A two parameter prestack time migration method and system

Abstract

The application discloses a two-parameter pre-stack time migration method and system, and the method comprises the following steps: obtaining an initial root mean square velocity model after dynamic correction velocity analysis of a target work area and pre-stack seismic data, and determining an initial stacking profile of pre-stack time migration; determining a plurality of local time windows in the initial stacking profile, and obtaining a root mean square velocity and a velocity variation factor corresponding to each local time window based on function inversion; determining an updated root mean square velocity model and a velocity variation factor model of the target work area according to the root mean square velocities and the velocity variation factors corresponding to all the local time windows; and performing two-parameter pre-stack time migration on the pre-stack seismic data based on the updated root mean square velocity model and the velocity variation factor model, so as to obtain an updated migration imaging trace set and an updated stacking profile. The application can effectively improve the ability of pre-stack time migration processing of more complex velocity variation, and obtain a higher-precision pre-stack time migration imaging result and a migration velocity model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of seismic exploration technology, and particularly relates to a two-parameter pre-stack time migration method and system. BACKGROUND

[0002] The purpose of seismic imaging is to find economic geological structures such as oil and gas reservoirs or mineral resources. Pre-stack time migration is one of the most important imaging techniques, which converts seismic data into seismic stratum reflection coefficients by using wave equation integral approximation, and then forms an underground structure image. The establishment of a migration velocity model is one of the key steps of pre-stack time migration, and a reasonable and accurate velocity model is very important and can greatly affect the imaging effect. The velocity used in pre-stack time migration is usually the root mean square velocity, which is actually an equivalent parameter reflecting the overlying stratum velocity. This velocity model is different from the true geological velocity model, but can provide a more accurate structure imaging result.

[0003] One advantage of using the root mean square velocity is that a proper velocity model can be obtained simply by velocity scanning, avoiding the difficulty of depth domain velocity modeling faced by using pre-stack depth migration methods. Pre-stack time migration is very sensitive to velocity analysis, and the error of velocity analysis will directly affect the migration imaging effect. Therefore, in practical applications, the velocity model needs to be iteratively adjusted through repeated pre-stack migration and velocity analysis until the best imaging effect is achieved. In addition, the conventional pre-stack time migration uses one root mean square velocity parameter, which cannot theoretically consider the problem of lateral inhomogeneous change of medium velocity.

[0004] The pre-stack time migration method can better image geological structures with relatively complex faults but not very severe lateral velocity variation. In order to further improve the adaptability of pre-stack time migration to lateral velocity variation, the curved ray method is currently mainly used. However, the coefficients in the curved ray travel time calculation process need to be calculated by fitting the time domain layer velocity model, and it is difficult to obtain a reasonable curved ray migration velocity model.

[0005] Therefore, the prior art still needs to be improved and improved. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a two-parameter pre-stack time migration method and system to solve the problem that the single parameter used in the pre-stack time migration technology of the prior art cannot well handle the lateral velocity variation.

[0007] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0008] In a first aspect, the present application provides a two-parameter pre-stack time migration method, wherein the method comprises:

[0009] obtain an initial root-mean-square (RMS) velocity model after velocity analysis of target work area and prestack seismic data, and determine an initial stack profile of prestack time migration corresponding to the RMS velocity model;

[0010] determine a plurality of local time windows in the initial stack profile, obtain a target function corresponding to the local time windows, and obtain RMS velocity and velocity variation factor corresponding to each of the local time windows based on the target function;

[0011] determine an updated RMS velocity model and an updated velocity variation factor model of the target work area according to the RMS velocity and the velocity variation factor corresponding to all of the local time windows;

[0012] perform two-parameter prestack time migration on the prestack seismic data based on the updated RMS velocity model and the updated velocity variation factor model to obtain updated migration gathers and an updated stack profile.

