Imaging method based on near-surface fine modeling

Abstract

The application provides an imaging method based on near-surface fine modeling, which comprises the following steps: step 1, extracting dispersion information of surface wave information in passive data through a phase shift method; step 2, obtaining shear wave velocity and stratum thickness information according to the dispersion information; step 3, calculating W-D curves and corresponding W / D-D curves; step 4, combining W-D information and W / D-D information to construct a target function; step 5, calculating a minimum value of the target function to determine longitudinal wave information; step 6, correcting a migration velocity field; and step 7, performing depth domain migration to obtain a migration profile more consistent with an underground structure. The imaging method based on near-surface fine modeling is used for improving the imaging effect in the depth domain, and the passive data in a short time range is utilized, so that the imaging effect is finally improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oilfield development, and particularly relates to an imaging method based on near-surface fine modeling. BACKGROUND

[0002] At present, seismic exploration of oil and gas fields gradually develops towards complex subsurface structure and reservoir region, which also puts forward higher requirements for the precision of migration images. The accuracy of migration velocity directly determines the quality of migration images, and efficient and accurate migration velocity is a key factor for improving the quality of migration imaging. Pre-stack depth migration is considered as an important revolution in seismic technology and is the most effective means for complex structure imaging. As to post-stack migration, most post-stack migrations are based on the idea of explosion reflection surface, which needs to be stacked first and then migrated. The horizontal stacking is restricted by the assumption of horizontal layered medium. When the stratum is inclined, the center profile obtained by horizontal stacking is no longer the zero offset profile, and with the increase of the inclination angle, the error between them will be larger, which makes it difficult to obtain good results for the migration of post-stack data of inclined stratum. Pre-stack migration can first return the wave field to the position in space, and then stack the wave field at the same position, which overcomes the influence of non-coherent stacking on subsequent migration. As to time migration, the migration velocity of time migration is based on the assumption of uniform medium or horizontal layered medium. When the velocity has a large lateral variation or the stratum has a large inclination angle, time migration does not satisfy the Shell law, and thus the imaging error of reflected waves is large.

[0003] The migration velocity field in actual production is usually obtained by velocity analysis, picking velocity and difference. The velocity field can be regarded as a highly smoothed actual underground velocity, which is reflected to the near-surface velocity and is usually much larger than the actual near-surface underground velocity. When the shallow velocity model is not accurate, the error caused by the model to the seismic wave field will be transmitted to the deep layer, and finally the imaging quality will be reduced. Surface wave is usually considered as an interference, but in recent years, people have gradually realized that this "passive data" also carries rich near-surface geological information.

[0004] Seismic nodes can perform full-time and full-space data acquisition. In addition to the commonly used reflection wave information, there is a large amount of passive data in the acquired data. How to change these signals into valuable resources and improve the quality of seismic data migration imaging has become an important research direction.

[0005] The passive data imaging method reported in the literature is mainly combined with Marchenko imaging (Jing Zhongyuan et al., 2017; Lomas and Curtis, 2019). Passive observation data has rich low-frequency information, which is the basis for microseismic exploration. Based on the low-frequency reconstruction of passive data, the reconstructed wideband seismic data is used as the input of the Marchenko method to reconstruct the Green function, which is the mainstream idea in the literature. Generally, due to the transmission of noise signals from deep underground to the surface, the high-frequency components are absorbed by the stratum due to the filtering effect of the earth, resulting in a large amount of low-frequency components in the received signals from the surface. These components are considered to be useful signals because they are truly transmitted from the underground medium, and they have an advantage over active sources at low frequencies. Figure 1 The flow of combined Marchenko imaging of active and passive sources is given. First, match the energy (amplitude) of passive and active sources, assuming that the intersection point of the frequency spectrum of active and passive sources is P; use a low-pass filter to filter out the low-frequency information of active and passive sources before the frequency spectrum intersection point P, and obtain the low-frequency information of active and passive sources; match filter the active source low-frequency information to the passive source low-frequency signal, and the result is the low-frequency data after the low-frequency reconstruction of the active source; Finally, normalize and sum the active source low-frequency data and the high-frequency data of the active source, and recover the energy (amplitude) based on the maximum amplitude of the original active source data to obtain the required wideband seismic data.

