A dual-parameter residual dynamic correction method

Abstract

The present invention discloses a dual-parameter residual moveout correction method, comprising the following steps: obtaining a common reflection point imaging gather and a time domain initial velocity model for seismic prestack migration in a target work area; determining an imaging stack profile; determining a time window; based on an objective function, inverting the common reflection point imaging gather and the time domain initial velocity model to obtain velocity and equivalent anisotropy parameters; determining a dual-parameter model based on the velocity and equivalent anisotropy parameters corresponding to all time windows; and performing high-order residual moveout correction on the common reflection point imaging gather based on the dual-parameter model to obtain a residual moveout corrected imaging gather and a corresponding imaging stack profile. The present invention takes into account the situation of non-uniform anisotropy and can describe more complex residual moveout curves to improve inversion accuracy, thereby obtaining a straighter reflection event. Furthermore, a local time window global optimization algorithm is used to simultaneously estimate the dual parameters of velocity and equivalent anisotropy. This method has higher accuracy and computational efficiency than conventional parameter scanning methods.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration technology, and in particular to a dual-parameter residual dynamic correction method. Background Art

[0002] High-precision seismic exploration places increasingly higher demands on the quality of common reflection point (CRP) imaging gathers generated by prestack migration. Straight imaging gather reflection events not only improve the quality of stack imaging but also provide a high-quality data foundation for seismic inversion problems such as amplitude-with-offset (AVO) analysis and attribute extraction. Conventional prestack time migration often results in a certain degree of curvature in the reflection events of the migrated imaging gathers due to travel-time errors caused by inaccurate velocity models or other factors (such as medium inhomogeneity and anisotropy). Residual dynamic correction (RMD) is often required to further flatten the gathers for better stack imaging and inversion results.

[0003] The residual dynamic correction after conventional prestack migration usually includes three steps. First, the CRP data set generated by prestack migration is subjected to back-movement correction using a two-parameter model to obtain the back-movement data set; then, the back-movement correction data set is re-analyzed and the velocity is picked using a standard hyperbolic back-movement correction; finally, the back-movement correction data set is leveled using the updated velocity model. Usually, this process requires repeated iterations, which requires a lot of manpower and computing costs. However, in some cases, it is difficult to completely level the event axis even through repeated iterations. This is because the theoretical basis of back-movement correction is based on the assumption of a uniform horizontal layered medium, while the actual formation medium is often much more complex.

[0004] To address the limitations of traditional residual dynamic correction (RMN) methods, existing solutions can be broadly divided into two categories. One involves directly correcting residual moveouts point by point using cross-correlation calculations with reference traces. This approach uses cross-correlation with the reference trace within a time window to calculate the time shift of each trace as the residual dynamic correction (RMN), which is then applied to the data for translational correction. This time shift actually includes the effects of residual statics, noise, and other interference, leading to temporal discontinuities in the seismic data. Furthermore, these methods fail to obtain the corresponding residual velocity, making it impossible to update the velocity model. Another approach primarily relies on residual velocity analysis to determine the residual dynamic correction (RMN). This method allows for iterative updates of the velocity model, but existing techniques generally employ a single-parameter residual velocity analysis, resulting in low accuracy for dipping interfaces and large-offset data. Consequently, the leveling effect on steeply dipped structural imaging gathers is less than ideal. Furthermore, these techniques are difficult to apply to complex situations such as anisotropic media.

[0005] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a dual-parameter residual dynamic correction method in response to the above-mentioned defects of the prior art, aiming to solve the problem of low correction accuracy for inclined interfaces and large offset distance data in the single-parameter residual velocity analysis in the prior art.

[0007] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0008] A dual-parameter residual dynamic correction method, comprising the steps of:

[0009] Obtain common reflection point imaging gathers and time domain initial velocity model of seismic prestack migration in the target area;

[0010] Determining an imaging stacking profile corresponding to the common reflection point imaging gather;

[0011] Determining a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection events in the imaging stack section;

[0012] Based on the objective function corresponding to the time window, the velocity and equivalent anisotropy parameters corresponding to the time window are obtained by inverting the common reflection point imaging gathers and the time domain initial velocity model;

[0013] Determine a two-parameter model of the target work area according to the velocities and equivalent anisotropy parameters corresponding to all time windows;

[0014] Based on the dual-parameter model, high-order residual moveout correction is performed on the common reflection point imaging gathers to obtain residual moveout corrected imaging gathers and corresponding imaging stacking sections.

