Irregular sampling observation system optimization method and device, equipment and storage medium
By optimizing the non-regular sampling observation system, the problem of insufficient illumination in seismic exploration is solved, more uniform wave field sampling and richer underground structure and fluid change details are achieved, and the optimization effect of seismic imaging quality and acquisition parameters is improved.
Patent Information
- Application Number
- CN202311725010.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-14
- Publication Date
- 2025-06-17
AI Technical Summary
In seismic exploration, the prior art is difficult to improve the illumination of underground acquisition under cost controllable and equipment limitations, providing more uniform wave field sampling and richer underground structure and fluid change details for subsequent processing and inversion.
By optimizing the non-regular sampling observation system, preset the grid cell size of the observation system according to the known geological model, divide the grid model, obtain the coordinates of the destination layer, and based on the maximum correlation value of the perceptual matrix vector and the maximum noise level of the imaging data, the greedy sequential algorithm is used to optimize the layout of the gun points and the detection point, forming an optimized non-regular observation system to determine the illumination of the destination layer coordinates.
Optimization of the weak lighting area for the target layer is achieved. By integrating the local gun channel density parameters on the ground and the surface reverse lighting parameters of the underground target geological bodies, lighting indicators reflecting the distribution characteristics of the non-regular gun inspection points on the surface are formed, which improves the lighting calculation efficiency and analysis accuracy, optimizes the acquisition parameters, and improves the quality of data imaging.
Smart Images
Figure CN120162923A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of seismic acquisition design in seismic exploration, and particularly relates to an optimization method and device, equipment, and storage medium for an irregular sampling observation system. Background Art
[0002] Three-dimensional seismic data acquisition is the primary link in seismic exploration, and the quality of field seismic data acquisition directly affects the interpretation accuracy and resolution of hydrocarbon-bearing structures and subsurface fluids. In recent years, domestic oil and gas exploration has gradually focused on unconventional, mid-deep, complex structures, and lithologic hydrocarbon reservoirs, posing higher requirements for the quality and efficiency of field seismic data acquisition. Under the conditions of controllable acquisition costs and acquisition equipment limitations, enhancing subsurface acquisition illumination and providing more uniform wavefield sampling and richer details of subsurface structure and fluid changes for subsequent processing and inversion are urgent problems to be solved in current seismic acquisition scheme design and optimization.
[0003] During the process of conventional seismic data acquisition, relatively regular time and space sampling are preferably used for signal excitation and reception. In recent years, seismic data acquisition methods based on irregular sampling and wavefield reconstruction theory can further reduce acquisition costs and better adapt to various complex surface environments. The purpose of seismic data acquisition is to provide high-fidelity wavefield information for subsequent processing and interpretation. There are certain differences in the impacts on subsurface illumination and illumination uniformity between spatial regularization and irregular sampling schemes, as well as between different spatial irregular sampling schemes.
[0004] The actual exploration target of interest is a specific subsurface geological body, and the illumination intensity and uniformity of the subsurface target are important guarantees for the quality of seismic imaging and inversion results. Illumination analysis for exploration targets is an important part of observation system optimization, migration imaging, and seismic inversion. Summary of the Invention
[0005] To solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide an optimization method and device, equipment, and storage medium for an irregular sampling observation system.
[0006] In a first aspect, embodiments of the present disclosure provide an optimization method for an irregular sampling observation system, including: Determining the grid cell size of a preset observation system according to the coordinates of shot points and geophone points in the preset observation system of a known geological model, where the preset observation system is a regular sampling observation system; Dividing the geological model into a grid model according to the grid cell size of the preset observation system, and obtaining the coordinates of the target layer in the grid model; Optimizing the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system; Based on the optimized irregular acquisition system, determine the illumination of the target layer coordinates.
[0007] In one possible implementation, the method for determining the grid cell size of the preset acquisition system according to the coordinates of the shot points and the geophone points in the known geological model includes: Analyze the coordinates of the shot points and the geophone points in the preset acquisition system, and respectively determine the longitudinal grid size and the transverse grid size of the shot points and the geophone points; Determine the grid cell size of the preset acquisition system according to the longitudinal grid size and the transverse grid size of the shot points and the geophone points.
