure 7As shown, the process of determining the location of the karst area in step S302 specifically includes the following steps:

[0153] Step S3021: Complete the RTM imaging of all single-shot data, use the Laplace filter to remove low-frequency noise, reconstruct the reflected wave field of the underground structure, and obtain a clear migration imaging result;

[0154] Step S3022: Use points and migration profiles to identify the low-frequency contour information of the abnormal body, and identify the low-resistivity abnormal area by analyzing the propagation characteristics of seismic waves at different depths and positions;

[0155] Step S3023: After identifying the low-frequency contour information, use the high-density resistivity method to delineate the location and floor depth of karst development through the apparent resistivity section, and further determine the specific location of the karst area.

[0156] Preferably, in step S3021 of this embodiment, RTM imaging is completed, the Laplace filter is used to remove low-frequency noise, and the reflected wave field is reconstructed; a clear migration imaging result is obtained; RTM imaging is a high-resolution imaging technology that can provide detailed images of the underground structure; the Laplace filter is used to remove low-frequency noise in the image, thereby improving the clarity of the image; in step S3022, points and migration profiles are used to identify the low-frequency contour information of the abnormal body and analyze the propagation characteristics of seismic waves; the low-resistivity abnormal area is identified; the propagation characteristics of seismic waves in different geological structures will be different, and abnormal areas in the geological structure can be identified by analyzing these characteristics; in step S3023, the high-density resistivity method is used to delineate the location and floor depth of karst development through the apparent resistivity section; the specific location of the karst area is determined; the high-density resistivity method is a geophysical exploration method that identifies geological structures by measuring the resistivity differences of underground media.

[0157] In summary, the clear imaging result of this embodiment provides a reliable basis for geological structure analysis and abnormal body identification; provides a target area for detailed exploration, helps to narrow the exploration scope and improve the exploration efficiency; provides key information for engineering design and geological disaster prevention; understanding the specific location and depth of the karst area is of great significance for avoiding geological disasters and ensuring engineering safety.

[0158] Furthermore, as Figure 8 shown, the process of extracting pure P-waves and pure S-waves in step S303 specifically includes the following steps:

[0159] Step S3031: Use the karst tunnel velocity model for reverse time migration imaging, and accurately reconstruct the underground structure by combining the information of the reflected wave field and the converted wave field; by separating the P-wave and S-wave signals, improve the imaging quality and reduce noise interference;

[0160] Step S3032: Based on the RTM imaging of the karst tunnel velocity model, by analyzing the differences between the reflected wave field and the converted wave field, identify the contour lines of underground faults, cavities or other geological structure anomalies;

[0161] Step S3033: Extract pure P-waves and pure S-waves from the full-wave data, and use the vector separation method to avoid changes in the wave field amplitude and phase; Compare the extracted pure P-waves and pure S-waves with the geological data for verification. If they do not match, adjust the parameters of the karst tunnel velocity model.

[0162] Preferably, in step S3031 of this embodiment, using the karst tunnel velocity model for reverse time migration imaging can simulate the situation of more complex media, image multiple waves, converted waves, etc., so as to better restore the geological structure morphology; By combining the information of the reflected wave field and the converted wave field, the information of different wave fields can be comprehensively utilized to improve the accuracy and integrity of imaging; Separating the P-wave and S-wave signals helps to reduce the interference between wave fields and improve the clarity and resolution of imaging; In step S3032, based on the RTM imaging, by analyzing the differences between the reflected wave field and the converted wave field, the contour lines of underground faults, cavities or other geological structure anomalies can be identified; The RTM technology has the advantage of amplitude preservation and can perform high-precision imaging on subtle and complex structures, which helps to accurately identify underground anomalies; In step S3033, extracting pure P-waves and pure S-waves from the full-wave data can avoid the interference between wave fields and improve the accuracy of data analysis; Using the vector separation method to avoid changes in the wave field amplitude and phase helps to maintain the original characteristics of the wave field and provides strong support for geological interpretation; Comparing the extracted pure P-waves and pure S-waves with the geological data for verification can verify the accuracy and reliability of the model. If they do not match, adjust the parameters of the karst tunnel velocity model, thereby improving the prediction ability of the model.

