A custom grid construction method and apparatus for compressive sensing reconstruction

Abstract

This invention discloses a method and apparatus for constructing a custom mesh for compressed sensing reconstruction. The method includes: constructing a mesh origin adjustment objective function for the custom mesh based on a sampling point regular mesh and a custom mesh; matching sampling points in the sampling point regular mesh and grid points in the custom mesh to determine the matching relationship and matching distance between sampling points and grid points; using the offset between the detector point and the excitation point in the actual observation system as a judgment threshold, deleting sampling points whose matching distance between sampling points and grid points exceeds the offset, and determining the average matching distance between the remaining sampling points and grid points; solving the mesh origin adjustment objective function using an optimization algorithm and adjusting the mesh origin position of the custom mesh; generating a new custom mesh using the adjusted mesh origin position of the custom mesh, and iterating the above steps until the average matching distance is minimized to construct the final custom mesh.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method and apparatus for constructing a custom grid for compressed sensing reconstruction. Background Technology

[0002] In practice, observation systems used for sampling are often complex and chaotic, especially due to constraints such as obstacles, leading to significant deviations from the initially pre-set sampling points. Irregular seismic data acquisition based on compressed sensing requires a pre-defined, regular observation system for the desired output. Therefore, how to construct the desired receiver or shot point locations based on the actual receiver or shot point locations in the observation system, thereby achieving better seismic data reconstruction, is a problem worthy of further research.

[0003] Traditional compressed sensing or irregular acquisition methods involve irregular gaps within a regular grid, resulting in a reconstructed grid that is typically identical to or a multiple of the original regular grid. This prevents the creation of custom grids tailored to specific subsurface targets or user requirements. In contrast, actual sampling and observation systems provide data such as point-to-point spacing, line-to-line spacing, survey line angles, and point and line numbers. Using this data, a corresponding grid can be generated from the given origin. Therefore, the problem of designing the desired output reconstructed grid can be transformed into finding the optimal grid origin. Once the optimal origin is found, a regular observation system can be constructed using user-provided data such as point-to-point spacing, line-to-line spacing, survey line angles, and point and line numbers, which improves the reconstruction accuracy of irregular seismic data. Summary of the Invention

[0004] In order to enable data processing based on user-defined grids, thereby improving the signal-to-noise ratio and accuracy of reconstructed irregularly acquired data, reducing the exploration risks of irregular seismic acquisitions, and enriching technical approaches and increasing the selection space, this invention provides a method and apparatus for constructing a custom grid for compressed sensing reconstruction.

[0005] In a first aspect, embodiments of the present invention provide a custom mesh construction method for compressed sensing reconstruction, which may include:

[0006] Based on the sampling point regular grid and the custom grid, a target function for adjusting the grid origin of the custom grid is constructed;

[0007] The matching is performed between sampling points in the sampling point rule grid and grid points in the custom grid to determine the matching relationship and matching distance between the sampling points and the grid points.

[0008] Using the offset between the detector point and the excitation point in the actual observation system as the judgment threshold, the sampling points whose matching distance between the sampling point and the grid point exceeds the offset are deleted, and the average matching distance between the remaining sampling points and the grid points is determined.

[0009] The objective function for adjusting the grid origin is solved using an optimization algorithm, and the grid origin position of the custom grid is adjusted accordingly.

[0010] A new custom grid is generated using the origin position of the adjusted custom grid, and the above steps are iterated until the average matching distance is minimized to construct the final custom grid.

[0011] In an optional embodiment, the above method may further include: determining the initial origin of the sampling points based on the location of the detector points or excitation points in the actual observation system, and generating a corresponding sampling point regular grid based on the initial origin.

[0012] In another optional embodiment, determining the initial origin of the sampling points based on the location of the detector or excitation point in the actual observation system, and generating a corresponding sampling point regular grid based on the initial origin, may include:

[0013] The initial origin of the sampling point is determined by rounding down from the position of the detector point or excitation point.

[0014] Based on the initial origin, the point distance, line distance, survey line angle, point number, and line number in the actual observation system, the sampling point regular grid is generated.

[0015] In another optional embodiment, solving the objective function for adjusting the grid origin using an optimization algorithm may include: solving the objective function for adjusting the grid origin using a heuristic optimization algorithm; wherein the heuristic optimization algorithm includes: particle swarm optimization, ant colony optimization, or greedy algorithm.

