Inversion method and system based on tunneling source noise base-high-order joint surface wave

Abstract

The invention belongs to the technical field of seismic exploration, and provides an inversion method and system based on tunneling source noise base-high-order joint surface waves. The method comprises the steps that shield tunneling source noise data in the TBM tunneling direction is acquired; preprocessing the tunneling source noise data to obtain a virtual source shot gather record; performing surface wave and body wave field separation on the virtual source shot gather record obtained after preprocessing through tau-p conversion; segmenting the data after surface body separation, carrying out cross-correlation processing on the segmented data, carrying out cross-correlation on all station data and the first station data, and sorting according to distances to form a new virtual source shot gather record; calculating a frequency dispersion spectrum according to a phase shift method and the new virtual source shot gather record; screening the frequency dispersion spectrums of the multiple segments of data, superposing the frequency dispersion spectrums meeting preset requirements, and extracting a frequency dispersion curve from the superposed frequency dispersion spectrums; and according to the initial model and an inversion algorithm, performing inversion imaging on the extracted frequency dispersion curve.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic exploration, and in particular relates to an inversion method and system based on tunneling source noise basis-high-order joint surface wave. Background Art

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

[0003] Shield tunneling machine construction has significant advantages, such as high degree of automation, fast excavation speed, little environmental interference, and high economic benefits. It is widely used in the construction of urban underground tunnels (subways, etc.). However, with the large-scale construction of urban shield tunnels, the problem of disaster prevention and control in tunnel construction has received more and more attention. Especially in the construction of rail transit tunnels represented by urban subways, there are unfavorable geology such as undulating bedrock surface, karst, isolated rocks, and uneven hardness; "unclear and unclear" unfavorable geological conditions will bring disaster risks such as sudden water inrush, stratum instability, machine damage, and surface collapse to the shield machine excavation, and may even cause serious consequences such as casualties and overturning of surface buildings. Therefore, it is of great significance to explore the unfavorable geological conditions ahead of the shield construction tunnel, which is an important prerequisite for avoiding geological disasters and ensuring the safe construction of shield tunnels.

[0004] Urban underground shield tunneling has higher requirements for advanced geological forecasting, but due to the densely built environment in the city, surface drilling is difficult to cover the entire shield tunneling line. The power line network, metal pipes, surface multi-physical field noise, etc. can easily have an adverse effect on surface geophysical exploration; the detection methods available in traditional drilling and blasting and TBM tunnel construction are difficult to directly copy and apply to shield tunnel construction.

[0005] The inventors have found that existing surface wave methods mostly use artificial excitation sources or natural environmental noise. Artificial sources have certain requirements for the site and have poor applicability. Natural environmental noise has a low frequency and poor resolution, making it difficult to balance detection depth and high resolution. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides an inversion method and system based on the tunneling source noise basis-high-order joint surface wave, which utilizes the characteristics of large tunneling noise energy, obvious high-frequency components of surface waves, and more developed high-order modes at a large buried depth of the earthquake source, improves the signal-to-noise ratio through data preprocessing and surface wave separation, and then optimizes the inversion imaging results, which is beneficial to improving the resolution of underground poor geological bodies and can provide a guidance plan for urban shield tunnel TBM excavation.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides an inversion method based on tunneling source noise basis-high-order joint surface roll, which comprises:

[0009] Obtain shield tunneling source noise data along the TBM excavation direction;

[0010] Preprocess the tunneling source noise data to obtain virtual source shot gather records;

[0011] The virtual source shot gather records obtained after preprocessing are used to separate the surface wave and body wave fields through τ-p transformation;

[0012] The data after surface separation are segmented, and the segmented data are cross-correlated. All station data are cross-correlated with the first station data, and then sorted by distance to form a new virtual source shot gather record; the dispersion spectrum is calculated based on the phase shift method and the new virtual source shot gather record;

[0013] Screen the dispersion spectra of multiple segments of data, superimpose the dispersion spectra that meet the preset requirements, and extract the dispersion curve from the superimposed dispersion spectra;

[0014] According to the initial model and inversion algorithm, the extracted dispersion curve is inverted and imaged.

