A shield construction random noise barrier monitoring method and system
By constructing a time-varying spatial coherent noise field and a frequency-domain coherent weighted spectrum weighting technique, the anti-interference and real-time problems of noise obstacle monitoring in shield tunneling were solved, and high-precision real-time monitoring and early warning of ground obstacles were realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU RAILWAY GROUP CO LTD
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-23
AI Technical Summary
Existing methods for monitoring noise barriers during tunnel boring machine (TBM) construction have poor anti-interference capabilities and insufficient real-time performance, making it difficult to accurately identify and provide real-time warnings of obstacles in the ground ahead.
Based on geological survey data, an elastic wave discrete model is established. The obstacle region is defined by the obstacle mask function, a time-varying spatial coherent noise field is constructed, and the propagation is simulated using the three-dimensional elastic wave discrete model. A damping absorption layer is set at the boundary, and the time-series three-dimensional matrix is output. A linear array of sensors is deployed behind the tunnel boring machine to collect signals and perform frequency domain coherent weighted spectrum. The main peak time delay inversion is extracted, and the ground propagation velocity and obstacle location distribution are output.
It enables real-time monitoring and early warning of geological obstacles, improves the accuracy of obstacle identification and the real-time response, and can use the equipment's own noise as a passive excitation to perform high-precision obstacle boundary identification during the tunnel boring machine's excavation process.
Smart Images
Figure CN122263455A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geophysical exploration and underground engineering construction monitoring technology, and in particular to a method and system for monitoring coherent noise barriers during shield tunneling. Background Technology
[0002] In the construction of urban subways, tunnels, and underground utility tunnels, tunnel boring machines (TBMs) are the primary excavation equipment. Due to the complex underground environment of cities, obstacles such as boulders, pile foundations, and pipeline remnants often exist in the strata. Failure to detect and identify these obstacles in a timely manner can lead to cutterhead damage, attitude deviation, or even machine shutdown. Therefore, real-time monitoring and early warning of obstacles ahead during TBM excavation is one of the key technologies for ensuring construction safety and efficiency. Current TBM obstacle detection mainly relies on two types of methods: one is active ultrasonic or seismic wave detection, which involves actively generating a wave field by deploying a seismic source and detectors and performing reflection analysis. While this method can achieve high resolution, it is difficult to deploy excitation sources in the TBM construction environment, and the equipment is costly, complex to operate, and lacks real-time capability. The other type is indirect identification methods based on construction parameters and geological responses, such as judging anomalies ahead by changes in torque, current, and earth pressure. However, this method has a delayed response and lacks spatial resolution, making it impossible to accurately determine the location and scale of obstacles.
[0003] In recent years, researchers have attempted to use the mechanical noise signals generated during the operation of tunnel boring machines (TBMs) for passive wavefield inversion. This type of method is based on the "noise field cross-correlation principle" and can reconstruct the propagation characteristics of the strata without adding external excitation, enabling real-time monitoring. However, traditional noise inversion methods have shortcomings. For example, noise sources are mostly randomly distributed, and spatial coherence cannot be controlled, leading to instability of the main peak of the cross-correlation signal. The noise field of the TBM is significantly affected by the operating conditions, with large variations in the signal-to-noise ratio, making it difficult to establish a stable model. Conventional cross-correlation analysis ignores frequency domain information, is susceptible to non-stationary noise interference, and has low resolution in the inversion results. Summary of the Invention
[0004] In view of the aforementioned existing problems, the present invention is proposed.
[0005] Therefore, this invention provides a method for monitoring coherent noise barriers during shield tunneling to solve the problems of poor anti-interference and insufficient real-time performance in the prior art.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for monitoring coherent noise barriers during shield tunneling, comprising: establishing an elastic wave discrete model based on geological survey data, defining the barrier region through a barrier mask function, and obtaining a three-dimensional elastic wave discrete model; deploying multiple random noise sources on the shield cutterhead, adjusting the correlation degree of the noise sources using a coherence control function, and adjusting the coherence length parameter based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field; using the three-dimensional elastic wave discrete model as the propagation medium to simulate the propagation of the time-varying spatial coherent noise field, and setting a damping absorption layer at the boundary to output a time-series three-dimensional matrix; A linear array of sensors is deployed behind the tunnel boring machine. Channel sampling is performed based on a time-series three-dimensional matrix, followed by bandpass filtering, amplitude normalization, and tiling expansion to obtain a multi-dimensional time series matrix. The sliding time window cross-correlation function is calculated pairwise for each multi-dimensional time series matrix. After transformation to the frequency domain, a frequency domain coherence weight spectrum is introduced for weighted superposition to obtain an enhanced cross-correlation function. The main peak time delay inversion is extracted, and the ground propagation velocity and obstacle location distribution are output. The main peak time delay field of the enhanced cross-correlation function is calculated, and the spatial gradient field of time delay drift is calculated. Gradient anomaly regions are extracted through spatial filtering, and obstacle boundaries are determined and visualized.