[0013] In an implementation manner, the determining of the initial stack profile of prestack time migration corresponding to the RMS velocity model comprises:

[0014] performing conventional prestack time migration on the prestack seismic data to obtain the initial stack profile, wherein the travel time calculation of the conventional prestack time migration is represented as:

[0015]

[0016] wherein, T0 represents zero-offset two-way travel time, r represents horizontal distance from imaging point to shot point or receiver point, V rms represents RMS velocity of imaging point position.

[0017] In an implementation manner, the determining of the plurality of local time windows in the initial stack profile comprises:

[0018] determining a plurality of time windows located at local positions on the reflection events according to interval selection of the initial stack profile in horizontal position.

[0019] In an implementation manner, the obtaining of the RMS velocity and the velocity variation factor corresponding to each of the local time windows based on the target function comprises:

[0020] inputting the prestack seismic data into each of the local time windows to perform local migration imaging and obtain local imaging results;

[0021] The objective function is used to inversely calculate the root mean square velocity and the velocity variation factor corresponding to each local time window.

[0022] In an implementation, the objective function is expressed as:

[0023]

[0024] wherein f represents the objective function, ||·|| represents a norm, w(Δt) represents a cross-correlation time shift stacking function, T(Δt) represents a weighting function, Δt represents a time sampling rate, V rms represents a root mean square velocity of an imaging point position, α v represents a velocity variation factor of the imaging point position.

[0025] In an implementation, the determining of the updated root mean square velocity model and the updated velocity variation factor model of the target area based on the root mean square velocities and the velocity variation factors corresponding to all the local time windows comprises:

[0026] The updated root mean square velocity model and the updated velocity variation factor model of the target area are determined by interpolation and smoothing based on the root mean square velocities and the velocity variation factors corresponding to all the local time windows.

[0027] In an implementation, the updated migration imaging gathers are expressed as:

[0028]

[0029] wherein,

[0030]

[0031]

[0032] wherein (x, y) represents a horizontal position of an imaging point, t represents a two-way time depth, h represents a shot-receiver offset, μ i represents an i-th seismic trace, n represents a total number of seismic traces, A represents an amplitude weight coefficient, τ s and τ g respectively represent two-parameter non-hyperbolic travel times of the imaging point to a shot point (x s , y s ) and a receiver (x g , y g ), α v is an updated velocity variation factor.

[0033] In a second aspect, the embodiments of the present application further provide a two-parameter pre-stack time migration device, wherein the device comprises:

[0034] an initial pre-stack time migration module, configured to obtain an initial root-mean-square (RMS) velocity model and pre-stack seismic data after dynamic correction velocity analysis of a target work area, and determine an initial stack profile of pre-stack time migration corresponding to the RMS velocity model;

[0035] a two-parameter inversion module, configured to determine a plurality of local time windows in the initial stack profile, obtain a target function corresponding to the local time windows, and obtain RMS velocity and a velocity variation factor corresponding to each of the local time windows based on the target function, wherein the local time windows are located on reflection events in an imaging stack profile;

[0036] a two-parameter model updating module, configured to determine an updated RMS velocity model and an updated velocity variation factor model of the target work area according to the RMS velocity and the velocity variation factor corresponding to all of the local time windows;

[0037] a two-parameter pre-stack time migration module, configured to perform two-parameter pre-stack time migration on the pre-stack seismic data based on the updated RMS velocity model and the updated velocity variation factor model, to obtain an updated migration imaging gather and an updated stack profile.

[0038] In a third aspect, the embodiments of the present application further provide a terminal device, wherein the terminal device comprises a memory, a processor, and a two-parameter pre-stack time migration program stored in the memory and executable on the processor, and the processor implements the steps of the two-parameter pre-stack time migration method of any one of the above solutions when executing the two-parameter pre-stack time migration program.

[0039] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, wherein the computer readable storage medium stores a two-parameter pre-stack time migration program, and the two-parameter pre-stack time migration program implements the steps of the two-parameter pre-stack time migration method of any one of the above solutions when executed by a processor.

