A method for automatic analysis of seismic migration velocity

Abstract

The invention discloses an automatic analysis method for seismic migration velocity, comprising the steps of: obtaining pre-stack seismic data of a target work area; determining an imaging stacking section corresponding to the pre-stack seismic data; determining a plurality of time windows and an objective function, and inverting to obtain a migration velocity value; determining a migration velocity model of the target work area according to the migration velocity values ​​corresponding to all time windows; and performing pre-stack time migration imaging on the pre-stack seismic data based on the migration velocity model to obtain a new imaging stacking section. The invention first obtains an imaging stacking section through the pre-stack seismic data of the target work area, selects a time window, and then inverts to obtain a migration velocity value corresponding to the time window based on the objective function, the migration velocity value is more accurate, the migration velocity model established according to the migration velocity value is more accurate, and the pre-stack time migration imaging is performed on the pre-stack seismic data based on the migration velocity model to obtain a new imaging stacking section, and the imaging effect is better.

Description

Technical Field

[0001] The invention relates to the technical field of seismic exploration, and in particular to an automatic analysis method for seismic offset velocity. Background Art

[0002] Velocity model is the key to seismic migration imaging. Existing migration velocity modeling technology mainly obtains stacking velocity through velocity spectrum energy group picking method based on pre-stack gathers, which is usually used as the initial migration velocity of pre-stack time migration. In practical application, it usually requires multiple iterations to obtain a more suitable migration velocity model. Therefore, the whole process usually requires high manpower and computing resource costs. Migration velocity analysis is mainly based on the residual time difference of imaging gathers, and whether the imaging gathers are flattened is used as the criterion for judging whether the migration velocity is accurate. For large-scale three-dimensional actual seismic data, the workload required for manual velocity picking is huge, and even occupies half of the seismic data processing process. There is an urgent need for automatic and efficient migration velocity analysis methods and technologies.

[0003] The existing automatic migration velocity analysis methods mainly include residual time difference inversion based on imaging gathers and constant velocity scanning analysis methods based on global search of imaging stacking profiles. Residual time difference inversion requires picking up accurate time differences for repeated migration iterations, which has low computational efficiency and is difficult to adapt to small offset missing and low signal-to-noise ratio data (residual time differences are difficult to pick up accurately); the migration velocity scanning technology selects velocity parameters by judging the focusing imaging effect of the reflection event axis under different constant velocity conditions, which relies on the experience and judgment of technicians. The processing results of different technicians may be inconsistent, and it is easily affected by related interference such as multiple waves. In addition, this method and technology is generally only suitable for local migration velocity updates, and it is difficult to perform global velocity analysis on large-scale three-dimensional data. Therefore, it is difficult for existing methods and technologies to efficiently and accurately obtain the time domain migration velocity model of large-scale three-dimensional seismic data, which seriously restricts the effect of seismic migration imaging and subsequent inversion interpretation.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for automatic analysis of seismic migration velocity in view of the above-mentioned defects of the prior art, aiming to solve the problem in the prior art of poor imaging effect caused by much manual intervention and low accuracy of the migration velocity model.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] A method for automatic analysis of seismic migration velocity, comprising the steps of:

[0008] Acquire pre-stack seismic data of the target work area;

[0009] Determining an imaging stacking section corresponding to the pre-stack seismic data;

[0010] Determine a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection event axes in the imaging stack section;

[0011] Determine the objective function corresponding to the time window, and invert to obtain the migration velocity value corresponding to the time window;

[0012] Determining the migration velocity model of the target work area according to the migration velocity values ​​corresponding to all time windows;

[0013] Based on the migration velocity model, prestack time migration imaging is performed on the prestack seismic data to obtain a new imaging stacking section.

[0014] The automatic seismic migration velocity analysis method, wherein the step of determining the imaging stacking section corresponding to the pre-stack seismic data comprises:

[0015] Determining a time domain velocity model corresponding to the pre-stack seismic data;

[0016] Based on the time domain velocity model, prestack time migration imaging is performed on the prestack seismic data to obtain an imaging stacking section corresponding to the prestack seismic data.

[0017] The automatic analysis method of seismic migration velocity, wherein the time domain velocity model comprises: a root mean square velocity field;

[0018] Determining the time domain velocity model corresponding to the pre-stack seismic data includes:

[0019] After preprocessing the prestack seismic data, common center point gathers are extracted;

[0020] The root mean square velocity field is obtained by performing stacking velocity analysis on the common center point gathers.