[0163] In summary, the reverse time migration imaging technology of this embodiment provides the possibility for the accurate reconstruction of underground structures, which helps engineers better understand the underground conditions; by combining the information of the reflected wave field and the converted wave field, the characteristics of underground structures can be more comprehensively revealed, providing strong support for geological interpretation; separating the P-wave and S-wave signals reduces noise interference and improves the imaging quality, providing a more reliable data basis for geological interpretation and engineering decision-making; identifying the contour lines of geological structure anomalies such as underground faults and cavities provides important geological basis for engineering design and construction; the high-precision RTM imaging technology helps engineers more accurately understand the underground conditions and avoid potential risks during the construction process; extracting pure P-waves and pure S-waves helps to more accurately analyze the characteristics of underground structures and provides strong support for geological interpretation; by comparing and verifying with geological data, the karst tunnel velocity model can be continuously optimized and improved, improving the prediction accuracy and reliability of the model; the application of the vector separation method helps to maintain the original characteristics of the wave field and provides a more reliable data basis for data analysis and geological interpretation.

[0164] As Figure 9 and Figure 22 shown, this embodiment also provides an embodiment of a high-sensitivity piezoelectric three-component sensor. In this embodiment, the high-sensitivity piezoelectric three-component sensor is applied to the seismic wave tunnel advanced prediction method of the acceleration probe as described in the above embodiment. The high-sensitivity piezoelectric three-component sensor includes: an automatic coupling actuator housing 1, a first folding rod 2, a second folding rod 3, a tensioning section 4, a base 14, an outer sleeve 15, a push rod handle 16, a wire pressing block 17, a terminal block 18, a first tensioning section 19, a second tensioning section 20, a third tensioning section 21, a fixing rivet 22, and a spring 23.

[0165] Among them, the automatic coupling actuator housing 1 is installed at the top of the high-sensitivity piezoelectric three-component sensor, and the push rod handle 16 is installed at the bottom of the high-sensitivity piezoelectric three-component sensor. The automatic coupling actuator housing 1 is connected to the push rod handle 16 through the first folding rod 2 and the second folding rod 3. The first folding rod 2 is connected to the second folding rod 3 through a connecting block. A tensioning section 4 is installed at the rear end of the automatic coupling actuator housing 1. The tensioning section 4 is installed on the base 14. An outer sleeve 15 is arranged outside the base 14. A wire pressing block 17 is arranged outside the push rod handle 16, and a terminal block 18 is arranged inside the wire pressing block 17. The tensioning section 4 is composed of a first tensioning section 19, a second tensioning section 20, and a third tensioning section 21. A plurality of fixing rivets 22 are installed inside the first tensioning section 19, the second tensioning section 20, and the third tensioning section 21. A spring 23 is sleeved on the fixing rivet 22 at the bottom.

[0166] The working principle of a high-sensitivity piezoelectric triaxial sensor is mainly based on the piezoelectric effect and mechanical coupling mechanism. Its core function is to convert mechanical vibration or pressure signals into electrical signals through piezoelectric materials, thereby achieving precise measurement of vibration or pressure changes in three directions (usually the X, Y, and Z axes). The following is a detailed analysis of its working principle:

[0167] The core component inside the sensor is the piezoelectric material (such as quartz or piezoelectric ceramics). When the material is subjected to mechanical stress (such as pressure or vibration), it generates electric charges, and the magnitude of the charges is proportional to the applied stress; this effect is called the direct piezoelectric effect. Conversely, when an electric field is applied, the piezoelectric material also undergoes deformation, which is the inverse piezoelectric effect. In a high-sensitivity piezoelectric triaxial sensor, the direct piezoelectric effect is mainly utilized to convert mechanical signals into electrical signals.

[0168] The sensor is designed to be able to measure vibration or pressure changes in three orthogonal directions (the X, Y, and Z axes) simultaneously. The vibration in each direction is transmitted to the corresponding piezoelectric element through the mechanical structure, and the piezoelectric element converts the vibration signal into an electrical signal. In this way, the sensor can capture the dynamic changes in three directions simultaneously.