[0016] In another optional embodiment, the objective function for adjusting the grid origin is:

[0017]

[0018] Wherein, the initial origin is (x0, y0), and the coordinates of the sampling points are (x0, y0). i ,y i The sampling point regular grid that matches the sampling point coordinates is... After custom grid adjustment, the origin coordinates are (x0+Δx, y0+Δy).

[0019] In a second aspect, embodiments of the present invention provide a method for compressed sensing reconstruction of seismic data, which may include: performing compressed sensing reconstruction of seismic data based on seismic data collected by an actual observation system and a custom grid;

[0020] The custom mesh is pre-built according to the custom mesh construction method for compressed sensing reconstruction described in the first aspect.

[0021] Thirdly, embodiments of the present invention provide a custom mesh construction apparatus for compressed sensing reconstruction, which may include:

[0022] The objective function construction module is used to construct the objective function for adjusting the origin of the custom grid based on the sampling point regular grid and the custom grid;

[0023] The matching module is used to match sampling points in the sampling point regular grid with grid points in the custom grid to determine the matching relationship and matching distance between sampling points and grid points;

[0024] The distance determination module is used to delete sampling points whose matching distance between the sampling point and the grid point exceeds the offset distance, using the offset distance between the detector point and the excitation point in the actual observation system as a judgment threshold, and to determine the average matching distance between the remaining sampling points and the grid points.

[0025] The optimization and adjustment module is used to solve the objective function for adjusting the grid origin using an optimization algorithm, and to adjust the grid origin position of the custom grid.

[0026] The mesh building module is used to generate a new custom mesh at the origin position of the adjusted custom mesh, and iterates the above steps until the average matching distance is minimized to build the final custom mesh.

[0027] Fourthly, embodiments of the present invention provide a method for compressed sensing reconstruction of seismic data, which may include: a reconstruction module for performing compressed sensing reconstruction of seismic data based on seismic data acquired by an actual observation system and a custom grid;

[0028] The custom mesh is pre-built according to the custom mesh construction method for compressed sensing reconstruction described in the first aspect.

[0029] Fifthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the custom grid construction method for compressed sensing reconstruction as described in the first aspect, or implements the compressed sensing reconstruction method for seismic data as described in the second aspect.

[0030] In a sixth aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the custom grid construction method for compressed sensing reconstruction as described in the first aspect, or the compressed sensing reconstruction method for seismic data as described in the second aspect.

[0031] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0032] This invention provides a method and apparatus for constructing a custom grid for compressed sensing reconstruction. The method is designed for target output observation systems with irregularly distributed sampling points. The constructed custom grid reconstructs seismic data, improving the signal-to-noise ratio and accuracy of the reconstructed irregularly acquired data, thereby reducing the exploration risks of irregular seismic acquisition.

[0033] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0034] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0035] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0036] Figure 1 This is a flowchart of a custom mesh construction method for compressed sensing reconstruction provided in an embodiment of the present invention;

[0037] Figure 2 This is a detailed flowchart of a custom mesh construction method for compressed sensing reconstruction provided in an embodiment of the present invention;

[0038] Figure 3 The simulated earthquake data provided in the embodiments of the present invention;

[0039] Figure 4 for Figure 3 Undersampled simulated seismic data;

[0040] Figure 5 These are the simulated sampling points provided in the embodiments of the present invention;

[0041] Figure 6 Provided in the embodiments of the present invention Figure 3 A custom grid plot constructed from simulated data;

[0042] Figure 7 for Figure 6 A magnified view of a portion of the image;

[0043] Figure 8 Based on Figure 6 Custom mesh reconstruction result image;

[0044] Figure 9 This is a diagram showing the mesh reconstruction result based on the initial origin design.

[0045] Figure 10 This is a diagram showing the actual detector point and excitation point layout provided in an embodiment of the present invention;

[0046] Figure 11 for Figure 10 Distribution diagram of the original detector points;

[0047] Figure 12 Based on Figure 10 A custom grid map of detector points is constructed from the detector point distribution map;

[0048] Figure 13 for Figure 12 A magnified view of a portion of the image;

[0049] Figure 14 for Figure 10 Distribution map of primary excitation points;

[0050] Figure 15 Based on Figure 10 A custom mesh map of excitation points constructed from the excitation point distribution map;

[0051] Figure 16 for Figure 15 A magnified view of a portion of the image;

[0052] Figure 17 The data provided in this embodiment of the invention are actual seismic data collected.

[0053] Figure 18 This is a seismic data map reconstructed based on a custom grid, provided in an embodiment of the present invention.