[0021] The automatic analysis method for seismic migration velocity, wherein the objective function is determined according to a cross-correlation stacking function and a weighting function, the cross-correlation stacking function is obtained by cross-correlation stacking of local migration imaging gathers between different offset distances, and the local migration imaging gathers are obtained by pre-stack migration imaging within the time window based on the migration velocity.

[0022] The automatic analysis method of seismic migration velocity, wherein the objective function is:

[0023]

[0024]

[0025] Where f represents the objective function, ||·|| represents the norm, w(Δτ) represents the cross-correlation superposition function, T(Δτ) represents the weighting function, Δτ represents the cross-correlation time difference, and h l represents the offset of offset group l, i, j represents the horizontal imaging point position index, n represents the number of sampling points in the time window, I represents the local migration imaging gather, τ0 represents the time depth of the imaging point, and m represents the time shift.

[0026] The automatic analysis method of seismic migration velocity, wherein the weighting function is:

[0027]

[0028] Wherein, α represents a constant related to the width of the window function, and e represents the base of the natural logarithm function; and / or

[0029] The local migration imaging gathers are:

[0030]

[0031]

[0032]

[0033]

[0034] Among them, τ s Indicates the distance from the imaging point (x, y, t0) to the shot point (x s ,y s ), x, y represent the x-axis and y-axis coordinates of the imaging point, t0 represents the zero offset one-way travel time of the imaging point, x s ,y s Respectively represent the x-axis and y-axis coordinates of the shot point, τ g Indicates the distance from the imaging point (x, y, t0) to the detection point (x g ,y g )'s travel time, x g ,y g They represent the x-axis and y-axis coordinates of the detection point, h represents the offset distance of the shot detection point, V e represents the displacement velocity, μ p represents the pth seismic data, and q represents the total number of seismic traces.

[0035] The automatic analysis method of seismic migration velocity, wherein:

[0036] The determining of the objective function corresponding to the time window and inverting to obtain the migration velocity value corresponding to the time window further includes:

[0037] Determine the global constraint range of the migration velocity;

[0038] Under the global constraint range, iterative inversion is performed to maximize the objective function corresponding to the time window until a preset condition is met, thereby obtaining the offset velocity value corresponding to the time window; wherein, during the iterative inversion process, the global constraint range is dynamically adjusted according to the directionality of the objective function.

[0039] The automatic analysis method of seismic migration velocity, wherein:

[0040] The preset conditions include:

[0041] The objective function value is greater than a preset threshold, or the number of iterations is greater than a preset number of iterations; and / or

[0042] The global constraint range has a minimum offset speed in the target work area and a maximum offset speed in the target work area, and the directionality of the objective function is determined according to the maximum value of the independent variable of the superposition cross-correlation function.

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0044] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0045] Beneficial effects: The present invention first obtains an imaging stacking section through pre-stack seismic data of a target work area, selects a time window, and then inverts to obtain an offset velocity value corresponding to the time window based on an objective function. The offset velocity value is more accurate, and an offset velocity model established according to the offset velocity value is more accurate. Pre-stack time migration imaging is performed on the pre-stack seismic data based on the offset velocity model to obtain a new imaging stacking section with better imaging effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the automatic analysis method of seismic migration velocity according to an embodiment of the present invention.

[0047] Figure 2 It is a schematic diagram of a velocity prestack migration imaging section and a selected local time window according to an embodiment of the present invention.

[0048] Figure 3 It is a schematic diagram of a local imaging gather for velocity analysis at a time window according to an embodiment of the present invention.

[0049] Figure 4 It is a schematic diagram of the time window objective function convergence curve of an embodiment of the present invention.

[0050] Figure 5 It is a schematic diagram of a migration velocity model obtained by automatic velocity analysis inversion according to an embodiment of the present invention.

[0051] Figure 6 It is a schematic diagram of an imaging cross section of a velocity model according to an embodiment of the present invention for migration.

[0052] Figure 7a It is a locally enlarged schematic diagram of the migration imaging results of the migration velocity model using the existing migration velocity analysis.

[0053] Figure 7b It is a locally enlarged schematic diagram of the migration imaging result of the migration velocity model automatically inverted using the method of the present invention.

[0054] Figure 8a It is a schematic diagram of migrating imaging gathers using the migration velocity model of existing migration velocity analysis.