[0006] Because of the selective effect of match filtering on signals, too much useless information will not be introduced in the expected output of the active and passive source low-frequency match filtering. This low-frequency reconstruction method of active source is called low-frequency reconstruction based on frequency advantage. The advantages of this method are: 1. The middle frequency part of the active source data is usually completely preserved, and the missing frequency band data can be based on the passive source data, which is reliable; 2. For areas with multiple wave development, Marchenko method can also be used to eliminate the influence of multiple waves on imaging.

[0007] The original intention of the above technical design is to use the low-frequency information of passive data to improve the imaging effect, but the frequency band of the detector used in microseismic exploration is different from that of the oil exploration detector, and the recording length is not in the same order of magnitude. Practical data analysis shows that the main component of passive data collected by oil exploration is surface wave, which cannot provide the expected low-frequency information. Only the recorded 2Hz surface wave information can be used to establish the velocity field of about 250m depth.

[0008] In the Chinese patent application with the application number: CN201510109148.3, a near-surface velocity modeling method and a modeling device are involved. The method comprises: collecting micro-logging data of a work area and interpreting the collected results to obtain micro-logging lithology layering data and velocity interpretation results; using a non-seismic exploration method to investigate the surface structure of the work area to obtain a surface structure interpretation profile; determining the mathematical relationship between the velocity of each stratum or each lithology layer and the non-seismic geophysical attribute according to the surface structure interpretation profile, the micro-logging lithology layering data and the velocity interpretation results; determining the near-surface velocity of each measuring point in the non-seismic exploration method according to the mathematical relationship between the velocity of each stratum or each lithology layer and the non-seismic geophysical attribute; and interpolating the near-surface velocity of each measuring point in the non-seismic exploration method to obtain the near-surface velocity data of each seismic shot point and geophone. The application improves the accuracy of the near-surface velocity model and is beneficial to improving the static correction effect of seismic data processing.

[0009] In the Chinese patent application with the application number: CN202110584553.6, an approximate true near-surface velocity modeling method and system are involved. The method uses the near-surface velocity model obtained by first arrival inversion as the initial velocity model, and determines the high-speed top interface based on it, and then calculates the corresponding surface elevation smoothing surface as the migration starting surface for velocity modeling and migration imaging. The near-surface velocity model is interpolated within the range determined by the high-speed top interface and the surface elevation smoothing surface to ensure that there is no velocity blank area in the calculation range. Based on this, the relevant velocity within the range determined after interpolation optimization is smoothed to obtain the approximate true near-surface velocity model in the depth domain velocity modeling. The modeling method can retain the background velocity while eliminating the imaging error problems caused by elevation mutations and local velocity anomalies, and can be effectively applied to various surface conditions, including undulating surface conditions and conditions with thick low velocity zones, and can effectively improve the imaging accuracy of subsequent pre-stack depth migration under various surface conditions.

[0010] In the Chinese patent application with the application number: CN201710905940.9, a near-surface velocity modeling method and device are involved. The method comprises: obtaining the near-surface inversion velocity model of the target near-surface and the static correction amount of the shot point and the geophone; determining the surface elevation smoothing surface of the target near-surface, and determining the surface elevation smoothing surface as the migration reference surface; determining the required travel time of the shot point and the geophone from the high-speed top interface to the surface elevation smoothing surface; updating the static correction amount according to the travel time; correcting the CMP gather data according to the updated static correction amount to obtain pre-migration CMP gather data; correcting the pre-migration CMP gather data to the migration reference surface; and obtaining a shallow surface layer velocity model according to the near-surface inversion velocity model and the pre-migration CMP gather data corrected to the migration reference surface. The application embodiment can improve the modeling accuracy and the pre-stack depth migration imaging accuracy.

