Imaging method based on near-surface fine modeling

Abstract

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

Description

Technical Field

[0001] The invention relates to the technical field of oilfield development, and in particular to an imaging method based on near-surface fine modeling. Background Art

[0002] At present, seismic exploration of oil and gas fields is gradually developing towards complex underground structures and reservoir areas, which also puts forward higher requirements on the accuracy of migration images. The accuracy of the migration speed directly determines the quality of the migration image. Efficient and accurate migration speed is the key factor to improve the quality of migration imaging. Prestack depth migration is considered to be an important revolution in seismic technology and the most effective means of imaging complex structures. As for post-stack migration, since most post-stack migrations are based on the idea of ​​explosive reflection surfaces, they need to be stacked first and then migrated. The horizontal stacking is restricted by the assumption of horizontal layered media. When the stratum is inclined, the central section obtained by horizontal stacking is no longer a zero-offset section. Moreover, as the inclination angle increases, the error between the two will increase, which makes it difficult to achieve good results for the migration of post-stack data of inclined strata. Pre-stack migration can first return the wave field to the position where it is generated in space, and then stack the wave field at the same position, thus overcoming the influence of non-co-phase stacking on subsequent migration. As far as time migration is concerned, since the migration velocity of time migration is based on the assumption of uniform medium or horizontal layered medium, when there is a sharp change in velocity laterally or the formation dip is large, the time migration does not satisfy the Shell law, and the reflection wave imaging error is large.

[0003] The migration velocity field in actual production is usually obtained through velocity analysis, velocity picking and difference. This velocity field can be regarded as a highly smoothed version of the actual underground velocity, which is usually much larger than the actual underground near-surface velocity when reflected in the near-surface velocity. When the shallow velocity model is inaccurate, the error caused by the seismic wave field will also be transmitted to the deep layer, eventually causing the image quality to deteriorate. Surface waves are usually considered to be a kind of interference, but in recent years, people have gradually realized that this "passive data" also carries rich near-surface geological information.

[0004] Seismic nodes can collect data in all time periods and spaces. In addition to the commonly used reflection wave information, the collected data also contains a large amount of passive data. How to "turn waste into treasure" and improve the quality of seismic data migration imaging has become an important research direction.

[0005] The passive data imaging research method reported in the literature is mainly combined with Marchenko imaging (Jin Zhongyuan et al., 2017; Lomas and Curtis, 2019). Passive observation data has rich low-frequency information, which is also the basis of current micro-seismic detection. The mainstream idea in the current literature is to reconstruct the low-frequency of active source data based on the low-frequency components of passive data, and then use the reconstructed broadband seismic data as the input of the Marchenko method for Green function reconstruction. Usually, since the noise signal is transmitted from deep underground to the surface, its high-frequency components are absorbed by the strata due to the filtering effect of the earth, resulting in a large number of low-frequency components in the signal received from the surface. Since these components are truly propagated from the underground medium, these low-frequency information are considered to be useful signals, which have advantages over active sources at the low-frequency end. Figure 1 The process of joint Marchenko imaging of active and passive sources is given. First, the energy (amplitude) of the passive source and the active source are matched, assuming that the intersection point of the active source and the passive source on the spectrum is P; a low-pass filter is used to filter out the low-frequency information of the active source and the passive source before the spectrum intersection point P, and the low-frequency information of both the active source and the passive source is obtained; the low-frequency information of the active source is matched with the low-frequency signal of the passive source to obtain the low-frequency data after the low-frequency reconstruction of the active source; finally, the low-frequency data of the active source and the high-frequency data of the active source are normalized and summed, and finally the energy (amplitude) is restored based on the maximum amplitude of the original active source data to obtain the required broadband seismic data.

[0006] Because matched filtering can selectively pass signals, too much useless information will not be introduced into the expected output after low-frequency matched filtering of active and passive sources. This method of low-frequency reconstruction of active sources is called low-frequency reconstruction based on frequency advantage. The advantages of this method are: 1. Usually the intermediate frequency part of active source data is completely preserved, and the data of missing frequency bands can be based on passive source data, which is reliable; 2. For areas with multiple wave development, the 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. However, the frequency bands of the detectors used in micro-motion detection and oil exploration detectors are different, and the recording length is not on the same order of magnitude. Actual data analysis shows that the passive data collected by oil exploration is mainly composed of surface waves, which cannot provide the expected low-frequency information. Only the information mainly recorded about 2Hz surface waves can be used to establish the velocity field at a depth of about 250m.

