Urban underground medium ultra-short time imaging method and system based on traffic noise tail wave

Abstract

The invention discloses an urban underground medium ultra-short time imaging method and system based on traffic noise tail waves, and relates to the technical field of geophysical exploration, and the method comprises the steps: obtaining traffic noise data, and carrying out the segmentation; performing mutual dry interference processing on each data segment to generate a virtual shot gather; according to the signal-to-noise ratio feature, the time-frequency energy distribution feature and the polarization feature of the virtual shot gather, a tail wave data segment is screened out from all the data segments; performing phase weighted stacking on the tail wave data segments; performing frequency dispersion imaging on the comprehensive data segment obtained by superposition to obtain a surface wave frequency dispersion curve; and performing inversion according to the surface wave dispersion curve to obtain an underground shear wave velocity structure image. The signal quality of the selected tail wave part is ensured through the signal-to-noise ratio, time frequency and polarization triple characteristics, high-precision and high-efficiency urban underground medium imaging is completed in an ultra-short time, the anti-interference capability is high, and the exploration cost is low.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, in particular to a city underground medium ultra-short time imaging method and system based on traffic noise coda. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] With the rapid development of urban construction, the demand for accurate and rapid detection of near-surface underground structures is increasingly urgent. Surface wave exploration, as an effective geophysical method, is widely used in such investigations. The traditional active source surface wave method needs to artificially excite a seismic source. Although it has advantages in controllability and signal-to-noise ratio, its use is often strictly limited in densely built and densely populated urban areas, and it has problems such as high cost and environmental disturbance. The passive source surface wave method uses natural environmental vibrations (such as noise generated by traffic and human activities) as a seismic source, has the significant advantages of non-destructive, environmentally friendly and low cost, and is more suitable for use in urban environments.

[0004] However, the current mainstream passive source surface wave technology (such as the spatial autocorrelation method and the passive multi-channel surface wave analysis method) generally has a key technical bottleneck: in order to extract stable and reliable surface wave signals from the strong random and weak energy background noise, it is necessary to record continuous noise for several hours or even several days for long-time stacking and averaging. The dependence on long-time data results in long field data acquisition period, low exploration efficiency, and difficulty in meeting the stringent requirements of urban engineering on timeliness.

[0005] In addition, there is abundant and strong energy traffic noise in urban environments, but existing passive source methods using traffic noise still treat it as a whole signal for processing, and fail to effectively exploit and utilize the more sensitive signal components to underground structures. SUMMARY

[0006] To solve the above problems, the present application provides a city underground medium ultra-short time imaging method and system based on traffic noise coda, which ensures the signal quality of the selected coda part through signal-to-noise ratio, time-frequency, and polarization triple characteristics, realizes high-precision and high-efficiency city underground medium imaging in ultra-short time, has strong anti-interference ability, and low exploration cost.

[0007] To achieve the above purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a city underground medium ultra-short time imaging method based on traffic noise coda, comprising: acquiring traffic noise data and segmenting; performing mutual coherence interference processing on each data segment to generate a virtual shot gather; According to the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics and polarization characteristics of the virtual shot gather, tail wave data segments are screened out from all data segments; The tail wave data segments are subjected to phase-weighted stacking; The comprehensive data segments obtained by stacking are subjected to dispersion imaging to obtain surface wave dispersion curves; The underground S-wave velocity structure image is obtained by inversion according to the surface wave dispersion curves.

[0008] As an optional implementation, the segmentation process includes: segmenting the traffic noise data by using a high overlap rate adaptive sliding window; wherein the segmentation duration is 5-40 seconds, and the overlap rate between adjacent data segments is not less than 50%.

[0009] As an optional implementation, the process of screening tail wave data segments includes: In the virtual shot gather, the wave field energy within a set range of surface wave apparent velocity is regarded as effective surface wave signal, and the wave field energy outside the range is regarded as noise, and thus the signal-to-noise ratio characteristics are calculated; The continuous wavelet transform is performed on the virtual shot gather to generate a time-frequency spectrum; the attenuation gradient of signal energy with time is calculated within a specific frequency range to determine the time-frequency energy distribution characteristics; The polarization linearity of the surface wave signal in the data segment is calculated by using three-component data to determine the polarization characteristics; After the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics and polarization characteristics are normalized respectively, the comprehensive scores of each data segment are obtained by weighted summation, and the data segment with the highest comprehensive score is taken as the tail wave data segment.