[0040] Beneficial effects: compared with the prior art, the application provides a two-parameter pre-stack time migration method, the application first acquires an initial root mean square velocity model after dynamic correction velocity analysis of a target work area and pre-stack seismic data, and determines an initial stack profile of pre-stack time migration corresponding to the root mean square velocity model. Then, a plurality of local time windows in the initial stack profile are determined, a target function corresponding to the local time windows is acquired, and a root mean square velocity and a velocity variation factor corresponding to each of the local time windows are obtained based on the target function, wherein the local time windows are located on reflection events in the imaging stack profile. Next, the updated root mean square velocity model and the updated velocity variation factor model of the target work area are determined according to the root mean square velocities and the velocity variation factors corresponding to all the local time windows. Finally, the pre-stack seismic data are subjected to two-parameter pre-stack time migration based on the updated root mean square velocity model and the updated velocity variation factor model, and an updated migration imaging gather and an updated stack profile are obtained. The application can effectively improve the ability of pre-stack time migration to deal with more complex velocity variation, and obtain a higher-precision pre-stack time migration imaging result and migration velocity model. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The flowchart of the specific embodiment of the two-parameter pre-stack time migration method provided by the application is shown.

[0042] Figure 2 The initial root mean square velocity model of the two-parameter pre-stack time migration method provided by the application is shown.

[0043] Figure 3 The imaging profile of pre-stack time migration and the selected local time window of the two-parameter pre-stack time migration method provided by the application are shown.

[0044] Figure 4 The local imaging gather obtained by the typical local time window optimization inversion in the two-parameter pre-stack time migration method provided by the application is shown.

[0045] Figure 5 The root mean square velocity model obtained by the inversion update in the two-parameter pre-stack time migration method provided by the application is shown.

[0046] Figure 6 The velocity variation factor model obtained by the inversion in the two-parameter pre-stack time migration method provided by the application is shown.

[0047] Figure 7 The updated stack profile of the two-parameter pre-stack time migration in the two-parameter pre-stack time migration method provided by the application is shown.

[0048] Figure 8A partial enlarged view of the imaging profile of the conventional pre-stack time migration technique and the two-parameter pre-stack time migration technique of the embodiment of the present application.

[0049] Figure 9 A comparison diagram of the imaging gather of the conventional pre-stack time migration technique and the two-parameter pre-stack time migration technique of the embodiment of the present application.

[0050] Figure 10 A functional principle diagram of the two-parameter pre-stack time migration device provided by the embodiment of the present application.

[0051] Figure 11 A principle block diagram of the terminal device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and effects of the present application clearer and more explicit, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0053] The embodiment provides a two-parameter pre-stack time migration method, which can effectively improve the ability of pre-stack time migration to deal with more complex velocity variation and obtain higher-precision pre-stack time migration imaging results and migration velocity models. In a specific application, the embodiment first acquires an initial root-mean-square velocity model after dynamic correction velocity analysis of a target work area and pre-stack seismic data, and determines an initial stack profile of pre-stack time migration corresponding to the root-mean-square velocity model. Then, a plurality of local time windows in the initial stack profile are determined, a target function corresponding to the local time windows is acquired, and a root-mean-square velocity and a velocity variation factor corresponding to each of the local time windows are obtained based on the target function, wherein the local time windows are located on reflection events in the imaging stack profile. Next, the updated root-mean-square velocity model and the updated velocity variation factor model of the target work area are determined according to the root-mean-square velocities and the velocity variation factors corresponding to all the local time windows. Finally, the pre-stack seismic data are subjected to two-parameter pre-stack time migration based on the updated root-mean-square velocity model and the updated velocity variation factor model, and updated migration imaging gathers and an updated stack profile are obtained.

[0054] Exemplary method

[0055] Based on the above embodiment, the present application further provides a two-parameter pre-stack time migration method, as shown in Figure 1 The two-parameter pre-stack time migration method of the embodiment includes the following steps:

[0056] Step S100, obtaining an initial root-mean-square (RMS) velocity model after NMO velocity analysis of a target work area and pre-stack seismic data, and determining an initial stack profile of pre-stack time migration corresponding to the RMS velocity model.

