Stratum velocity profile exploration method and system based on micro-motion technology

Abstract

The invention discloses a formation velocity profile exploration method and system based on a micro-motion technology, belongs to a geological exploration technology, and utilizes the micro-motion technology to carry out formation velocity profile exploration on a large physical model. According to the method, a plurality of single-component low-frequency detectors are arranged on a straight line, micro-motion data are obtained, screening and cross-correlation processing are carried out to obtain a correlation coefficient, a correlation curve is constructed, and a speed model is determined. Meanwhile, the active source data is subjected to frequency dispersion spectrum calculation, an initial model is determined, a simulated frequency dispersion spectrum curve is obtained, and the model is updated to determine a speed model. And generating a stratum velocity profile based on the velocity model and the spatial position of the large physical model. According to the method, the purpose of determining the formation velocity profile of the large physical model by using the micro-motion technology is achieved, the application range of the micro-motion technology is expanded, and the exploration accuracy is improved.

Description

Technical Field

[0001] The present invention relates to a geological exploration technology, and more specifically, mainly to a formation velocity profile exploration method and system based on micro-motion technology. Background Art

[0002] With the development of science and technology, the demand for exploration and detection of strata is increasing. Currently, the commonly used stratum exploration technologies include active surface wave exploration and passive surface wave exploration. Among them, active surface wave exploration cannot be implemented in cities and noise control areas, and passive surface wave exploration is greatly affected by site conditions, resulting in unstable data quality and affecting exploration accuracy. Micro-seismic technology, as a kind of passive surface wave exploration method, uses natural micro-seismic signals on the surface for exploration. It has the advantages of no need for artificial excitation sources, suitable for urban areas and noise control areas, less affected by site conditions in the quality of the collected data, and can provide deeper stratum information. However, in reality, micro-seismic technology also has many disadvantages, such as high site requirements, which limits the scope of application of micro-seismic technology, the high speed of some micro-seismic technologies, which cannot accurately reflect stratum information, and low efficiency, which cannot meet the actual application needs. Therefore, it is necessary to study and improve the exploration methods of micro-seismic technology. Summary of the invention

[0003] One of the purposes of the present invention is to address the above-mentioned shortcomings and provide a formation velocity profile exploration method and system based on micro-motion technology, in the hope of solving the technical problems in the prior art of using micro-motion technology for geological exploration, such as high site requirements, inability to accurately reflect formation information, and low efficiency.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] On one hand, the present invention provides a formation velocity profile exploration method based on micro-vibration technology, the method comprising the following steps:

[0006] Step A: multiple single-component low-frequency geophones respectively acquire single-component low-frequency micro-motion data of multiple exploration points.

[0007] Step B: determining a correlation curve corresponding to a first exploration point based on single-component low-frequency micro-motion data of multiple exploration points; the first exploration point is any one of the multiple exploration points.

[0008] The correlation curve is obtained by first screening and processing the single-component low-frequency micro-motion data of multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point; then cross-correlating the first stable micro-motion data and the second stable micro-motion data through the following formula to obtain the correlation coefficient Rfh(x) of other exploration points.

[0009] Rfh(x)=∫[f(x)*h(x)]dx

[0010] Where f(x) is the first stable micro-motion data, h(x) is the second stable micro-motion data, * is the convolution operator, and dx is the integral variable.

[0011] The correlation curve is determined by the correlation coefficient Rfh(x).

[0012] Step C, obtaining active source data related to the first exploration point according to the correlation curve; then performing dispersion spectrum calculation on the active source data by the following formula to obtain a measured dispersion spectrum curve u(x,f).

[0013] u(x,f)=A(f)*exp(j*k*x)

[0014] Where A(f) is the amplitude, k is the wave number, j is the imaginary unit, x is the distance, and exp is the exponential function;

[0015] Step D, obtaining the initial model by measuring the dispersion spectrum curve u(x,f) from the following formula;

[0016] H=(u*4) / f

[0017] Where H is the formation depth, u is the surface wave velocity, and f is the frequency;

[0018] Step E, obtaining a velocity model after updating the initial model;

[0019] Step F: generating a formation velocity profile according to the spatial position of the velocity model and the formation model.

