An environmental noise imaging method and system for improving imaging accuracy of a power grid tower foundation

Abstract

The present application relates to a kind of environmental noise imaging method for improving the imaging precision of power grid tower foundation, specific steps include: arranging multiple detectors in target exploration area, environmental noise data is collected;Based on environmental noise data, initial three-dimensional transverse wave velocity model is obtained by surface wave tomography inversion technique;Local non-uniform grid encryption processing is carried out to each detector in initial three-dimensional transverse wave velocity model The coarse velocity grid, generate fine velocity grid;And based on the distance between detector and the adjacent grid node of fine velocity grid, fine grid is dynamically updated;The accurate speed value at each detector position is calculated using bilinear interpolation algorithm in the encryption fine velocity grid after updating is completed;The accurate speed value at each detector position in fine grid is used to carry out iterative update to initial three-dimensional transverse wave velocity model, and high-resolution three-dimensional transverse wave velocity structure model is obtained.

Description

Technical Field

[0001] This invention relates to the field of exploration geophysical data processing and parameter inversion technology, specifically to an environmental noise imaging method for improving the imaging accuracy of power grid tower bases. Background Technology

[0002] With the expansion and increasing complexity of modern power grid construction, geophysical exploration methods, characterized by their speed and non-destructive nature, are widely used in tower foundation exploration. However, the core challenge in this field lies in the extremely limited exploration area, while demanding very high vertical and lateral resolution of underground structures and geotechnical parameters. This presents a dual challenge to traditional geophysical methods (such as high-density electrical resistivity tomography and ground-penetrating radar). On the one hand, in shallow, small-scale conditions, these methods are susceptible to interference from field sources and suffer from limitations in spatial distribution and insufficient depth penetration. On the other hand, transmission lines in Fujian Province often traverse complex terrains such as mountains and valleys, facing complex geological conditions including variable lithology and dramatic undulations in weathered layers and bedrock surfaces. This makes it difficult for traditional exploration methods to comprehensively capture key geological interfaces and adverse geological bodies. Ultimately, geological models constructed based on this limited and highly uncertain information are insufficient to support accurate assessments of the stability and bearing capacity of high-tower foundations.

[0003] Passive source imaging in the frequency domain of seismic exploration, employing single-point observations and natural sources, demonstrates good adaptability to the complex curvilinear terrain and variable surface conditions of Fujian Province due to its flexible deployment, lack of artificial excitation, and low cost, making it highly suitable for shallow tower foundation exploration. However, this technique is typically based on a laterally uniform two-dimensional initial velocity model. This simplified model struggles to accurately reflect true lateral underground variations, thus limiting its effectiveness in supporting precise evaluation and maintenance of soil bearing capacity at later mountainous tower sites. To overcome this limitation, surface wave tomography inversion technology can be introduced. This method extracts the surface wave cross-correlation function recorded between any two measuring points and inverts its dispersion characteristics, effectively constructing a three-dimensional shear wave velocity structure revealing lateral variations. Utilizing this technology to provide an initial velocity field with lateral variation characteristics for passive source frequency domain imaging is expected to significantly improve the accuracy and reliability of the final imaging model, thereby better meeting the design and evaluation needs of tower foundation engineering.

[0004] In the inversion process, the subsurface medium is first discretized into a regular velocity grid model, and theoretical phase velocities and travel times are calculated at the grid points using numerical simulation methods (such as the finite difference method). However, since the station locations are usually not on the grid nodes, bilinear interpolation is needed to estimate the station values ​​using the velocities of surrounding grid points. The problem is that bilinear interpolation is essentially a smoothing operation, which blurs sharp changes within the grid and geological boundaries. When the lateral velocity changes drastically, this method inevitably introduces significant errors. The most direct solution is to globally refine the grid, which effectively suppresses smoothing errors, but the dramatic increase in the number of grid points leads to excessively high computational costs and severe resource redundancy.