[0055] Figure 8b It is a schematic diagram of the migration velocity model migration imaging gather automatically inverted by the method of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] Please also see Figure 1-Figure 8b The present invention provides some embodiments of a method for automatic analysis of seismic migration velocity.

[0058] like Figure 1 As shown, the automatic analysis method of seismic migration velocity of the present invention comprises the following steps:

[0059] Step S100: Acquire pre-stack seismic data of the target work area.

[0060] Specifically, pre-stack seismic data may be seismic data collected by offshore cable transport, and the pre-stack seismic data are collected by a single-side shooting and single-side receiving observation system. The pre-stack seismic data include: the number of shots, the range of the shot points in the survey line, the shot spacing, the number of traces in each shot, the trace spacing, the minimum offset, etc.

[0061] Step S200: determining an imaging stacking section corresponding to the pre-stack seismic data.

[0062] Specifically, an existing method is used to obtain an imaging stacking section corresponding to pre-stack seismic data.

[0063] Step S200 specifically includes:

[0064] Step S210: Determine a time domain velocity model corresponding to the pre-stack seismic data.

[0065] Step S220: Based on the time domain velocity model, pre-stack time migration imaging is performed on the pre-stack seismic data to obtain an imaging stacking section corresponding to the pre-stack seismic data.

[0066] Specifically, the time domain velocity model corresponding to the prestack seismic data is determined based on the prestack seismic data; finally, the prestack time migration imaging is performed based on the time domain velocity model to obtain the imaging stacking profile. Velocity is an important parameter when seismic waves propagate in underground media. Usually, the observed seismic data is in the time domain in the vertical direction. Therefore, a time domain velocity model is established. The root mean square velocity or stacking velocity is the velocity that best fits the definition of imaging seismic data in the time domain, and is also the basis for later depth domain imaging.

[0067] Here, the time domain velocity model is used as the initial velocity model to perform prestack time migration imaging on prestack seismic data to obtain an imaging stack section. Figure 2 As shown in the figure, the root mean square velocity model is used as the initial velocity model to perform prestack time migration imaging on all prestack seismic data to obtain an imaging stacking section. Since the initial velocity model is not accurate enough, the imaging effect is not very good, especially in the medium and deep layers.

[0068] Step S210 specifically includes:

[0069] Step S211, after pre-processing the pre-stack seismic data, extract common center point gathers.

[0070] Step S212: Perform stacking velocity analysis on the common center point gathers to obtain a root mean square velocity field.

[0071] Specifically, the pre-stack seismic data is pre-processed to obtain pre-processed pre-stack seismic data; then the common middle point gather (CMP) of the pre-processed pre-stack seismic data is extracted, and then the common middle point gather is used to perform stacking velocity analysis to obtain a root mean square velocity model. Of course, other methods can also be used to obtain a time domain velocity model. Pre-processing includes multiple wave removal, noise removal and other pre-processing.

[0072] Step S300, determining a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection event axes in the imaging stack section.

[0073] Specifically, after obtaining the imaging stack section, several time windows are selected in the imaging stack section. The time windows are located on the event axis. The position on the event axis where the signal-to-noise ratio is greater than the preset signal-to-noise ratio can be selected as the time window, or the local part where the event axis is clearer can be selected as the time window. Figure 2As shown in Figure 2, 48 time windows were selected on the phase axis of the imaging stack section, and the distribution is as follows: Figure 2 As shown by the cross in .

[0074] Step S400: determine the objective function corresponding to the time window, and invert to obtain the migration velocity value corresponding to the time window.

[0075] Specifically, based on the migration velocity, the selected time window is locally migrated to generate local imaging gathers and the corresponding objective function is constructed, and the migration velocity value corresponding to the time window is inverted. The simulated annealing method can be used for inversion.

[0076] Step S400 specifically includes:

[0077] Step S410: For each time window, determine the objective function corresponding to the time window.

[0078] Specifically, the objective function is determined according to a cross-correlation stacking function and a weighting function. The cross-correlation stacking function is obtained by cross-correlation stacking of local migration imaging gathers between different offsets. The local migration imaging gathers are obtained by pre-stack migration imaging within the time window based on the migration velocity.

[0079] Specifically, for each time window, the objective function is obtained according to the cross-correlation stacking function and the weighting function. The cross-correlation stacking function is obtained by cross-correlation stacking of local migration imaging gathers between different offsets. The local migration imaging gathers are obtained according to the migration velocity and pre-stack seismic data.