[0020] Preferably, a further technical solution is: in step D, the initial model is updated by obtaining a simulated dispersion spectrum curve through a theoretical model or an empirical model, and then the error factor E between the simulated dispersion spectrum curve and the measured dispersion spectrum curve u(x, f) is calculated by the following formula.

[0021]

[0022] In the formula, u sim is the simulated dispersion spectrum curve, and N is the number of frequency sampling points.

[0023] The initial model is updated based on the error factor, and the error factor is minimized by updating the initial model, thereby determining the velocity model.

[0024] A further technical solution is that the stratum model is a large physical model or an actual stratum model.

[0025] A further technical solution is: the formation velocity profile in step F is calculated by the following formula:

[0026] v(x, y) = f(H(x, y))

[0027] Where v(x,y) is the formation velocity profile, H(x,y) is the formation depth, and f(H(x,y)) is the relationship between surface wave velocity and formation depth.

[0028] A further technical solution is that the multiple single-component low-frequency detectors in step A are distributed on the same straight line at a preset interval, and the exploration points are located in a large physical model or an actual formation model.

[0029] Another aspect of the present invention provides a formation velocity profile exploration system based on micro-motion technology, the system comprising:

[0030] The acquisition unit is used to obtain single-component low-frequency micro-motion data of multiple exploration points.

[0031] The determination unit is used to determine a correlation curve corresponding to a first exploration point; the first exploration point is any one of the multiple exploration points. The correlation curve is obtained by first screening the single-component low-frequency micro-motion data of the multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point; then performing cross-correlation processing on the first stable micro-motion data and the second stable micro-motion data through the following formula to obtain the correlation coefficient Rfh(x) of other exploration points.

[0032] Rfh(x)=∫[f(x)*h(x)]dx

[0033] Where f(x) is the first stable micro-motion data, h(x) is the second stable micro-motion data, * is the convolution operator, and dx is the integral variable.

[0034] The correlation curve is determined by the correlation coefficient Rfh(x).

[0035] The velocity model unit is used to obtain active source data related to the first exploration point according to the correlation curve; then the dispersion spectrum of the active source data is calculated by the following formula to obtain the measured dispersion spectrum curve u(x,f).

[0036] u(x,f)=A(f)*exp(j*k*x)

[0037] Where A(f) is the amplitude, k is the wave number, j is the imaginary unit, x is the distance, and exp is the exponential function.

[0038] The initial model is obtained by measuring the dispersion spectrum curve u(x,f) from the following formula.

[0039] H=(u*4) / f

[0040] Where H is the formation depth, u is the surface wave velocity, and f is the frequency.

[0041] The velocity model is obtained after updating the initial model.

[0042] The formation velocity profile generating unit is used to generate the formation velocity profile according to the spatial position of the velocity model and the formation model.

[0043] Preferably, a further technical solution is that the velocity model unit updates the initial model by obtaining a simulated dispersion spectrum curve through a theoretical model or an empirical model, and then calculating an error factor E between the simulated dispersion spectrum curve and the measured dispersion spectrum curve u(x, f) through the following formula.

[0044]

[0045] In the formula, u sim is the simulated dispersion spectrum curve, and N is the number of frequency sampling points.

[0046] The initial model is updated based on the error factor, and the error factor is minimized by updating the initial model, thereby determining the velocity model.

[0047] A further technical solution is that the stratum model is a large physical model or an actual stratum model.

[0048] A further technical solution is: the formation velocity profile is calculated by the following formula.

[0049] v(x, y) = f(H(x, y))

[0050] Where v(x,y) is the formation velocity profile, H(x,y) is the formation depth, and f(H(x,y)) is the relationship between surface wave velocity and formation depth.

[0051] A further technical solution is that a plurality of single-component low-frequency detectors are distributed on the same straight line at a preset interval, and the exploration points are located in a large physical model or an actual formation model.

[0052] Compared with the prior art, one of the beneficial effects of the present invention is that through the application of micro-motion technology, the formation velocity profile of the large physical model is determined, thereby improving the accuracy of exploration and the application scope of the micro-motion technology, and by improving data screening and processing methods, and optimizing the dispersion spectrum calculation process, the limitations of the prior art are overcome, and high-quality, low-interference formation velocity profile exploration is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1The present invention is a flowchart for describing an embodiment of the present invention.