[0054] Figure 19 for Figure 18 A magnified view of a portion of the image;

[0055] Figure 20 This is a seismic data map reconstructed from a regular grid based on an initial point arrangement, provided in an embodiment of the present invention.

[0056] Figure 21 for Figure 20 A magnified view of a portion of the image;

[0057] Figure 22This is a schematic diagram of the structure of a custom mesh construction device for compressed sensing reconstruction provided in an embodiment of the present invention. Detailed Implementation

[0058] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0059] In practice, the inventors discovered that existing compressed sensing reconstruction technology can only densify the original regular mesh by multiple times. It cannot realize the reconstruction process for actual underground target requirements or user-defined meshes. In view of the above problems, the present invention is proposed to provide a method and apparatus for constructing a custom mesh for compressed sensing reconstruction that overcomes or at least partially solves the above problems.

[0060] This invention provides a method for constructing a custom mesh for compressed sensing reconstruction, referring to... Figure 1 As shown, the construction method may include the following steps:

[0061] Step S11: Based on the sampling point regular grid and the custom grid, construct the grid origin adjustment objective function of the custom grid.

[0062] Step S12: Match the sampling points in the sampling point rule grid with the grid points in the custom grid to determine the matching relationship and matching distance between the sampling points and the grid points.

[0063] Step S13: Using the offset distance between the detector point and the excitation point in the actual observation system as the judgment threshold, delete the sampling points whose matching distance between the sampling points and grid points exceeds the offset distance, and determine the average matching distance between the remaining sampling points and grid points.

[0064] Step S14: Solve the objective function for adjusting the grid origin using an optimization algorithm, and adjust the position of the grid origin of the custom grid.

[0065] Step S15: Generate a new custom grid with the grid origin position of the adjusted custom grid, and iterate the above steps until the average matching distance is minimized to construct the final custom grid.

[0066] The construction method provided in this embodiment of the invention, for target output observation systems with irregularly arranged sampling points, constructs a custom grid to reconstruct seismic data, which improves the signal-to-noise ratio and accuracy of the reconstructed irregularly acquired data, thereby reducing the exploration risk of irregular seismic acquisition.

[0067] In one specific embodiment, the present invention also provides a detailed method for constructing a custom mesh for compressed sensing reconstruction, referencing... Figure 2 As shown, the construction method may include the following steps:

[0068] Step S21: Based on the location of the detector point or excitation point in the actual observation system, determine the initial origin of the sampling point, and generate the corresponding sampling point regular grid based on the initial origin.

[0069] In this step, the initial origin of the sampling points is first determined by rounding down the position of the detector point or excitation point in the actual observation system. Then, based on the initial origin, the point distance, line distance, survey line angle, point number, and line number in the actual observation system, a regular grid of sampling points is generated.

[0070] In this embodiment of the invention, the initial origin of the sampling points can be the first sampling point, that is, the points around the lower left sampling point, in the actual sampling point coordinates (x... i ,y i The initial origin (x0, y0) of the sampling points is obtained by rounding down from the bottom left sampling point. After determining the initial origin (x0, y0), a regular grid of sampling points corresponding to the initial origin (x0, y0) can be generated based on the point spacing, line spacing, survey line angle, point number, and line number in the actual observation system.

[0071] In the embodiments of the present invention, Figure 3 Taking the provided simulated seismic data as an example, the data size is 128*128*128, and the sampling time interval is 0.004s. In this embodiment, the simulated seismic acquisition data is obtained through forward modeling, i.e., according to... Figure 5 The principle of 60% missing points and positional jitter is as shown. Figure 4 The image shows undersampled seismic data. The simulated sampling points for the simulated point data in this example are shown below. Figure 5 As shown, assuming (1,1) is the grid origin, angles are set to 15° and 90° respectively, and the point and line spacing is 5m. Each test has 100 sampling points, for a total of 100 survey lines. A grid is generated based on this data, with 60% of the grid points randomly missing. Half of the remaining grid points are randomly jittered within the range of [-2.5m, 2.5m] around their corresponding regular grid points. In this simulation data, the coordinates of the first sampling point in the lower left corner are (1.012, -0.361). Rounding this down, we select (1,0) as the initial origin. The optimal origin is then solved using the constructed method, and the target grid points are output.

[0072] Step S22: Based on the sampling point regular grid and the custom grid, construct the grid origin adjustment objective function of the custom grid.