[0011] In the Chinese patent application with the application number: CN201810940741.6, a multi-scale near-surface tomography velocity modeling method and a modeling system are involved, which comprises: step 1: inputting first arrival wave travel time and an initial velocity model; step 2: calculating theoretical travel time and ray path based on the initial velocity model; step 3: obtaining a velocity modification amount, and modifying the initial velocity model to obtain a current velocity model according to the velocity modification amount; step 4: comparing the velocity modification amount with a preset velocity modification amount threshold; step 5: if the velocity modification amount is greater than or equal to the preset velocity modification amount threshold, then comparing the maximum offset with an offset threshold; step 6: if the maximum offset is greater than or equal to the offset threshold, then taking the current velocity model as a final velocity model. The multi-scale near-surface tomography velocity modeling method of the application improves the inversion precision by inputting the first arrival wave travel time and the initial velocity model and modifying the initial velocity model according to the velocity modification amount, and can establish a high-precision near-surface velocity model.

[0012] The above prior art is quite different from the present application, and cannot solve the technical problems we want to solve. Therefore, we have invented a new imaging method based on near-surface fine modeling. SUMMARY

[0013] The purpose of the present application is to provide an imaging method based on near-surface fine modeling for improving the imaging effect in the depth domain, utilizing passive data in a short time range, and ultimately improving the imaging effect.

[0014] The purpose of the present application can be achieved by the following technical measures: an imaging method based on near-surface fine modeling, which comprises:

[0015] Step 1: extracting dispersion information of surface wave information in passive data by phase shift method;

[0016] Step 2: obtaining shear wave velocity and stratum thickness information according to the dispersion information;

[0017] Step 3: calculating W-D curve and corresponding W / D-D curve;

[0018] Step 4: combining W-D information and W / D-D information to construct a target function;

[0019] Step 5: calculating the minimum value of the target function to determine the P-wave information;

[0020] Step 6: correcting the migration velocity field;

[0021] Step 7: performing depth domain migration to obtain a migration profile more consistent with the underground structure.

[0022] The purpose of the present application can also be achieved by the following technical measures:

[0023] In step 1, passive data is mainly surface wave and strong interference, surface wave has obvious dispersion property, strong interference has not, and long time passive data recording makes dispersion extraction more accurate, low frequency and high frequency more balanced, wider frequency band, more helpful for reliable velocity structure inversion.

[0024] In step 2, seek a person for the set theory near-surface S-wave velocity profile, the profile of S-wave velocity, layer thickness information is calculated by forward calculation of dispersion data and actual dispersion data extracted from field receiving to achieve the best fitting, which is the S-wave velocity and thickness information.

[0025] In step 3, according to the sensitivity of Poisson's ratio to the relationship between wavelength W-depth D, calculate the W-D curve corresponding to the dispersion curve, and calculate the corresponding W / D-D curve.

[0026] In step 3, P-wave velocity has little effect on the shape of dispersion curve, so it is impossible to invert the dispersion curve to get the P-wave velocity structure; according to the analysis, the relationship between surface wave wavelength W and detection depth D and P-wave velocity has a relatively stable correlation.

[0027] In step 3, according to the obtained S-wave velocity profile, first calculate the average S-wave velocity Vsz at a certain depth:

[0028] V sz =∑ n h i / ∑ n (h i / V i ),

[0029] Where n is the depth sequence, h i and V i are the thickness and S-wave velocity corresponding to the depth number; through the relationship between wavelength and phase velocity to display the fundamental dispersion curve, find the depth and wavelength corresponding to the same velocity of S-wave average velocity curve and dispersion curve, that is, the W-D relationship curve of the model.

[0030] In step 3, for any point on the W-D relationship curve, calculate the ratio of the abscissa W to the ordinate D to form a new W / D-D curve.

[0031] In step 4, combine W-D information and W / D-D information to construct the objective function:

[0032]

[0033] Where N is the number of depth points, Wr i obs and Wr ical where W represents the ratio of wavelength to depth at the observed and calculated i-th depth point, respectively; max(W i obs ) and max(Wr i obs These are the observed wavelength value and the maximum value of the wavelength-to-depth ratio, respectively; normalization was performed when calculating the objective function to address problems of different orders of magnitude.

