Near-surface velocity modeling method and device with adaptive damping regularization

Abstract

A near-surface modeling method, device, and electronic device with adaptive damping regularization. The near-surface velocity modeling method with adaptive damping regularization includes: (1) performing ray tracing on an initial near-surface velocity model, calculating the ray paths from each shot point to the geophone point, and then establishing a travel-time tomography kernel function K; (2) calculating the theoretical travel time based on the initial near-surface velocity model and the ray paths, and calculating the travel-time difference Δt between the theoretical travel time and the true travel time; (3) obtaining an adaptive damping factor D based on the travel-time tomography kernel function K; (4) establishing a tomography equation based on the travel-time tomography kernel function K, the travel-time difference Δt, and the adaptive damping factor D, and solving the tomography equation to obtain an updated near-surface velocity. By introducing adaptive damping regularization, the present invention can more stably and efficiently obtain a near-surface velocity model with good stability and high accuracy, providing a high-quality near-surface velocity model for subsequent migration imaging.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical exploration, and more specifically, to a near-surface velocity modeling method, device and electronic equipment with adaptive damping regularization, which can be applied to near-surface velocity modeling in geophysical exploration. Background Art

[0002] With the continuous development of oil and gas exploration in China, areas with undulating surfaces such as the piedmont zone have become the focus of current seismic exploration work. In these exploration areas with undulating surfaces, the sharp changes in near-surface elevation and velocity have caused relatively serious impacts on the acquisition, processing and interpretation of seismic data. The piedmont zones in the Kuqa Depression and Sichuan region have the dual complex characteristics of complex surface and complex underground structures, and are the hotspots of current seismic exploration processing research. When processing seismic data in exploration areas with complex near-surface characteristics, an accurate near-surface velocity model is the prerequisite and foundation for achieving good results in high-precision migration imaging, and is the key to implementing the structure.

[0003] The conventional practice of near-surface velocity modeling is to first perform first arrival picking, then establish an initial near-surface velocity, then perform ray tracing, calculate travel time residuals, solve equations, and then update the near-surface velocity, and continuously iterate to finally obtain the near-surface velocity. However, the tomography equation in conventional near-surface modeling is ill-conditioned. Without regularization constraints, local extrema will occur. In the subsequent iteration process, the high-speed anomaly makes the ray density in this area higher and higher, and it cannot be solved, ultimately resulting in inaccurate modeling. Moreover, the conventional damping operator is the regularization coefficient multiplied by the identity matrix. There are still two problems here. One is that it is difficult to determine the regularization coefficient, and the other is that the situation of local extrema cannot be alleviated. Therefore, it is expected to propose a method that can reduce the occurrence of local extrema in the near-surface velocity inversion result, making the near-surface model more stable and with higher accuracy. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an adaptive damping regularization near-surface modeling method, device and electronic equipment. Among them, the adaptive damping regularization near-surface modeling method introduces an adaptive damping operator constraint when solving the tomography equation, so that the near-surface velocity inversion result reduces the occurrence of local extrema, and the near-surface model is more stable and has higher accuracy.

[0005] To achieve the above object, the first aspect of the present invention provides an adaptive damping regularization near-surface velocity modeling method, including:

[0006] Step 1: Perform ray tracing on the initial near-surface velocity model, calculate the ray path from each shot point to the receiving point, and then establish a travel time tomography kernel function K;

[0007] Step 2: Calculate the theoretical travel time based on the initial near-surface velocity model and the ray path, and calculate the travel time difference Δt between the theoretical travel time and the true travel time;

[0008] Step 3: Obtain the adaptive damping factor D based on the travel time tomography kernel function K;

[0009] Step 4: Establish a tomography equation based on the travel time tomography kernel function K, the travel time difference Δt, and the adaptive damping factor D, solve the tomography equation, and obtain the updated near-surface velocity.

[0010] Optionally, use the fourth-order Runge-Kutta formula to calculate the ray tracing equation (1) to obtain the ray path from the shot point to the geophone:

[0011]

[0012] where x, y, and z respectively represent the three-dimensional space coordinates, p x , p y , p z represent the slowness in the x, y, and z directions at the current coordinate, v represents the velocity, τ represents the time, θ, represent the dip angle and azimuth angle.

[0013] Optionally, the travel time tomography kernel function K is an m×n matrix, m represents the total number of rays, n represents the number of grids, and the i-th column in K represents the length of the i-th ray passing through the grid.

[0014] Optionally, sum the columns of the travel time tomography kernel function K and perform matrix diagonalization to obtain the adaptive damping factor D, as shown in formula (2):

[0015] D = diag(1 T K) (2)

[0016] where 1 represents a 1×n vector of 1s, and diag represents the diagonalization operation.