[0015] The dual-parameter residual dynamic correction method, wherein determining the imaging stacking profile corresponding to the common reflection point imaging gather includes:

[0016] determining the stretched portion and the noise portion of imaging traces at different offsets in the common reflection point imaging gather;

[0017] Setting the imaging result values ​​of the stretched part and the noise part to zero to obtain updated migration results of different offset distances;

[0018] The updated migration results of different offset distances are superimposed to obtain an imaging stacking section corresponding to the common reflection point imaging gather.

[0019] The dual-parameter residual dynamic correction method, wherein determining the objective function corresponding to the time window and inverting to obtain the velocity and equivalent anisotropy parameters corresponding to the time window, includes:

[0020] For each time window, extracting a local imaging gather of the time window according to the common reflection point imaging gather, and extracting an initial velocity of the time window according to the time domain initial velocity model;

[0021] Based on a simulated annealing algorithm, an iterative inversion is performed according to the local imaging gathers and the initial velocity to maximize the objective function corresponding to the time window until a first preset condition is satisfied, thereby obtaining the velocity and equivalent anisotropy parameter corresponding to the time window.

[0022] The dual-parameter residual dynamic correction method, wherein the objective function is:

[0023]

[0024]

[0025] Δτ i =argmax Δt C[G i (t0,V e ,η e ),G i+1 (t0,V e ,η e )](Δt)

[0026] Where f represents the objective function, ||·|| represents the norm, w(Δt) represents the cross-correlation superposition function, T(Δt) represents the weighting function, Δt represents the time sampling rate, i represents the offset index, N represents the number of offset groups, and Δt max represents the maximum time difference of the cross-correlation calculation within the time window, Δτ represents the cross-correlation time difference with the adjacent trace, C[·] represents the cross-correlation calculation, G(·) represents the local imaging gather after time difference correction using the corresponding experimental parameters, t0 represents the center time of the time window, V e Indicates the test speed, η e represents the equivalent anisotropy parameter, the objective function ranges from 0 to 1, and the objective function is maximized when the experimental parameter is equal to the true parameter.

[0027] In the dual-parameter residual dynamic correction method, the first preset condition includes:

[0028] The objective function value is greater than the preset threshold, or the number of iterations is greater than the preset number of iterations.

[0029] The dual-parameter residual dynamic correction method, wherein the dual-parameter model includes: a velocity model and an equivalent anisotropic parameter model;

[0030] Determining the dual-parameter model of the target work area according to the velocities and equivalent anisotropy parameters corresponding to all time windows includes:

[0031] Interpolating and smoothing the velocities corresponding to all time windows to obtain a velocity model of the target work area;

[0032] The equivalent anisotropic parameters corresponding to all time windows are interpolated and smoothed to obtain an equivalent anisotropic parameter model of the target work area.

[0033] The dual-parameter residual moveout correction method, wherein the high-order residual moveout correction is performed on the common reflection point imaging gather based on the dual-parameter model to obtain the imaging gather after residual moveout correction and the corresponding imaging stacking section, comprises:

[0034] Based on the velocity model and the equivalent anisotropy parameter model, high-order residual moveout is calculated from the common reflection point imaging gathers, and the gathers are time-shifted to obtain imaging gathers after residual moving average correction;

[0035] An imaging stacking section corresponding to the residual normalized imaging gather is determined according to the residual normalized imaging gather.