[0007] As a preferred embodiment of the method for monitoring coherent noise obstruction during shield tunneling as described in this invention, the specific steps for obtaining the three-dimensional elastic wave discrete model are as follows: Based on geological survey data, an elastic wave discrete model of the shield tunneling area was established, and P-wave velocity, S-wave velocity and density parameters were set according to lithology to obtain a three-dimensional stratigraphic model. The computational domain is divided into uniform grids to form regular voxel grids. Obstacle regions are set in the three-dimensional geological model, and the location and extent of the obstacles are identified by mask functions to obtain a three-dimensional discrete model of elastic waves.
[0008] As a preferred embodiment of the method for monitoring coherent noise barriers during tunnel boring machine (TBM) construction according to the present invention, the specific steps for constructing the time-varying spatial coherent noise field are as follows: Multiple random noise sources are deployed around the shield cutterhead and nacelle. The signal of each noise source is limited to a fixed frequency band by a bandpass filter, forming a set of multi-source random noise signals. Set the spatial distribution function and control the excitation position and amplitude ratio of each random noise source in the multi-source random noise signal set to generate a noise source excitation signal set; The correlation between random noise sources within the noise source excitation signal set is adjusted by a coherent modulation function. The coherent modulation function dynamically adjusts the coherence length parameter based on the noise source spacing and the current noise field signal-to-noise ratio. The coherence length parameter is updated in real time based on the cross-correlation signal-to-noise ratio, thus forming a time-varying spatial coherent noise field.
[0009] As a preferred embodiment of the method for monitoring coherent noise obstruction during shield tunneling as described in this invention, the specific steps for outputting the time-series three-dimensional matrix are as follows: A three-dimensional finite difference time-domain algorithm is used to simulate the propagation of time-varying spatial coherent noise field. A three-dimensional elastic wave discrete model is used as the propagation medium, and the velocity and displacement of each grid node of the three-dimensional elastic wave discrete model are updated in time step. An exponential absorption layer is set at the outer boundary of the three-dimensional elastic wave discrete model, and the wave field displacement response time series data is output and saved in the form of a time series three-dimensional matrix.
[0010] As a preferred embodiment of the method for monitoring coherent noise obstruction during shield tunneling as described in this invention, the specific steps for obtaining the multidimensional time series matrix are as follows: A linear array of sensors is deployed behind the tunnel boring machine, and the spatial correspondence between each channel of the linear array of sensors and the time-series three-dimensional matrix is determined. Based on spatial correspondence, channel sampling is performed on the wave field displacement response time series data to obtain array channel response signals. Bandpass filtering, amplitude normalization, and tiling expansion preprocessing are then performed to obtain multi-channel signals. The multi-channel signals are then tiled and expanded along the time axis to form a multi-dimensional time series matrix.
[0011] As a preferred embodiment of the shield tunneling coherent noise obstacle monitoring method of the present invention, the specific steps for outputting the inversion of stratum propagation velocity and obstacle location distribution are as follows: Select any two channels of signals in the multidimensional time series matrix and perform cross-correlation operation according to the sliding time window to obtain the cross-correlation function. Transform the cross-correlation function to the frequency domain to obtain the cross-correlation spectrum. Introducing a frequency domain coherent weight spectrum in the frequency domain, adaptively weighting and superimposing different frequency band components of the cross-correlation spectrum, yields an enhanced cross-correlation function. The main peak delay is extracted from the enhanced cross-correlation function and inverted in combination with the channel spacing to output the formation propagation velocity and obstacle location distribution.
[0012] As a preferred embodiment of the shield tunneling coherent noise obstacle monitoring method of the present invention, the step of calculating the sliding time window cross-correlation function of the multidimensional time series matrix pairwise includes performing cross-correlation operation on the channel signals corresponding to adjacent receiver channels according to the sliding time window, and extracting the initial time delay feature characterizing the signal propagation characteristics from the cross-correlation function.