[0010] As an optional implementation, the underground S-wave velocity structure image is obtained by a multi-mode joint inversion method according to the surface wave dispersion curves; wherein the objective function of the multi-mode joint inversion method is: ; wherein, is the underground model parameter to be inverted, is the weight of the i-th dispersion data point, is the forward response function; is the norm; is the velocity observed by the i-th dispersion data point; is the frequency observed by the i-th dispersion data point; and M is the number of data points.

[0011] As an optional implementation, the formula of phase-weighted stacking is: ; wherein, ​​​is a comprehensive data segment obtained by stacking, is a signal of the jth trace, is an instantaneous phase of the qth trace, v is a balance coefficient for controlling coherence and incoherence, and N is the total number of traces stacked.

[0012] As an alternative embodiment, the comprehensive data segment obtained by stacking is transformed from a time-space domain to a frequency-phase velocity domain to generate a dispersion energy map, and a surface wave dispersion curve is obtained by picking a peak value of an energy group on the dispersion energy map.

[0013] In a second aspect, the present application provides an urban underground medium ultra-short-time imaging system based on traffic noise coda waves, comprising: A preprocessing module configured to acquire traffic noise data and segment the data; An interference processing module configured to perform mutual coherence interference processing on each data segment to generate a virtual shot gather; A screening module configured to screen coda wave data segments from all data segments according to signal-to-noise ratio characteristics, time-frequency energy distribution characteristics, and polarization characteristics of the virtual shot gather; A stacking module configured to perform phase-weighted stacking on the coda wave data segments; A dispersion analysis module configured to perform dispersion imaging on a comprehensive data segment obtained by stacking to obtain a surface wave dispersion curve; An inversion module configured to obtain an underground shear wave velocity structure image by inversion according to the surface wave dispersion curve.

[0014] In a third aspect, the present application provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein when the computer instructions are run by the processor, the method of the first aspect is completed.

[0015] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method of the first aspect is completed.

[0016] In a fifth aspect, the present application provides a computer program product comprising a computer program, wherein when the computer program is executed by a processor, the method of the first aspect is completed.

[0017] Compared with the prior art, the present application has the following beneficial effects: In view of the problems that the existing passive source method relies on long-time noise recording and cannot utilize effective signals in ultra-short-time data, the application provides an urban underground medium ultra-short-time imaging method and system based on traffic noise tail waves, traffic noise is collected through a detector array; high-overlap sliding windows are used to segment continuous noise in an ultra-short time; virtual shot gathers are obtained by processing each segment of data mutually coherently, so that tail wave data segments are screened from all segments; phase weighting stacking is performed on the tail wave segments to enhance the coherence of surface waves; and finally, dispersion imaging and multi-mode joint inversion are performed on the stacking results to obtain the underground transverse wave velocity structure. The application realizes high-precision and high-efficiency urban underground medium imaging that can be completed only by using several seconds to tens of seconds of data, has the outstanding advantages of strong anti-interference ability and low exploration cost. Moreover, the application directly uses traffic noise in the city as a seismic source, does not need artificial excitation, has little influence on urban traffic and environment, and has wide applicability.

[0018] The application ensures the signal quality of the selected tail wave part through the three characteristics of signal-to-noise ratio, time-frequency and polarization, is more sensitive to medium changes, and has stronger anti-interference ability.

[0019] In the dispersion curve obtained by the application, high-order mode surface waves are often more developed, which provides more constraints for inversion and helps to improve the inversion precision and resolution of the velocity model.

[0020] The advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be learned through the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show the embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0022] Figure 1 A flowchart of the urban underground medium ultra-short-time imaging method based on traffic noise tail waves provided for the embodiment 1 of the application is shown in the figure. Figure 2 A schematic diagram of a traffic noise collection observation system provided for the embodiment 1 of the application is shown in the figure. Figure 3 A single traffic noise record diagram collected in the embodiment 1 of the application is shown in the figure. Figure 4 A high-quality dispersion diagram obtained from a single traffic noise provided for the embodiment 1 of the application is shown in the figure. Figure 5 A higher-quality dispersion diagram obtained by stacking multiple traffic noises provided for the embodiment 1 of the application is shown in the figure. DETAILED DESCRIPTION

[0023] The application will be further described below in connection with the drawings and examples.