[0015] As an implementation method, the operations of preprocessing the tunneling source noise data include resampling, removing instrument response, removing the mean, removing the linear trend, bandpass filtering, time domain normalization and spectral whitening.

[0016] As an implementation method, the dispersion spectra of multiple segments of data are screened based on deep learning.

[0017] As an implementation method, a signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data, and a dispersion energy graph that is greater than the threshold meets the requirements, and a dispersion energy graph that is less than the threshold does not meet the requirements.

[0018] As an implementation method, the objective function of inversion imaging of the extracted dispersion curve is expressed as:

[0019]

[0020] Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1; m and n are the number of frequency points.

[0021] The second aspect of the present invention provides an inversion system based on tunneling source noise basis-high-order joint surface wave, which comprises:

[0022] An acquisition module is used to acquire shield tunneling source noise data along the TBM tunneling direction;

[0023] A preprocessing module, which is used to preprocess the tunneling source noise data and obtain virtual source shot gather records;

[0024] A surface-volume separation module is used to separate the surface wave and body wave fields of the virtual source shot gather records obtained after preprocessing through τ-p transformation;

[0025] The dispersion module is used to segment the data after surface separation, perform cross-correlation processing on the segmented data, cross-correlate all station data with the first station data, and then sort them by distance to form a new virtual source shot gather record; the dispersion spectrum is calculated based on the phase shift method and the new virtual source shot gather record;

[0026] A screening module is used to screen the dispersion spectra of multiple segments of data, superimpose the dispersion spectra that meet the preset requirements, and extract the dispersion curve from the superimposed dispersion spectra;

[0027] The inversion module is used to perform inversion imaging on the extracted dispersion curve according to the initial model and the inversion algorithm.

[0028] As an implementation mode, in the screening module, a signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data, and a dispersion energy graph that is greater than the threshold meets the requirements, and a dispersion energy graph that is less than the threshold does not meet the requirements.

[0029] As an implementation mode, in the inversion module, the objective function of inversion imaging of the extracted dispersion curve is expressed as:

[0030]

[0031] Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1; m and n are the number of frequency points.

[0032] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the inversion method based on the tunneling source noise basis-high-order joint surface roll as described above.

[0033] A fourth aspect of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the inversion method based on the tunneling source noise basis-high-order joint surface wave as described above are implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention obtains high-frequency surface wave information by acquiring excavation source noise data, thereby improving detection resolution, obtaining more modal dispersion information, and improving inversion accuracy;

[0036] (2) The present invention can obtain a clearer dispersion energy spectrum through surface separation;

[0037] (3) The present invention effectively reduces human interference, improves the quality of dispersion energy spectrum, and reduces inversion errors by deep learning to screen dispersion curves.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 It is a flow chart of an inversion method based on tunneling source noise basis-high-order joint surface wave according to an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of arrangement of geophones along the shield tunneling direction according to an embodiment of the present invention;

[0042] Figure 3 is a preprocessing flow chart of an embodiment of the present invention;

[0043] Figure 4 It is a schematic diagram of the structure of an inversion system based on tunneling source noise basis-high-order joint surface wave according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0047] Embodiment 1

[0048] like Figure 1 As shown, this embodiment provides an inversion method based on tunneling source noise basis-high-order joint surface roll, which specifically includes the following steps:

[0049] S101: Acquire shield tunneling source noise data along the TBM tunneling direction.

[0050] In the specific implementation process of step S101, the detectors are linearly arranged along the TBM excavation direction to obtain the shield excavation source noise data. The survey line layout range is mainly divided into two parts: the cement hardened part and the green belt land. The detectors arranged on the cement hardened road surface are fixed by punching and installed, and sandbags are covered above the detectors to prevent gusts from interfering with the detectors; the detectors arranged in the green belt land are arranged semi-buried by digging pits. The sampling rate parameter should be set to no less than 2K to meet the subsequent data processing accuracy requirements.

[0051] Surface observation systems such as Figure 2 As shown, the number of detectors should be no less than 12, arranged linearly along the tunneling direction of the shield machine, and the data collection time should be long enough.

[0052] S102: Preprocess the tunneling source noise data to obtain virtual source shot gather records.