[0054] Figure 2 A schematic diagram for explaining filtered wave micromotion data in one embodiment of the present invention

[0055] Figure 3 It is a schematic diagram of correlation coefficient micro-motion data for illustrating one embodiment of the present invention.

[0056] Figure 4 It is a schematic diagram of fitting micro-motion data for explaining the correlation coefficient of each frequency point and the Bessel function in one embodiment of the present invention.

[0057] Figure 5 It is a schematic diagram of an inversion profile for illustrating an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Data acquisition is the basis of microseismic exploration, and observation system design is the key to ensure the acquisition of high-quality data. The present invention uses a two-dimensional observation system to collect data from artificial models of different formations. The observation system parameters include pulse width 0.5MHz, preamplifier 34dB, postamplifier 10dB, voltage 200V, number of shots 454, number of channels 368, minimum offset 16mm, shot distance 2mm, channel distance 1mm, maximum offset 383mm, sampling rate 0.2us, seismic wavelet main frequency of about 30Hz, sampling points 4096, water depth 150mm. Filtered wave microseismic data such as Figure 2 As shown in Figure 1, the acquisition system has the characteristics of fully automatic servo mechanical positioning control, high spatial accuracy, large acquisition area and high-speed acquisition. The collected experimental data are filtered and inverted using geogiga software. Figure 5 shown.

[0060] refer to Figure 1 As shown, one embodiment of the present invention is a formation velocity profile exploration method based on micro-vibration technology, which is performed according to the following steps:

[0061] S1. Arrange multiple single-component low-frequency geophones at preset intervals on a straight line to obtain single-component low-frequency micro-motion data of multiple exploration points.

[0062] S2. Screen and process the single-component low-frequency micro-motion data of multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point. The key to this step is to ensure that the screened micro-motion data is of high quality and can reflect the real geological information. During the screening process, judgment can be made based on characteristics such as amplitude and frequency.

[0063] S3. Perform cross-correlation processing on the first stable micro-motion data and the second stable micro-motion data to obtain correlation coefficients of other exploration points. Cross-correlation processing can be performed by complex conjugation and reverse translation to measure the similarity between the two micro-motion signals by calculating the cross-correlation coefficient. The calculation formula of the correlation coefficient is as follows:

[0064] Rfh(x)=∫[f(x)*h(x)]dx

[0065] Among them, f(x) represents the first stable micro-motion data, h(x) represents the second stable micro-motion data, * represents the convolution operation, Rfh(x) represents the correlation coefficient, and dx represents the integral variable.

[0066] S4. Determine the correlation curve corresponding to the first exploration point based on the correlation coefficient, such as Figure 3 As shown, in Figure 3 In the figure, the pink curve is the smoothed curve. By calculating the correlation coefficients between different exploration points and the first exploration point, a correlation curve can be constructed to measure the similarity between each exploration point and the first exploration point. The correlation curve can be constructed by fitting methods such as linear regression and polynomial regression. Figure 4 shown.

[0067] S5. Obtain active source data related to the first exploration point according to the correlation curve. The key to this step is to convert the micro-seismic data into equivalent active source data, thereby improving the accuracy and efficiency of exploration.

[0068] S6. Perform dispersion spectrum calculation on the active source data to obtain a measured dispersion spectrum curve. The dispersion spectrum calculation can be performed by performing Fourier transform, inverse Fourier transform and other operations on the active source data to obtain a dispersion spectrum curve. The formula for the measured dispersion spectrum curve is as follows:

[0069] u(x,f)=A(f)*exp(j*k*x)

[0070] Among them, u(x,f) represents the dispersion spectrum curve, A(f) represents the amplitude, k represents the wave number, j represents the imaginary unit, x represents the distance, and exp represents the exponential function.

[0071] S7. Determine the initial model based on the measured dispersion spectrum curve. The initial model can be determined by measuring the relationship between the dispersion spectrum curve and the formation depth and surface wave velocity. The formula of the initial model is as follows:

[0072] H=(u*4) / f

[0073] Among them, H represents the depth of the formation, u represents the surface wave velocity, and f represents the frequency.

[0074] S8. Obtaining a simulated dispersion spectrum curve. A simulated dispersion spectrum curve can be obtained through a theoretical model or an empirical model, so as to be used to verify and optimize the measured dispersion spectrum curve.