[0057] Step S200, determining a plurality of local time windows in the initial stack profile, obtaining a target function corresponding to the local time windows, and inversely deriving RMS velocity and velocity variation factor corresponding to each of the local time windows based on the target function, wherein the local time windows are located on reflection events in the imaging stack profile.

[0058] Step S300, determining an updated RMS velocity model and an updated velocity variation factor model of the target work area according to RMS velocities and velocity variation factors corresponding to all of the local time windows.

[0059] Step S400, performing two-parameter pre-stack time migration on the pre-stack seismic data based on the updated RMS velocity model and the updated velocity variation factor model to obtain an updated migration gather and an updated stack profile.

[0060] The principle of the embodiment is as follows:

[0061] Based on the assumption of uniform horizontal layered medium, the travel time calculation of the conventional pre-stack time migration is represented as:

[0062]

[0063] In the formula, T0 represents zero-offset two-way travel time, r represents the horizontal distance from an imaging point to a shot point or a receiver, V rms represents the RMS velocity of the imaging point position. The travel time curve of one imaging point is determined by one RMS velocity. In order to improve the adaptability of time migration to lateral velocity variation, it is considered that the actual seismic wave propagation velocity for different ray incidence angles is different for an imaging point. Since the seismic wave with a larger incidence angle has a longer propagation path in a higher velocity stratum, the actual RMS velocity also increases with the increase of the incidence angle. The increased velocity can be represented as a function of the ray parameter.

[0064] By introducing the updated velocity variation factor α v , the RMS velocity related to the ray parameter can be represented as:

[0065] V rms (p r )=V rms (1+α v p r ) (2)

[0066] wherein the ray parameter is:

[0067]

[0068] Therefore, the travel time expressed by the above two parameters can be represented as:

[0069]

[0070] Therefore, for an imaging point, two equivalent parameters (V rms , α v ) are needed to determine the imaging result after pre-stack time migration. When α v = 0, the above formula degenerates into the single parameter expression (1). Therefore, the imaging gather of the two-parameter pre-stack time migration can be represented as:

[0071]

[0072] wherein:

[0073]

[0074]

[0075] In the formula, (x, y) represents the horizontal position of the imaging point, t represents the two-way time depth, h represents the offset of the shot-receiver point, μ i represents the i-th seismic trace, n represents the total number of seismic traces, A represents the amplitude weight coefficient, τ s and τ g respectively represent the two-parameter non-hyperbolic travel time of the imaging point to the shot point (x s , y s ) and the receiver point (x g , y g ). Therefore, the calculation of the two-parameter travel time expressed by formula (4) can realize the improved two-parameter pre-stack time migration.

[0076] Specifically, the embodiment first acquires the initial root mean square velocity model after dynamic correction velocity analysis of a target work area and pre-stack seismic data. Then, the pre-stack seismic data is subjected to conventional pre-stack time migration to obtain the initial stacked profile. The velocity estimation of the pre-stack time migration is actually a nonlinear parameter optimization problem, and since the imaging result of an imaging point is only related to the equivalent velocity of the point, the embodiment can independently invert and estimate the local time window.

[0077] The embodiment can select intervals according to the initial stack section in horizontal position to determine a plurality of time windows located at local positions on reflection events. Then, the pre-stack seismic data is input into each of the local time windows to perform local migration imaging to obtain local imaging results. The objective function is used to inversely calculate the local imaging results of each of the local time windows to obtain the root mean square velocity and the velocity variation factor corresponding to each of the local time windows. Specifically, for two-parameter pre-stack time migration, we need to estimate V rms and a v two parameters. For the two-parameter estimation problem, the simple parameter scanning technique is usually inefficient and difficult to guarantee accuracy. Therefore, in order to realize reliable and efficient two-parameter estimation, the embodiment adopts a global optimization fast simulated annealing method algorithm to simultaneously estimate the two parameters.