[0036] The dual-parameter residual dynamic correction method, wherein the high-order residual moveout is:

[0037] R h =C2H 2 +C4H 4 +O(H 6 )

[0038]

[0039]

[0040] Among them, R h represents the high-order residual time difference, C2 and C4 represent the second-order and fourth-order coefficient terms respectively, H represents the offset of the shot check point, O represents the high-order truncation term, T0 represents the zero-offset travel time, V t represents the true velocity, V0 represents the initial inaccurate velocity for generating the imaging gather, η t represents the true equivalent anisotropy parameter, and η0 represents the initial inaccurate equivalent anisotropy parameter.

[0041] 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.

[0042] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0043] Beneficial Effects: This method accounts for non-uniform VTI anisotropy, enabling the description of more complex residual moveout curves and improving inversion accuracy, thereby obtaining straighter reflection events. Furthermore, the method employs a local time window global optimization algorithm to simultaneously estimate both velocity and equivalent anisotropy parameters, resulting in higher accuracy and computational efficiency than conventional parameter scanning methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 4 is a flow chart of a dual-parameter residual dynamic correction method according to an embodiment of the present invention.

[0045] Figure 2 Schematic diagram of the time domain initial velocity model according to an embodiment of the present invention.

[0046] Figure 3 Schematic diagram of the initial pre-stack time migration imaging section and the selected calculation time window according to an embodiment of the present invention.

[0047] Figure 4 Schematic diagram of a time difference corrected gather obtained by a typical local time window optimization inversion according to an embodiment of the present invention.

[0048] Figure 5a Schematic diagram of a velocity model obtained by inversion updating according to an embodiment of the present invention;

[0049] Figure 5b Schematic diagram of an equivalent anisotropic parameter model according to an embodiment of the present invention.

[0050] Figure 6a This is a schematic diagram of the original CRP gather.

[0051] Figure 6b FIG. 4 is a schematic diagram of a CRP gather after residual dynamic correction according to an embodiment of the present invention.

[0052] Figure 7 Schematic diagram of the comparison of stacked imaging sections before and after residual dynamic correction (a) original stacked imaging section, (b) stacked imaging section after residual dynamic correction using this technology. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, 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 intended to limit the present invention.

[0054] Please also see Figure 1-Figure 7 , the present invention provides some embodiments of a dual-parameter residual dynamic correction method.

[0055] like Figure 1 As shown, the dual-parameter residual dynamic correction method of the present invention includes the following steps:

[0056] Step S100: Acquire common reflection point imaging gathers and time domain initial velocity model of seismic prestack migration in the target work area.

[0057] Specifically, the pre-stack seismic data can be the actual collected three-dimensional seismic data, which is pre-processed and pre-stack time migration imaging using conventional seismic data processing procedures to obtain the common reflection point imaging gather (CRP) after migration and the time domain initial velocity model.

[0058] Step S200: Determine the imaging stacking profile corresponding to the common reflection point imaging gather.

[0059] Specifically, an existing method is used to obtain an imaging stacking profile corresponding to a common reflection point imaging gather.

[0060] Step S200 specifically includes:

[0061] Step S210, determining the stretched portion and the noise portion of imaging traces at different offsets in the common reflection point imaging gather;

[0062] Step S220: setting the imaging result values ​​of the stretched part and the noise part to zero to obtain updated offset results of different offset distances;

[0063] Step S230: superimpose the updated migration results of different offset distances to obtain an imaging stacking section corresponding to the common reflection point imaging gather.

[0064] Specifically, the common reflection point imaging gathers are stretched, cut, and then stacked to create an imaging stack section. Setting the amplitude of the portion stretched by more than 50% and the noise portion to zero and stacking the updated migration results at different offsets can improve the signal-to-noise ratio of the imaging stack section.

[0065] Step S300: Determine a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection events in the imaging stack section.

[0066] 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 area where the event axis is clearer can be selected as the time window. Figure 2 As shown, 84 time windows were selected on the phase axis of the imaging stack section, and the distribution is as follows Figure 2 As shown in the rectangle.

[0067] Step S400: Based on the objective function corresponding to the time window, the velocity and equivalent anisotropy parameters corresponding to the time window are obtained by inversion according to the common reflection point imaging gathers and the time domain initial velocity model.