[0075] S9. The theoretical model is mainly based on the physical characteristics of the strata and the propagation law of sound waves in different strata. Through geological surveys of the target area, physical parameters such as density and elastic modulus of the relevant strata are obtained. These physical parameters determine the propagation speed v of sound waves in the strata, and its calculation follows the wave equation:

[0076]

[0077] Among them, u is the displacement of the wave field, is the Laplace operator, and v is the wave velocity.

[0078] Assuming that the formation is homogeneous and isotropic, a numerical model of the wave equation is constructed. Numerical simulation methods, such as the finite difference method (FDM) or the finite element method (FEM), are used to solve the wave equation to simulate the propagation of sound waves in the formation.

[0079] Through the simulated wave equation, the relationship between frequency f and wave number k(f) is derived:

[0080]

[0081] Here, v(H) is the wave velocity at the formation depth H, and the theoretical model provides an accurate estimate of this wave velocity.

[0082] S10. The empirical model is obtained by analyzing and regressing historical exploration data, and is suitable for estimating the propagation law of sound waves under complex geological conditions. Collect historical exploration data in the target area and similar geological conditions, focusing on the relationship between depth H and sound wave velocity u(H). Use statistical analysis methods to establish a regression model of depth and wave velocity, as shown below:

[0083] u(H)=a0+a1H+a2H 2 +…+a n H n

[0084] Among them, a0, a1, ..., a n are the coefficients obtained through regression analysis.

[0085] The fitted empirical model is verified by applying it to exploration data under known geological conditions to verify its accuracy. Based on the verification results, necessary adjustments and corrections are made to the model parameters to improve the accuracy of the prediction.

[0086] S11. Through the above theoretical model or empirical model, a simulated dispersion spectrum curve can be generated. The specific steps are as follows:

[0087] Using the wave velocity v(H) or u(H) in the model, calculate the wave number k(f) at each frequency:

[0088]

[0089] Perform Fourier transform on the formation, convert the time domain signal into frequency domain signal, and obtain the simulated dispersion spectrum curve u sim (f):

[0090]

[0091] S12. Determine the error factor between the simulated dispersion spectrum curve and the measured dispersion spectrum curve, update the initial model based on the error factor, and determine the velocity model related to the first exploration point. The calculation formula of the error factor is as follows:

[0092]

[0093] Where u(x,f) represents the measured dispersion spectrum curve, u sim Represents the simulated dispersion spectrum curve, and N represents the number of frequency sampling points. The velocity model is determined by continuously updating the initial model to minimize the error factor.

[0094] S13. Based on the spatial position of the velocity model and the large physical model, generate the formation velocity profile of the large physical model. The key to this step is to combine the velocity model with the actual formation structure to obtain the formation velocity profile. The calculation formula of the formation velocity profile is as follows:

[0095] v(x, y) = f(H(x, y))

[0096] Among them, v(x,y) represents the formation velocity profile, H(x,y) represents the formation depth, and f(H(x,y)) represents the relationship between surface wave velocity and formation depth.

[0097] Another embodiment of the present invention is a system for executing the above method, specifically the system includes an acquisition unit, a determination unit, a velocity model unit and a formation velocity profile generation unit, wherein:

[0098] The acquisition unit is used to obtain single-component low-frequency micro-motion data of multiple exploration points.

[0099] The determining unit is used to determine a correlation curve corresponding to a first exploration point; the first exploration point is any one of the multiple exploration points.

[0100] The correlation curve is obtained by first screening and processing the single-component low-frequency micro-motion data of multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point; then cross-correlating the first stable micro-motion data and the second stable micro-motion data through the following formula to obtain the correlation coefficient Rfh(x) of other exploration points.

[0101] Rfh(x)=∫[f(x)*h(x)]dx

[0102] Where f(x) is the first stable micro-motion data, h(x) is the second stable micro-motion data, * is the convolution operator, and dx is the integral variable.

[0103] The correlation curve is determined by the correlation coefficient Rfh(x).

[0104] The velocity model unit is used to obtain active source data related to the first exploration point according to the correlation curve; then the dispersion spectrum of the active source data is calculated by the following formula to obtain the measured dispersion spectrum curve u(x,f).