[0013] As a preferred embodiment of the method for monitoring coherent noise obstruction during shield tunneling as described in this invention, the specific steps for outputting the visualization results are as follows: The peak delay field is formed based on the peak delay of the enhanced cross-correlation function. The spatial gradient field of the delay drift is calculated on the peak delay field. The spatial gradient field is spatially filtered to extract the gradient anomaly region, and the gradient anomaly region is used as the basis for determining the obstacle boundary. The obstacle distance is estimated based on the time delay difference of the main peak time delay field and combined with the ground propagation velocity, and the visualization results are output.
[0014] As a preferred embodiment of the shield tunneling coherent noise obstacle monitoring method of the present invention, the method outputs obstacle spatial coordinates and abnormal intensity index after outputting visualization results, performs parameter adjustment and risk warning, and transmits them to the construction control terminal through a graphical interface.
[0015] Secondly, this invention provides a coherent noise barrier monitoring system for tunnel boring machine (TBM) construction, comprising: a stratum and barrier modeling module, used to establish an elastic wave discrete model based on geological survey data and define the barrier region through a barrier mask function to obtain a three-dimensional elastic wave discrete model; a noise field modeling module, used to deploy multiple random noise sources on the TBM cutterhead, adjust the correlation degree of the noise sources using a coherence control function, and adjust the coherence length parameter based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field; a wave field simulation module, used to simulate the propagation of the time-varying spatial coherent noise field using the three-dimensional elastic wave discrete model as the propagation medium, and set a damping absorption layer at the boundary to output a time-series three-dimensional matrix; and signal acquisition and preprocessing. The shield tunneling machine has several modules: a processing module for deploying a linear array of sensors behind the tunnel boring machine (TBM), a frequency domain coherent enhanced cross-correlation inversion module for calculating the sliding time window cross-correlation function pairwise on the multidimensional time series matrix, transforming it to the frequency domain and introducing a frequency domain coherent weight spectrum for weighted superposition to obtain the enhanced cross-correlation function, extracting the main peak time delay inversion, and outputting the ground propagation velocity and obstacle location distribution; and an obstacle identification and distance estimation module for calculating the main peak time delay field of the enhanced cross-correlation function and calculating the spatial gradient field of the time delay drift, extracting gradient anomaly regions through spatial filtering, determining obstacle boundaries, and outputting visualization results.
[0016] The beneficial effects of this invention are as follows: by introducing a coherent modulation mechanism in the noise field modeling stage, adaptive adjustment of the coherent characteristics between noise sources is achieved; combined with frequency domain weighted cross-correlation inversion technology, the main peak response and propagation consistency of the stratum signal are effectively enhanced; and based on drift field analysis, high-precision identification of obstacle boundaries is achieved, enabling real-time monitoring and early warning of stratum obstacles by using the equipment's own operating noise as a passive excitation during the tunnel boring machine's excavation process. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of a method for monitoring coherent noise obstructions during shield tunneling.
[0019] Figure 2 This is a schematic diagram of a coherent noise obstruction monitoring system during shield tunneling.
[0020] Figure 3 A three-dimensional schematic diagram of the geological formation, obstacles, and receiver array layout.
[0021] Figure 4 This is a schematic diagram of the coherent distribution of noise sources on the cutterhead surface of a tunnel boring machine.
[0022] Figure 5 This is a flowchart of cross-correlation inversion and distance estimation.
[0023] Figure 6 This is a comparison of the cross-correlation curves before and after frequency domain coherence enhancement.
[0024] Figure 7 This is a schematic diagram of the peak delay versus distance estimation curve for obstacle detection. Detailed Implementation
[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.
[0028] Reference Figures 1-7 As one embodiment of the present invention, this embodiment provides a method for monitoring coherent noise obstruction during shield tunneling, comprising the following steps: S1. Based on geological survey data, an elastic wave discrete model is established, and the obstacle region is defined by the obstacle mask function to obtain a three-dimensional elastic wave discrete model.
[0029] S1.1. Based on geological survey data, establish an elastic wave discrete model of the shield tunneling area, and set the P-wave velocity, S-wave velocity and density parameters according to the lithology to obtain a three-dimensional stratum model.