[0017] Optionally, the tomography equation is as shown in formula (3):

[0018]

[0019] where λ represents the damping term regularization parameter, 0 represents a 1×n vector of 0s, Δs is the slowness update amount, with n columns, Δt is the travel time difference, with m rows.

[0020] Optionally, the range of the damping term regularization parameter λ is 0.1 to 1.

[0021] Optionally, the near-surface velocity modeling method based on adaptive damping regularization further includes:

[0022] Repeat the above steps 1 to 4 with the updated near-surface velocity as the near-surface velocity until the preset number of iterations is reached.

[0023] The second aspect of the present invention provides an apparatus for near-surface velocity modeling with adaptive damping regularization, including:

[0024] A travel-time tomography kernel function establishment module, configured to perform ray tracing on an initial near-surface velocity model, calculate the ray path from each shot point to the geophone point, and further establish a travel-time tomography kernel function K;

[0025] A travel-time difference calculation module, configured to calculate the theoretical travel time based on the initial near-surface velocity model and the ray path, and calculate the travel-time difference Δt between the theoretical travel time and the true travel time;

[0026] An adaptive damping factor D calculation module, configured to obtain an adaptive damping factor D based on the travel-time tomography kernel function K;

[0027] A tomography equation solving module, configured to establish a tomography equation based on the travel-time tomography kernel function K, the travel-time difference Δt, and the adaptive damping factor D, and solve the tomography equation to obtain an updated near-surface velocity.

[0028] The third aspect of the present invention provides an electronic device, including:

[0029] A memory, storing executable instructions;

[0030] A processor, which runs the executable instructions in the memory to implement the method for near-surface velocity modeling with adaptive damping regularization according to any one of the first aspect.

[0031] The fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the method for near-surface velocity modeling with adaptive damping regularization according to any one of the first aspect.

[0032] The effect of the present invention is that the method for near-surface modeling with adaptive damping regularization solves the problems and difficulties such as unstable velocity models and local extrema obtained by conventional near-surface modeling methods, which still cannot be effectively solved even by introducing conventional damping regularization. By introducing adaptive damping regularization, a near-surface velocity model with good stability and high accuracy can be obtained more stably and efficiently, providing a high-quality near-surface velocity model for subsequent migration imaging.

[0033] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail with reference to the accompanying drawings, in which, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0035] Figure 1 It is a flowchart of the near-surface velocity modeling method with adaptive damping regularization in the embodiments of the present invention.

[0036] Figure 2 It is a comparison of conventional near-surface velocity modeling and near-surface velocity modeling with adaptive damping regularization in the embodiments of the present invention. (a) Conventional near-surface velocity modeling, (b) Near-surface velocity modeling with adaptive damping regularization.

[0037] Figure 3 It is a comparison of migration imaging of conventional near-surface velocity modeling and migration imaging of near-surface velocity modeling with adaptive damping regularization in the embodiments of the present invention. (a) Migration imaging of conventional near-surface velocity modeling, (b) Migration imaging of near-surface velocity modeling with adaptive damping regularization. Detailed implementation manners

[0038] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0039] A near-surface velocity modeling method with adaptive damping regularization includes:

[0040] Step 1: Perform ray tracing on the initial near-surface velocity model, calculate the ray paths from each shot point to the receiver point, and then establish the travel-time tomography kernel function K;

[0041] Step 2: Calculate the theoretical travel time based on the initial near-surface velocity model and the ray paths, and calculate the travel-time difference Δt between the theoretical travel time and the true travel time;

[0042] Step 3: Obtain the adaptive damping factor D based on the travel-time tomography kernel function K;

[0043] Step 4: Establish a tomography equation based on the travel-time tomography kernel function K, the travel-time difference Δt, and the adaptive damping factor D, and solve the tomography equation to obtain the updated near-surface velocity.

[0044] Optionally, use the fourth-order Runge-Kutta formula to calculate the ray-tracing equation (1) to obtain the ray paths from the shot point to the receiver point:

[0045]

[0046] where x, y, and z respectively represent the three-dimensional space coordinates, px , p y , p z represents the slowness in the x, y, and z directions at the current coordinate, v represents the velocity, τ represents the time, and θ, represents the dip angle and azimuth angle.

[0047] Optionally, the travel-time tomography kernel function K is an m-by-n matrix, where m represents the total number of rays and n represents the number of grids. The i-th column in K represents the length of the i-th ray passing through the grids.

[0048] Optionally, sum the columns of the travel-time tomography kernel function K and perform matrix diagonalization to obtain the adaptive damping factor D, as shown in Equation (2):

[0049] D = diag(1 T K) (2)

[0050] where 1 represents a 1-by-n vector of 1s, and diag represents the diagonalization operation.