[0008] In the Chinese patent application with application number: CN201510109148.3, a near-surface velocity modeling method and modeling device are involved, which includes: collecting micro-logging data in the work area and interpreting the collected results to obtain micro-logging lithology layering data and velocity interpretation results; using non-seismic exploration methods to conduct surface structure surveys in the work area to obtain surface structure interpretation profiles; determining the mathematical relationship between the velocity of each formation or each lithology layer and the non-seismic geophysical attributes based on the surface structure interpretation profiles, micro-logging lithology layering data and velocity interpretation results; determining the near-surface velocity of each measuring point in the non-seismic exploration method based on the mathematical relationship between the velocity of each formation or each lithology layer and the non-seismic geophysical attributes; interpolating the near-surface velocity of each measuring point in the non-seismic exploration method to obtain near-surface velocity data of each seismic shot point and receiver point. The invention improves the accuracy of the near-surface velocity model and is conducive to improving the static correction effect of seismic data processing.

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

[0010] In the Chinese patent application with application number: CN201710905940.9, a near-surface velocity modeling method and device are involved, which includes: obtaining a near-surface inversion velocity model of the target near-surface and static correction values ​​of shot points and receiver points; determining the surface elevation smooth surface of the target near-surface, and determining the surface elevation smooth surface as the migration reference surface; determining the travel time required for shot points and receiver points from the high-speed top interface to the surface elevation smooth surface; updating the static correction value according to the travel time; correcting the CMP gather data according to the updated static correction value to obtain the pre-migration CMP gather data; correcting the pre-migration CMP gather data to the migration reference surface; obtaining a shallow surface velocity model according to the near-surface inversion velocity model and the pre-migration CMP gather data corrected to the migration reference surface. This application embodiment can improve the modeling accuracy and pre-stack depth migration imaging accuracy.

[0011] In the Chinese patent application with application number: CN201810940741.6, a multi-scale near-surface tomographic velocity modeling method and modeling system are involved, including: step 1: input the first arrival wave travel time and the initial velocity model; step 2: calculate the theoretical travel time and ray path based on the initial velocity model; step 3: obtain the velocity modification, and modify the initial velocity model according to the velocity modification to obtain the current velocity model; step 4: compare the velocity modification with the preset velocity modification threshold; step 5: if the velocity modification is greater than or equal to the preset velocity modification threshold, compare the maximum shot offset with the shot offset threshold; step 6: if the maximum shot offset is greater than or equal to the shot offset threshold, use the current velocity model as the final velocity model. The multi-scale near-surface tomographic velocity modeling method of the invention improves the inversion accuracy by inputting the first arrival wave travel time and the initial velocity model, and modifying the initial velocity model according to the velocity modification, and can establish a high-precision near-surface velocity model.

[0012] The above existing technologies are all significantly different from the present invention and fail to solve the technical problem we want to solve. For this reason, we have invented a new imaging method based on near-surface fine modeling. Summary of the invention

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

[0014] The object of the present invention can be achieved by the following technical measures: an imaging method based on near-surface fine modeling, the imaging method based on near-surface fine modeling comprising:

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

[0016] Step 2, inverting the shear wave velocity and formation thickness information based on the dispersion information;

[0017] Step 3, calculate the WD curve and the corresponding W / DD curve;

[0018] Step 4, combining WD information with W / DD information to construct the objective function;

[0019] Step 5, calculating the minimum value of the objective function to determine the longitudinal wave information;

[0020] Step 6, correcting the offset velocity field;

[0021] Step 7: Perform depth domain migration to obtain a migration profile that better fits the underground structure.

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

[0023] In step 1, passive data are mainly surface waves and strong interference. Surface waves have obvious dispersion properties, while strong interference does not. In addition, long-term passive data recording makes dispersion extraction more accurate, low and high frequencies more balanced, and the frequency band wider, which is more conducive to reliable velocity structure inversion.

[0024] In step 2, a certain artificially set theoretical near-surface shear wave velocity profile is sought, and the dispersion data of the shear wave velocity and layer thickness information of the profile obtained by forward calculation is matched best with the actual dispersion data extracted by field reception. This profile is the shear wave velocity and thickness information.

[0025] In step 3, according to the sensitivity of Poisson's ratio to the wavelength W-depth D relationship, the WD curve corresponding to the dispersion curve is calculated, and the corresponding W / DD curve is calculated at the same time.

[0026] In step 3, the P-wave velocity has very little effect on the dispersion curve morphology, so the dispersion curve cannot be inverted to obtain the P-wave velocity structure; 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.