[0053] like Figure 3 As shown in the figure, the preprocessing operations for the tunneling source noise data include resampling, removing instrument response, removing mean, removing linear trend, bandpass filtering, time domain normalization and spectrum whitening. Through data preprocessing, higher quality virtual source shot records can be obtained to reduce the errors in subsequent dispersion curve extraction and inversion.

[0054] S103: The virtual source shot gather records obtained after preprocessing are used to separate the surface wave and body wave fields through τ-p transformation.

[0055] Shield tunneling noise has the characteristics of strong energy and wide bandwidth, and the source is buried deep, and the high-order dispersion mode of surface waves is more developed, which is conducive to improving the depth and resolution of surface wave exploration. However, there is a large amount of body wave information in the signal data received at the surface level, which greatly interferes with the subsequent dispersion curve extraction and inversion imaging. In order to eliminate the interference of body waves, the τ-p transformation method is used to separate the surface wave and body wave fields.

[0056] The τ-p transformation transforms data from the tx domain to the τ-p domain, which is equivalent to a coordinate transformation from a mathematical point of view. This allows the complex time-distance relationship formed by the cross-interference of various waves in the tx domain to be separated in the τ-p domain, thereby achieving the effect of separating surface waves and body waves.

[0057] Convert the two-dimensional signal y(t,x) in the tx domain to the two-dimensional signal Y(τ,p) in the τ-p domain. The calculation formula is as follows:

[0058]

[0059] Among them, t represents time and x represents distance;

[0060] τ represents the intercept on the time axis, and p represents the instantaneous slope (slowness) of the time-distance curve.

[0061] Different types of waves in the tx domain will be distributed in different regions after being transformed to the τ-p domain due to their different apparent velocities. Therefore, effective signals can be extracted and body waves can be removed based on the regional distribution rules of different types of waves.

[0062] The signal data after removing the body wave is transformed back to the tx domain through inverse transformation. The calculation formula is as follows:

[0063]

[0064] Where H represents the Hilbert transform of the function Y(τ,p). After the surface separation, compared with before the separation, the high-frequency part has less noise, the high-order mode is more obvious, and the continuity of the fundamental mode is better.

[0065] S104: segmenting the data after surface separation, cross-correlating the segmented data, cross-correlating all station data with the first station data, and then sorting them by distance to form a new virtual source shot gather record; and calculating the dispersion spectrum based on the phase shift method and the new virtual source shot gather record.

[0066] In the process of calculating the dispersion energy spectrum according to the phase shift method and the tunneling source noise data:

[0067] Perform a one-dimensional Fourier transform on the virtual shot collection record d(x,t) in the tx domain along the time t direction to obtain

[0068] U(x,ω)=∫d(x,t)e iωt dt (1)

[0069] The spectrum U(x,ω) can be written as the product of the following two terms:

[0070] U(x,ω)=p(x,ω)A(x,ω) (2)

[0071] Where: p(x,ω) represents the phase spectrum; A(x,ω) represents the amplitude spectrum

[0072] Formula (2) can be written as follows

[0073] U(x,ω)=e -iΦx A(x,ω) (3)

[0074] Where Φ=ω / V ω , ω is the circular frequency, V ω is the phase velocity corresponding to the frequency component ω.

[0075] Integrating equation (3), we get

[0076]

[0077] The integral shown in equation (4) can be regarded as the superposition of the wave field of a certain frequency in equation (3) after a certain phase shift and amplitude normalization along the offset direction. Therefore, for a given frequency ω, if the following equation is satisfied

[0078]

[0079] Then V(φ,ω) will have a maximum value. Conversely, the phase velocity corresponding to the maximum point can be determined from the maximum point on the V(φ,ω) spectrum. The V(φ,ω) spectrum can be transformed into the dispersion energy spectrum according to formula (5).

[0080] S105: screening the dispersion spectra of multiple segments of data, superimposing the dispersion spectra that meet the preset requirements, and extracting the dispersion curve from the superimposed dispersion spectra.

[0081] After calculating the dispersion energy spectrum of the segmented data separately, it is necessary to screen out the dispersion spectra that meet the requirements for superposition, and then extract the dispersion curve. The traditional method of using manual judgment is very subjective, and the resulting errors will affect the subsequent inversion results. Therefore, it is proposed to screen the dispersion energy spectrum based on deep learning.