[0080] The objective function is:

[0081]

[0082]

[0083] Where f represents the objective function, ||·|| represents the norm, w(Δτ) represents the cross-correlation superposition function, T(Δτ) represents the weighting function, Δτ represents the cross-correlation time difference, and h l represents the offset of offset group l, i, j represents the horizontal imaging point position index, n represents the number of sampling points in the time window, I represents the local migration imaging gather, τ0 represents the time depth of the imaging point, and m represents the time shift.

[0084] The weighting function may be a Gaussian weighting function or a parabolic function. For example, a Gaussian weighting function is used, and the weighting function is:

[0085]

[0086] Wherein, α represents a constant related to the width of the window function, and e represents the base of the natural logarithm function.

[0087] Specifically, α is set as needed, for example, 0.1. Practical experience shows that this objective function can effectively improve the convergence speed and stability of the inversion.

[0088] The local migration imaging gathers are:

[0089]

[0090]

[0091]

[0092]

[0093] Among them, τ s Indicates the distance from the imaging point (x, y, t0) to the shot point (x s ,y s ), x, y represent the x-axis and y-axis coordinates of the imaging point, t0 represents the zero offset one-way travel time of the imaging point, x s ,y s Respectively represent the x-axis and y-axis coordinates of the shot point, τ g Indicates the distance from the imaging point (x, y, t0) to the detection point (x g ,y g )'s travel time, x g ,y g They represent the x-axis and y-axis coordinates of the detection point, h represents the offset distance of the shot detection point, V e represents the displacement velocity, μ p represents the pth seismic data, and q represents the total number of seismic traces.

[0094] Step S400 also includes:

[0095] Step S420: determine the global constraint range of the offset velocity.

[0096] Step S430: Under the global constraint range, iterative inversion is performed to maximize the objective function corresponding to the time window until a preset condition is met, thereby obtaining the migration velocity value corresponding to the time window; wherein, during the iterative inversion process, the constraint range is dynamically adjusted according to the directionality of the objective function.

[0097] In order to speed up the iterative inversion, the iterative inversion is performed under the constraints of the global constraint range to reduce the number of iterations. During the iterative inversion, the objective function is maximized until the preset conditions are met, and the offset velocity value corresponding to the time window is obtained. The offset velocity value obtained through iterative inversion is more accurate. The global constraint range has a minimum offset velocity in the target work area and a maximum offset velocity in the target work area, that is, the offset velocity is between the minimum offset velocity and the maximum offset velocity. Specifically, the global constraint range can be expressed as:

[0098] V gmin ≤V e ≤V gmax

[0099] Among them, V e represents the displacement velocity, V gmin Indicates the minimum offset speed in the work area, V gmax Indicates the maximum offset speed in the work area.

[0100] The directionality of the objective function can be determined based on the maximum value of the independent variable of the cross-correlation stacking function. When the maximum value of the independent variable of the cross-correlation stacking function is > 0, it indicates that the local migration imaging gather is bent upward, and the migration speed is too small, and the minimum and maximum migration speeds need to be increased. When the maximum value of the independent variable of the cross-correlation stacking function is < 0, it indicates that the local migration imaging gather is bent downward, and the migration speed is too large, and the minimum and maximum migration speeds need to be reduced.

[0101] The maximum value of the independent variable of the cross-correlation superposition function is:

[0102] C = arg max[w(Δτ)]

[0103] According to the size of C, the directionality of the objective function is determined, thereby dynamically adjusting the global constraint range.

[0104] The preset conditions include:

[0105] The objective function value is greater than the preset threshold, or the number of iterations is greater than the preset number of iterations.

[0106] Specifically, when the objective function is maximized, if the objective function value is greater than a preset threshold or the number of iterations is greater than a preset number of iterations, the iterative inversion is stopped, and the migration velocity value under the objective function value is output as the migration velocity value corresponding to the time window. Based on the objective function corresponding to each time window, iterative inversion is performed to obtain the migration velocity values ​​corresponding to each time window.

[0107] Step S500: Determine the migration velocity model of the target work area according to the migration velocity values ​​corresponding to all time windows.

[0108] Specifically, the migration velocity model of the target work area is determined according to the migration velocity values ​​corresponding to all time windows. Specifically, the migration velocity values ​​of other positions can be supplemented by interpolation smoothing to obtain the migration velocity model of the target work area.

[0109] Step S600: Based on the migration velocity model, pre-stack time migration imaging is performed on the pre-stack seismic data to obtain a new imaging stacking section.