[0027] In step 3, based on the acquired shear wave velocity profile, the average shear wave velocity Vsz at a certain depth is first calculated:

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

[0029] Where n is the depth sequence, h i With V i is the thickness and shear wave velocity corresponding to the depth number; the fundamental dispersion curve is displayed through the relationship between wavelength and phase velocity, and the corresponding depth and wavelength when the shear wave average velocity curve and the velocity in the dispersion curve are the same can be found to obtain the WD relationship curve of the model.

[0030] In step 3, for any point on the WD relationship curve, the ratio between the ordinate W and the abscissa D is calculated to form a new W / DD curve.

[0031] In step 4, the WD information is combined with the W / DD information to construct the objective function:

[0032]

[0033] Where N is the number of depth points, Wr i obs With Wr ical are the ratios of the wavelength and depth corresponding to the observed and calculated i-th depth point, respectively; max(W i obs ) and max(Wr i obs ) are the maximum values ​​of the observed wavelength and the ratio of wavelength to depth, respectively. For problems of different orders of magnitude, normalization was performed when calculating the objective function.

[0034] In step 5, the required P-wave information is obtained by using the Monte Carlo full-space search algorithm in a nonlinear inversion manner to obtain the P-wave velocity profile corresponding to the minimum value of the objective function.

[0035] In step 6, the inverted near-surface P- 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 in improving the quality of migration imaging.

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

[0037] In step 7, the single-shot seismic data is first migrated, and then the imaging results of each shot are superimposed to obtain the final imaging section; 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, where u s is the source wave field, u r It is the detection wave field.

[0038] The imaging method based on near-surface fine modeling in the present invention inverts near-surface P- and S-wave velocities based on surface wave information, uses the inverted near-surface velocities to correct the offset velocity field, and uses the corrected offset velocity field for depth offset to ultimately improve the imaging effect. Surface waves are generally considered to be a kind of interference, but in recent years, people have gradually realized that this "passive data" also carries rich near-surface geological information. Based on surface wave information, the present invention first obtains dispersion information through a phase shift method, and extracts S-wave velocity and stratum thickness, and then combines WD information with W / DD information to construct an objective function and minimize the objective function to obtain P-wave velocity; in seismic exploration, obtaining an accurate near-surface velocity model is of great significance for improving the accuracy of seismic imaging in complex areas. The present invention turns to using passive data in a short period of time to invert near-surface P- and S-wave velocities, which are then used to correct the offset velocity field. The final imaging result can better characterize the underground stratum structure information. This imaging method based on fine near-surface modeling turns to utilizing the dispersion properties of passive data to invert the velocity structure of the near-surface, which is used to improve the imaging effect in the depth domain, making use of passive data within a short time range, and ultimately improving the imaging effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Flow chart of combined active and passive source Marchenko imaging;

[0040] Figure 2 A flowchart of a specific embodiment of the imaging method based on near-surface fine modeling of the present invention;

[0041] Figure 3 It is a schematic diagram showing the comparison of the velocity field of a simple model before and after correction in a specific embodiment of the present invention;

[0042] Figure 4 It is a schematic diagram of depth migration results before and after correction of near-surface velocity of the migration velocity field in a specific embodiment of the present invention;

[0043] Figure 5 It is a schematic diagram of depth migration results before and after correction of near-surface velocity of the migration velocity field in a specific embodiment of the present invention;

[0044] Figure 6 It is a schematic diagram showing the comparison of the actual underground velocity field before and after correction in a specific embodiment of the present invention;

[0045] Figure 7 It is a schematic diagram of the depth migration results before and after the near-surface velocity of the migration velocity field is corrected in a specific embodiment of the present invention;

[0046] Figure 8 It is a schematic diagram of depth migration results before and after correction of near-surface velocity of the migration velocity field in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0047] It should be noted that the following detailed descriptions are exemplary and are 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.

[0048] 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 and / or combinations thereof.

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

[0050] (1) First, the surface wave information in the passive data is extracted through the phase shift method. Passive data mainly consists of surface waves and strong interference. Surface waves have obvious dispersion properties, while strong interference does not. In addition, the long-term recording of passive data makes the dispersion extraction more accurate, the low frequency and high frequency are more balanced, the frequency band is wider, and it is more conducive to reliable velocity structure inversion.

[0051] (2) Obtain shear wave velocity and formation thickness information by inverting the dispersion information. Seek a certain artificially set theoretical near-surface shear wave velocity profile, and use the dispersion data obtained by forward calculation of the shear wave velocity, layer thickness and other information of the profile to achieve the best fit with the actual dispersion data extracted by field reception. 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, the WD curve corresponding to the dispersion curve is calculated, and the corresponding W / DD curve is also calculated.