[0034] In step 5, the P-wave velocity profile that minimizes the objective function is obtained through the Monte Carlo full-space search algorithm in a nonlinear inversion manner, which is the desired P-wave information.

[0035] In step 6, the inverted near-surface P-wave and S-wave velocities are used to correct the migration velocity field. The accuracy of the migration velocity directly determines the quality of the migration image. Efficient and accurate migration velocity is a key factor in improving the quality of migration imaging.

[0036] In step 7, the RTM migration method is used to apply the obtained migration velocity field to the depth domain migration to obtain a migration profile that better fits the subsurface structure.

[0037] In step 7, the single-shot seismic record data is first migrated, and then the imaging results of each shot are superimposed to obtain the final imaging profile; the expression for the zero-delay cross-correlation imaging condition is: I(x,z)=∫u s (x,z,t)u r (x,z,t)dt, where u s It is the source wave field, u r It is the detector wave field.

[0038] The imaging method based on near-surface fine modeling in the application is based on the inversion of near-surface longitudinal and transverse wave velocities from surface wave information, uses the inverted near-surface velocities to correct the migration velocity field, and uses the corrected migration velocity field for depth migration to finally improve the imaging effect. Surface waves are usually considered as interference, but in recent years, people have gradually realized that this "passive data" also carries rich near-surface geological information. The application is based on surface wave information to first obtain dispersion information by phase shift method, and extract transverse wave velocity and formation thickness, then combine W-D information and W / D-D information to construct an objective function and minimize the objective function to obtain longitudinal wave velocity; in seismic exploration, obtaining an accurate near-surface velocity model is of great significance to improve the imaging precision in complex areas. The application turns to use passive data in a short time to invert near-surface longitudinal and transverse wave velocities, and then uses the velocities to correct the migration velocity field, and finally obtains imaging results that can better depict the underground formation structure information. The imaging method based on near-surface fine modeling turns to use the dispersion property of passive data to invert the velocity structure of the near-surface, and uses the velocity structure to improve the imaging effect in the depth domain, uses passive data in a short time range, and finally improves the imaging effect. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 Flowchart for joint Marchenko imaging of active and passive sources

[0040] Figure 2 Flowchart for a specific embodiment of the imaging method based on near-surface fine modeling of the application

[0041] Figure 3 Schematic diagram for comparison before and after simple model velocity field correction in a specific embodiment of the application

[0042] Figure 4 Schematic diagram of depth migration results before and after near-surface velocity correction of the migration velocity field in a specific embodiment of the application

[0043] Figure 5 Schematic diagram of depth migration results before and after near-surface velocity correction of the migration velocity field in a specific embodiment of the application

[0044] Figure 6 Schematic diagram for comparison before and after actual underground velocity field correction in a specific embodiment of the application

[0045] Figure 7 Schematic diagram of depth migration results before and after near-surface velocity correction of the migration velocity field in a specific embodiment of the application

[0046] Figure 8 Schematic diagram of depth migration results before and after near-surface velocity correction of the migration velocity field in a specific embodiment of the application Detailed Implementation

[0047] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, 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 invention pertains.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0049] like Figure 2 As shown, Figure 2 This is a flowchart of the imaging method based on near-surface fine modeling of the present invention. The imaging method based on near-surface fine modeling includes:

[0050] (1) First, the dispersion information of the surface wave information in the passive data is extracted by the phase shift method. The passive data mainly consists of surface waves and strong interference. Surface waves have obvious dispersion properties, while strong interference does not. Moreover, the recording of passive data for a long time makes the dispersion extraction more accurate, the low frequency and high frequency are more balanced, the bandwidth is wider, and it is more conducive to reliable velocity structure inversion.

[0051] (2) Obtain shear wave velocity and formation thickness information by inversion based on dispersion information. Seek a theoretical near-surface shear wave velocity profile that is artificially set. The dispersion data obtained by forward modeling the shear wave velocity, formation thickness and other information of this profile is best matched with the actual dispersion data extracted from the field. This profile is the shear wave velocity and thickness information.