[0078] First, for the above-mentioned optimization inversion problem of determining the local time window, the embodiment constructs the following objective function:

[0079]

[0080] In the formula, f represents the objective function, ||·|| represents the norm, w(Δt) represents the cross-correlation time shift stack function, T(Δt) represents the weighting function, Δt represents the time sampling rate, V rms represents the root mean square velocity of the imaging point position, a v represents the velocity variation factor of the imaging point position.

[0081] During the inversion calculation process, for the local imaging gathers generated by the trial parameter migration, the corresponding time difference can be obtained by the cross-correlation calculation of adjacent traces in the gather, and the cross-correlation time shift stack function can be obtained by stacking the time difference, which can be represented as:

[0082]

[0083] In the formula: l represents the offset index, m represents the local imaging point index, and Δτ represents the cross-correlation time difference with adjacent imaging traces, which can be represented as:

[0084] Δτ l = argmax Δt C[I(x,t,h l ),I(x,(t+Δt),h l+k ](Δt) (8)

[0085] In the formula, C[.] represents the cross-correlation calculation, and I represents the local imaging gather after the trial parameter migration.

[0086] For the selected local time window, the local imaging gathers obtained by migration using the experimental parameters are used to calculate the objective function value. The optimal inversion parameters for each time window can be obtained by optimizing the objective function through the fast simulated annealing algorithm. rms ,α v ) are interpolated and smoothed to determine the inversion parameter model of the target area, namely the updated root mean square velocity model and the updated velocity change factor model. Then, two-parameter prestack time migration imaging is performed using Equation (5).

[0087] The following combination Figures 2-9 The content of this embodiment is further described in detail. The data of this embodiment is a 3D seismic data actually collected on land. The data has been analyzed using conventional CMP gather dynamic correction velocity analysis to obtain the root mean square velocity model.

[0088] According to step S100, the pre-processed pre-stack seismic data of the work area and the root mean square velocity model corresponding to line number 415 are obtained. The pre-stack seismic data obtained are from line numbers 126 to 625, with a minimum offset of 50 meters and a maximum offset of 4450 meters, a total of 500 lines, and about 400G of data. The root mean square velocity model obtained is as follows: Figure 2 shown.

[0089] Furthermore, this embodiment uses pre-stack time migration to obtain migration imaging gathers and then performs stretching, cutting and stacking. Figure 3 The resulting imaging stack profile is shown in Figure 2. Specific imaging parameters include: CDP (horizontal position) range from 70 to 1200, a total of 1131 CDPs, a CDP interval of 10 meters, an output imaging gather offset interval of 100 meters, 45 imaging gathers per CDP, an imaging time depth of 4.5 seconds, and a sampling rate of 2 milliseconds.

[0090] Further, according to step S200, this embodiment selects 73 local time windows with relatively clear events based on the offset stacking 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.

[0091] Furthermore, this embodiment performs fast simulated annealing optimization inversion on two parameters (i.e., root mean square velocity and velocity variation factor) in the selected local time window. Figure 4 The figure shows the local imaging gathers generated by the optimal inversion parameters in a typical window (CDP800, 2020ms). It can be seen that the reflection event axes of the imaging gathers are very straight, indicating that the parameter values ​​obtained by inversion are relatively accurate.

[0092] Further, the embodiment according to step S300 obtains two parameters (i.e. root mean square velocity and velocity variation factor) of each local time window position by inverting all selected windows, and obtains the inverted parameter model by interpolating and smoothing the two parameters. Figure 5 is the updated root mean square velocity model, Figure 6 is the velocity variation factor model.

[0093] Further, the embodiment according to step S400 uses the velocity model and the velocity variation factor parameter model to perform two-parameter pre-stack time migration. Figure 7 is the imaging stacked section, Figure 8 is the target layer image of the existing pre-stack time migration imaging result. By comparison, it can be seen that the imaging section of the embodiment has clearer events, and the imaging of steep dip angles is obviously improved, and the energy focusing is better.