[0053] (4) Combine WD information and W / DD information to construct the objective function: Where N is the number of depth points, Wr i obs With Wr i cal are the ratios of the wavelength and depth corresponding to the observed and calculated i-th depth point, respectively. max(W i obs ) and max(Wr i obs) are the maximum values ​​of the observed wavelength and the ratio of wavelength to depth, respectively. For problems of different orders of magnitude, normalization was performed when calculating the objective function;

[0054] (5) Through the Monte Carlo full-space search algorithm, in a nonlinear inversion manner, the P-wave velocity profile corresponding to the minimum value of the objective function is obtained, which is the desired P-wave information;

[0055] (6) The near-surface P- and S-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. Efficient and accurate migration velocity is a key factor in 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. First, the single-shot seismic record data is migrated, and then the imaging results of each shot are superimposed to obtain the final imaging profile.

[0057] The following are several specific embodiments of the present invention.

[0058] Example 1

[0059] In the specific embodiment 1 of the present invention, Figure 2 The flowchart of the imaging method based on near-surface fine modeling of the present invention is as follows:

[0060] In step 101, the surface wave information in the passive data is firstly extracted by the phase shift method. Passive data mainly includes surface waves and strong interference. Surface waves have obvious dispersion properties, while strong interference does not. In addition, the long-term recording of passive data makes the dispersion extraction more accurate, the low frequency and high frequency are more balanced, the frequency band is wider, and it is more conducive to the reliability of velocity structure inversion. The process enters step 102.

[0061] In step 102, the shear wave velocity and formation thickness information are obtained by inversion based on the dispersion information. A certain artificially set theoretical near-surface shear wave velocity profile is sought, and the dispersion data of the shear wave velocity, layer thickness and other information of the profile obtained by forward calculation and the actual dispersion data extracted by field reception are optimally fitted. The profile is the shear wave velocity and thickness information. The process enters step 103.

[0062] In step 103, the WD curve corresponding to the dispersion curve is calculated based on the sensitivity of Poisson's ratio to the wavelength (W)-depth (D) relationship, and the corresponding W / DD curve is calculated at the same time. The P-wave velocity has very little effect on the dispersion curve morphology, so it is impossible to invert the dispersion curve to obtain the P-wave velocity structure. 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. Based on the acquired S-wave velocity profile, the average S-wave 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 With V i The thickness and shear wave velocity corresponding to the depth number are shown by the relationship between wavelength and phase velocity. The WD relationship curve of the model can be obtained by finding the depth and wavelength corresponding to the same velocity in the shear wave average velocity curve and the dispersion curve. For any point on the curve, the ratio between the ordinate W and the abscissa D is calculated to form a new W / DD curve. The process enters step 104.

[0063] In step 104, the WD information is combined with the W / DD information to construct an objective function: Where N is the number of depth points, Wr i obs With Wr i cal are the ratios of the wavelength and depth corresponding to the observed and calculated i-th depth point, respectively. max(W i obs ) and max(Wr i obs ) are respectively the maximum values ​​of the observed wavelength value and the wavelength-to-depth ratio. For problems of different orders of magnitude, normalization is performed when calculating the objective function. The process proceeds to step 105.

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

[0065] In step 106, the near-surface P- and S-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. Efficient and accurate migration velocity is a key factor in improving the quality of migration imaging. The process proceeds to step 107.

[0066] In step 107, 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. First, the single-shot seismic record data is migrated, and then the imaging results of each shot are superimposed 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, where u s is the source wave field, u r It is the detection wave field.

[0067] Through the above process, passive data in a short period of time are utilized to invert the near-surface P- and S-wave velocities, which are then used to correct the offset velocity field. The final imaging results can better characterize the underground stratigraphic structure information.

[0068] Example 2

[0069] In the specific embodiment 2 of the present invention, Figure 3 This is a simple model used to test the effect of the corrected migration velocity field on the migration results. From top to bottom are the true velocity model, the migration velocity field obtained after the smoothing model, and the corrected migration velocity field. The large-scale smoothed velocity model is used to simulate the migration velocity field obtained by velocity analysis and other techniques in the actual data, and the near-surface velocity of the migration velocity field obtained based on 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 there is an obvious low-velocity zone on the surface of the corrected migration velocity field, while the velocity of the uncorrected velocity field is higher on the surface.