[0024] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0025] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of the application will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Unless otherwise required by context, singular terms shall include pluralities and vice versa. The singular forms "a", "an", and "the" include plural referents unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "contains", "containing" as used herein, are specifically intended to be open-ended and to mean including, but not limited to.

[0026] The embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0027] Embodiment 1 The traffic noise record contains a part of the coda generated by vehicle vibration and scattered multiple times through the underground medium. This part of the signal contains rich underground structure information and has stronger resistance to random interference. Therefore, the embodiment provides a city underground medium ultra-short-time imaging method based on traffic noise coda, which breaks away from the dependence on long-time noise recording, quickly and accurately identifies and extracts high-quality coda signals from short and non-steady traffic noise recording, and realizes underground medium ultra-short-time and high-resolution imaging through multi-dimensional identification and intelligent enrichment of the coda signals in traffic noise, frequency dispersion imaging and inversion. The imaging is completed in a few seconds to tens of seconds only by using the coda part of the traffic noise, greatly improving the exploration efficiency.

[0028] As shown in Figure 1 , comprising: acquiring traffic noise data and segmenting; interferometrically processing each data segment to generate a virtual shot gather; screening coda data segments from all data segments according to the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics and polarization characteristics of the virtual shot gather; phase-weighted stacking the coda data segments; frequency dispersion imaging the integrated data segment obtained by stacking to obtain a surface wave dispersion curve; According to the dispersion curve inversion of surface wave, the underground shear wave velocity structure image is obtained.

[0029] The method of the embodiment will be described in detail below. Figure 1 The method of the embodiment will be described in detail below.

[0030] S1: Lay out the observation system and collect traffic noise data, as shown in Figures 2-3 .

[0031] Specifically, lay out an array of seismic detectors in the area to be measured on the urban road and collect traffic noise generated by the passing vehicles in real time.

[0032] Among them, the detector is preferably a three-component digital seismograph, arranged along a straight line at equal intervals, and the channel spacing is preferably 2 meters.

[0033] Among them, the detector should ensure good coupling with the ground. When collecting data, the time sampling interval is set to 1-5 milliseconds. Collect continuous ambient noise data, and the recording duration should cover multiple vehicle passing events.

[0034] S2: Data segmentation and preprocessing.

[0035] Specifically, the continuous traffic noise data collected is segmented and processed using a high overlap rate adaptive sliding window.

[0036] Among them, the segmentation duration is selected between 5 seconds and 40 seconds according to the actual noise source characteristics and the exploration target depth, and is preferably 4-10 seconds.

[0037] Among them, the adjacent data segments are intercepted using a sliding time window, and the overlap rate is not less than 50%, preferably 80%-95%. The high overlap sliding window method aims to capture as many high-quality coda fragments as possible from non-stationary noise.

[0038] S3: Coda enrichment; this step is the core of ensuring the quality of ultra-short-time imaging, aiming to intelligently select data segments dominated by coda from vehicle vibration and fully scattered by underground media from all data segments. Among them, the data segment dominated by coda refers to the signal energy of the data segment mainly coming from the scattered wave field of the vehicle vibration reaching the receiver after multiple scattering by the underground medium.

[0039] Specifically, it includes: S3-1: Interference processing of each data segment to generate a virtual shot gather .

[0040] Specifically: In the frequency domain, calculate the cross-correlation function of the noise data recorded by any two receiving points and , and . ; wherein, and are two receiving points and a frequency domain representation of the recorded noise data, denotes complex conjugate; is frequency.

[0041] S3-2: Select the tail wave data segment from all data segments by comprehensively evaluating the signal-to-noise ratio feature, time-frequency energy distribution feature and polarization feature of the virtual shot gather.

[0042] Specifically: (1) Signal-to-noise ratio feature.