[0094] Further, the embodiment extracts the migration imaging gathers at the section CDP 550 for comparison, as shown in Figure 9 By comparison, it can be found that the embodiment further flattens the imaging gathers, especially the large offset part, which also shows that the embodiment can improve the imaging effect of pre-stack time migration.

[0095] The application improves the travel time calculation accuracy in pre-stack time migration by introducing a new independent velocity-related parameter (velocity variation factor), expresses more complex non-hyperbolic characteristics of travel time, and further improves the ability of pre-stack time migration to adapt to the lateral variation of velocity, and improves the imaging effect. For the two-parameter modeling problem, the application uses a global optimization algorithm based on local imaging gathers, and simultaneously inverts two parameters, thereby improving the efficiency and accuracy of parameter modeling. Moreover, the application directly uses pre-stack seismic data for migration velocity analysis, and does not need to store pre-stack migration gathers, and has higher parameter inversion accuracy than the residual moveout analysis method based on pre-stack gathers.

[0096] Exemplary device

[0097] Based on the above embodiment, the application further provides a two-parameter pre-stack time migration device, as shown in Figure 10As shown, the apparatus of this embodiment includes: an initial pre-stack time migration module 10, a two-parameter inversion module 20, a two-parameter model update module 30, and a two-parameter pre-stack time migration module 40. The initial pre-stack time migration module 10 of this embodiment is used to obtain an initial root mean square velocity model and pre-stack seismic data after dynamic correction velocity analysis of the target work area, and determine an initial stack profile for pre-stack time migration corresponding to the root mean square velocity model. The two-parameter inversion module 20 is used to determine a number of local time windows in the initial stack profile, obtain an objective function corresponding to the local time window, and invert the root mean square velocity and velocity change factor corresponding to each local time window based on the objective function, wherein the local time window is located on the reflection event in the imaging stack profile. The two-parameter model update module 30 is used to determine an updated root mean square velocity model and an updated velocity change factor model for the target work area based on the root mean square velocities and velocity change factors corresponding to all the local time windows. The two-parameter prestack time migration module 40 is used to perform two-parameter prestack time migration on the prestack seismic data based on the updated root mean square velocity model and the updated velocity variation factor model to obtain updated migration imaging gathers and updated stack sections.

[0098] The working principles of each module in the two-parameter pre-stack time migration device of this embodiment are the same as the principles of each step in the above method embodiment, and will not be repeated here.

[0099] Based on the above embodiment, the present invention further provides a terminal device, the principle block diagram of the terminal device can be as follows: Figure 11 The terminal device may include one or more processors 100 ( Figure 11 Only one is shown in the figure), a memory 101, and a computer program 102 stored in the memory 101 and executable on one or more processors 100, for example, a program for two-parameter prestack time migration. When the one or more processors 100 execute the computer program 102, the steps of the two-parameter prestack time migration method embodiment can be implemented. Alternatively, when the one or more processors 100 execute the computer program 102, the functions of the modules / units of the two-parameter prestack time migration apparatus embodiment can be implemented, without limitation herein.

[0100] In one embodiment, the processor 100 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0101] In one embodiment, the memory 101 can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 101 can include both the internal storage unit and the external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the terminal device. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0102] Those skilled in the art can understand that, Figure 11 The principle block diagram shown in the figure is only a block diagram of part of the structure related to the present application, and does not constitute a limitation on the terminal device to which the present application is applied. The specific terminal device can include more or less components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, operating database or other medium used in the embodiments of the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0104] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A two parameter prestack time migration method characterized by, The method comprises: acquiring an initial root-mean-square velocity model after velocity analysis of a target work area and prestack seismic data, and determining an initial stack profile of prestack time migration corresponding to the root-mean-square velocity model; determining a plurality of local time windows in the initial stack profile, acquiring a target function corresponding to the local time windows, and inversely deriving a root-mean-square velocity and a velocity variation factor corresponding to each of the local time windows based on the target function, wherein the local time windows are located on reflection events in an imaging stack profile; determining an updated root-mean-square velocity model and an updated velocity variation factor model of the target work area according to the root-mean-square velocities and the velocity variation factors corresponding to all of the local time windows; performing two-parameter prestack time migration on the prestack seismic data based on the updated root-mean-square velocity model and the updated velocity variation factor model to obtain an updated migration gather and an updated stack profile.