[0169] The function of the automatic coupling actuator housing 1 and its folding rod structure (the first folding rod 2 and the second folding rod 3) is to ensure that the sensor can be coupled with the measurement object quickly and stably; this design improves the response speed and measurement accuracy of the sensor, especially in an environment of high-frequency vibration or rapidly changing pressure.

[0170] The tensioning section 4 consists of the first tensioning section 19, the second tensioning section 20, and the third tensioning section 21, and is tensioned internally through fixed rivets 22 and springs 23. This structure can effectively absorb external vibration or impact, avoid interference with the core components of the sensor, and ensure that the piezoelectric elements are always in the best working state.

[0171] The sensor outputs the electrical signals generated by the piezoelectric elements to external devices (such as a data acquisition system or a signal processor) through the wiring terminal 18. These signals can be used for further analysis or control after being amplified, filtered, and digitized.

[0172] In summary, the high-sensitivity piezoelectric triaxial sensor converts mechanical vibration or pressure signals into electrical signals through the piezoelectric effect, and combines the design of the automatic coupling actuator and the tensioning section to achieve high-precision measurement of vibration or pressure in three directions. This type of sensor is widely used in fields such as monitoring, industrial vibration detection, and aerospace, and has the characteristics of high sensitivity, fast response, and stable reliability.

[0173] Such as Figures 10 - 13As shown, the original X, Y, and Z component data of the piezoelectric acceleration sensor show good consistency, can clearly capture the direct wave and acoustic wave signals, have a wide frequency spectrum range, and are rich in low-frequency, medium-frequency, and high-frequency information. In contrast, the consistency of each component in the original X, Y, and Z component data of the electromagnetic moving coil velocity sensor is poor. Due to the extrusion of the shear wave compression modulus in the borehole, the Y component causes high-frequency signal loss, and only low-frequency signals are collected. Its frequency spectrum range is narrow, limited to low-frequency and medium-frequency information.

[0174] Through Figure 14 the original data, it can be found that the first arrival of the acceleration sensor is clear and the takeoff is clean. The first arrival of the velocity sensor has an envelope signal, and the takeoff is blurred with a tail spike envelope, which is not conducive to the identification of the direct wave velocity.

[0175] Figure 15 The blue curve in represents the distribution of the data energy collected by the velocity sensor in different frequency bands. It can be seen from the figure that the frequency band range of the velocity sensor is narrow, and there is almost no signal response after 500 Hz. The red curve represents the frequency band range of the acceleration sensor. It can be seen from the figure that the acceleration sensor has a wider frequency band range, and the signal response extends to 1500 Hz.

[0176] As Figure 16 shown, this embodiment also provides an embodiment of the seismic wave tunnel advanced prediction system for the acceleration probe. In this embodiment, the seismic wave tunnel advanced prediction system for the acceleration probe is applied to the seismic wave tunnel advanced prediction method for the acceleration probe as described in the above embodiment. The seismic wave tunnel advanced prediction system for the acceleration probe includes:

[0177] A seismic wave acquisition module 5, which is used to arrange a piezoelectric acceleration three-component sensor near the tunnel working face, collect the X, Y, and Z three-component data of the tunnel seismic wave by using the piezoelectric acceleration three-component sensor, and record the direct wave, reflected wave, and acoustic wave signals; perform denoising processing on the X, Y, and Z three-component data;

[0178] A model establishment module 6, which is used to establish a karst tunnel velocity model including the longitudinal wave velocity and the shear wave velocity according to the tunnel geological data, perform forward simulation on the elastic wave equation by using the finite difference method, and generate theoretical wave field data; perform forward continuation on the wave field data obtained by the forward simulation to generate a forward propagating wave field; perform backward continuation on the actually collected wave field data to generate a backward propagating wave field; perform cross-correlation operation on the forward continuation wave field and the backward continuation wave field to generate PP wave and PS wave migration profiles;

[0179] The advanced prediction module 7 is used to perform dot sum and dot product operations on the migration data of PP waves and PS waves to generate a dot sum migration profile and a dot product migration profile; identify the low-frequency contour information of abnormal bodies using the dot sum migration profile to determine the location of karst areas; extract the high-frequency detail information of abnormal bodies using the dot product migration profile; fuse the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information, and comprehensive interpretation result to generate a tunnel advanced prediction report.