[0070] Figure 4 It 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 present invention; by comparing the phase axis, it can be seen that the continuity of the phase axis becomes better after correction, and the structural morphology becomes clearer.

[0071] Figure 5 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 present invention; by comparing the 250th record, it can be seen that at a depth of 1500m, the waveform of the migration velocity field after correction and the waveform of the true model have a good correspondence, the same phase, and almost the same amplitude, while the waveform of the simple smoothing model has a large error.

[0072] Example 3

[0073] In specific embodiment 3 of the present invention, Figure 6 This is a schematic diagram showing the comparison of the actual underground velocity field before and after correction;

[0074] Figure 7It is a schematic diagram of the depth migration results before and after the near-surface velocity of the migration velocity field is corrected in the specific embodiment 3 of the present invention; by comparing the phase axis in the dotted frame in the figure, it can be seen that the continuity of the phase axis becomes better after correction, the structural morphology is clearer, the small structure is more obvious, and the underground stratum structure can be further portrayed.

[0075] Figure 8 Schematic diagram of the depth migration results before and after the near-surface velocity of the migration velocity field is corrected in specific embodiment 3 of the present invention; the waveforms and depth wavenumber spectra of tracks 901 and 1101 in the figure show that the energy distribution of the depth wavenumber spectrum after correction is balanced, and the migration imaging effect is further improved.

[0076] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. 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.

[0077] Except for the technical features described in the specification, all other technical features are known technologies 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 includes: Step 1, extracting the dispersion information of the surface wave information in the passive data by using the phase shift method; Step 2, inverting the shear wave velocity and formation thickness information based on the dispersion information; Step 3, calculate the WD curve and the corresponding W / DD curve; Step 4, combining WD information with W / DD information to construct the objective function; Step 5, calculating the minimum value of the objective function to determine the longitudinal wave information; Step 6, correcting the offset velocity field; Step 7: Perform depth domain migration to obtain a migration profile that better fits the underground structure.

2. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 1, passive data are mainly surface waves and strong interference. Surface waves have obvious dispersion properties, while strong interference does not. In addition, long-term passive data recording makes dispersion extraction more accurate, low and high frequencies more balanced, and the frequency band wider, which is more conducive to reliable velocity structure inversion.

3. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 2, a certain artificially set theoretical near-surface shear wave velocity profile is sought, and the dispersion data of the shear wave velocity and layer thickness information of the profile obtained by forward calculation is matched best with the actual dispersion data extracted by field reception. This profile is the shear wave velocity and thickness information.

4. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 3, according to the sensitivity of Poisson's ratio to the wavelength W-depth D relationship, the WD curve corresponding to the dispersion curve is calculated, and the corresponding W / DD curve is calculated at the same time.

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 very little effect on the dispersion curve morphology, so the dispersion curve cannot be inverted to obtain the P-wave velocity structure; 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.

6. The imaging method based on near-surface fine modeling according to claim 5, characterized in that: In step 3, based on the acquired shear wave velocity profile, the average shear wave 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 With V i is the thickness and shear wave velocity corresponding to the depth number; the fundamental dispersion curve is displayed through the relationship between wavelength and phase velocity, and the corresponding depth and wavelength when the shear wave average velocity curve and the velocity in the dispersion curve are the same can be found to obtain the WD relationship curve of the model.

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 WD relationship curve, the ratio between the ordinate W and the abscissa D is calculated to form a new W / DD curve.

8. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 4, the WD information is combined with the W / DD information to construct the objective function: Where N is the number of depth points, Wr i obs With Wr i cal are the ratios of the wavelength and depth corresponding to the observed and calculated i-th depth point, respectively; max(W i obs ) and max(Wr i obs ) are the maximum values ​​of the observed wavelength and the ratio of wavelength to depth, respectively. For problems of different orders of magnitude, normalization was performed when calculating the objective function.

9. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 5, the required P-wave information is obtained by using the Monte Carlo full-space search algorithm in a nonlinear inversion manner to obtain the P-wave velocity profile corresponding to the minimum value of the objective function.

10. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 6, the inverted near-surface P- 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 in improving the quality of migration imaging.

11. The imaging method based on near-surface fine modeling according to claim 1, characterized in that: In step 7, the RTM migration method is used to obtain the migration velocity field for depth domain migration to obtain a migration profile that better fits the underground structure.

12. The imaging method based on near-surface fine modeling according to claim 11, characterized in that: In step 7, the single-shot seismic data is first migrated, and then the imaging results of each shot are superimposed to obtain the final imaging section; 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, where u s is the source wave field, u r It is the detection wave field.

Images