[0068] Specifically, the corresponding local imaging gathers are extracted for the selected time window and the corresponding objective function is constructed. The velocity and equivalent anisotropy parameters corresponding to the time window are then inverted. The simulated annealing method can be used for the inversion.

[0069] Step S400 specifically includes:

[0070] Step S410: For each time window, extract a local imaging gather of the time window according to the common reflection point imaging gather, and extract an initial velocity of the time window according to the time domain initial velocity model.

[0071] Step S420: Based on the simulated annealing algorithm, iterative inversion is performed according to the local imaging gathers and the initial velocity to maximize the objective function corresponding to the time window until a first preset condition is met, thereby obtaining the velocity and equivalent anisotropy parameters corresponding to the time window.

[0072] Specifically, for each time window, a simulated annealing algorithm is used for iterative optimization to maximize the objective function. When the first preset condition is met, the velocity and equivalent anisotropy parameters of the time window are obtained. When the first preset condition is not met, the velocity and equivalent anisotropy parameters are continued to be iteratively optimized to maximize the objective function corresponding to the time window.

[0073] Specifically, first, for each time window, the cross-correlation time difference Δτ with the adjacent trace is calculated based on the central time t0 and the initial equivalent anisotropy parameter η0 of the local imaging gather G in the time window, the initial velocity V0 in the time window, and the time sampling rate Δt. Then, the cross-correlation time shift superposition function w(Δt) is calculated based on the cross-correlation time shift superposition function w(Δt) and the weighting function T(Δt). Based on the objective function f, the initial velocity V0 and the initial equivalent anisotropy parameter η0 are updated to obtain the velocity V e and the equivalent anisotropy parameter η e , then calculate the objective function f, and further update the speed V e and the equivalent anisotropy parameter η e , until the first preset condition is met, the iteration ends and the final speed V is output e and the equivalent anisotropy parameter η e .

[0074] The objective function is:

[0075]

[0076]

[0077] Δτ i =argmax Δt C[G i (t0,V e ,η e ),G i+1 (t0,V e ,η e )](Δt)

[0078] Where f represents the objective function, ||·|| represents the norm, w(Δt) represents the cross-correlation superposition function, T(Δt) represents the weighting function, Δt represents the time sampling rate, i represents the offset index, N represents the number of offset groups, and Δt max represents the maximum time difference of the cross-correlation calculation within the time window, Δτ represents the cross-correlation time difference with the adjacent trace, C[·] represents the cross-correlation calculation, G(·) represents the local imaging gather after time difference correction using the corresponding input test parameters, t0 represents the center time of the time window, V e Indicates the test speed, η e represents the experimental equivalent anisotropy parameter, the objective function ranges from 0 to 1, and the objective function is maximum when the experimental parameter is equal to the true parameter.

[0079] The first preset condition includes:

[0080] The objective function value is greater than the preset threshold, or the number of iterations is greater than the preset number of iterations.

[0081] Specifically, when the objective function is maximized, if the objective function value exceeds a preset threshold or the number of iterations exceeds a preset number, the iterative inversion is stopped, and the velocity and equivalent anisotropy parameter at that objective function value are output as the velocity and equivalent anisotropy parameter corresponding to the time window, respectively. Based on the objective function corresponding to each time window, iterative inversion is performed to obtain the velocity and equivalent anisotropy parameter corresponding to each time window.

[0082] Step S500: Determine a two-parameter model of the target work area according to the velocities and equivalent anisotropy parameters corresponding to all time windows.

[0083] Specifically, the two-parameter model of the target work area is determined based on the velocity and equivalent anisotropy parameters corresponding to all time windows.

[0084] The dual-parameter model includes: a velocity model and an equivalent anisotropic parameter model; step S500 includes:

[0085] Step S510: interpolate and smooth the velocities corresponding to all time windows to obtain a velocity model of the target work area.

[0086] Step S520: interpolate and smooth the equivalent anisotropic parameters corresponding to all time windows to obtain an equivalent anisotropic parameter model of the target work area.

[0087] Specifically, the interpolation smoothing method can be used to supplement the velocity and equivalent anisotropy parameters at other locations, thereby obtaining a dual-parameter model of the target work area.