[0051] Optionally, the tomography equation is as shown in Equation (3):

[0052]

[0053] where λ represents the regularization parameter of the damping term, 0 represents a 1-by-n vector of 0s, Δs is the slowness update amount with n columns, and Δt is the travel-time difference with m rows.

[0054] Conventional damping regularization is as follows:

[0055]

[0056] where λ represents the regularization parameter of the damping term, I is the identity matrix, the matrix is n-by-n, and 0 represents a 1-by-n vector of 0s. Conventional damping regularization has two problems. One is that it is difficult to determine the regularization coefficient, and the other is that it cannot alleviate the local extreme situation. In this application, the adaptive damping factor D is introduced into the tomography equation, which can more stably and efficiently obtain a near-surface velocity model with good stability and high accuracy, providing a high-quality near-surface velocity model for subsequent migration imaging.

[0057] Optionally, the range of the regularization parameter λ of the damping term is 0.1 to 1.

[0058] Optionally, the method for near-surface velocity modeling with adaptive damping regularization further includes:

[0059] Taking the updated near-surface velocity as the near-surface velocity, and repeating steps 1 to 4 until a preset number of iterations is reached.

[0060] Example 1:

[0061] An adaptive damping regularization method for near-surface velocity modeling, comprising:

[0062] Step 1: Perform ray tracing on the initial near-surface velocity model, calculate the ray paths from each shot point to the geophone points, and then establish a travel-time tomography kernel function K;

[0063] Step 2: Calculate the theoretical travel time based on the initial near-surface velocity model and the ray paths, and calculate the travel-time difference Δt between the theoretical travel time and the true travel time;

[0064] Step 3: Obtain an adaptive damping factor D based on the travel-time tomography kernel function K;

[0065] Step 4: Establish a tomography equation based on the travel-time tomography kernel function K, the travel-time difference Δt, and the adaptive damping factor D, solve the tomography equation, and obtain an updated near-surface velocity.

[0066] This method solves the problems of unstable velocity models and local extrema obtained by conventional near-surface velocity modeling methods. By introducing adaptive damping regularization, a more stable and efficient near-surface velocity model with good stability and high accuracy can be obtained, providing a high-quality near-surface velocity model for subsequent migration imaging. The technical effects of the present invention are illustrated by the following comparison with reference to embodiments:

[0067] Figure 2 Figure (a) shows the results of conventional near-surface modeling and near-surface modeling with adaptive damping regularization. Among them, (a) is conventional near-surface modeling, and (b) is near-surface modeling with adaptive damping regularization. It can be seen from Figure 2 (a) that there are many spherical local high values in the conventional modeling method, and adaptive damping regularization can solve this problem, and the obtained model is relatively smooth and natural as shown in (b);

[0068] Figure 3 Figure (a) shows the migration imaging of conventional near-surface modeling and (b) shows the migration imaging of near-surface modeling with adaptive damping regularization. By comparison, it can be clearly seen that the imaging quality and signal-to-noise ratio in (b) are higher, which indicates that the near-surface velocity model obtained by the adaptive damping regularization algorithm is more stable, and finally a high-quality migration imaging result is obtained.

[0069] Embodiment 2:

[0070] An adaptive damping regularization device for near-surface velocity modeling, comprising:

[0071] The travel-time tomography kernel building module is used to perform ray tracing on the initial near-surface velocity model, calculate the ray paths from each shot point to the geophone point, and then build the travel-time tomography kernel K;

[0072] The travel-time difference calculation module calculates the theoretical travel time based on the initial near-surface velocity model and the ray paths, and calculates the travel-time difference Δt between the theoretical travel time and the true travel time;

[0073] The adaptive damping factor D calculation module obtains the adaptive damping factor D based on the travel-time tomography kernel K;

[0074] The tomography equation solving module builds a tomography equation based on the travel-time tomography kernel K, the travel-time difference Δt, and the adaptive damping factor D, and solves the tomography equation to obtain the updated near-surface velocity.

[0075] Optionally, the fourth-order Runge-Kutta formula is used to calculate the ray tracing equation (1) to obtain the ray paths from the shot point to the geophone point:

[0076]

[0077] where x, y, and z respectively represent the three-dimensional space coordinates, and p x , p y , p z represent the slowness in the x, y, and z directions at the current coordinate, v represents the velocity, τ represents the time, and θ, represent the dip angle and azimuth angle.

[0078] Optionally, the travel-time tomography kernel K is an m×n matrix, m represents the number of all rays, n represents the number of grids, and the i-th column in K represents the length of the i-th ray passing through the grid.