2. The two parameter prestack time migration method of claim 1, wherein, The method comprises: performing conventional prestack time migration on the prestack seismic data to obtain the initial stack profile, wherein travel time calculation of the conventional prestack time migration is represented as: In the formula, T0 represents zero-offset two-way travel time, r represents the horizontal distance from the imaging point to the shot point or the receiver point, V rms represents the root-mean-square velocity of the imaging point position.

3. The 2-parameter prestack time migration method of claim 1, wherein, The method comprises: selecting a plurality of time windows located at local positions on reflection events according to the initial stack profile by interval selection in horizontal positions.

4. The 2P prestack time migration method of claim 1, wherein, The method comprises: inputting the prestack seismic data into each of the local time windows to perform local migration imaging and obtain local imaging results; inversely deriving the root-mean-square velocity and the velocity variation factor corresponding to each of the local time windows by using the target function to inversely process the local imaging results of each of the local time windows one by one.

5. The 2P prestack time migration method of claim 4, wherein, The target function is represented as: where f represents the objective function, ||·|| represents a norm, w(Δt) represents a cross-correlation time-shifted stack function, T(Δt) represents a weighting function, Δt represents a time sampling rate, V rms represents a root-mean-square velocity of the imaged point location, a v represents a velocity variation factor of the imaged point location.

6. The 2p prestack time migration method of claim 2, wherein, The method comprises: determining the updated root-mean-square velocity model and the updated velocity variation factor model of the target work area by interpolation and smoothing based on the root-mean-square velocities and the velocity variation factors corresponding to all of the local time windows.

7. The 2p prestack time migration method of claim 2, wherein, The updated migration gather is represented as: wherein where (x, y) represents the horizontal position of the imaging point, t represents the two-way time depth, h represents the offset of the shot point, μ i represents the i-th seismic trace, n represents the total number of seismic traces, A represents the amplitude weight coefficient, τ s and τ g respectively represent the two-parameter non-hyperbolic travel time from the imaging point to the shot point (x s ,y s ) and the receiver point (x g ,y g ), and α v is the updated velocity variation factor.

8. A two parameter prestack time migration apparatus, characterized by, The device comprises: an initial prestack time migration module configured to acquire an initial root-mean-square velocity model after velocity analysis of a target work area and prestack seismic data, and determine an initial stack profile of prestack time migration corresponding to the root-mean-square velocity model; a two-parameter inversion module configured to determine a plurality of local time windows in the initial stack profile, acquire a target function corresponding to the local time windows, and inversely derive a root-mean-square velocity and a velocity variation factor corresponding to each of the local time windows based on the target function, wherein the local time windows are located on reflection events in an imaging stack profile; and The two-parameter model updating module is configured to determine an updated root-mean-square velocity model and an updated velocity variation factor model of the target area according to the root-mean-square velocities and the velocity variation factors corresponding to all the local time windows. The two-parameter pre-stack time migration module is configured to perform two-parameter pre-stack time migration on the pre-stack seismic data based on the updated root-mean-square velocity model and the updated velocity variation factor model to obtain an updated migration imaging gather and an updated stacked profile.

9. A terminal device, comprising: The terminal device comprises a memory, a processor, and a two-parameter pre-stack time migration program stored in the memory and executable on the processor. When the processor executes the two-parameter pre-stack time migration program, the steps of the two-parameter pre-stack time migration method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a two-parameter pre-stack time migration program. When the two-parameter pre-stack time migration program is executed by the processor, the steps of the two-parameter pre-stack time migration method according to any one of claims 1-7 are implemented.

Images