[0110] Specifically, after the migration velocity model is obtained, prestack time migration imaging is performed on the prestack seismic data based on the migration velocity model to obtain a new imaging stacking section.

[0111] In order to solve the problem that the existing migration velocity analysis technology consumes a lot of manpower and computing resource costs and is difficult to obtain an accurate migration velocity model, the present invention first obtains an imaging stacking section through the pre-stack seismic data of the target work area, selects a time window, and then inverts the migration velocity value corresponding to the time window based on the objective function. The migration velocity value is more accurate, and the migration velocity model established according to the migration velocity value is more accurate. Pre-stack time migration imaging is performed on the pre-stack seismic data based on the migration velocity model to obtain a new imaging stacking section with better imaging effect.

[0112] The invention is based on the local migration imaging gather cross-correlation stacking weighted objective function, and adopts the fast simulated annealing method to automatically invert the local migration velocity. It is a localized global optimization method with high computational efficiency and independent of the initial velocity model. The migration velocity automatic analysis method of the invention can reduce the human intervention of seismic prestack migration imaging, greatly improve the work efficiency of migration velocity analysis, and obtain a reliable migration velocity model, which can serve the next step of more refined wave equation velocity inversion and depth domain migration imaging. Specific embodiment 1

[0114] The data is a seismic data collected by an actual offshore cable. The pre-stack seismic data set contains 3560 shots, with the shot points ranging from 3275m to 92475m in the survey line, a shot spacing of 25m, 240 channels per shot, a channel spacing of 12.5m, a minimum offset of 275m, and an observation system of single-side shooting and single-side reception. The data set has a total of 854400 channels, a record length of 8s, a sampling interval of 2ms, and a size of about 13G. The defined imaging range is 10km to 35km, the imaging interval is 6.25m, the output profile duration is 8s, and the time sampling interval is 2ms.

[0115] After conventional pre-processing such as multiple removal and noise removal, the pre-stack seismic data is pre-processed to extract the common center point gather, and the stacking velocity analysis is performed using the common center point gather to obtain the root mean square velocity model. The root mean square velocity model is used as the initial velocity model to perform pre-stack time migration imaging on all pre-stack seismic data. Figure 2 This is the imaging stacking section obtained by the initial migration velocity model. Since the initial velocity model is not accurate enough, the imaging effect is not very good, especially in the middle and deep layers. According to the migration imaging results, 48 ​​local time windows with clear event axes are selected, distributed as follows Figure 2 Indicated by the cross in the middle.

[0116] Automatic migration velocity inversion analysis is performed on the selected time windows in turn. Figure 3 The local velocity analysis imaging gather at a typical window (20km, 3s) is shown. It can be seen that the reflection event axes of the imaging gather are flattened, indicating that the migration velocity obtained by inversion is relatively accurate. Figure 4 The convergence of the inversion process is shown. It can be seen that it usually converges in about 30 times, indicating that this method has high computational efficiency.

[0117] By inverting all selected windows, the migration velocity of each local imaging point can be obtained. By interpolating and smoothing them, the inverted updated migration velocity model can be obtained, such as Figure 5 This velocity model is the final velocity model obtained by automatic analysis of this method.

[0118] Using this migration velocity model for prestack time migration imaging, more accurate migration imaging results are obtained, such as Figure 6 As shown in the figure, we can see that the in-phase axis is well focused and positioned, and the imaging effect is significantly improved.

[0119] A comparison is made with the existing advanced technologies. Figure 7a It is a partial enlarged image of the imaging section of the velocity model obtained by the prior art. Figure 7b This is a partial enlarged view of the imaging section of the velocity model obtained by the technology of the present invention. By comparison, it can be seen that the imaging section of the technology of the present invention has clearer phase axes and better focus.

[0120] The migration imaging gathers at a horizontal distance of 20 km were extracted for comparison. Figure 8a It is the migration imaging gather generated by the velocity model obtained by the existing technology. Figure 8b This is the migration imaging gather generated by the velocity model obtained by the technology of the present invention. By comparison, it can be found that the reflection event axis of the imaging gather obtained by the technology of the present invention is straighter, which also shows that the migration velocity model obtained by the technology of the present invention is more accurate.