[0105] u(x,f)=A(f)*exp(j*k*x)

[0106] Where A(f) is the amplitude, k is the wave number, j is the imaginary unit, x is the distance, and exp is the exponential function.

[0107] The initial model is obtained by measuring the dispersion spectrum curve u(x,f) from the following formula.

[0108] H=(u*4) / f

[0109] Where H is the formation depth, u is the surface wave velocity, and f is the frequency.

[0110] The velocity model is obtained after updating the initial model.

[0111] The formation velocity profile generating unit is used to generate the formation velocity profile according to the spatial position of the velocity model and the formation model.

[0112] In this embodiment, the velocity model unit updates the initial model by obtaining a simulated dispersion spectrum curve through a theoretical model or an empirical model, and then calculating an error factor E between the simulated dispersion spectrum curve and the measured dispersion spectrum curve u(x, f) through the following formula.

[0113]

[0114] In the formula, u sim is the simulated dispersion spectrum curve, and N is the number of frequency sampling points.

[0115] The initial model is updated based on the error factor, and the error factor is minimized by updating the initial model, thereby determining the velocity model.

[0116] The above-mentioned formation model is a large physical model or an actual formation model.

[0117] The above formation velocity profile is calculated by the following formula:

[0118] v(x, y) = f(H(x, y))

[0119] Where v(x,y) is the formation velocity profile, H(x,y) is the formation depth, and f(H(x,y)) is the relationship between surface wave velocity and formation depth.

[0120] Based on the above-mentioned embodiments of the present invention, it can be known that the technical solution disclosed in the present invention is intended to use micro-motion technology to perform formation velocity profile exploration on a large physical model. The method arranges multiple single-component low-frequency detectors on a straight line, obtains micro-motion data, and performs screening and cross-correlation processing to obtain correlation coefficients, constructs correlation curves, and determines the velocity model. At the same time, the active source data is subjected to dispersion spectrum calculation, the initial model is determined, the simulated dispersion spectrum curve is obtained, and the model is updated to determine the velocity model. Based on the velocity model and the spatial position of the large physical model, a formation velocity profile is generated. The method achieves the purpose of determining the formation velocity profile of a large physical model using micro-motion technology, expands the application scope of micro-motion technology, and improves exploration accuracy.

[0121] In addition to the above, it should be noted that "one embodiment", "another embodiment", "embodiment", etc. mentioned in this specification refer to the specific features, structures or characteristics described in conjunction with the embodiment included in at least one embodiment generally described in this application. The same expression appearing in multiple places in the specification does not necessarily refer to the same embodiment. Further, when describing a specific feature, structure or characteristic in conjunction with any embodiment, it is claimed that the realization of such feature, structure or characteristic in conjunction with other embodiments also falls within the scope of the present invention.

[0122] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and improvements may be made to the components and / or layout of the subject combination layout. In addition to variations and improvements made to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A formation velocity profile exploration method based on micro-vibration technology, characterized in that The method comprises the following steps: Multiple single-component low-frequency geophones respectively acquire single-component low-frequency micro-motion data of multiple exploration points; Determine a correlation curve corresponding to a first exploration point based on single-component low-frequency micro-motion data of multiple exploration points; the first exploration point is any one of the multiple exploration points; The correlation curve is obtained by first screening the single-component low-frequency micro-motion data of multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point; then performing cross-correlation processing on the first stable micro-motion data and the second stable micro-motion data through the following formula to obtain the correlation coefficient Rfh(x) of other exploration points; Rhn(x)=∫[f(x)*h(x)]dx Where f(x) is the first stable micro-motion data, h(x) is the second stable micro-motion data, * is the convolution operator, and dx is the integral variable; Determine the correlation curve by the correlation coefficient Rfh(x); Active source data related to the first exploration point is obtained according to the correlation curve; then dispersion spectrum is calculated on the active source data by the following formula to obtain a measured dispersion spectrum curve u(x, f); u(x,f)=A(f)*exp(j*k*x) Where A(f) is the amplitude, k is the wave number, j is the imaginary unit, x is the distance, and exp is the exponential function; The initial model is obtained by measuring the dispersion spectrum curve u(x, f) from the following formula: H=(u*4) / f Where H is the formation depth, u is the surface wave velocity, and f is the frequency; After updating the initial model, a velocity model is obtained; A formation velocity profile is generated according to the spatial positions of the velocity model and the formation model.