[0052] (3) Based on the sensitivity of Poisson's ratio to the wavelength (W)-depth (D) relationship, calculate the WD curve corresponding to the dispersion curve, and at the same time calculate the corresponding W / DD curve.

[0053] (4) Combine WD information with W / DD information to construct the objective function: Where N is the number of depth points, Wr i obs With Wr i cal λ and δ are the ratios of wavelength to depth corresponding to the observed and calculated i-th depth points, respectively. max(W) i obs ) and max(Wr i obs) are the observed wavelength value and the maximum value of the wavelength-depth ratio, respectively. Normalization is performed when calculating the objective function for the problem of different orders of magnitude;

[0054] (5) The longitudinal wave velocity profile corresponding to the minimum value of the objective function is obtained by the Monte Carlo full-space search algorithm in a nonlinear inversion manner, and is the sought longitudinal wave information;

[0055] (6) The near-surface longitudinal and transverse wave velocities obtained by inversion are used to correct the migration velocity field. The accuracy of the migration velocity directly determines the quality of the migration image, and efficient and accurate migration velocity is a key factor for improving the quality of migration imaging;

[0056] (7) The obtained migration velocity field is used for depth domain migration to obtain a migration profile that is more consistent with the underground structure. RTM is one of the most commonly used migration methods, which first migrates single-shot seismic record data, then stacks the imaging results of each shot to obtain the final imaging profile.

[0057] The following are several specific embodiments of the application

[0058] Embodiment 1

[0059] In the specific embodiment 1 of the application, Figure 2 is a flowchart of the imaging method based on near-surface fine modeling of the application. The imaging method based on near-surface fine modeling includes the following steps:

[0060] In step 101, the frequency dispersion information of the surface wave information in the passive data is extracted by the phase shift method. The passive data mainly includes surface waves and strong interference. The surface wave has obvious frequency dispersion property, and the strong interference does not. Long-time passive data recording makes the frequency dispersion extraction more accurate, the low and high frequencies more balanced, and the frequency band wider, which is more helpful to the reliability of the velocity structure inversion. The flow enters step 102.

[0061] In step 102, the transverse wave velocity and the stratum thickness information are obtained by inversion according to the frequency dispersion information. The theoretical near-surface transverse wave velocity profile set by a person is sought. The transverse wave velocity and the layer thickness information of the profile are calculated by forward calculation to obtain the frequency dispersion data, which are fitted with the actual frequency dispersion data received and extracted in the field to achieve the best fitting. The profile is the transverse wave velocity and thickness information. The flow enters step 103.

[0062] In step 103, the W-D curve corresponding to the dispersion curve is calculated according to the sensitivity of the Poisson ratio to the wavelength (W)-depth (D) relationship, and the corresponding W / D-D curve is calculated. The P-wave velocity has little effect on the shape of the dispersion curve, so the P-wave velocity structure cannot be obtained by inverting the dispersion curve. According to the analysis, there is a relatively stable correlation between the relationship between the surface wave wavelength (W) and the detection depth (D) and the P-wave velocity. According to the obtained S-wave velocity profile, the S-wave average velocity (Vsz) at a certain depth is first calculated: V sz =∑ n h i / ∑ n (h i / V i ), where n is the depth sequence, h i and V i are the thickness and S-wave velocity corresponding to the depth number. The fundamental dispersion curve is displayed by the relationship between the wavelength and the phase velocity, and the depth and wavelength corresponding to the same velocity in the S-wave average velocity curve and the dispersion curve are found, that is, the W-D relationship curve of the model is obtained. For any point on the curve, the ratio of the ordinate W to the abscissa D is calculated to form a new W / D-D curve. The process goes to step 104.