[0008] In one possible implementation, the method further includes: Determine the grid cell size for model analysis according to the grid cell size of the preset acquisition system, where the longitudinal size and the transverse size of the grid cells of the geological model are respectively 1 / n of the preset acquisition system, and n is a positive integer and a multiple of 2; Determine the illumination of the target layer coordinates of the pre-optimization regular acquisition system and the post-optimization irregular acquisition system according to the grid cell size for model analysis; Compare the illumination of the target layer coordinates of the pre-optimization regular acquisition system and the post-optimization irregular acquisition system to ensure the illumination intensity and illumination uniformity of the target layer.
[0009] In one possible implementation, the method for optimizing the preset acquisition system based on the maximum correlation value of the sensing matrix vectors and the maximum noise level of the imaging data to obtain the post-optimization irregular acquisition system includes: Taking the minimum of both the maximum correlation value of the sensing matrix vectors and the maximum noise level of the imaging data as the optimization objective, and taking the distance between the shot point and the geophone point within the preset safety distance as the constraint condition, use the greedy sequential algorithm to irregularly arrange at least one of the shot points and the geophone points. When the number of iterations reaches the preset number of iterations, output the iteration result reaching the preset number of iterations, where the iteration result includes the post-optimization irregular acquisition system and the sampling parameters.
[0010] In one possible implementation, the maximum correlation value of the sensing matrix vectors is obtained through the following expression:
[0011] =
[0012] where is the sensing matrix is the maximum correlation value of the i-th and j-th column vectors of the sensing matrix, is the receiving point vector in the sensing matrix, is the excitation point vector in the sensing matrix, The maximum noise level of the imaging data is obtained through the following expression:
[0013] where, is the point at the imaging data of the maximum noise level, is the maximum time of the target layer, is the up-going data of the receiving point at time t, is the down-going data of the excitation point at time t.
[0014] In a possible implementation manner, determining the illumination of the coordinates of the target layer based on the optimized irregular observation system includes: Based on the positions of the shot points and the geophone points in the optimized irregular observation system and the incident signal of the seismic wave when the shot point is excited, determining the frequency-domain source-to illumination and the time-domain source-to illumination at the underground space points of the target layer.
[0015] In a possible implementation manner, the frequency-domain source-to illumination at the underground space points of the target layer is determined through the following expression:
[0016] where, is the frequency-domain source-to illumination at the underground space point of the target layer, is the shot point, is the receiving point, is the frequency of the incident signal, is the source-to single-frequency wavefield response, The time-domain source-to illumination at the underground space points of the target layer is determined through the following expression:
[0017] where, is the time-domain source-to illumination at the underground space point of the target layer, is the shot point, is the receiving point, is the frequency of the incident signal, is the source-to single-frequency wavefield response.
[0018] In a second aspect, an embodiment of the present disclosure provides an optimized device for an irregular sampling observation system, including: A first determination module, configured to determine the grid cell size of a preset observation system according to the coordinates of shot points and geophone points in the preset observation system of a known geological model, where the preset observation system is a regular sampling observation system; A division module, configured to divide the geological model into a grid model according to the grid cell size of the preset observation system, and obtain the target layer coordinates in the grid model; An optimization module, configured to optimize the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data, and obtain an optimized irregular observation system; A second determination module, configured to determine the illumination of the target layer coordinates based on the optimized irregular observation system.
[0019] In a third aspect, an embodiment of the present disclosure provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store a computer program; The processor is configured to implement the above-mentioned irregular sampling observation system optimization method when executing the program stored on the memory.
[0020] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. The computer program is characterized in that when the computer program is executed by a processor, the above-mentioned irregular sampling observation system optimization method is implemented.