[0180] Preferably, the seismic wave acquisition module 5 in this embodiment can accurately acquire the X, Y, and Z three-component data of seismic waves by arranging piezoelectric acceleration three-component sensors near the tunnel working face; collect direct waves, reflected waves, and acoustic wave signals, providing a basis for seismic wave analysis; perform denoising processing on the collected three-component data to improve the signal-to-noise ratio of the data and make the analysis more accurate; the model establishment module 6 establishes a karst tunnel velocity model based on tunnel geological data, providing necessary parameters for the forward simulation of the finite difference method; perform forward simulation on the elastic wave equation using the finite difference method to generate theoretical wave field data, which can be used for comparison and analysis with actual wave field data; generate PP wave and PS wave migration profiles by forward continuation and backward continuation of the wave field and performing cross-correlation operations, which can reflect the geological structure information in front of the tunnel; the advanced prediction module 7 performs dot sum and dot product operations on the migration data of PP waves and PS waves to generate a dot sum migration profile and a dot product migration profile, which can reflect different characteristic information of abnormal bodies; identify the low-frequency contour information of abnormal bodies using the dot sum migration profile to determine the location of karst areas; extract the high-frequency detail information of abnormal bodies using the dot product migration profile, which helps to more precisely depict the shape and characteristics of abnormal bodies; fuse the dot sum migration data and the dot product migration data to generate a comprehensive interpretation result, improving the accuracy and reliability of abnormal body identification.

[0181] In summary, the high-sensitivity piezoelectric acceleration three-component detector in this embodiment is beneficial to improving the consistency of seismic data in the X, Y, and Z components, having a wider frequency spectrum range, clear direct wave arrivals and acoustic waves, rich low-frequency, medium-frequency, and high-frequency information, and can improve the signal-to-noise ratio of seismic data; DE-RTM can prevent errors in separating wave fields and improve the signal fidelity; DE-RTM effectively utilizes P-wave and S-wave data, realizes high-resolution imaging of abnormal bodies in karst areas, and improves the karst detection accuracy; use dot sum migration data to identify the low-frequency contour information of abnormal bodies and use dot product migration data to identify the high-frequency detail information of abnormal bodies. By combining dot sum and dot product migration data, more reliable interpretation results can be obtained.

[0182] Such as Figure 17As shown in the figure, this embodiment also provides an embodiment of the seismic wave tunnel advanced prediction experiment of the acceleration probe. In this embodiment, the seismic wave tunnel advanced prediction experiment of the acceleration probe is applied to the seismic wave tunnel advanced prediction method of the acceleration probe in the above-mentioned embodiment. The seismic wave tunnel advanced prediction experiment of the acceleration probe includes:

[0183] The experimental site is selected in a water diversion tunnel in a certain county, a certain district, and a certain city in China. The data acquisition instrument used is the TETSP-2 tunnel seismic advanced detection system. Seismic waves are generated on the tunnel wall by hammering, and four three-component sensors are used to collect seismic data. The sensors are placed in the boreholes, and the borehole depth is 30 cm. The left sidewall of the tunnel was hammered 54 times, and the right sidewall of the tunnel was hammered 50 times. The sampling interval of the sampled data is 41.7 μs, and the sampling length is 8192 sampling points (for the specific construction drawing, refer to the appendix Figure 18 );

[0184] The signal-to-noise ratio of the original seismic data is relatively large; for the X and Y component seismic data after exponential gain (AGC) processing in the original seismic data, it is possible to more effectively distinguish the direct wave (Di), Rayleigh surface wave (RS), and some interference waves (N1 and N2) (for the specific original seismic data diagram, refer to the appendix Figure 18 );

[0185] Using the information after data processing, RTM imaging is realized by using the P-wave and S-wave velocity models. The resolution of the PS-wave migration contour line is higher than that of the PP-wave migration contour line, especially in front of the tunnel. Compared with the PP-wave migration contour line A, the dot product migration imaging improves the resolution and contour line of the abnormal interface, reduces the potential size of the abnormal interface, and more effectively concentrates the imaging area. The reflection interface of the abnormal body can be divided into two interfaces. Compared with the point and migration profile, the dot product migration imaging profile has higher resolution and can accurately identify the contour line of the abnormal body (for the specific schematic diagram, refer to the appendix Figure 19 ).