[0063] In step 104, the W-D information and the W / D-D information are combined to construct the objective function: where N is the number of depth points, Wr i obs and Wr i cal are the wavelength and depth ratios corresponding to the ith depth point observed and calculated, respectively. max(W i obs ) and max(Wr i obs ) are the maximum values of the observed wavelength and the wavelength and depth ratio, respectively. In order to solve the problem of different orders of magnitude, normalization is performed when calculating the objective function. The process goes to step 105.

[0064] In step 105, the P-wave velocity profile corresponding to the minimum value of the objective function is obtained by the Monte Carlo full-space search algorithm in a nonlinear inversion manner, that is, the P-wave information is obtained. The process goes to step 106.

[0065] In step 106, the inverted near-surface P-wave and S-wave velocities are used to correct the migration velocity field. The accuracy of the migration velocity directly determines the quality of the migration image, and efficient and accurate migration velocity is a key factor to improve the quality of migration imaging. The process goes to step 107.

[0066] In step 107, the obtained migration velocity field is used for depth domain migration to obtain a migration profile more consistent with the subsurface structure. RTM is one of the most commonly used migration methods at present, which first migrates single-shot seismic record data, and then stacks the imaging results of each shot to obtain the final imaging profile. The expression of the zero-lag cross-correlation imaging condition is: I(x,z) = ∫u s (x,z,t)u r (x,z,t)dt, wherein u s is the source wave field, and u r is the receiver wave field.

[0067] Through the above process, the passive data in a short time is utilized to invert the near-surface P-wave and S-wave velocities, which are then used to correct the migration velocity field, and finally the imaging result can better depict the subsurface structure information.

[0068] Embodiment 2

[0069] In the specific embodiment 2 of the application, Figure 3 is a simple model for testing the influence of the corrected migration velocity field on the migration result. From top to bottom are the true velocity model, the migration velocity field obtained after smoothing the model, and the corrected migration velocity field. The migration velocity field based on the surface wave inversion in the actual data is simulated by directly modifying the near-surface velocity of the smoothed velocity model. It can be seen that the corrected migration velocity field has a clear low-velocity zone at the surface, while the uncorrected velocity field has a higher velocity at the surface.

[0070] Figure 4 is a schematic diagram of the depth migration results before and after the correction of the near-surface velocity of the migration velocity field in the specific embodiment 2 of the application; by comparing the same phase axis, it can be seen that the continuity of the corrected phase axis is better, and the structural pattern is clearer.

[0071] Figure 5 is a schematic diagram of the depth migration results before and after the correction of the near-surface velocity of the migration velocity field in the specific embodiment 2 of the application; by comparing the 250th record, it can be seen that at a depth of 1500m, the waveform of the corrected migration velocity field and the true model have a good correspondence, the phase is the same, and the amplitude is almost the same, while the waveform of the simple smoothing model has a large error.

[0072] Embodiment 3

[0073] In the specific embodiment 3 of the application, Figure 6 is a schematic diagram of the comparison of the actual subsurface velocity field before and after correction;

[0074] Figure 7The figure is the depth migration result before and after correcting the near-surface velocity of the migration velocity field in the specific embodiment 3 of the present application; by comparing the same phase axis in the dashed box in the figure, it can be seen that the continuity of the same phase axis after correction is better, the structural form is clearer, small structures are more obvious, and the underground stratum structure can be further depicted.

[0075] Figure 8 The figure is the depth migration result before and after correcting the near-surface velocity of the migration velocity field in the specific embodiment 3 of the present application; the waveforms of the 901th trace and the 1101th trace and the depth wave spectrum, and the energy distribution of the corrected depth wave spectrum is balanced, and the migration imaging effect is further improved.

[0076] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for limiting the present application, although the present application is described in detail with reference to the foregoing embodiments, for those skilled in the art, it still can modify the technical scheme recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0077] In addition to the technical features described in the specification, they are known to those skilled in the art.