[0021] The above technical solutions provided by the embodiments of the present disclosure have at least some or all of the following advantages compared with the prior art: For the irregular sampling observation system optimization method described in the embodiments of the present disclosure, the grid cell size of the preset observation system is determined according to the coordinates of shot points and geophone points in the preset observation system of a known geological model, where the preset observation system is a regular sampling observation system; the geological model is divided into a grid model according to the grid cell size of the preset observation system, and the target layer coordinates in the grid model are obtained; the preset observation system is optimized based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data, and an optimized irregular observation system is obtained; the illumination of the target layer coordinates is determined based on the optimized irregular observation system, which can face the weak illumination area of the target layer space, fuse the local shot density parameter on the ground reflecting the ground and the reverse illumination parameter of the underground target geological body on the surface, form an illumination index reflecting the distribution characteristics of irregular shot and geophone points on the surface, avoid repeated calculation of the seismic wave field, improve the illumination calculation efficiency and analysis accuracy, and achieve the purpose of efficient acquisition parameter optimization through illumination index optimization. Description of the Drawings
[0022] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.
[0023] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the accompanying drawings required for use in the description of the embodiments or the related art will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0024] Figure 1 Schematically shows a schematic flow diagram of an optimization method for an irregular sampling observation system according to an embodiment of the present disclosure; Figure 2a Figures 2b and 2c schematically show a schematic diagram of the lighting calculation results of a regular layout of a 12.5m * 12.5m grid according to an embodiment of the present disclosure; Figure 3a Figures 3b and 3c schematically show a schematic diagram of the lighting calculation results of a regular layout of a 6.25m * 6.25m grid according to an embodiment of the present disclosure; Figure 4a Figures 4b and 4c schematically show a schematic diagram of the lighting calculation results of an irregular layout of a 6.25m * 6.25m grid according to an embodiment of the present disclosure; Figure 5 Schematically shows a structural block diagram of an optimization device for an irregular sampling observation system according to an embodiment of the present disclosure; Figure 6 Schematically shows a structural block diagram of an electronic device according to an embodiment of the present disclosure. Detailed implementation manners
[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts belong to the scope of protection of the present disclosure.
[0026] See Figure 1 , embodiments of the present disclosure provide an optimization method for an irregular sampling observation system, including the following steps: S1. Determine the grid cell size of the preset observation system according to the coordinates of the shot points and geophone points in the preset observation system of the known geological model, where the preset observation system is a regular sampling observation system.
[0027] S2. Divide the geological model into a grid model according to the grid cell size of the preset observation system, and obtain the coordinates of the target layer in the grid model.
[0028] In this embodiment, the geological model is a horizontal layered model.
[0029] S3. Optimize the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system.
[0030] S4. Determine the illumination of the coordinates of the target layer based on the optimized irregular observation system.
[0031] In this embodiment, in step S1, the determining the grid cell size of the preset observation system according to the coordinates of the shot points and geophone points in the preset observation system of the known geological model includes: Analyze the coordinates of the shot points and geophone points in the preset observation system, and respectively determine the longitudinal grid size and transverse grid size of the shot points and geophone points; According to the longitudinal grid size and transverse grid size of the shot points and geophone points, determine the grid cell size of the preset observation system, ensuring that in the grid model divided from the geological model according to the grid cell size of the preset observation system, the coordinates of the shot points and geophone points are all on the grid points, which is convenient for directly moving the shot points and geophone points of the preset observation system from the initial grid points to other grid points when optimizing the positions of the shot points and geophone points.
[0032] In this embodiment, the method further includes: Determine the grid cell size for model analysis according to the grid cell size of the preset observation system, where the longitudinal size and transverse size of the grid cells of the geological model are respectively 1 / n of the preset observation system, and n is a positive integer and a multiple of 2; Determine the illumination of the coordinates of the target layer of the regular observation system before optimization and the irregular observation system after optimization according to the grid cell size for model analysis; Compare the illumination of the coordinates of the target layer of the regular observation system before optimization and the irregular observation system after optimization to ensure the illumination intensity and illumination uniformity of the target layer.
[0033] In this embodiment, in step S3, the optimizing the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system includes: Taking the minimum of the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data as the optimization objective, and taking the distance between the shot point and the receiver point within the preset safety distance as the constraint condition, a greedy sequential algorithm is used to irregularly arrange at least one of the shot points and the receiver points. When the number of iterations reaches the preset number of iterations, the iterative result reaching the preset number of iterations is output, where the iterative result includes the optimized irregular observation system and sampling parameters.