2. The formation velocity profile exploration method based on micro-vibration technology according to claim 1 is characterized in that The initial model is updated in the following way: A simulated dispersion spectrum curve is obtained through a theoretical model or an empirical model, and then an error factor E between the simulated dispersion spectrum curve and the measured dispersion spectrum curve u(x, f) is calculated through the following formula; In the formula, u sim To simulate the dispersion spectrum curve, N is the number of frequency sampling points; The initial model is updated based on the error factor, and the error factor is minimized by updating the initial model, thereby determining the velocity model.

3. The formation velocity profile exploration method based on micro-vibration technology according to claim 1 is characterized in that: The formation model is a large physical model or an actual formation model.

4. The formation velocity profile exploration method based on micro-vibration technology according to claim 1 or 3, characterized in that The formation velocity profile is calculated by the following formula: v(x, y) = f(H(x, y)) Where v(x,y) is the formation velocity profile, H(x,y) is the formation depth, and f(H(x,y)) is the relationship between surface wave velocity and formation depth.

5. The formation velocity profile exploration method based on micro-vibration technology according to claim 1 is characterized in that: A plurality of single-component low-frequency geophones are distributed on the same straight line at a preset interval, and the exploration points are located in a large physical model or an actual formation model.

6. A formation velocity profile exploration system based on micro-vibration technology, characterized in that The system comprises: an acquisition unit for acquiring single-component low-frequency micro-motion data of a plurality of exploration points; A determination unit, configured to determine a correlation curve corresponding to a first exploration point; the first exploration point is any one of a plurality of exploration points; The correlation curve is obtained by first screening the single-component low-frequency micro-motion data of multiple exploration points to obtain the first stable micro-motion data and the second stable micro-motion data corresponding to the first exploration point; then performing cross-correlation processing on the first stable micro-motion data and the second stable micro-motion data through the following formula to obtain the correlation coefficient Rfh(x) of other exploration points; Rfh(x)=∫[f(x)*h(x)]dx Where f(x) is the first stable micro-motion data, h(x) is the second stable micro-motion data, * is the convolution operator, and dx is the integral variable; Determine a correlation curve by means of the correlation coefficient Rfh(x); The velocity model unit is used to obtain active source data related to the first exploration point according to the correlation curve; then the dispersion spectrum of the active source data is calculated by the following formula to obtain the measured dispersion spectrum curve u(x, f); u(x,f)=A(f)*exp(j*k*x) Where A(f) is the amplitude, k is the wave number, j is the imaginary unit, x is the distance, and exp is the exponential function; The initial model is obtained by measuring the dispersion spectrum curve u(x, f) from the following formula: H=(u*4) / f Where H is the formation depth, u is the surface wave velocity, and f is the frequency; After updating the initial model, a velocity model is obtained; The formation velocity profile generating unit is used to generate the formation velocity profile according to the spatial position of the velocity model and the formation model.

7. The formation velocity profile exploration system based on micro-vibration technology according to claim 6 is characterized in that: The velocity model unit updates the initial model in the following way: A simulated dispersion spectrum curve is obtained through a theoretical model or an empirical model, and then an error factor E between the simulated dispersion spectrum curve and the measured dispersion spectrum curve u(x, f) is calculated through the following formula; In the formula, u sim To simulate the dispersion spectrum curve, N is the number of frequency sampling points; The initial model is updated based on the error factor, and the error factor is minimized by updating the initial model, thereby determining the velocity model.

8. The formation velocity profile exploration system based on micro-vibration technology according to claim 6 is characterized in that: The formation model is a large physical model or an actual formation model.

9. The formation velocity profile exploration system based on micro-vibration technology according to claim 6 or 8, characterized in that The formation velocity profile is calculated by the following formula: v(x, y) = f(H(x, y)) Where v(x,y) is the formation velocity profile, H(x,y) is the formation depth, and f(H(x,y)) is the relationship between surface wave velocity and formation depth.

10. The formation velocity profile exploration system based on micro-vibration technology according to claim 6, characterized in that: A plurality of single-component low-frequency geophones are distributed on the same straight line at a preset interval, and the exploration points are located in a large physical model or an actual formation model.

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