[0005] The existing Chinese invention patent "CN120972249A" proposes a general, unoptimized implementation scheme for relatively homogeneous scenarios in an airport pavement inspection method based on seismic background noise imaging technology. When dealing with complex geological conditions, this scheme suffers from insufficient local accuracy due to inherent defects in the interpolation method, and may also face low computational efficiency when pursuing higher accuracy. Therefore, it is necessary to develop a targeted variable mesh refinement strategy, that is, to refine the mesh only in key areas near the station. This method can obtain more accurate theoretical phase velocities and travel times within a controllable computational overhead, thereby significantly improving the characterization accuracy of three-dimensional laterally varying structures to meet the core requirement of frequency imaging for high-resolution velocity fields. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention proposes an environmental noise imaging method and system for improving the imaging accuracy of power grid tower bases.

[0007] The technical solution of the present invention is as follows: On the one hand, this invention proposes an environmental noise imaging method to improve the imaging accuracy of power grid tower bases, the specific steps of which include: Multiple geophones were deployed in the target exploration area to collect environmental noise data; Based on environmental noise data, an initial three-dimensional shear wave velocity model is obtained through surface wave tomography inversion technique. The coarse velocity grid containing each detector in the initial three-dimensional shear wave velocity model is locally non-uniformly refined to generate a fine velocity grid; and the fine grid is dynamically updated based on the distance between the detector and the neighboring grid nodes of the fine velocity grid. The precise velocity value at each detector location is calculated using a bilinear interpolation algorithm in the updated encrypted fine velocity grid. The initial three-dimensional shear wave velocity model is iteratively updated using the precise velocity values ​​at each detector location within the fine grid to obtain a high-resolution three-dimensional shear wave velocity structure model.

[0008] In a preferred embodiment, the local non-uniform mesh refinement process specifically includes: If the detector is on the boundary of the coarse velocity grid, the velocity value of the coarse grid point is directly assigned to the corresponding point in the fine grid. If the detector is not on the boundary of the coarse velocity grid, then on the boundary of the coarse velocity grid where each detector is located, a fine velocity grid node and a set of node equations are generated by linear interpolation. Then, the set of equations of the fine velocity grid node is solved to obtain the refined fine velocity grid.

[0009] In a preferred embodiment, the step of performing linear interpolation on the boundary of the coarse velocity grid specifically includes: Interpolation is performed in the x-direction, and the interpolation formula is:

[0010] In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid. Interpolation is performed in the y-direction, using the following formula:

[0011] In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid.

[0012] In a preferred embodiment, the step of generating fine velocity grid nodes and node equations through linear interpolation specifically includes: For the coordinates of the four nodes A, B, C, and D of the fine velocity grid generated by interpolation within the coarse velocity grid: A = V(x m+1 / k y n+1 / k B=V(x) m+1 / k y n+2 / k C=V(x) m+2 / k y n+1 / k D=V(x) m+2 / k y n+2 / k The velocity values ​​can be calculated from the coordinates of nodes on the coarse velocity grid boundary that have already been interpolated, and the nodal equations are constructed as follows:

[0013] In the formula, A, B, C, and D are the velocity values ​​of the nodes inside the fine mesh, respectively.

[0014] In a preferred embodiment, the dynamic updating of the fine grid based on the distance between the detector and neighboring grid nodes of the fine velocity grid specifically involves: Preset the distance threshold between the detector and neighboring grid nodes; After performing a mesh refinement, calculate the distance between the detector and the neighboring mesh nodes of the current fine mesh containing it, and take the maximum distance. If the maximum distance is greater than the distance threshold, the current fine grid containing the detector is used as the new coarse velocity grid, and the above encryption and judgment process is repeated. If the maximum distance is less than or equal to the distance threshold, then encryption is stopped and the current fine velocity grid is output.

[0015] In a preferred embodiment, the high-resolution three-dimensional shear wave velocity structure model is used as the initial velocity model for frequency domain passive source imaging technology to generate underground structure images for tower foundation engineering assessment.

[0016] As a preferred embodiment, the iterative update of the initial three-dimensional shear wave velocity model adopts a damped least squares inversion algorithm based on least squares, and uses the precise velocity value at the detector position as the basis for theoretical travel time calculation.