[0121] Compared with the prior art, the present invention has the following advantages:

[0122] 1. No need to manually pick up the velocity spectrum, the accurate migration velocity can be quickly and automatically inverted;

[0123] 2. Independent of the initial velocity model, the optimal migration velocity model suitable for the migration method can be obtained by autonomous inversion;

[0124] 3. The use of local imaging can combine the existing prior information in the inversion process to avoid the effects of false picking and multiple wave interference;

[0125] 4. Localized inversion is used to reduce parameters, and the fast simulated annealing method can quickly converge to the global optimum, with high computational efficiency. The present invention can quickly process time domain migration velocity analysis tasks of large-scale actual data, quickly obtain high-precision prestack time migration imaging results, and at the same time, through simple time-depth conversion, it can also provide a reliable initial velocity model for depth domain migration.

[0126] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for automatic analysis of seismic migration velocity, characterized in that: Includes steps: Acquire pre-stack seismic data of the target work area; Determining an imaging stacking section corresponding to the pre-stack seismic data; Determine a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection event axes in the imaging stack section; Determine the objective function corresponding to the time window, and invert to obtain the migration velocity value corresponding to the time window; Determining the migration velocity model of the target work area according to the migration velocity values ​​corresponding to all time windows; Based on the migration velocity model, prestack time migration imaging is performed on the prestack seismic data to obtain a new imaging stacking section.

2. The automatic analysis method of seismic migration velocity according to claim 1, characterized in that: Determining the imaging stacking section corresponding to the pre-stack seismic data includes: Determining a time domain velocity model corresponding to the pre-stack seismic data; Based on the time domain velocity model, prestack time migration imaging is performed on the prestack seismic data to obtain an imaging stacking section corresponding to the prestack seismic data.

3. The automatic analysis method of seismic migration velocity according to claim 2, characterized in that: The time domain velocity model includes: a root mean square velocity field; Determining the time domain velocity model corresponding to the pre-stack seismic data includes: After preprocessing the prestack seismic data, common center point gathers are extracted; The root mean square velocity field is obtained by performing stacking velocity analysis on the common center point gathers.

4. The automatic analysis method of seismic migration velocity according to any one of claims 1 to 3, characterized in that: The objective function is determined according to a cross-correlation stacking function and a weighting function. The cross-correlation stacking function is obtained by cross-correlation stacking of local migration imaging gathers between different offsets. The local migration imaging gathers are obtained by pre-stack migration imaging within the time window based on the migration velocity.

5. The method for automatic analysis of seismic migration velocity according to claim 4, characterized in that: The objective function is: Where f represents the objective function, ||·|| represents the norm, w(Δτ) represents the cross-correlation superposition function, T(Δτ) represents the weighting function, Δτ represents the cross-correlation time difference, and h l represents the offset of offset group l, i, j represents the horizontal imaging point position index, n represents the number of sampling points in the time window, I represents the local migration imaging gather, τ0 represents the time depth of the imaging point, and m represents the time shift.

6. The automatic analysis method of seismic migration velocity according to claim 5, characterized in that: The weighting function is: Wherein, α represents a constant related to the width of the window function, and e represents the base of the natural logarithm function; and / or The local migration imaging gathers are: Among them, τ s Indicates the distance from the imaging point (x, y, t0) to the shot point (x s ,y s ), x, y represent the x-axis and y-axis coordinates of the imaging point, t0 represents the zero offset one-way travel time of the imaging point, x s ,y s Respectively represent the x-axis and y-axis coordinates of the shot point, τ g Indicates the distance from the imaging point (x, y, t0) to the detection point (x g ,y g )'s travel time, x g ,y g They represent the x-axis and y-axis coordinates of the detection point, h represents the offset distance of the shot detection point, V e represents the displacement velocity, μ p represents the pth seismic data, and q represents the total number of seismic traces.

7. The method for automatic analysis of seismic migration velocity according to claim 6, characterized in that: The determining of the objective function corresponding to the time window and inverting to obtain the migration velocity value corresponding to the time window further includes: Determine the global constraint range of the migration velocity; Under the global constraint range, iterative inversion is performed to maximize the objective function corresponding to the time window until a preset condition is met, thereby obtaining the offset velocity value corresponding to the time window; wherein, during the iterative inversion process, the global constraint range is dynamically adjusted according to the directionality of the objective function.

8. The method for automatic analysis of seismic migration velocity according to claim 7, characterized in that: The preset conditions include: The objective function value is greater than a preset threshold, or the number of iterations is greater than a preset number of iterations; and / or The global constraint range has a minimum offset speed in the target work area and a maximum offset speed in the target work area, and the directionality of the objective function is determined according to the maximum value of the independent variable of the cross-correlation superposition function.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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