[0030] Specifically, establishing a three-dimensional geological model involves constructing a cuboid computational domain in a three-dimensional Cartesian coordinate system. This computational domain is then divided into a two-layer medium structure along the depth direction. The upper medium region is defined as depths from 0m to 25m, and the lower medium region is defined as depths from 25m to 30m, separated by a horizontal interface at depth 25m. Within the upper medium region, 15m in front of the tunnel boring machine cutterhead, three parallel vertical cylindrical geometries are embedded as obstacle models, penetrating the upper depth. Simultaneously, the location of the tunnel boring machine cutterhead and the coordinates of the linear array sensor array located 21m behind the cutterhead are marked in the model mesh, resulting in a three-dimensional geometric model that includes geological strata, columnar obstacles, and the observation system.
[0031] S1.2. The computational domain is divided into uniform grids to form regular voxel grids. Obstacle regions are set in the three-dimensional geological model. The location and extent of the obstacles are identified by mask functions to obtain a three-dimensional discrete model of elastic waves.
[0032] Specifically, the obstacle area is defined in the three-dimensional geological model, and the obstacle location and range are identified by the obstacle mask function. The medium parameters of the obstacle are significantly increased relative to the surrounding strata to reflect the abnormal wave velocity characteristics. The obstacle is set about 15 meters in front of the shield tunnel face, penetrates the upper strata, and has a diameter of about 0.9 meters. To meet the needs of numerical simulation, the computational domain is discretized using a regular voxel grid. The grid size is determined based on one-tenth of the minimum wavelength. An absorbing boundary layer is set at the boundary of the three-dimensional stratigraphic model to reduce the influence of boundary reflection on the wave field. After modeling is completed, the parameters of the three-dimensional stratigraphic model and the layer boundary information are stored in a unified manner to form a three-dimensional elastic wave discrete model for subsequent propagation simulation modules to read.
[0033] S2. Multiple random noise sources are deployed on the shield cutterhead. The correlation degree of the noise sources is adjusted using a coherence control function, and the coherence length parameter is adjusted based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field.
[0034] S2.1. Multiple random noise sources are deployed around the shield cutterhead and nacelle. The signal of each noise source is limited to a fixed frequency band by a bandpass filter, forming a set of multi-source random noise signals.
[0035] Specifically, multiple random noise sources are deployed on the cutterhead of the tunnel boring machine to simulate the mechanical vibration noise of the cutterhead, nacelle and drive system during operation. The noise signals are limited to the 50-200Hz frequency band by a bandpass filter to conform to the actual equipment noise characteristics, forming a set of multi-source random noise signals.
[0036] S2.2. Set the spatial distribution function and control the excitation position and amplitude ratio of each random noise source in the multi-source random noise signal set to generate a noise source excitation signal set.
[0037] Specifically, based on the formation of a multi-source random noise signal set, a spatial distribution function is set to control the excitation position and amplitude ratio of each random noise source in space, generating a noise source excitation signal set. This ensures that the spatial distribution of noise source excitation matches the structure and operating conditions of the tunnel boring machine.
[0038] S2.3. The correlation between random noise sources in the noise source excitation signal set is adjusted by the coherent modulation function. The coherent modulation function dynamically adjusts the coherence length parameter according to the noise source spacing and the current noise field signal-to-noise ratio. The coherence length parameter is updated in real time according to the cross-correlation signal-to-noise ratio to form a time-varying spatial coherent noise field.
[0039] Specifically, a spatial coherence matrix is constructed, and the first coherence matrix on the shield cutterhead is defined. The noise source and the first The spatial Euclidean distance between the noise sources is A Gaussian decay function is used to construct the time interval. Spatial coherence coefficient , It should be noted that the expression for the Gaussian decaying correlation function with coherence length as the scale parameter is: ; in, The spatial coherence length parameter at the current moment characterizes the effective correlation range of the noise source in space; is the spatial coherence coefficient between all noise source pairs.
[0040] For a set of multi-source random noise signals (let's say they are mutually uncorrelated random vectors) Modulation is performed. This is achieved by modulating the coherence matrix. Cholesky decomposition yields the lower triangular matrix. ,Right now The coherent noise field excitation signal with specified spatial correlation is calculated. The expression is: ; Adaptive update of coherence length parameter: Calculate the cross-correlation signal-to-noise ratio of the current inversion result. The signal-to-noise ratio (SNR) is defined as the ratio of the peak energy of the cross-correlation function to the average energy of the background noise; based on the SNR and the target SNR... The deviation is used to update the coherence length parameter at the next time step according to the following iterative rules. The expression is: ; in, To update the step size factor, which controls the magnitude of parameter adjustment; The sensitivity coefficient (obtained through offline numerical simulation or calibration using historical construction data); The hyperbolic tangent function is used to limit the adjustment range of a single update and prevent parameter oscillation. Through the update mechanism, when the actual signal-to-noise ratio is lower than the target value, the coherence length is automatically increased to enhance the signal superposition effect; conversely, the coherence length is decreased to improve spatial resolution, forming a time-varying spatial coherent noise field.