[0017] On the other hand, this invention proposes an environmental noise imaging system to improve the imaging accuracy of power grid tower bases, comprising: The data acquisition module deploys multiple geophones in the target exploration area to collect environmental noise data. The initial velocity model construction module obtains the initial three-dimensional shear wave velocity model based on environmental noise data and surface wave tomography inversion technology. The local non-uniform mesh refinement module performs local non-uniform mesh refinement on the coarse velocity mesh containing each detector in the initial three-dimensional shear wave velocity model to generate a fine velocity mesh; and dynamically updates the fine mesh based on the distance between the detector and the neighboring mesh nodes of the fine velocity mesh. The precise velocity value calculation module uses a bilinear interpolation algorithm to calculate the precise velocity value at each detector position in the updated encrypted fine velocity grid. The initial velocity model update module uses the precise velocity values ​​at each detector position within the fine grid to iteratively update the initial three-dimensional shear wave velocity model, thereby obtaining a high-resolution three-dimensional shear wave velocity structure model.

[0018] On the other hand, the present invention proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements an environmental noise imaging method for improving the imaging accuracy of power grid tower bases as described in any embodiment of the present invention.

[0019] On the other hand, the present invention proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an environmental noise imaging method for improving the imaging accuracy of power grid tower bases as described in any embodiment of the present invention.

[0020] The present invention has the following beneficial effects: 1. The non-uniform grid encryption of the present invention can dynamically allocate computing resources according to actual needs, thereby transforming bilinear interpolation from an indiscriminate smoothing tool into a fine constraint method that can be adapted to local conditions.

[0021] 2. The method of the present invention ensures that the surface wave tomography analysis inversion method can invert a high-precision lateral change velocity model that more realistically reflects the shallow structure, laying a solid foundation for obtaining high-resolution frequency imaging results. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a coarse velocity grid. Figure 2 This is a schematic diagram of a locally non-uniform grid. Figure 3 A schematic diagram showing the location of the work area and the layout of the survey lines; Figure 4 This is a schematic diagram of the frequency domain passive source imaging results for measurement point 6. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0025] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0027] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0028] Example 1: An environmental noise imaging method for improving the imaging accuracy of power grid tower bases includes the following steps: Step 1: Deploy multiple geophones in the target exploration area to collect environmental noise data; The purpose of this step is to acquire the raw seismic signal data for subsequent surface wave tomography inversion processing. Specific implementation includes field investigation, scheme design, equipment deployment, and data recording.

[0029] Exploration targets and work area overview: First, clarify the exploration objectives, such as: determining the thickness of the overburden layer and the undulation of the bedrock surface beneath a certain transmission line tower, or detecting the distribution of specific adverse geological bodies.

[0030] Describe the geographical location, topography, and known geological conditions of the work area.

[0031] Observation system design: Arrangement: The survey lines are designed based on the terrain conditions and exploration objectives. Linear arrangements (suitable for strip-shaped exploration), L-shaped or T-shaped arrangements (to understand the structure in two perpendicular directions), or two-dimensional area grid arrangements (to obtain three-dimensional velocity structure) can be used. In complex mountainous terrain, a curved arrangement adapted to the terrain can be adopted.

[0032] Equipment deployment and data acquisition: Equipment selection: Choose a broadband or short-period seismic detector suitable for low-frequency environmental noise acquisition. Ensure that its sensitivity, dynamic range, and frequency response meet the requirements for extracting surface wave signals.

[0033] Deployment requirements: The seismic detector is securely coupled to the ground to reduce noise caused by wind, human activity, etc. In soft ground, it may be necessary to dig a pit for burial.

[0034] Accurately measuring and recording the three-dimensional coordinates (usually longitude, latitude, and elevation, or planar coordinates and elevation relative to a certain base point) of each detector is the basis for subsequent inversion imaging.

[0035] Data acquisition parameters: Sampling rate: Set according to the Nyquist sampling theorem and the highest frequency of the signal to be analyzed, typically 100 Hz or higher, to ensure effective recording of surface waves.

[0036] Recording duration: Passive source imaging relies on data over a sufficiently long period to obtain a stable empirical Green's function. The duration of a single continuous recording session typically ranges from several hours to several days, with the total duration depending on the signal-to-noise ratio and target accuracy.