[0088] Step S600: Based on the dual-parameter model, high-order residual moveout correction is performed on the common reflection point imaging gathers to obtain residual moveout corrected imaging gathers and corresponding imaging stacking sections.

[0089] Specifically, after obtaining the dual-parameter model, the high-order residual moveout corresponding to each trace of the imaging gather is calculated based on the dual-parameter model, and the residual moveout correction is performed on the common reflection point imaging gather to obtain the imaging gather after residual dynamic correction and the corresponding imaging stacking profile.

[0090] Step S600 specifically includes:

[0091] Step S610: Based on the velocity model and the equivalent anisotropy parameter model, high-order residual moveout is calculated from the common reflection point imaging gathers, and the gathers are time-shifted to obtain imaging gathers after residual dynamic correction.

[0092] Step S620: Determine an imaging stacking section corresponding to the residual imaging gather after normal movement correction according to the residual imaging gather after normal movement correction.

[0093] Specifically, the corresponding imaging stacking section can be obtained by stacking the remaining imaging gathers after dynamic correction.

[0094] The goal of residual moving average (RMNO) correction is to flatten the reflection events in the gathers through residual moveout analysis, which is essentially an optimization problem. For dual-parameter estimation, simple parameter sweeping techniques are often inefficient and lack accuracy. Therefore, to achieve reliable and efficient dual-parameter estimation, this paper employs a global optimization fast simulated annealing algorithm for dual-parameter estimation.

[0095] Specifically, according to the velocity model, the velocity V e The coefficient C2 is calculated by the zero offset travel time T0 and initial velocity V0 of the common reflection point imaging gather. e , equivalent anisotropy parameter η eAnd the zero offset travel time T0, initial velocity V0, initial equivalent anisotropy parameter η0, and calculation coefficient C4 of the common reflection point imaging gather. Then, the high-order residual time difference R is calculated based on the shot check point offset H and coefficients C2 and C4 of the common reflection point imaging gather. h Based on the high-order residual time difference R h , update the initial velocity V0 and the initial equivalent anisotropy parameter η0, and get the true velocity V t and the true equivalent anisotropy parameter η t , and then calculate the high-order residual time difference R h , and further update the real speed V t and the true equivalent anisotropy parameter η t , until the first preset condition is met, the iteration ends and the final true speed V is output t and the true equivalent anisotropy parameter η t The final real speed V can be output according to t and the true equivalent anisotropy parameter η t , and obtain the imaging gathers after residual dynamic correction.

[0096] Based on the assumption of uniform horizontal layered medium and considering weak VTI anisotropy, Taylor expansion is performed and the high-order residual time difference is:

[0097] R h =C2H 2 +C4H 4 +O(H 6 )

[0098]

[0099]

[0100] Among them, R h represents the high-order residual time difference, C2 and C4 represent the second-order and fourth-order coefficient terms respectively, H represents the offset of the shot check point, O represents the high-order truncation term, T0 represents the zero-offset travel time, V t represents the true velocity, V0 represents the initial inaccurate velocity for generating the imaging gather, η t represents the true equivalent anisotropy parameter, and η0 represents the initial inaccurate equivalent anisotropy parameter.

[0101] In order to address the low efficiency of existing residual dynamic correction technologies and the inability to effectively flatten reflection events for large offset data and steep-angle structures, the present invention proposes a dual-parameter residual dynamic correction method based on local time window optimization. This method uses a high-order form to consider the anisotropy of non-uniform VTIs, which can describe more complex residual time difference curves and improve inversion accuracy, thereby obtaining flatter reflection events. In addition, the present invention uses a local time window global optimization algorithm to simultaneously estimate the dual parameters of velocity and equivalent anisotropy. This method has higher accuracy and computational efficiency than conventional parameter scanning methods. The residual dynamic correction method of the present invention can not only efficiently implement high-order residual dynamic correction and obtain high-precision stacking imaging results, but also update the velocity model and anisotropy parameter model, further improving the accuracy of velocity analysis. Specific embodiment 1

[0103] The imaging gathers and migration velocity model corresponding to the line number 415 of the work area were obtained. The CDP range of the imaging gathers obtained for this imaging line is from 70 to 1200, with a total of 1131 CDPs, a CDP interval of 10 meters, a minimum offset of 50 meters, a maximum offset of 4450 meters, and an offset interval of 100 meters. The imaging gathers corresponding to each CDP contain 45 traces, a data length of 4.5 seconds, and a sampling rate of 2 milliseconds. The migration velocity model obtained is as follows: Figure 2 shown.