[0082] A signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data. A dispersion energy map that is greater than the threshold meets the requirements, and a dispersion energy map that is less than the threshold does not meet the requirements. A neural network model is established, and the two dispersion energy maps are used as training data to train the constructed network model. The trained neural network model is used to screen and superimpose the dispersion spectrum, which can effectively reduce the inversion error.

[0083] S106: Perform inversion imaging on the extracted dispersion curve according to the initial model and the inversion algorithm.

[0084] Specifically, the high-order modal components in the dispersion energy obtained by the shield tunneling vibration noise are more obvious than the high-order modal components obtained by the surface environmental noise. The embodiment of the present invention utilizes the high-order modes in the surface wave dispersion energy to perform a joint inversion of the base-high-order dispersion curve. The objective function of inverting the extracted dispersion curve is expressed as:

[0085]

[0086] Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1. m and n are the number of frequency points.

[0087] The weight coefficient indicates the sensitivity of different modal dispersion curves to the velocity in the medium-deep formation. The dispersion curve of the higher-order mode is more sensitive to the velocity in the medium-deep formation than the fundamental-order dispersion curve.

[0088] This embodiment acquires tunneling noise signal data; pre-processes the acquired data; converts the pre-processed data into the τ-p domain, extracts effective signals and removes body waves according to the regional distribution rules of different types of waves, and then inverts the surface wave data after separating the body waves back to the tx domain, cross-correlates the data after segmentation, calculates the dispersion energy spectrum of each segment of data according to the phase shift method, and extracts fundamental-high-order dispersion curves according to the obtained dispersion energy spectrum; screens dispersion curves based on deep learning, automatically removes dispersion curves that do not meet the requirements, and superimposes dispersion curves that meet the requirements; wherein the dispersion curves include fundamental and high-order dispersion curves; establishes an initial formation model according to the dispersion curves; and inverts the tunneling noise data according to the initial model and the inversion algorithm. The present invention obtains the surface wave information in the high-frequency band by acquiring the noise data of the tunneling source, thereby improving the detection resolution, obtaining more modal dispersion information, and improving the inversion accuracy; a clearer dispersion energy spectrum can be obtained through surface separation; deep learning screening of dispersion curves can effectively reduce human interference, improve the quality of the dispersion energy spectrum, and reduce inversion errors.

[0089] Embodiment 2

[0090] like Figure 4 As shown, this embodiment provides an inversion system based on tunneling source noise basis-high-order joint surface wave, which specifically includes the following modules:

[0091] (1) an acquisition module, which is used to obtain shield tunneling source noise data along the TBM excavation direction;

[0092] (2) a preprocessing module, which is used to preprocess the tunneling source noise data and obtain virtual source shot gather records;

[0093] (3) a surface-volume separation module, which is used to separate the surface wave and the volume wave field of the virtual source shot gather records obtained after preprocessing through τ-p transformation;

[0094] (4) a dispersion module, which is used to segment the data after surface separation, perform cross-correlation processing on the segmented data, cross-correlate the data of all stations with the data of the first station, and then sort them by distance to form a new virtual source shot gather record; and calculate the dispersion spectrum based on the phase shift method and the new virtual source shot gather record;

[0095] (5) a screening module, which is used to screen the dispersion spectra of multiple segments of data, superimpose the dispersion spectra that meet the preset requirements, and extract the dispersion curve from the superimposed dispersion spectra;

[0096] Specifically, in the screening module, a signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data, and a dispersion energy graph that is greater than the threshold is a dispersion energy graph that meets the requirements, and a dispersion energy graph that is less than the threshold is a dispersion energy graph that does not meet the requirements.

[0097] (6) An inversion module, which is used to perform inversion imaging on the extracted dispersion curve according to the initial model and the inversion algorithm.

[0098] Specifically, in the inversion module, the objective function of inversion imaging of the extracted dispersion curve is expressed as:

[0099]

[0100] Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1. m and n are the number of frequency points.