[0034] In this embodiment, the optimized irregular observation system and sampling parameters include, but are not limited to, the minimum shot point spacing, the minimum receiver point spacing, the maximum shot point spacing, the maximum receiver point spacing, the number of iterations, the adjusted number of shot points, the adjusted number of receiver points, and the positions of the shot and receiver points.
[0035] In this embodiment, the maximum correlation value of the sensing matrix vector is obtained through the following expression:
[0036] =
[0037] where is the maximum correlation value of the i-th and j-th column vectors of the sensing matrix is the receiver point vector in the sensing matrix, is the source point vector in the sensing matrix, is the source point vector in the sensing matrix, The maximum noise level of the imaging data is obtained through the following expression:
[0038] where is the maximum noise level of the imaging data at point is the imaging data at point is the maximum noise level of, is the maximum time of the target layer, is the up-going data of the receiver point at time t, is the down-going data of the source point at time t.
[0039] In this embodiment, in step S4, determining the illumination of the target layer coordinates based on the optimized irregular observation system includes: Based on the positions of the shot points and receiver points in the optimized irregular observation system and the incident signal of the seismic wave when the shot point is excited, determining the frequency-domain source-to-illumination and time-domain source-to-illumination at the underground space point of the target layer.
[0040] In this embodiment, the frequency-domain source-to-illumination at the underground space point of the target layer is determined through the following expression:
[0041] Among them, is the frequency-domain source illumination at the underground space point of the target layer at, is the shot point, is the receiver point, is the incident signal frequency, is the source-direction single-frequency wavefield response, The time-domain source illumination at the underground space point of the target layer is determined through the following expression:
[0042] Among them, is the time-domain source illumination at the underground space point of the target layer at, is the shot point, is the receiver point, is the incident signal frequency, is the source-direction single-frequency wavefield response.
[0043] Taking the geological model as the Marmousi 3D model as an example, the longitudinal direction of the model is the east-west direction, with a length of 9.0 km, the transverse direction is the north-south direction, with a width of 2.5 km, and the depth is 3.0 km, to explain the optimization method of the irregular sampling observation system of the present disclosure: The first step is to analyze the shot point coordinates and determine its grid cell According to the layout requirements, analyze the existing shot point coordinates, and analyze and determine the longitudinal grid size and the transverse grid size: 12.5 m. The second step is to analyze the geophone point coordinates and determine its grid cell According to the layout requirements, analyze the existing types of geophone point coordinates, and analyze and determine the longitudinal grid size and the transverse grid size: 12.5 m.
[0044] The third step is to determine the grid cell of the optimized observation system based on the shot point coordinates and the geophone point coordinates, and establish a model analysis grid based on the minimum grid of the optimized observation system According to the regular sampling shot point coordinates, the geophone points and their acquisition parameters, analyze and determine the grid cell of the optimized observation system: 12.5 m. Divide the Marmousi 3D model according to the grid cell of the optimized observation system to obtain a grid model.
[0045] Determine the forward modeling and analysis grid size according to the minimum grid of the optimized observation system: 6.25.
[0046] The fourth step is to design the optimal irregular shot and receiver points based on the Marmousi 3D model Based on the Marmousi 3D model, analyze and determine the irregular acquisition system and sampling parameters. Among them, the irregular acquisition system and sampling parameters include the positions of shot points and receivers, etc. The method of irregular shot points and regular receivers can be adopted. For 6 shots and 6 lines with 120 channels of reception, the average point distance of shot points is 250 m (75 m - 425 m), the line distance is 750 m; the point distance of receivers is 50 m, and the line distance is 250 m.
[0047] In the fifth step, based on the grid model, specify the illumination analysis interface range. Based on the grid model, determine the coordinates of the target layer to be analyzed in the Marmousi 3D model.
[0048] In the sixth step, input the designed optimal irregular shot points and receivers, that is, the shot-point and receiver relationship file, and use the grid model to perform illumination calculation for the given target layer.