[0104] The pre-stack migration imaging gathers are stretched and cut and then stacked. Figure 3 The obtained imaging stack section is 84 local time windows with clear event axes are selected based on the offset stack section. The window size in the horizontal direction is 3 CDPs and the time direction size is 200ms. The distribution is as follows: Figure 3 As shown in the box.

[0105] The velocity and equivalent anisotropy dual-parameter fast simulated annealing optimization inversion is performed on the selected local time window in turn. Figure 4 The local time difference correction gather at a typical window (CDP700, 2795ms) is shown. It can be seen that the reflection phase axis of the imaging gather is very straight, indicating that the parameter values ​​obtained by inversion are relatively accurate.

[0106] The stacking velocity and equivalent anisotropy parameters of each local imaging point are obtained by inverting all selected windows. The inversion updated velocity model and equivalent anisotropy parameter model can be obtained by interpolating and smoothing them, such as Figure 5a is the updated velocity model, Figure 5b is the updated equivalent anisotropic parameter model.

[0107] The velocity model and the equivalent anisotropy parameter model were used to perform a two-parameter residual time difference correction. We extracted the imaging gathers at CDP 750 for comparison. Figure 6a is the original migration imaging gather, Figure 6b This is the imaging gather corrected using the present invention. The dashed lines are the two calibrated target reflection events. By comparison, we can see that the present invention further flattens the imaging gather, especially at large offsets. This also demonstrates that the present invention can update the velocity and equivalent anisotropy parameter models.

[0108] The stacked imaging sections before and after residual dynamic correction were compared. Figure 7 a in the middle is the original imaging section of the target area. Figure 7 Figure b is the target area imaging section obtained by the technology of the present invention. By comparison, we can see that the imaging section of the technology of the present invention has clearer phase axes and can better separate thin interlayers, such as Figure 7 As shown in the box above the middle part; In addition, the imaging at steep angles is significantly improved, and the energy focusing is better, as shown in Figure 7 As shown in the lower left box.

[0109] By processing all target imaging lines according to the above process, the residual dynamic correction results for all CRP gathers can be obtained. This method can be effectively applied to 3D seismic prestack gather data, providing flatter CRP gathers and higher-precision migration imaging results, while also generating updated velocity models and anisotropy parameter models.

[0110] Compared with the prior art, the present invention has the following advantages:

[0111] 1. Using a high-order form to consider VTI anisotropy can describe more complex residual moveout curves to improve inversion accuracy, obtain straighter imaging gather reflection events, and improve stacking imaging effects and AVO inversion accuracy;

[0112] 2. The local time window dual-parameter global optimization algorithm has higher accuracy and computational efficiency than traditional parameter scanning technology, and can effectively implement high-order dual-parameter residual dynamic correction, while updating the velocity model and anisotropic parameter model;

[0113] 3. The present invention can efficiently handle the residual dynamic correction problem of large-scale two-dimensional and three-dimensional real data, and obtain more accurate pre-stack time migration imaging results and time domain velocity and equivalent anisotropy parameter models.