[0041] S3. Using a three-dimensional elastic wave discrete model as the propagation medium, the propagation of time-varying spatial coherent noise field is simulated, and a damping absorption layer is set at the boundary to output a time-series three-dimensional matrix.
[0042] S3.1. A three-dimensional finite difference time-domain algorithm is used to simulate the propagation of time-varying spatial coherent noise field. A three-dimensional elastic wave discrete model is used as the propagation medium, and the velocity and displacement of each grid node of the three-dimensional elastic wave discrete model are updated in time step.
[0043] Specifically, a three-dimensional elastic wave discrete model is used as the propagation medium to simulate the propagation of time-varying spatial coherent noise fields. The propagation simulation adopts a three-dimensional finite difference time-domain algorithm to update the velocity and displacement components of each grid node in the three-dimensional elastic wave discrete model step by step in order to obtain the propagation response of elastic waves in the formation.
[0044] S3.2. An exponential absorption layer is set on the outer boundary of the three-dimensional elastic wave discrete model to output the wave field displacement response time series data and save it in the form of a time series three-dimensional matrix.
[0045] Specifically, an exponential absorption layer is set at the outer boundary of the three-dimensional elastic wave discrete model to attenuate the outward propagating reflected waves and reduce the impact of boundary reflection on the simulation results. During the simulation, the time series data of the wave field displacement response is output and stored in the form of a time series three-dimensional matrix according to the spatial grid and time dimension. The time-series three-dimensional matrix serves as input data for subsequent signal acquisition and channel sampling steps, simulating the formation response signals acquired by the sensor array at the corresponding spatial location.
[0046] S4. A linear array of sensors is deployed behind the tunnel boring machine. Channel sampling is performed based on the time-series three-dimensional matrix, and bandpass filtering, amplitude normalization, and tiling expansion operations are performed to obtain a multi-dimensional time series matrix.
[0047] S4.1. Deploy a linear array sensor array behind the tunnel boring machine and determine the spatial correspondence between each channel of the linear array sensor array and the time-series three-dimensional matrix.
[0048] Specifically, a linear array of sensors with 24 receivers is deployed behind the tunnel boring machine along the tunneling direction. The first receiver is 21 meters away from the cutterhead, with a spacing of 0.3 meters. The array collects the ground response signal in real time, with the sampling frequency set to 1kHz. The spatial correspondence between each channel of the linear array of sensors and the time-series three-dimensional matrix is determined to realize the mapping between the simulated output wave field and the array channel response.
[0049] S4.2. Based on the spatial correspondence, channel sampling is performed on the wave field displacement response time series data to obtain the array channel response signal. Bandpass filtering, amplitude normalization and tiling expansion preprocessing are then performed to obtain multi-channel signals. The multi-channel signals are then tiled and expanded along the time axis to form a multi-dimensional time series matrix.
[0050] Specifically, based on spatial correspondence, channel sampling is performed on the wavefield displacement response time series data to obtain array channel response signals. Bandpass filtering is then performed on the array channel response signals to reduce interference in non-working frequency bands. The signal amplitude is normalized to reduce energy differences between different channels. The multi-channel signals are then flattened and expanded along a unified time axis to form a multi-dimensional time series matrix with consistent structure, which is used for subsequent cross-correlation calculations and frequency domain coherence enhancement processing.
[0051] S5. Calculate the sliding time window cross-correlation function for each pair of multidimensional time series matrices, transform it to the frequency domain, introduce the frequency domain coherence weight spectrum for weighted superposition, obtain the enhanced cross-correlation function, extract the main peak time delay inversion, and output the formation propagation velocity and obstacle location distribution.
[0052] S5.1. Select any two channel signals in the multidimensional time series matrix and perform cross-correlation operation according to the sliding time window to obtain the cross-correlation function. Transform the cross-correlation function to the frequency domain to obtain the cross-correlation spectrum.