[0049] According to the shot-point and receiver relationship file designed irregularly, use the grid model to perform illumination calculation for the given target layer coordinates. Compared with directly using the analysis grid to calculate illumination, the time is greatly shortened.
[0050] In the seventh step, based on the illumination calculation results, perform illumination intensity and illumination uniformity analysis for the target layer on the analysis grid. Figures 2 to 4 are respectively the illumination calculation results of performing illumination calculation for the given target layer coordinates on the models obtained by dividing the geological model using the grid model and through the analysis grid according to the shot-point and receiver relationship file designed irregularly and the regular shot-point and receiver relationship file.
[0051] The irregular sampling acquisition system optimization method of the present disclosure can improve the spatial illumination calculation efficiency, improve the illumination analysis accuracy, enable more efficient optimization of the acquisition parameters, and is beneficial to improving the data imaging quality.
[0052] See Figure 5 , the embodiments of the present disclosure provide an irregular sampling acquisition system optimization device, including: The first determination module 11 is used to determine the grid cell size of the preset acquisition system according to the coordinates of the shot points and receivers in the preset acquisition system of the known geological model, where the preset acquisition system is a regular sampling acquisition system; The division module 12 is used to divide the geological model into a grid model according to the grid cell size of the preset acquisition system, and obtain the coordinates of the target layer in the grid model; The optimization module 13 is used to optimize the preset acquisition system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain the optimized irregular acquisition system; The second determination module 14 is used to determine the illumination degree of the coordinates of the target layer based on the optimized irregular acquisition system.
[0053] The non-regular sampling observation system optimization device of the present disclosure can be used for observation system optimization and improving seismic imaging quality.
[0054] For the implementation processes of the functions and roles of each unit in the above device, please refer to the implementation processes of the corresponding steps in the above method for details, which will not be elaborated here.
[0055] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without creative efforts.
[0056] In the above embodiments, any combination of the first determination module 11, the division module 12, the optimization module 13, and the second determination module 14 can be combined and implemented in one module, or any one of the modules can be split into multiple modules. Or, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of the first determination module 11, the division module 12, the optimization module 13, and the second determination module 14 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits and other hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware or in an appropriate combination of any several of them. Or, at least one of the first determination module 11, the division module 12, the optimization module 13, and the second determination module 14 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions can be executed.
[0057] Refer to Figure 6 As shown, the electronic device provided by the embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140. Among them, the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140; The memory 1130 is used to store a computer program; The processor 1110, when executing the program stored in the memory 1130, implements the optimization method for the irregular sampling observation system as follows: According to the coordinates of the shot points and geophone points in the preset observation system of the known geological model, determine the grid cell size of the preset observation system, where the preset observation system is a regular sampling observation system; Divide the geological model into a grid model according to the grid cell size of the preset observation system, and obtain the target layer coordinates in the grid model; Optimize the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system; Based on the optimized irregular observation system, determine the illumination of the target layer coordinates.
[0058] The above communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
[0059] The communication interface 1120 is used for communication between the above electronic device and other devices.
[0060] The memory 1130 can include a Random Access Memory (RAM), and can also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 can also be at least one storage device located far from the aforementioned processor 1110.
[0061] The above processor 1110 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
[0062] Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for optimizing an irregular sampling observation system as described above is implemented.
[0063] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist alone without being assembled into the device / apparatus. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method for optimizing an irregular sampling observation system according to the embodiments of the present disclosure is implemented.
[0064] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device.
[0065] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.
[0066] The above description is only the specific implementation manners of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features claimed herein.
Claims
1. An optimization method for an irregular sampling observation system, characterized in that, The method includes: Determining the grid cell size of a preset observation system according to the coordinates of shot points and geophone points in the preset observation system of a known geological model, where the preset observation system is a regular sampling observation system; Dividing the geological model into a grid model according to the grid cell size of the preset observation system, and obtaining the target layer coordinates in the grid model; Optimizing the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system; Determining the illumination of the target layer coordinates based on the optimized irregular observation system.