[0043] In the virtual shot gather, the wave field energy of the surface wave apparent velocity within a set range (such as 200-800 m / s) is regarded as effective surface wave signal, and the wave field energy outside the range is regarded as noise, and thus the signal-to-noise ratio SNR is calculated.

[0044] (2) Time-frequency energy distribution feature; on the time-frequency spectrum, the tail wave signal shows energy continuous attenuation and uniform distribution within a specific frequency range.

[0045] Therefore, the continuous wavelet transform or S transform is performed on the virtual shot gather to generate a time-frequency spectrum; the energy attenuation gradient of the signal with time within the surface wave main frequency (such as 5-20 Hz) bandwidth is calculated, the more gentle the energy attenuation and the more uniform the distribution on the time axis, the higher the time-frequency energy distribution score E, and the more in line with the tail wave characteristics, and the data segment with gentle energy attenuation and uniform distribution is identified as high-quality tail wave data segment.

[0046] (3) Polarization feature.

[0047] Using three-component data (i.e. using vertical and horizontal component data recorded by a three-component seismograph), the polarization linearity P of the surface wave signal in the data segment is calculated, and the data segment with linearity higher than a preset threshold (such as 0.7) is identified as a high-quality tail wave data segment.

[0048] (4) The signal-to-noise ratio feature, time-frequency energy distribution feature and polarization feature of each data segment are normalized to obtain , , respectively; a weighted comprehensive evaluation function F is used to quantitatively score each data segment to obtain a comprehensive score of each data segment; according to the comprehensive score of the data segment, one or more data segments with the highest comprehensive score are selected as tail wave data segments.

[0049] The weighted comprehensive evaluation function F is: ; wherein, For the normalized signal-to-noise ratio, For the normalized time-frequency energy distribution score, Scoring for normalized polarization linearity; , , These are the weighting coefficients, and If take , , .

[0050] S4: To further improve signal quality, phase-weighted superposition is performed on the selected high signal-to-noise ratio virtual shot gather records to enhance the phase consistency of the surface wave signal in the frequency domain in the wake, suppress incoherent noise, and thus obtain surface wave records with extremely high signal-to-noise ratio.

[0051] The phase-weighted superposition formula is: ; in, It is a superimposed signal. It is the virtual gun set of the j-th trajectory. is the instantaneous phase of the q-th trajectory, v is the balance coefficient controlling the coherence and incoherence, and N is the total number of superimposed trajectories; It is the imaginary unit.

[0052] This step significantly enhances coherent signals with consistent phase characteristics by weighted averaging of the phase spectra of multiple records in the frequency domain, while suppressing random noise, thereby obtaining a composite surface wave record with an extremely high signal-to-noise ratio.

[0053] S5: For the superimposed and enhanced virtual shot gather records, a high-resolution linear Radon transform method is used to transform the virtual shot gather records from the time-space domain to the frequency-phase velocity domain, generating a dispersion energy map. On the dispersion energy map, the high-value regions of energy clusters characterize the dispersion features of surface waves. By picking the peak values ​​of the energy clusters, the surface wave dispersion curve can be obtained, such as... Figures 4-5 As shown.

[0054] S6: Based on the surface wave dispersion curve, a multi-mode joint inversion method that does not rely on pattern recognition is used to obtain the subsurface shear wave velocity structure image.

[0055] The objective function of the multi-mode joint inversion method is: ; in, The parameters of the underground model to be inverted are: For the first The weight of each dispersion data point The forward response function is based on the Haskell-Thomson transfer matrix algorithm; The selected norm; For the first The velocity observed at each dispersion data point; For the first The frequency observed at each dispersion data point; M is the number of data points.

[0056] This inversion method can simultaneously fit the dispersion data of all modes picked on the dispersion energy map without prior identification of the mode to which each data point belongs, thus avoiding inversion errors caused by mode misidentification and finally obtaining a reliable one-dimensional shear wave velocity profile.