[0053] Specifically, any two channel signals in the multidimensional time series matrix are selected, and cross-correlation is performed using a sliding time window to obtain the cross-correlation function. Cross-correlation is first performed on the channel signals corresponding to adjacent receiver channels, and the initial time delay features characterizing the signal propagation characteristics are extracted from the cross-correlation function as the basic input for subsequent frequency domain enhancement processing.
[0054] S5.2. Introduce a frequency domain coherent weight spectrum in the frequency domain, adaptively weight and superimpose different frequency band components of the cross-correlation spectrum to obtain an enhanced cross-correlation function, extract the main peak delay from the enhanced cross-correlation function, and perform inversion in combination with the channel spacing to output the formation propagation velocity and obstacle location distribution.
[0055] Specifically, any two channel signals in the multidimensional time series matrix are selected, and cross-correlation is performed using a sliding time window to obtain the cross-correlation function. Cross-correlation can be performed on the channel signals corresponding to adjacent receiver channels first, and the initial time delay features characterizing the signal propagation characteristics are extracted from the cross-correlation function as the basic input for subsequent frequency domain enhancement processing.
[0056] It should be noted that calculating the sliding time window cross-correlation function for each pair of multidimensional time series matrices includes performing cross-correlation operations on the channel signals corresponding to adjacent receiver channels according to the sliding time window, and extracting the initial time delay feature characterizing the signal propagation characteristics from the cross-correlation function.
[0057] Furthermore, a sliding time window cross-correlation operation is performed on the signals of adjacent receiver channels to obtain the initial time delay characteristics. After converting the cross-correlation function to the frequency domain, the power spectrum is calculated, and a frequency domain weighted spectrum is constructed based on the coherence of signals in each frequency band. By using the weighted spectrum to weight and superimpose the cross-correlation components of different frequency bands, the main cross-correlation peak can be significantly enhanced, random noise can be suppressed, and the time delay of the main cross-correlation peak can be extracted. By combining the receiver spacing to calculate the local propagation velocity variation, the propagation delay distribution curve along the direction of the receiver array is obtained, providing a basis for obstacle identification and distance estimation.
[0058] S6. Calculate the peak time delay field of the enhanced cross-correlation function and the spatial gradient field of the time delay drift. Extract the gradient anomaly region through spatial filtering, determine the obstacle boundary, and output the visualization result.
[0059] S6.1. Based on the peak delay of the enhanced cross-correlation function, a peak delay field is formed, and the spatial gradient field of the delay drift is calculated for the peak delay field. The spatial gradient field is spatially filtered to extract the gradient anomaly region, and the gradient anomaly region is used as the basis for determining the obstacle boundary.
[0060] Specifically, such as Figure 7 As shown, after extracting the time delay distribution of the main peak, the average propagation wave velocity of the strata is used as a basis for further analysis. If obvious time delay anomalies or local peaks appear in the obstacle distance conversion curve, it indicates that there is wave speed disturbance or obstacle reflection in that direction.
[0061] It should be noted that the expression for converting obstacle distance is: ; in, Indicates the distance to the obstacle. Indicates the average propagation velocity of waves in the strata. Indicates the peak delay of cross-correlation. Indicates the main peak of mutual correlation. Indicates the reference propagation velocity used for obstacle distance conversion.
[0062] S6.2. Estimate the obstacle distance based on the time delay difference of the main peak time delay field and the ground propagation velocity, and output the visualization results.
[0063] Specifically, the obstacle distance is estimated based on the time delay difference in the main peak time delay field and in combination with the ground propagation velocity, using the assumed Rayleigh wave velocity. The equivalent distance distribution between the obstacle and the cutterhead can be calculated through the main peak time delay curve. Combined with the curve shape, the approximate location and scale of the obstacle can be determined. The output is in the form of two-dimensional profile or three-dimensional visualization, which can be used for obstacle monitoring and risk warning during shield tunneling.
[0064] Furthermore, after outputting the visualization results, the system outputs the spatial coordinates of the obstacles and the abnormal intensity index, performs parameter adjustments and risk warnings, and transmits them to the construction control terminal through a graphical interface.