[0037] Step 2: Based on environmental noise data, obtain an initial three-dimensional shear wave velocity model using surface wave tomography inversion technology; The surface wave tomography inversion technique has been briefly introduced in the background section, and will not be repeated here.

[0038] Step 3: For the coarse velocity grid containing each detector in the initial three-dimensional shear wave velocity model, perform local non-uniform mesh refinement processing to generate a fine velocity grid; and dynamically update the fine grid based on the distance between the detector and the neighboring grid nodes of the fine velocity grid. This step specifically involves: performing local non-uniform interpolation on the grid containing the detector. The interpolation method is the core of variable mesh technology and is used to calculate function values ​​at the interface between coarse and fine meshes. The specific steps for performing linear interpolation on the boundary of the coarse velocity grid are as follows: Interpolation is performed in the x-direction, and the interpolation formula is:

[0039] In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid. Interpolation is performed in the y-direction, using the following formula:

[0040] In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid.

[0041] The specific steps for generating fine velocity grid nodes and nodal equations through linear interpolation are as follows: For the coordinates of the four nodes A, B, C, and D of the fine velocity grid generated by interpolation within the coarse velocity grid: A = V(x m+1 / k y n+1 / k B=V(x) m+1 / k y n+2 / k C=V(x) m+2 / k y n+1 / k D=V(x) m+2 / k y n+2 / k The velocity values ​​can be calculated from the coordinates of nodes on the coarse velocity grid boundary that have already been interpolated, and the nodal equations are constructed as follows:

[0042] In the formula, A, B, C, and D are the velocity values ​​of the nodes inside the fine mesh, respectively.

[0043] Based on the above steps, a specific example will be used for illustration: For example... Figure 1 and Figure 2 As shown, the blue squares represent the finer mesh points after interpolation, while the points where the coarse and fine meshes coincide are still represented by the dots of the coarse mesh. The specific interpolation steps are as follows: First, at the points where the coarse and fine meshes coincide, the velocity value of the coarse mesh point is directly assigned to the corresponding point in the fine mesh. Then, for non-coincident nodes on the boundary, a linear interpolation formula is used to calculate the velocity value of the fine mesh point, which is obtained by interpolating from adjacent coarse mesh points. The interpolation formula in the x-direction is:

[0044] The interpolation formula in the y-direction is:

[0045] Where k=0,1,2,3 are the interpolation indices for the fine grid; and for the four points inside the grid (light blue squares)... , , , It can be calculated from points on the boundary, and a system of linear equations can be established by solving it: .

[0046] Step 4: Calculate the precise velocity value at each detector location using bilinear interpolation in the updated encrypted fine velocity grid. In this embodiment, in practical engineering applications, especially facing complex tower-base geological conditions with drastic changes in shallow velocities, the resolution achieved by a single mesh refinement is often insufficient to accurately capture subtle structural changes. Therefore, the non-uniform mesh refinement process can be designed as a dynamically iterative closed-loop system. First, a distance threshold between the station and its neighboring grid points needs to be initialized as a convergence criterion for the iteration. Subsequently, the system enters a "calculation-determination-refinement" loop: in each iteration, if the current distance does not meet the accuracy requirement (i.e., is greater than the threshold), mesh refinement is automatically performed. This loop continues until the distance calculation result is lower than the threshold, marking the completion of refinement. Finally, the velocity value of the station's location is calculated based on the refined mesh that meets the accuracy requirements.

[0047] The specific steps of the "calculation-decision-encryption" loop are as follows: Preset the distance threshold between the detector and neighboring grid nodes; After performing a mesh refinement, calculate the distance between the detector and the neighboring mesh nodes of the current fine mesh containing it, and take the maximum distance. If the maximum distance is greater than the distance threshold, the current fine grid containing the detector is used as the new coarse velocity grid, and the above encryption and judgment process is repeated. If the maximum distance is less than or equal to the distance threshold, then encryption is stopped and the current fine velocity grid is output.

[0048] Step 5: Iteratively update the initial three-dimensional shear wave velocity model using the precise velocity values ​​at each detector location within the fine grid to obtain a high-resolution three-dimensional shear wave velocity structure model.