[0057] Example 2 This embodiment provides an ultra-short-time imaging system for urban underground media based on traffic noise wake waves, including: The preprocessing module is configured to acquire traffic noise data and segment it. The interference processing module is configured to perform mutual interference processing on each data segment to generate a virtual gun set; The filtering module is configured to filter out the wake wave data segment from all data segments based on the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics, and polarization characteristics of the virtual shot set; The overlay module is configured to perform phase-weighted overlay on the wake data segments; The dispersion analysis module is configured to perform dispersion imaging on the superimposed composite data segment to obtain the surface wave dispersion curve. The inversion module is configured to invert the subsurface shear wave velocity structure image based on the surface wave dispersion curve.

[0058] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0059] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0060] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0061] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0062] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0063] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0064] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0065] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0066] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0067] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0068] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0069] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for ultra-short-time imaging of urban underground media based on traffic noise wake waves, characterized in that, include: Acquire traffic noise data and segment it; Each data segment undergoes mutual interference processing to generate a virtual gun set; Based on the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics, and polarization characteristics of the virtual shot set, the wake wave data segment is selected from all data segments; Phase-weighted superposition of the wake data segments; Dispersion imaging is performed on the composite data segments obtained by superposition to obtain the surface wave dispersion curve; The subsurface shear wave velocity structure image is obtained by inversion from the surface wave dispersion curve.

2. The method for ultra-short-time imaging of urban underground media based on traffic noise wake waves as described in claim 1, characterized in that, The segmentation process includes: segmenting traffic noise data using an adaptive sliding window with a high overlap rate; the segmentation time is 5-40 seconds, and the overlap rate between adjacent data segments is not less than 50%.

3. The method for ultra-short-time imaging of urban underground media based on traffic noise wake waves as described in claim 1, characterized in that, The process of filtering wakewave data segments includes: The virtual gun is concentrated, and the wave field energy with surface wave apparent velocity within a set range is regarded as the effective surface wave signal, while the wave field energy outside this range is regarded as noise. The signal-to-noise ratio characteristics are calculated accordingly. A continuous wavelet transform is performed on the virtual gun set to generate the time spectrum; the attenuation gradient of the signal energy over time is calculated within a specific frequency range to determine the time-frequency energy distribution characteristics; The polarization linearity of the surface wave signal in the data segment is calculated using the three-component data to determine the polarization characteristics. After normalizing the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics, and polarization characteristics, the comprehensive score of each data segment is obtained by weighted summation, and the data segment with the highest comprehensive score is the wake data segment.

4. The method for ultra-short-time imaging of urban underground media based on traffic noise wake waves as described in claim 1, characterized in that, The subsurface shear wave velocity structure image is obtained using a multi-mode joint inversion method based on surface wave dispersion curves; the objective function of the multi-mode joint inversion method is: ； in, The parameters of the underground model to be inverted are: For the first The weight of each dispersion data point This is the forward response function; It is a norm; For the first The velocity observed at each dispersion data point; For the first The frequency observed at each dispersion data point; M is the number of data points.

5. The method for ultra-short-time imaging of urban underground media based on traffic noise wake waves as described in claim 1, characterized in that, The formula for phase-weighted superposition is: ； in, It is a composite data segment obtained by superposition. It is the signal of the j-th trajectory. is the instantaneous phase of the q-th trajectory, v is the balance coefficient controlling the coherence and incoherence, and N is the total number of superimposed trajectories.

6. The method for ultra-short-time imaging of urban underground media based on traffic noise wake waves as described in claim 1, characterized in that, The composite data segment obtained by superposition is transformed from the time-space domain to the frequency-phase velocity domain to generate a dispersion energy map. The surface wave dispersion curve is obtained by picking the peak value of the energy cluster on the dispersion energy map.

7. A short-time imaging system for urban underground media based on traffic noise wake waves, characterized in that, include: The preprocessing module is configured to acquire traffic noise data and segment it. The interference processing module is configured to perform mutual interference processing on each data segment to generate a virtual gun set; The filtering module is configured to filter out the wake wave data segment from all data segments based on the signal-to-noise ratio characteristics, time-frequency energy distribution characteristics, and polarization characteristics of the virtual shot set; The overlay module is configured to perform phase-weighted overlay on the wake data segments; The dispersion analysis module is configured to perform dispersion imaging on the superimposed composite data segment to obtain the surface wave dispersion curve. The inversion module is configured to invert the subsurface shear wave velocity structure image based on the surface wave dispersion curve.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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