[0186] Preferably, through numerical simulation and field tests, it is found that by using mathematical transformation methods such as polarization filtering, F-K transformation, and τ-p transformation, pure P-waves and pure S-waves are extracted from the full-wave data of P-wave and S-wave data migration imaging. Either the Kirchhoff migration method or the equal travel-time plane migration method can be used to realize P-wave and S-wave migration imaging. Since an accurate boundary needs to be established when separating the wave fields, it is difficult to separate pure P-waves and S-waves, thus introducing additional noise. These noises will cause migration in the imaging and lead to migration artifacts, affecting the impedance interface of the real abnormal body.

[0187] RTM is considered the most accurate migration method. The DE-RTM algorithm uses the methods of P-wave forward continuation, P-wave and S-wave reverse continuation to achieve simultaneous migration of multi-wave and multi-component data. DE-RTM can effectively utilize P-waves and S-waves, and at the same time can prevent wavefield separation errors and improve signal fidelity. The collected data proves the advantages of using DE-RTM technology in improving imaging resolution.

[0188] The dot and imaging conditions and dot product imaging conditions for multi-wave and multi-component RTM data are proposed. The dot and migration profile exhibits a higher signal-to-noise ratio, lower resolution, and shows certain migration artifacts. The dot product migration profile has greater resolution and effectively reduces migration artifacts. The low-frequency contour details of the anomaly are interpreted using the dot and migration data, and the high-frequency contour details of the anomaly are interpreted using the dot product migration data. By fusing the dot and and dot product migration data, more reliable interpretation results can be obtained.

[0189] In summary, the present invention obtains a high-sensitivity piezoelectric acceleration three-component probe. This probe is used for tunnel seismic wave advance prediction, and on this basis, the DE-RTM method is proposed to solve the problems of tunnel advance detection and imaging in engineering. At the same time, a method for interpreting multi-wave migration data is also proposed.

[0190] As Figure 20 shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 12 includes a processor 121 and a memory 122 coupled to the processor 121. The memory 122 stores program instructions for implementing the seismic wave tunnel advance prediction method of the acceleration probe in any of the above embodiments. The processor 121 is used to execute the program instructions stored in the memory 122 to perform seismic wave tunnel advance prediction of the acceleration probe. Among them, the processor 121 can also be called a CPU (Central Processing Unit, central processing unit). The processor 121 may be an integrated circuit chip with signal processing capabilities. The processor 121 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0191] Further, Figure 21The figure is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium 13 in the embodiment of the present application stores program instructions 131 that can implement all the above methods. Among them, the program instructions 131 can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets. In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0192] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0193] The specific implementation manners of the invention have been described in detail above, but they are only examples, and the invention is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution to the invention is also within the scope of the present invention. Therefore, any equivalent transformation, modification, and improvement made without departing from the spirit and principle of the present invention should be covered by the scope of the present invention.

Claims

1. A seismic wave tunnel advance prediction method using an acceleration probe, characterized in that: The seismic wave tunnel advance prediction method of the acceleration probe comprises: A piezoelectric acceleration three-component sensor is arranged near the tunnel working face, and the X, Y, and Z three-component data of the tunnel seismic wave are collected by the piezoelectric acceleration three-component sensor, and the direct wave, reflected wave and sound wave signals are recorded; the X, Y, and Z three-component data are denoised; Based on the tunnel geological data, a karst tunnel velocity model including longitudinal wave velocity and transverse wave velocity is established, and the elastic wave equation is forward simulated by the finite difference method to generate theoretical wave field data; the wave field data of the forward simulation is forward extended to generate the forward propagation wave field; the actual wave field data collected is reverse extended to generate the reverse propagation wave field; the forward extension wave field and the reverse extension wave field are cross-correlated to generate the PP wave and PS wave migration profiles; Perform point sum and dot product operations on the offset data of PP waves and PS waves to generate point sum offset profiles and dot product offset profiles; use the point sum offset profiles to identify the low-frequency contour information of the anomaly and determine the location of the karst area; use the dot product offset profiles to extract the high-frequency detail information of the anomaly; fuse the point sum offset data with the dot product offset data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information and comprehensive interpretation results to generate a tunnel advance prediction report.