Claims

1. An imaging method based on near-surface fine modeling, characterized in that, The imaging method based on near-surface fine modeling comprises: Step 1, extracting dispersion information of the surface wave information in the passive data by the phase shift method; Step 2, obtaining the shear wave velocity and stratum thickness information according to the dispersion information inversion; Step 3, calculating the W-D curve and the corresponding W / D-D curve; Step 4, combining the W-D information and the W / D-D information to construct a target function; Step 5, calculating the minimum value of the target function to determine the P-wave information; Step 6, correcting the migration velocity field; Step 7, performing depth domain migration to obtain a migration profile more consistent with the underground structure; In step 4, the W-D information and the W / D-D information are combined to construct a target function: where N is the number of depth points, Wr i obs and Wr i cal are the observed and calculated wavelength-to-depth ratios for the ith depth point, respectively; max(W i obs ) and max(Wr i obs ) are the maximum values of the observed wavelength values and wavelength-to-depth ratios, respectively; normalization is performed when calculating the objective function to address the problem of different orders of magnitude.

2. The imaging method based on near-surface fine modeling according to claim 1, characterized in that, In step 1, the passive data is surface wave and strong interference, the surface wave has obvious dispersion property, the strong interference does not have, and the long-time passive data recording makes the dispersion extraction more accurate, the low frequency and the high frequency are more balanced, the frequency band is wider, and it is more helpful for reliable velocity structure inversion.

3. The imaging method based on near-surface fine modeling of claim 1, wherein, In step 2, a certain theoretical near-surface shear wave velocity profile is set, the shear wave velocity and layer thickness information of the profile are calculated by forward calculation to obtain dispersion data, and the actual dispersion data extracted in the field are fitted to be best, and the profile is the shear wave velocity and thickness information.

4. The imaging method based on near-surface fine modeling of claim 1, wherein, In step 3, according to the sensitivity of Poisson's ratio to the wavelength W-depth D relationship, the W-D curve corresponding to the dispersion curve is calculated, and the corresponding W / D-D curve is also calculated.

5. The imaging method based on near-surface fine modeling according to claim 4, characterized in that, In step 3, the P-wave velocity has little effect on the shape of the dispersion curve, so it is impossible to obtain the P-wave velocity structure by inversion of the dispersion curve; according to the analysis, there is a relatively stable correlation between the wavelength W and the detection depth D of the surface wave and the P-wave velocity.

6. The imaging method based on near-surface fine modeling according to claim 5, characterized in that, In step 3, according to the obtained shear wave velocity profile, the average shear wave velocity Vsz at a certain depth is calculated first: V sz =∑ n h i / ∑ n (h i / V i ), where n is the depth sequence, h i With V i The thickness and shear wave velocity corresponding to the depth sequence; by the relationship between wavelength and phase velocity to show the base dispersion curve, looking for the same time corresponding to the depth and wavelength of the average shear wave velocity curve and dispersion curve, that is, the W-D relationship curve.

7. The imaging method based on near-surface fine modeling according to claim 6, characterized in that, In step 3, for any point on the W-D relationship curve, the ratio between the abscissa W and the ordinate D is calculated to form a new W / D-D curve.

8. The near-surface fine modeling based imaging method of claim 1, wherein, In step 5, by using the Monte Carlo full-space search algorithm, the P-wave velocity profile corresponding to the minimum value of the target function is obtained by nonlinear inversion, which is the P-wave information.

9. The near-surface fine modeling based imaging method of claim 1, wherein, In step 6, the near-surface P-wave and shear wave velocities obtained by inversion are used to correct the migration velocity field; the accuracy of the migration velocity directly determines the quality of the migration image, and efficient and accurate migration velocity is a key factor to improve the quality of migration imaging.

10. The near-surface fine modeling based imaging method of claim 1, wherein, In step 7, the RTM migration method is used, and the obtained migration velocity field is used for depth domain migration to obtain a migration profile more consistent with the underground structure.

11. The imaging method based on near-surface fine modeling according to claim 10, characterized in that, In step 7, first, the single-shot seismic record data is migrated, and then the imaging results of each shot are stacked to obtain the final imaging profile; the expression of the zero-delay cross-correlation imaging condition is: I(x,z) = ∫u s (x,z,t)u r (x,z,t)dt, wherein u s is a wave field of a seismic source, and u r is a wave field of a receiver.

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