[0049] In this embodiment, the final high-resolution three-dimensional shear wave velocity structure model is used as the initial velocity model for frequency-domain passive source imaging technology to generate underground structure images for tower foundation engineering assessment. The iterative update of the initial three-dimensional shear wave velocity model adopts a damped least squares inversion algorithm based on least squares, and uses the precise velocity value at the detector location as the basis for theoretical travel time calculation.

[0050] Based on the above steps, let's take an example to illustrate: The work area is located in Qingliu Lijia, a 35kV transmission and transformation project situated in a township within a county. The site is an alluvial plain, and the substation is near a village road. A total of 11 measuring lines and 330 measuring points were deployed, with a point spacing of 1m and a total measuring line length of 307m. (See details...) Figure 3 .

[0051] For detailed results, please see [link / details]. Figure 4 The orientation of survey line 6 is roughly perpendicular to the dam axis. The imaging profile of this line shows a clear and continuous interface between the dam body (sandy cohesive soil) and the dam foundation (granite). The boundary between the strongly weathered granite layer and the moderately weathered granite layer within the dam foundation is not very distinct, indicating that their physical properties are extremely similar. The boundary is determined by the envelope between the quasi-stratified seismic facies and the blocky, chaotic seismic facies. Comparison of these results with borehole data shows a good match, with an error of only 0.9m.

[0052] Accordingly, the present invention also provides an apparatus for implementing the above-mentioned environmental noise imaging technique. The apparatus logically includes a data processing unit and a storage unit, which collaboratively complete the following process through programmed control: First, the data processing unit reads in the geophone observation data and coordinate information of the seismic station and establishes an initial velocity grid model based on preset rules. Then, for each station coordinate, the unit automatically executes the adaptive local grid refinement algorithm of the present invention; that is, after determining that the station geophone does not coincide with a grid node, it iteratively performs local grid subdivision and interpolation calculations until the distance between the station geophone and neighboring grid nodes meets a preset accuracy threshold. Next, on the finally obtained locally refined grid that meets the accuracy requirements, the accurate theoretical velocity value at the station is obtained through bilinear interpolation, driving the subsequent surface wave tomography inversion process, iteratively optimizing until a high-precision velocity model reflecting lateral variations is output. Finally, the obtained velocity model is stored in the storage unit and can be further output for imaging and geological interpretation. The device can be implemented using a general-purpose or special-purpose computing device with integrated corresponding program instructions. Its core lies in transforming the method into an automatically executable systematic solution through the aforementioned process-oriented data processing and control.

[0053] Example 2: An environmental noise imaging system for improving the imaging accuracy of power grid tower bases includes: The data acquisition module deploys multiple geophones in the target exploration area to collect environmental noise data. The initial velocity model construction module obtains the initial three-dimensional shear wave velocity model based on environmental noise data and surface wave tomography inversion technology. The local non-uniform mesh refinement module performs local non-uniform mesh refinement on the coarse velocity mesh containing each detector in the initial three-dimensional shear wave velocity model to generate a fine velocity mesh; and dynamically updates the fine mesh based on the distance between the detector and the neighboring mesh nodes of the fine velocity mesh. The precise velocity value calculation module uses a bilinear interpolation algorithm to calculate the precise velocity value at each detector position in the updated encrypted fine velocity grid. The initial velocity model update module uses the precise velocity values ​​at each detector position within the fine grid to iteratively update the initial three-dimensional shear wave velocity model, thereby obtaining a high-resolution three-dimensional shear wave velocity structure model.

[0054] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An environmental noise imaging method for improving the imaging accuracy of power grid tower bases, characterized in that, The specific steps include: Multiple geophones were deployed in the target exploration area to collect environmental noise data; Based on environmental noise data, an initial three-dimensional shear wave velocity model is obtained through surface wave tomography inversion technique. The coarse velocity grid containing each detector in the initial three-dimensional shear wave velocity model is locally non-uniformly refined to generate a fine velocity grid; and the fine grid is dynamically updated based on the distance between the detector and the neighboring grid nodes of the fine velocity grid. The precise velocity value at each detector location is calculated using a bilinear interpolation algorithm in the updated encrypted fine velocity grid. The initial three-dimensional shear wave velocity model is iteratively updated using the precise velocity values ​​at each detector location within the fine grid to obtain a high-resolution three-dimensional shear wave velocity structure model.

2. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 1, characterized in that, The local non-uniform grid refinement process specifically includes: If the detector is on the boundary of the coarse velocity grid, the velocity value of the coarse grid point is directly assigned to the corresponding point in the fine grid. If the detector is not on the boundary of the coarse velocity grid, then on the boundary of the coarse velocity grid where each detector is located, a fine velocity grid node and a set of node equations are generated by linear interpolation. Then, the set of equations of the fine velocity grid node is solved to obtain the refined fine velocity grid.

3. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 2, characterized in that, The specific steps for performing linear interpolation on the boundary of the coarse velocity grid are as follows: Interpolation is performed in the x-direction, and the interpolation formula is: In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid. Interpolation is performed in the y-direction, using the following formula: In the formula, m is the starting node index of the coarse velocity grid in the x direction, n is the starting node index of the coarse velocity grid in the y direction, and k is the interpolation index of the fine velocity grid.

4. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 3, characterized in that, The specific steps for generating fine velocity grid nodes and node equations through linear interpolation are as follows: For the coordinates of the four nodes A, B, C, and D of the fine velocity grid generated by interpolation within the coarse velocity grid: A = V(x m+1 / k y n+1 / k B=V(x) m+1 / k y n+2 / k C=V(x) m+2 / k y n+1 / k D=V(x) m+2 / k y n+2 / k The velocity values ​​can be calculated from the coordinates of nodes on the coarse velocity grid boundary that have already been interpolated, and the nodal equations are constructed as follows: In the formula, A, B, C, and D are the velocity values ​​of the nodes inside the fine mesh, respectively.

5. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 1, characterized in that, The specific steps for dynamically updating the fine mesh based on the distance between the detector and neighboring mesh nodes of the fine velocity mesh are as follows: Preset the distance threshold between the detector and neighboring grid nodes; After performing a mesh refinement, calculate the distance between the detector and the neighboring mesh nodes of the current fine mesh containing it, and take the maximum distance. If the maximum distance is greater than the distance threshold, the current fine grid containing the detector is used as the new coarse velocity grid, and the above encryption and judgment process is repeated. If the maximum distance is less than or equal to the distance threshold, then encryption is stopped and the current fine velocity grid is output.

6. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 1, characterized in that, The high-resolution three-dimensional shear wave velocity structure model is used as the initial velocity model for frequency domain passive source imaging technology to generate underground structure images for tower foundation engineering assessment.

7. The environmental noise imaging method for improving the imaging accuracy of power grid tower bases according to claim 1, characterized in that, The iterative update of the initial three-dimensional shear wave velocity model adopts a damped least squares inversion algorithm based on least squares, and uses the accurate velocity value at the detector position as the basis for theoretical travel time calculation.

8. An environmental noise imaging system for improving the imaging accuracy of power grid tower bases, characterized in that, include: The data acquisition module deploys multiple geophones in the target exploration area to collect environmental noise data. The initial velocity model construction module obtains the initial three-dimensional shear wave velocity model based on environmental noise data and surface wave tomography inversion technology. The local non-uniform mesh refinement module performs local non-uniform mesh refinement on the coarse velocity mesh containing each detector in the initial three-dimensional shear wave velocity model to generate a fine velocity mesh; and dynamically updates the fine mesh based on the distance between the detector and the neighboring mesh nodes of the fine velocity mesh. The precise velocity value calculation module uses a bilinear interpolation algorithm to calculate the precise velocity value at each detector position in the updated encrypted fine velocity grid. The initial velocity model update module uses the precise velocity values ​​at each detector position within the fine grid to iteratively update the initial three-dimensional shear wave velocity model, thereby obtaining a high-resolution three-dimensional shear wave velocity structure model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements an environmental noise imaging method for improving the imaging accuracy of power grid tower bases as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements an environmental noise imaging method for improving the imaging accuracy of power grid tower bases as described in any one of claims 1 to 7.

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