2. The method for advance prediction of seismic wave tunnels using an acceleration probe according to claim 1, characterized in that: The process of collecting X, Y, and Z three-component data of tunnel seismic waves includes the following steps: Four three-component sensors are used to collect seismic data at the target tunnel face. The sensors are placed in the borehole to collect X, Y, and Z components of seismic waves. Conduct noise checks on the sensor and its environment, and use a piezoelectric three-component acceleration sensor to collect seismic wave data; The collected seismic wave data is denoised, and after denoising, the data is further corrected and analyzed.

3. The seismic wave tunnel advance prediction method of the acceleration probe according to claim 1 is characterized in that: The process of forward modeling the elastic wave equation includes the following steps: Based on the tunnel geological data, the rock type, geological structure and groundwater distribution information of the tunnel are analyzed in detail, and the finite difference method is used to simulate the two-dimensional full-space wave field to establish a karst tunnel velocity model including longitudinal wave velocity and shear wave velocity; Design two blastholes and two geophones; use the two-dimensional elastic decoupled wave equation with finite difference approximation to perform forward simulation on the karst tunnel velocity model and generate theoretical wave field data; The forward continuous longitudinal wave and the backward continuous longitudinal wave are correlated to realize the PP wave migration imaging; the forward extension of the longitudinal wave and the reverse extension of the shear wave are correlated to obtain the PS wave migration imaging result.

4. The method for advance prediction of earthquake wave tunnels using an acceleration probe according to claim 3, characterized in that: RTM imaging is based on the forward model, and the first-order stress-velocity elastic decoupling equation is: The elastic wave decoupling equation is: in=in p +in s in=in p +in s Among them, λ and μ are the Lame constants of the medium, t is the time variable, (τ yy ,τ xx ,τ yx ) are stress tensors, (u, w) are the vibration velocities in the horizontal and vertical directions; V p and V s are the longitudinal wave velocity and the transverse wave velocity respectively; the partial differential equation is transformed into a second-order equation in time and a twelfth-order equation in space; a perfectly matched layer is used as the boundary condition; u p ,w p Represents the horizontal and vertical vibration velocity components of the longitudinal wave; u s ,w s Represents the horizontal and vertical vibration velocity components of the shear wave; The formula for generating the migration imaging results of PP and PS waves by cross-correlation calculation of the forward and reverse extended wave fields is: PP wave migration imaging formula: PS wave migration imaging formula: In the formula, I PP (x,z) represents the migration imaging result of PP wave; I PS (x,z) represents the migration imaging result of PS wave; u p (x,z,t) represents the vibration velocity field of the longitudinal wave; w s (x,z,t) represents the vibration velocity field of the shear wave; t1,t2 represent the time integration range.

5. The method for advance prediction of earthquake wave tunnels using an acceleration probe according to claim 3, characterized in that: The process of establishing a karst tunnel velocity model including longitudinal wave velocity and shear wave velocity includes the following steps: Obtain geological background information of the tunnel area and conduct detailed analysis of the tunnel rock type, geological structure and groundwater distribution information based on the tunnel geological data; The finite difference method is used to simulate the two-dimensional full-space wave field, and the initial condition model including the longitudinal wave velocity and the shear wave velocity is established. The numerical model is calibrated through field monitoring data. The finite difference method is used to simulate the two-dimensional full-space wave field. Based on the wave field simulation results, a karst tunnel velocity model containing longitudinal wave velocity and shear wave velocity is generated to reflect the physical properties of the medium around the tunnel. The generated velocity model is then verified by data and the accuracy of the model prediction results is compared and evaluated.