[0073] This step adjusts the position of the initial origin based on the objective function of the grid origin adjustment. The adjusted position is (x0+Δx, y0+Δy).

[0074] The objective function for adjusting the origin of the custom mesh constructed in this step is:

[0075]

[0076] Wherein, the initial origin is (x0, y0), and the coordinates of the sampling points are (x0, y0). i ,y i The sampling point regular grid that matches the sampling point coordinates is... After custom grid adjustment, the origin coordinates are (x0+Δx, y0+Δy).

[0077] Since the objective function is a non-convex and nonlinear function, the global optimum cannot be directly obtained. Therefore, a heuristic optimization algorithm is considered for solving the problem. During the calculation, optimization algorithms such as particle swarm optimization (PSO) can be used. The basic principle of this algorithm is to simulate the search and learning process of particles in the solution space, finding the optimal solution through cooperation and information sharing. By continuously updating the particle positions and velocities and utilizing information from historical and global optimum positions, PSO can effectively search for the minimum value of complex multivariable functions, possessing global search capability and good convergence. After obtaining (Δx, Δy), a re-iteration is required, and in each iteration, the farthest biased point is removed to reduce interference with the overall solution result.

[0078] Step S23: Match the sampling points in the sampling point rule grid with the grid points in the custom grid to determine the matching relationship and matching distance between the sampling points and the grid points.

[0079] Step S24: Using the offset distance between the detector point and the excitation point in the actual observation system as the judgment threshold, delete the sampling points whose matching distance between the sampling points and grid points exceeds the offset distance, and determine the average matching distance between the remaining sampling points and grid points.

[0080] Step S25: Solve the objective function for adjusting the grid origin using an optimization algorithm, and adjust the position of the grid origin of the custom grid.

[0081] In this step, a heuristic optimization algorithm is used to solve the objective function for adjusting the grid origin; wherein, the heuristic optimization algorithm may include: particle swarm optimization, ant colony optimization, or greedy algorithm.

[0082] Step S26: Generate a new custom grid with the grid origin position of the adjusted custom grid, and iterate the above steps until the average matching distance is minimized to construct the final custom grid.

[0083] The number of iterations in this step can be set to 10, or the iteration can stop when Δx + Δy is less than 0.001.

[0084] Reference Figure 6 The final custom mesh shown demonstrates that almost all blue dots (sampling points) fall within the target output points (red circles). Figure 7 The enlarged view shows this more clearly. Further testing was conducted with different initial origin selections, as shown in Table 1. This table displays the origin results obtained with different initial points, and compares the total distance calculated after matching the generated target mesh with the sampling points at each origin. All three sets of initial points ultimately yielded a near-optimal solution, verifying that the designed scheme can solve for the optimal design origin and possesses stability.

[0085] Table 1 Comparison of results with different initial points

[0086] Initial point selection (1,0) (0,0) (3,3) Optimal solution The origin of the solution (0.9996,0.9998) (1,0.9988) (1.0003,1) (1,1) Total distance 2962.8 2965.6 2963.1 2962.4

[0087] This invention verifies the signal-to-noise ratio and continuity of reconstructed seismic data through compressed sensing reconstruction. Specifically, compressed sensing reconstruction is performed based on seismic data acquired by an actual observation system and a custom grid.

[0088] In this example, a regular grid with a desired output grid size of dx = 7m and dy = 10m is used. The original points after optimization processing are then used to lay out a complete regular grid (custom grid), compared to directly selecting... Figure 3 A complete regular grid is laid out at the bottom left corner, and the reconstruction results are as follows: Figure 8 and Figure 9 As shown in the red arrow, the optimized grid points designed in this invention are reconstructed, resulting in a stronger signal-to-noise ratio and better continuity of the in-phase axis.

[0089] In addition, this embodiment of the invention also provides specific actual seismic data for verification. The point distribution information of this seismic data is designed as follows: the distance between receiving points and the distance between excitation points are 50m, and the line spacing is 200m. An irregular missing technique is used, with 25% irregular missing for both receiving and excitation points along the line direction. During actual construction, due to the influence of obstacles and other factors in the construction area, some sampling points deviated from the original design positions, resulting in a more complex distribution of the actual collected data points. The distribution diagram of the detector points and excitation points of this actual data is shown below. Figure 10As shown (red represents the excitation point, and blue represents the receiving point), the main parameters are a point spacing of 50m and a line spacing of 200m, with measurement line directions of 332.2° and 62.2° respectively. Then, the target output grid is fitted to the detector point and the excitation point respectively using the method of the present invention.