[0101] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0102] Embodiment 3

[0103] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the inversion method based on the tunneling source noise basis-high-order joint surface roll as described above are implemented.

[0104] Embodiment 4

[0105] This embodiment provides 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, the steps in the inversion method based on the tunneling source noise basis-high-order joint surface roll as described above are implemented.

[0106] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of the processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An inversion method based on tunneling source noise basis-high-order joint surface roll, characterized in that: include: Obtain shield tunneling source noise data along the TBM excavation direction; Preprocess the tunneling source noise data to obtain virtual source shot gather records; The virtual source shot gather records obtained after preprocessing are used to separate the surface wave and body wave fields through τ-p transformation; The data after surface separation are segmented, and the segmented data are cross-correlated. All station data are cross-correlated with the first station data, and then sorted by distance to form a new virtual source shot gather record; the dispersion spectrum is calculated based on the phase shift method and the new virtual source shot gather record; Screen the dispersion spectra of multiple segments of data, superimpose the dispersion spectra that meet the preset requirements, and extract the dispersion curve from the superimposed dispersion spectra; According to the initial model and inversion algorithm, the extracted dispersion curve is inverted and imaged.

2. The inversion method based on tunneling source noise basis-high-order joint surface roll according to claim 1, characterized in that: The preprocessing operations of the tunneling source noise data include resampling, removing instrument response, removing the mean, removing the linear trend, bandpass filtering, time domain normalization and spectral whitening.

3. The inversion method based on tunneling source noise basis-high-order joint surface roll according to claim 1, characterized in that: Filter the dispersion spectra of multiple data segments based on deep learning.

4. The inversion method based on tunneling source noise basis-high-order joint surface roll according to claim 1, characterized in that: A signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data. A dispersion energy graph that is greater than the threshold meets the requirements, and a dispersion energy graph that is less than the threshold does not meet the requirements.

5. The inversion method based on tunneling source noise basis-high-order joint surface roll according to claim 1, characterized in that: The objective function of inversion imaging of the extracted dispersion curve is expressed as: Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1; m and n are the number of frequency points.

6. An inversion system based on tunneling source noise basis-high-order joint surface wave, characterized in that: include: An acquisition module is used to acquire shield tunneling source noise data along the TBM tunneling direction; A preprocessing module, which is used to preprocess the tunneling source noise data and obtain virtual source shot gather records; A surface-volume separation module is used to separate the surface wave and body wave fields of the virtual source shot gather records obtained after preprocessing through τ-p transformation; The dispersion module is used to segment the data after surface separation, perform cross-correlation processing on the segmented data, cross-correlate all station data with the first station data, and then sort them by distance to form a new virtual source shot gather record; the dispersion spectrum is calculated based on the phase shift method and the new virtual source shot gather record; A screening module is used to screen the dispersion spectra of multiple segments of data, superimpose the dispersion spectra that meet the preset requirements, and extract the dispersion curve from the superimposed dispersion spectra; The inversion module is used to perform inversion imaging on the extracted dispersion curve according to the initial model and the inversion algorithm.

7. The inversion system based on tunneling source noise basis-high-order joint surface wave according to claim 6, characterized in that: In the screening module, a signal-to-noise ratio threshold is set for the dispersion energy spectrum calculated from the segmented data. A dispersion energy graph that is greater than the threshold meets the requirements, and a dispersion energy graph that is less than the threshold does not meet the requirements.

8. The inversion system based on tunneling source noise basis-high-order joint surface wave according to claim 6, characterized in that: In the inversion module, the objective function of inversion imaging of the extracted dispersion curve is expressed as: Where: V R基阶反演 represents the fundamental order dispersion curve obtained by inversion, V R高阶反演 represents the high-order dispersion curve obtained by inversion; V R基阶实测 and V R高阶实测 is the target dispersion curve to be fitted; α1 and α2 represent the weight coefficients of the fundamental and higher-order dispersion curves respectively, and α1+α2=1; m and n are the number of frequency points.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the inversion method based on tunneling source noise base-high-order joint surface roll as described in any one of claims 1 to 5 are implemented.

10. 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, the steps in the inversion method based on tunneling source noise basis-high-order joint surface roll are implemented as described in any one of claims 1 to 5.

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