[0114] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, 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 dual-parameter residual dynamic correction method, characterized in that: Including steps: Obtain common reflection point imaging gathers and time domain initial velocity model of seismic prestack migration in the target area; Determining an imaging stacking profile corresponding to the common reflection point imaging gather; Determining a plurality of time windows in the imaging stack section; wherein the time windows are located on reflection events in the imaging stack section; Based on the objective function corresponding to the time window, the velocity and equivalent anisotropy parameters corresponding to the time window are obtained by inverting the common reflection point imaging gathers and the time domain initial velocity model; Determine a two-parameter model of the target work area according to the velocities and equivalent anisotropy parameters corresponding to all time windows; Based on the dual-parameter model, high-order residual moveout correction is performed on the common reflection point imaging gathers to obtain residual moveout corrected imaging gathers and corresponding imaging stacking sections; Determining the objective function corresponding to the time window and inverting to obtain the velocity and equivalent anisotropy parameters corresponding to the time window include: For each time window, extracting a local imaging gather of the time window according to the common reflection point imaging gather, and extracting an initial velocity of the time window according to the time domain initial velocity model; Based on a simulated annealing algorithm, iteratively inverting the local imaging gathers and the initial velocity to maximize the objective function corresponding to the time window until a first preset condition is satisfied, thereby obtaining the velocity and equivalent anisotropy parameter corresponding to the time window; The objective function is: =argmax Δt C[G i (t0,V e ,or e ),G i+1 (t0,V e ,or e )](Δt) Where f represents the objective function, ||·|| represents the norm, w(Δt) represents the cross-correlation superposition function, T(Δt) represents the weighting function, Δt represents the time sampling rate, i represents the offset index, N represents the number of offset groups, and Δt max represents the maximum time difference of cross-correlation calculation within the time window, represents the cross-correlation time difference with the adjacent traces, C[·] represents the cross-correlation calculation, G(·) represents the local imaging gather after time difference correction using the corresponding input test parameters, t0 represents the center time of the time window, V e Indicates the test speed, η e represents the experimental equivalent anisotropy parameter, the objective function ranges from 0 to 1, and the objective function is maximum when the experimental parameter is equal to the true parameter.

2. The dual-parameter residual dynamic correction method according to claim 1, characterized in that: Determining the imaging stacking profile corresponding to the common reflection point imaging gather includes: determining the stretched portion and the noise portion of imaging traces at different offsets in the common reflection point imaging gather; Setting the imaging result values ​​of the stretched part and the noise part to zero to obtain updated migration results of different offset distances; The updated migration results of different offset distances are superimposed to obtain an imaging stacking section corresponding to the common reflection point imaging gather.

3. The dual-parameter residual dynamic correction method according to claim 1, characterized in that: The first preset condition includes: The objective function value is greater than the preset threshold, or the number of iterations is greater than the preset number of iterations.

4. The dual-parameter residual dynamic correction method according to any one of claims 1 to 3, characterized in that: The dual-parameter model includes: a velocity model and an equivalent anisotropic parameter model; Determining the dual-parameter model of the target work area according to the velocities and equivalent anisotropy parameters corresponding to all time windows includes: Interpolating and smoothing the velocities corresponding to all time windows to obtain a velocity model of the target work area; The equivalent anisotropic parameters corresponding to all time windows are interpolated and smoothed to obtain an equivalent anisotropic parameter model of the target work area.

5. The dual-parameter residual dynamic correction method according to claim 4, characterized in that: The method of performing high-order residual moveout correction on the common reflection point imaging gather based on the dual-parameter model to obtain an imaging gather after residual moving average correction and a corresponding imaging stacking profile includes: Based on the velocity model and the equivalent anisotropy parameter model, high-order residual moveout is calculated from the common reflection point imaging gathers, and the gathers are time-shifted to obtain imaging gathers after residual moving average correction; An imaging stacking section corresponding to the residual normalized imaging gather is determined according to the residual normalized imaging gather.

6. The dual-parameter residual dynamic correction method according to claim 5, characterized in that: The high-order residual time difference is: R h =C2H 2 +C4H 4 +O(H 6 ) Among them, R h represents the high-order residual time difference, C2 and C4 represent the second-order and fourth-order coefficient terms respectively, H represents the offset of the shot check point, O represents the high-order truncation term, T0 represents the zero-offset travel time, V t represents the true velocity, V0 represents the initial inaccurate velocity for generating the imaging gather, η t represents the true equivalent anisotropy parameter, and η0 represents the initial inaccurate equivalent anisotropy parameter.

7. 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 6 are implemented.

8. 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 6 are implemented.

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