6. The seismic wave tunnel advance prediction method using an acceleration probe according to claim 1, characterized in that: The process of generating point and offset profiles and dot product offset profiles includes the following steps: Perform dot sum and dot product operations on the offset data of the PP and PS waves; Using the karst tunnel velocity model for RTM imaging, point and offset profiles are used to identify the low-frequency contour information of the anomaly and determine the location of the karst area; Using the information after data processing, RTM imaging is realized by using the longitudinal and shear wave velocity models, the contours of the anomaly are identified, and pure longitudinal and shear waves are extracted from the full-wave data of longitudinal and shear wave data migration imaging.

7. The method for advance prediction of seismic wave tunnels using an acceleration probe according to claim 6, characterized in that: Two-dimensional elastic wave P x P x , P y P y , P x S x , P y S y Each corresponding component can realize cross-correlation migration imaging, PP is P y P y , PS is P y P y ; I when PP and PS offset data are added sum , I product It is the result of the dot product of PP and PS offset data, and its calculation process is as follows: I product =(∑ t P forward ·P reverse ) · (∑ t P forward ·S reverse )。 8. The method for advance prediction of earthquake wave tunnels using an acceleration probe according to claim 6, characterized in that: The process of determining the location of karst areas includes the following steps: Complete RTM imaging of all single-shot data, use Laplace filters to remove low-frequency noise, reconstruct the reflection wave field of underground structures, and obtain migration imaging results; Use point and offset profiles to identify low-frequency contour information of anomalies, and identify low-resistance anomaly areas by analyzing the propagation characteristics of seismic waves at different depths and locations; After identifying the low-frequency contour information, the high-density electrical method is used to delineate the location of karst development and the depth of the bottom plate through the apparent resistivity section to further determine the specific location of the karst area.

9. The method for advance prediction of earthquake wave tunnels using an acceleration probe according to claim 6, characterized in that: The process of extracting pure longitudinal waves and pure shear waves includes the following steps: Reverse time migration imaging is performed using the karst tunnel velocity model to reconstruct the underground structure by combining the information of the reflected wave field and the converted wave field; the P-wave and S-wave signals are separated; Based on the RTM imaging of the karst tunnel velocity model, the contours of underground faults and cavity anomalies are identified by analyzing the differences between the reflected wave field and the converted wave field. Pure longitudinal waves and pure shear waves are extracted from the full-wave data, and the extracted pure longitudinal waves and pure shear waves are compared and verified with the geological data. If they do not match, the parameters of the karst tunnel velocity model are adjusted.

10. A seismic wave tunnel advance prediction system of an acceleration probe, which is applied to the seismic wave tunnel advance prediction method of an acceleration probe as claimed in any one of claims 1 to 9, characterized in that: The seismic wave tunnel advance prediction system of the acceleration probe comprises: The seismic wave acquisition module is used to arrange a piezoelectric acceleration three-component sensor near the tunnel working face, collect the X, Y, and Z three-component data of the tunnel seismic wave using the piezoelectric acceleration three-component sensor, record the direct wave, reflected wave and sound wave signals; and perform denoising on the X, Y, and Z three-component data; The model building module is used to build a karst tunnel velocity model including longitudinal wave velocity and transverse wave velocity according to tunnel geological data, use the finite difference method to perform forward simulation on the elastic wave equation to generate theoretical wave field data; use the wave field data of the forward simulation to perform forward extension to generate a forward propagation wave field; perform reverse extension on the actual wave field data collected to generate a reverse propagation wave field; perform cross-correlation operation on the forward extension wave field and the reverse extension wave field to generate PP wave and PS wave migration profiles; The advanced prediction module is used to perform point sum and dot product operations on the offset data of PP waves and PS waves to generate point sum offset sections and dot product offset sections; use the point sum offset sections to identify the low-frequency contour information of the anomaly and determine the location of the karst area; use the dot product offset section to extract the high-frequency detail information of the anomaly; fuse the point sum offset data with the dot product offset data to generate a comprehensive interpretation result; integrate the low-frequency contour information, high-frequency detail information and comprehensive interpretation results to generate a tunnel advanced prediction report.

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