[0065] This embodiment also provides a coherent noise obstacle monitoring system for tunnel boring machine (TBM) construction, including: a stratum and obstacle modeling module, used to establish an elastic wave discrete model based on geological survey data, and define the obstacle region through an obstacle mask function to obtain a three-dimensional elastic wave discrete model; a noise field modeling module, used to deploy multiple random noise sources on the TBM cutterhead, adjust the correlation degree of the noise sources using a coherence control function, and adjust the coherence length parameter based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field; a wave field simulation module, used to simulate the propagation of the time-varying spatial coherent noise field using the three-dimensional elastic wave discrete model as the propagation medium, and set a damping absorption layer at the boundary to output a time-series three-dimensional matrix; and a signal acquisition and preprocessing module. The first module is used to deploy a linear array of sensors behind the tunnel boring machine (TBM). It performs channel sampling based on a time-series 3D matrix and performs bandpass filtering, amplitude normalization, and tiling expansion operations to obtain a multi-dimensional time series matrix. The second module is a frequency domain coherent enhanced cross-correlation inversion module. It calculates the sliding time window cross-correlation function pairwise on the multi-dimensional time series matrix, transforms it to the frequency domain, introduces frequency domain coherent weight spectrum for weighted superposition, obtains the enhanced cross-correlation function, extracts the main peak time delay inversion, and outputs the ground propagation velocity and obstacle location distribution. The third module is an obstacle identification and distance estimation module. It calculates the main peak time delay field of the enhanced cross-correlation function and the spatial gradient field of the time delay drift. It extracts gradient anomaly regions through spatial filtering, determines obstacle boundaries, and outputs visualization results.
[0066] In summary, this invention achieves adaptive adjustment of the coherence characteristics between noise sources by introducing a coherent modulation mechanism during the noise field modeling stage; effectively enhances the peak response and propagation consistency of the stratum signal by combining frequency domain weighted cross-correlation inversion technology; and realizes high-precision identification of obstacle boundaries based on drift field analysis. It can utilize the equipment's own operating noise as a passive excitation during the tunnel boring machine's excavation process to achieve real-time monitoring and early warning of stratum obstacles.
[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for monitoring coherent noise obstruction during shield tunneling, characterized in that: include, Based on geological survey data, an elastic wave discrete model was established, and the obstacle region was defined by the obstacle mask function to obtain a three-dimensional elastic wave discrete model. Multiple random noise sources are deployed on the shield cutterhead. The correlation degree of the noise sources is adjusted by the coherence control function, and the coherence length parameter is adjusted based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field. Using a three-dimensional elastic wave discrete model as the propagation medium, the propagation of time-varying spatial coherent noise field is simulated, and a damping absorption layer is set at the boundary to output a time-series three-dimensional matrix. A linear array of sensors is deployed behind the tunnel boring machine. Channel sampling is performed based on a time-series three-dimensional matrix, and bandpass filtering, amplitude normalization, and tiling expansion operations are performed to obtain a multi-dimensional time series matrix. The sliding time window cross-correlation function is calculated pairwise for the multidimensional time series matrix. After transformation to the frequency domain, the frequency domain coherent weight spectrum is introduced for weighted superposition to obtain the enhanced cross-correlation function. The main peak time delay inversion is extracted, and the stratigraphic propagation velocity and obstacle location distribution are output. The peak time delay field of the enhanced cross-correlation function is calculated, and the spatial gradient field of the time delay drift is calculated. The gradient anomaly region is extracted by spatial filtering, and the obstacle boundary is determined and the visualization result is output.
2. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The specific steps for obtaining the three-dimensional discrete model of elastic waves are as follows. Based on geological survey data, an elastic wave discrete model of the shield tunneling area was established, and P-wave velocity, S-wave velocity and density parameters were set according to lithology to obtain a three-dimensional stratigraphic model. The computational domain is divided into uniform grids to form regular voxel grids. Obstacle regions are set in the three-dimensional geological model, and the location and extent of the obstacles are identified by mask functions to obtain a three-dimensional discrete model of elastic waves.
3. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The specific steps for constructing the time-varying spatial coherent noise field are as follows: Multiple random noise sources are deployed around the shield cutterhead and nacelle. The signal of each noise source is limited to a fixed frequency band by a bandpass filter, forming a set of multi-source random noise signals. Set the spatial distribution function and control the excitation position and amplitude ratio of each random noise source in the multi-source random noise signal set to generate a noise source excitation signal set; The correlation between random noise sources within the noise source excitation signal set is adjusted by a coherent modulation function. The coherent modulation function dynamically adjusts the coherence length parameter based on the noise source spacing and the current noise field signal-to-noise ratio. The coherence length parameter is updated in real time based on the cross-correlation signal-to-noise ratio, thus forming a time-varying spatial coherent noise field.
4. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The specific steps for outputting the three-dimensional temporal matrix are as follows. A three-dimensional finite difference time-domain algorithm is used to simulate the propagation of time-varying spatial coherent noise field. A three-dimensional elastic wave discrete model is used as the propagation medium, and the velocity and displacement of each grid node of the three-dimensional elastic wave discrete model are updated in time step. An exponential absorption layer is set at the outer boundary of the three-dimensional elastic wave discrete model, and the wave field displacement response time series data is output and saved in the form of a time series three-dimensional matrix.
5. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The specific steps for obtaining the multidimensional time series matrix are as follows: A linear array of sensors is deployed behind the tunnel boring machine, and the spatial correspondence between each channel of the linear array of sensors and the time-series three-dimensional matrix is determined. Based on spatial correspondence, channel sampling is performed on the wave field displacement response time series data to obtain array channel response signals. Bandpass filtering, amplitude normalization, and tiling expansion preprocessing are then performed to obtain multi-channel signals. The multi-channel signals are then tiled and expanded along the time axis to form a multi-dimensional time series matrix.
6. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The specific steps for outputting the stratum propagation velocity and obstacle location distribution are as follows. Select any two channels of signals in the multidimensional time series matrix and perform cross-correlation operation according to the sliding time window to obtain the cross-correlation function. Transform the cross-correlation function to the frequency domain to obtain the cross-correlation spectrum. Introducing a frequency domain coherent weight spectrum in the frequency domain, adaptively weighting and superimposing different frequency band components of the cross-correlation spectrum, yields an enhanced cross-correlation function. The main peak delay is extracted from the enhanced cross-correlation function and inverted in combination with the channel spacing to output the formation propagation velocity and obstacle location distribution.
7. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: The step of calculating the sliding time window cross-correlation function pairwise for the multidimensional time series matrix includes performing cross-correlation operations on the channel signals corresponding to adjacent receiver channels according to the sliding time window, and extracting the initial time delay feature characterizing the signal propagation characteristics from the cross-correlation function.
8. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 6, characterized in that: The specific steps for generating the visualization results are as follows. The peak delay field is formed based on the peak delay of the enhanced cross-correlation function. The spatial gradient field of the delay drift is calculated on the peak delay field. The spatial gradient field is spatially filtered to extract the gradient anomaly region, and the gradient anomaly region is used as the basis for determining the obstacle boundary. The obstacle distance is estimated based on the time delay difference of the main peak time delay field and combined with the ground propagation velocity, and the visualization results are output.
9. The method for monitoring coherent noise obstruction during shield tunneling as described in claim 1, characterized in that: After outputting the visualization results, the obstacle spatial coordinates and abnormal intensity indicators are output, parameters are adjusted and risk warnings are issued, and the results are displayed and transmitted to the construction control terminal through a graphical interface.
10. A coherent noise obstruction monitoring system for shield tunneling, based on the coherent noise obstruction monitoring method for shield tunneling as described in any one of claims 1 to 9, characterized in that: include, The stratigraphy and obstacle modeling module is used to establish an elastic wave discrete model based on geological exploration data, and to define the obstacle region through the obstacle mask function to obtain a three-dimensional elastic wave discrete model. The noise field modeling module is used to deploy multiple random noise sources on the shield cutterhead, adjust the correlation of the noise sources using the coherence control function, and adjust the coherence length parameter based on the signal-to-noise ratio of the inversion feedback to construct a time-varying spatial coherent noise field. The wave field simulation module is used to simulate the propagation of time-varying spatial coherent noise fields using a three-dimensional elastic wave discrete model as the propagation medium, and sets a damping absorption layer at the boundary to output a time-series three-dimensional matrix. The signal acquisition and preprocessing module is used to deploy a linear array of sensors behind the tunnel boring machine, perform channel sampling based on the time-series three-dimensional matrix, and perform bandpass filtering, amplitude normalization and tiling expansion operations to obtain a multi-dimensional time series matrix. The frequency domain coherent enhanced cross-correlation inversion module is used to calculate the sliding time window cross-correlation function pairwise on the multidimensional time series matrix, transform it to the frequency domain, introduce frequency domain coherent weight spectrum weighted superposition to obtain the enhanced cross-correlation function, extract the main peak time delay inversion, and output the formation propagation velocity and obstacle location distribution. The obstacle recognition and distance estimation module is used to calculate the peak time delay field of the enhanced cross-correlation function and the spatial gradient field of the time delay drift. It extracts gradient anomaly regions through spatial filtering, determines obstacle boundaries, and outputs visualization results.