2. The method according to claim 1, characterized in that, The determining the grid cell size of the preset observation system according to the coordinates of shot points and geophone points in the preset observation system of a known geological model includes: Analyzing the coordinates of shot points and geophone points in the preset observation system, and respectively determining the longitudinal grid size and the transverse grid size of the shot points and the geophone points; Determining the grid cell size of the preset observation system according to the longitudinal grid size and the transverse grid size of the shot points and the geophone points.
3. The method according to claim 1, characterized in that, The method further includes: Determining the grid cell size for model analysis according to the grid cell size of the preset observation system, where the longitudinal size and the transverse size of the grid cells of the geological model are respectively 1 / n of the preset observation system, n is a positive integer and a multiple of 2; Determining the illumination of the target layer coordinates of the regular observation system before optimization and the optimized irregular observation system according to the grid cell size for model analysis; Comparing the illumination of the target layer coordinates of the regular observation system before optimization and the optimized irregular observation system to ensure the illumination intensity and illumination uniformity of the target layer.
4. The method according to claim 1, characterized in that, The optimizing the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data to obtain an optimized irregular observation system includes: Taking the minimum of both the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data as the optimization objective, and taking the distance between the shot point and the geophone point within a preset safety distance as the constraint condition, and using a greedy sequential algorithm to irregularly arrange at least one of the shot points and the geophone points. When the number of iterations reaches the preset number of iterations, outputting the iteration result that reaches the preset number of iterations, where the iteration result includes the optimized irregular observation system and the sampling parameters.
5. The method according to claim 4, characterized in that, The maximum correlation value of the sensing matrix vector is obtained through the following expression: ψ = ψ r + ψ s where μ is the maximum correlation value between the i-th and j-th column vectors of the sensing matrix ψ, and ψ r is the receiving point vector in the sensing matrix, and ψ s is the excitation point vector in the sensing matrix The maximum noise level of the imaging data is obtained through the following expression: where σ is the maximum noise level of the imaging data I(x, y) at the point (x, y), and t max is the maximum time of the target layer, f(t, x r , y r ) is the up-going data of the receiving point at time t, and f(t, x s , y s ) is the down-going data of the shot point at time t.
6. The method according to claim 1, characterized in that, The determining the illumination of the target layer coordinates based on the optimized irregular observation system includes: Based on the positions of the shot points and the geophone points in the optimized irregular observation system and the incident signal of the seismic wave when the shot point is excited, determining the frequency-domain source-to-illumination and time-domain source-to-illumination at the underground space points of the target layer.
7. The method according to claim 6, characterized in that, Determining the frequency-domain source-to-illumination at the underground space points of the target layer through the following expression: I(r′) = ∑∑|G(r s , r, ω)| 2 where \(I(r')\) is the frequency - domain source - directed illumination at the subsurface point \(r'\) of the target layer, \(r\) s is the shot point, \(r\) is the receiver point, \(\omega\) is the incident signal frequency, \(G(r\) s , r, \(\omega)\) is the source - directed single - frequency wave - field response. Determining the time-domain source-to-illumination at the underground space points of the target layer through the following expression: I′(r′) = ∑∑G 2 (r s , r, t) Among them, I′(r′) is the illumination from the time-domain source at the subsurface space point r′ of the target layer, r s is the shot point, r is the receiver, ω is the incident signal frequency, G(r s , r, ω) is the source-to-single-frequency wavefield response.
8. An optimization device for an irregular sampling observation system, characterized in that, including: A first determination module for determining the grid cell size of the preset observation system according to the coordinates of shot points and geophone points in the preset observation system of a known geological model, where the preset observation system is a regular sampling observation system; A partitioning module, configured to partition a geological model into a grid model according to the grid cell size of a preset observation system, and obtain the coordinates of the target layer in the grid model; An optimization module, configured to optimize the preset observation system based on the maximum correlation value of the sensing matrix vector and the maximum noise level of the imaging data, and obtain an optimized irregular observation system; A second determination module, configured to determine the illumination of the coordinates of the target layer based on the optimized irregular observation system.
9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor is configured to implement the irregular sampling observation system optimization method described in any one of claims 1-7 when executing the programs stored on the memory.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that, The computer program, when executed by the processor, implements the irregular sampling observation system optimization method described in any one of claims 1-7.