[0090] First, the detector points are fitted, such as... Figure 11 The image shows the original detector point distribution diagram. Figure 12 The calculated target output grid shows that most points are located within the target output grid. Figure 13 for Figure 12 The image shows a magnified view of a portion of the data, illustrating the areas with a good fit, where most points lie within the target output circle. Next, the image shows the fitting of the target output grid points for the excitation points; the original excitation point distribution is as follows. Figure 14 The distribution of the excitation points shown is more disordered, with more local missing points and off-center points. Figure 15 For the calculation results, Figure 16 The results show local fitting, and overall the fitting results for the target output grid of the detector and excitation point are better.

[0091] The above construction method can obtain a target output mesh and ensure that as many points as possible fall on the target output mesh. Although some densely packed off-center points cannot be fitted, this method can be further improved by refining the mesh to allow more points to fall on the target mesh.

[0092] The effectiveness of the method of the present invention is verified by using the data reconstruction results. Figure 17 For the actual collected data, firstly, according to the desired output grid size of 50m for the receiving point and 30m for the excitation point (not an integer multiple of the original design grid size), a complete regular grid is laid out using the original points optimized by the method of this invention. The reconstruction results are as follows: Figure 18 and Figure 20 As shown in the image, the optimized grid points designed in this invention undergo reconstruction processing, as clearly indicated by the red arrows. After reconstruction, the data signal-to-noise ratio and the continuity of the in-phase axis are significantly improved. Through a magnified view and comparison, the magnified image (…) Figure 19 and Figure 21 Within the red box in the diagram, it can be more clearly seen that the reconstruction result after reconstructing the mesh using the method of this invention is better, and the continuity of the phase axis is better.

[0093] Based on the same inventive concept, this embodiment of the invention also provides a method for compressed sensing reconstruction of seismic data, which includes: performing compressed sensing reconstruction of seismic data based on seismic data collected by an actual observation system and a custom grid;

[0094] The custom mesh is pre-built according to the custom mesh construction method described above for compressed sensing reconstruction.

[0095] Based on the same inventive concept, this invention also provides a custom mesh construction device for compressed sensing reconstruction, referring to... Figure 22 As shown, the device may include: an objective function construction module 11, a matching module 12, a distance determination module 13, an optimization and adjustment module 14, and a mesh construction module 15. Its working principle is as follows:

[0096] Objective function construction module 11 is used to construct an objective function for adjusting the origin of a custom mesh based on a sampling point regular mesh and a custom mesh;

[0097] Matching module 12 is used to match sampling points in the sampling point rule grid and grid points in the custom grid to determine the matching relationship and matching distance between sampling points and grid points;

[0098] The distance determination module 13 is used to use the offset distance between the detector point and the excitation point in the actual observation system as a judgment threshold to delete the sampling points whose matching distance between the sampling points and the grid points exceeds the offset distance, and to determine the average matching distance between the remaining sampling points and the grid points.

[0099] The optimization and adjustment module 14 is used to solve the objective function of mesh origin adjustment using an optimization algorithm, and to adjust the position of the mesh origin of the custom mesh;

[0100] The mesh building module 15 is used to generate a new custom mesh with the mesh origin position of the adjusted custom mesh, and iterates the above steps until the average matching distance is minimized to build the final custom mesh.

[0101] In an optional embodiment, refer to Figure 22 As shown, the above-mentioned device may further include: a generation module 10, which is used to determine the initial origin of the sampling points based on the position of the detector point or excitation point in the actual observation system, and generate a corresponding sampling point regular grid based on the initial origin.

[0102] In one specific embodiment, the generation module 10 is specifically used for:

[0103] The initial origin of the sampling point is determined by rounding down from the position of the detector point or excitation point.

[0104] Based on the initial origin, the point distance, line distance, survey line angle, point number, and line number in the actual observation system, the sampling point regular grid is generated.

[0105] In another optional embodiment, the optimization and adjustment module 14 is specifically used to: solve the objective function for adjusting the grid origin using a heuristic optimization algorithm; wherein the heuristic optimization algorithm includes: particle swarm optimization, ant colony optimization, or greedy algorithm.

[0106] Based on the same inventive concept, this embodiment of the invention also provides a seismic data compressed sensing reconstruction device, including: a reconstruction module, used to perform seismic data compressed sensing reconstruction based on seismic data collected by an actual observation system and a custom grid;

[0107] The custom mesh is pre-built according to the custom mesh construction method described above for compressed sensing reconstruction.