[0079] Optionally, the travel-time tomography kernel K is summed in the column direction and matrix diagonalization is performed to obtain the adaptive damping factor D, as shown in formula (2):

[0080] D = diag(1 T K) (2)

[0081] where 1 represents a 1×n vector of 1s, and diag represents the diagonalization operation.

[0082] Optionally, the tomography equation is as shown in formula (3):

[0083]

[0084] where λ represents the damping term regularization parameter, 0 represents a 1×n vector of 0s, Δs is the slowness update amount, n columns, and Δt is the travel-time difference, m rows.

[0085] Optionally, the range of the damping term regularization parameter λ is 0.1 to 1.

[0086] Optionally, the near-surface velocity modeling method with adaptive damping regularization further includes:

[0087] Taking the updated near-surface velocity as the near-surface velocity, and repeatedly executing the above steps 1 to 4 until a preset number of iterations is reached.

[0088] Embodiment III:

[0089] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0090] The memory stores executable instructions.

[0091] The processor runs the executable instructions in the memory to implement the near-surface velocity modeling method with adaptive damping regularization.

[0092] This memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and these computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. This volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. This non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0093] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0094] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present invention.

[0095] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0096] Embodiment IV:

[0097] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the near-surface velocity modeling method with adaptive damping regularization.

[0098] A computer-readable storage medium according to an embodiment of the present invention stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0099] The above computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).

[0100] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An adaptive damping regularization method for near-surface velocity modeling, characterized in that Including: Step 1: Conduct ray tracing on the initial near-surface velocity model, calculate the ray paths from each shot point to the geophone points, and then establish the travel-time tomography kernel function K; Step 2: Calculate the theoretical travel time based on the initial near-surface velocity model and the ray path, and calculate the travel time difference between the theoretical travel time and the true travel time ; Step 3: Obtain the adaptive damping factor D based on the travel-time tomography kernel function K; Step 4: Based on the travel-time tomography kernel function K, the travel-time difference and the adaptive damping factor D, establish a tomography equation, solve the tomography equation, and obtain an updated near-surface velocity; Sum the travel-time tomography kernel function K in the column direction and perform matrix diagonalization to obtain the adaptive damping factor D, as shown in formula (2): （2） Among them, denotes 's 1 vector, and diag denotes the diagonalization operation.

2. The near-surface velocity modeling method with adaptive damping regularization according to claim 1, wherein Use the fourth-order Runge-Kutta formula to calculate the ray-tracing equation (1) to obtain the ray paths from the shot points to the geophone points: （1） where x, y, and z respectively represent three-dimensional space coordinates, indicating slowness in the x, y, and z directions at the current coordinates, and v represents velocity, representing time, representing dip angle and azimuth.

3. The near-surface velocity modeling method with adaptive damping regularization according to claim 2, wherein The travel-time tomography kernel function K is an m×n matrix, where m represents the total number of rays and n represents the number of grids. The i-th column in K represents the length of the ray passing through the i-th grid.

4. The near-surface velocity modeling method with adaptive damping regularization according to claim 1, characterized in that The tomography equation is as shown in formula (3): （3） wherein represents the damping term regularization parameter, is represented as the zero vector of the slowness update amount, n columns, is the traveltime difference, m rows.

5. The near-surface velocity modeling method with adaptive damping regularization according to claim 4, characterized in that The damping term regularization parameter ranges from 0.1 to 1.

6. The near-surface velocity modeling method with adaptive damping regularization according to claim 1, wherein Also including: Take the updated near-surface velocity as the near-surface velocity and repeat steps 1 to 4 until a preset number of iterations is reached.

7. An apparatus for near-surface velocity modeling with adaptive damping regularization, characterized in that, Including: A travel-time tomography kernel function establishment module for conducting ray tracing on the initial near-surface velocity model, calculating the ray paths from each shot point to the geophone points, and then establishing the travel-time tomography kernel function K; Travel time difference calculation module, calculates the theoretical travel time based on the initial near-surface velocity model and the ray path, and calculates the travel time difference between the theoretical travel time and the true travel time ; An adaptive damping factor D calculation module for obtaining the adaptive damping factor D based on the travel-time tomography kernel function K; Tomography equation solving module, based on the travel-time tomography kernel function K, travel-time difference and the adaptive damping factor D to establish a tomography equation, solve the tomography equation, and obtain an updated near-surface velocity; Sum the travel-time tomography kernel function K in the column direction and perform matrix diagonalization to obtain the adaptive damping factor D, as shown in formula (2): （2） Among them, represents a 1-vector, and diag represents the diagonalization operation.

8. An electronic device, characterized in that, The electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the adaptive damping regularization-based near-surface velocity modeling method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the adaptive damping regularization-based near-surface velocity modeling method according to any one of claims 1-6.

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