[0108] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described custom grid construction method for compressed sensing reconstruction, or implements the above-described compressed sensing reconstruction method for seismic data.

[0109] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned custom grid construction method for compressed sensing reconstruction, or implements the above-mentioned compressed sensing reconstruction method for seismic data.

[0110] The principles by which the above-mentioned devices, media, and related equipment in the embodiments of the present invention solve the problem are similar to those of the aforementioned methods. Therefore, their implementation can refer to the implementation of the aforementioned methods, and repeated details will not be repeated.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a custom mesh for compressed sensing reconstruction, characterized in that, include: Based on the sampling point regular grid and the custom grid, a target function for adjusting the grid origin of the custom grid is constructed; The matching is performed between sampling points in the sampling point rule grid and grid points in the custom grid to determine the matching relationship and matching distance between the sampling points and the grid points. Using the offset between the detector point and the excitation point in the actual observation system as the judgment threshold, the sampling points whose matching distance between the sampling point and the grid point exceeds the offset are deleted, and the average matching distance between the remaining sampling points and the grid points is determined. The objective function for adjusting the grid origin is solved using an optimization algorithm, and the grid origin position of the custom grid is adjusted accordingly. A new custom grid is generated using the origin position of the adjusted custom grid, and the above steps are iterated until the average matching distance is minimized to construct the final custom grid.

2. The method according to claim 1, characterized in that, Also includes: Based on the location of the detector or excitation point in the actual observation system, the initial origin of the sampling point is determined, and a corresponding sampling point regular grid is generated based on the initial origin.

3. The method according to claim 2, characterized in that, Based on the location of the detector or excitation point in the actual observation system, the initial origin of the sampling points is determined, and a corresponding sampling point regular grid is generated based on the initial origin, including: The initial origin of the sampling point is determined by rounding down from the position of the detector point or excitation point. Based on the initial origin, the point distance, line distance, survey line angle, point number, and line number in the actual observation system, the sampling point regular grid is generated.

4. The method according to claim 1, characterized in that, The step of solving the objective function for adjusting the grid origin using an optimization algorithm includes: solving the objective function for adjusting the grid origin using a heuristic optimization algorithm; wherein the heuristic optimization algorithm includes: particle swarm optimization, ant colony optimization, or greedy algorithm.

5. The method according to any one of claims 1 to 4, characterized in that, The objective function for adjusting the grid origin is: Wherein, the initial origin is (x0, y0), and the coordinates of the sampling points are (x0, y0). i ,y i The sampling point regular grid that matches the sampling point coordinates is... After custom grid adjustment, the origin coordinates are (x0+Δx, y0+Δy).

6. A method for compressed sensing reconstruction of seismic data, characterized in that, include: Seismic data compression and sensing reconstruction based on seismic data collected by actual observation systems and custom grids; The custom mesh is pre-built using the custom mesh construction method for compressed sensing reconstruction according to any one of claims 1 to 5.

7. A custom mesh construction apparatus for compressed sensing reconstruction, characterized in that, include: The objective function construction module is used to construct the objective function for adjusting the origin of the custom grid based on the sampling point regular grid and the custom grid; The matching module is used to match sampling points in the sampling point regular grid with grid points in the custom grid to determine the matching relationship and matching distance between sampling points and grid points; The distance determination module is used to use the offset distance between the detector point and the excitation point in the actual observation system as a judgment threshold to delete the sampling points whose matching distance between the sampling points and the grid points exceeds the offset distance, and to determine the average matching distance between the remaining sampling points and the grid points. The optimization and adjustment module is used to solve the objective function for adjusting the grid origin using an optimization algorithm, and to adjust the grid origin position of the custom grid. The mesh building module is used to generate a new custom mesh at the origin position of the adjusted custom mesh, and iterates the above steps until the average matching distance is minimized to build the final custom mesh.

8. A seismic data compression sensing reconstruction device, characterized in that, include: The reconstruction module is used for compressed sensing reconstruction of seismic data based on seismic data acquired by actual observation systems and custom grids; The custom mesh is pre-built using the custom mesh construction method for compressed sensing reconstruction according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the custom grid construction method for compressed sensing reconstruction as described in any one of claims 1 to 5, or the compressed sensing reconstruction method for seismic data as described in claim 6.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the custom grid construction method for compressed sensing reconstruction as described in any one of claims 1 to 5, or the compressed sensing reconstruction method for seismic data as described in claim 6.

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