Compressed sensing-based optimized layout method and apparatus for surface area point locations

Abstract

The present application discloses a compressed sensing-based optimized layout method and apparatus for surface area point locations. The method comprises: obtaining initial offset data and using same as current offset data; and repeatedly executing the following steps until an iteration termination condition is met, and outputting optimal offset data: performing point location offset optimization on the basis of construction observation system parameters, a theoretical deployment SPS file and the current offset data to obtain an offset-optimized SPS file; deleting repeated point locations in the offset-optimized SPS file to obtain compressed sensing data; performing simulated reconstruction on the compressed sensing data on the basis of a preset undersampling ratio and a fold; performing simulated reconstruction feasibility verification by comparing the simulated reconstruction result with a theoretical deployment SPS acquisition data attribute, to obtain a reconstruction feasibility verification result; and when it is determined on the basis of the reconstruction feasibility verification result that the iteration termination condition is not met, adjusting the current offset data. The present application has high point location optimization accuracy.

Description

Surface area point optimization layout method and device based on compressed sensing

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202311633426.6 filed on November 30, 2023, and cites the contents disclosed in the above patent application as part of this application. Technical Field

[0003] The present application relates to the technical field of optimized layout of seismic acquisition design points, and in particular to a system and method for optimized layout of surface points based on compressed sensing. Background Art

[0004] This section is intended to provide a background or context to the embodiments of the present application that are recited in the claims. No admission is made that the description herein is prior art by virtue of its inclusion in this section.

[0005] Before implementing seismic exploration in the field, the observation system to be used for the project is generally determined through previous data analysis, model forward modeling, etc., in combination with the geological exploration task (depth of the target layer). The main parameters include the number of receiving lines, receiving line spacing, receiving point spacing (trace spacing), excitation point line spacing, excitation point spacing, maximum offset distance, etc., to form a preset SPS (Shell Prospecting Support Files) format file.

[0006] During field implementation, the preset SPS gun checkpoint locations are optimized on-site based on the field obstacle survey information, and corresponding markings (flags, mounds, paint, paper strips, etc.) are made. For some projects that use pile-free construction, only the coordinates of the optimized points are collected, and no physical marking of the points is performed in the field.

[0007] In traditional geophysical exploration, data sampling follows the Nyquist sampling theorem in both time and space (to recover the original signal without distortion from a discrete signal, the sampling frequency should be greater than twice the signal's highest frequency). However, the advent of compressed sensing (CS) has transcended and extended this classical sampling theorem, garnering widespread attention in fields such as image processing, geophysics, medical imaging, computer science, signal processing, and applied mathematics. CS creatively combines the L1-norm minimization sparsity constraint with an offset matrix to achieve optimal sparse signal reconstruction performance.

[0008] Since the introduction of compressed sensing, many oil companies have conducted pilot trials based on it, achieving excellent results in indoor data reconstruction. However, during the design phase, point densification is often employed followed by offset, jitter sampling, or quadrat sampling to achieve point sparseness, reducing exploration costs or achieving higher data density with the same investment.

[0009] When the compressed sensing sparse design points based on the above method are implemented on the surface, they are restricted by point layout and are prone to situations where they cannot be laid out or a large number of points need to be offset. There is no technology in the industry designed based on compressed sensing solutions in surface areas.

[0010] Summary of the Invention

[0011] In a first aspect, embodiments of the present application provide a method for optimizing the layout of surface points based on compressed sensing. This method can optimize points based on compressed sensing during surface construction (especially in complex surface areas), with high accuracy, and improves the flexibility of field point layout on the surface (especially in complex surfaces) while reducing exploration costs. The method includes:

[0012] Get the initial offset data and use it as the current offset data;

[0013] Repeat the following steps until the iteration termination condition is met, and output the current offset data when the iteration termination condition is met as the optimal offset data:

[0014] Perform point offset optimization based on the construction observation system parameters, the theoretically deployed SPS file, and the current offset data to obtain the optimized SPS file. Point offset optimization includes point offset optimization and / or detection point offset optimization.

[0015] Delete duplicate points in the SPS file after offset optimization to obtain compressed sensing data;

[0016] According to the preset empty point ratio and coverage times, the compressed sensing data is simulated and reconstructed to obtain the simulated reconstruction result;

[0017] By comparing the reconstruction simulation results with the theoretical deployment SPS acquisition data attributes, the feasibility of the simulation reconstruction is verified and the reconstruction feasibility verification results are obtained;

[0018] When it is determined that the iteration termination condition is not satisfied according to the reconstruction feasibility verification result, the current offset data is adjusted.

[0019] In a second aspect, embodiments of the present application further provide a surface area point optimization layout device that can perform compressed sensing-based point optimization during surface area construction (especially in complex surface areas), with high accuracy, and improves the flexibility of field point layout on the surface (especially in complex surfaces) while reducing exploration costs. The device includes:

[0020] The offset data module is used to obtain the initial offset data and use it as the current offset data;

[0021] The iteration module is used to repeatedly execute the following steps until the iteration termination condition is met, and output the current offset data when the iteration termination condition is met as the optimal offset data:

[0022] The offset optimization module is used to optimize the point offset based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data, and obtain the SPS file after the offset optimization. The point offset optimization includes point offset optimization and / or detection point offset optimization.

[0023] The compressed sensing data acquisition module is used to delete duplicate points in the SPS file after the offset optimization to obtain compressed sensing data;

[0024] The reconstruction module is used to simulate and reconstruct the compressed sensing data according to the preset empty shot ratio and coverage times to obtain the simulated reconstruction results;

[0025] The feasibility verification module is used to verify the feasibility of the simulation reconstruction by comparing the simulation reconstruction results with the attributes of the theoretically deployed SPS acquisition data, and obtain the reconstruction feasibility verification results;

[0026] The adjustment module is used to adjust the current offset data when it is determined that the iteration termination condition is not met according to the reconstruction feasibility verification result.

[0027] In a third aspect, an embodiment of the present application also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned surface area point optimization layout method based on compressed sensing is implemented.

[0028] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned surface area point optimization layout method based on compressed sensing.

[0029] In a fifth aspect, an embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned surface area point optimization layout method based on compressed sensing.

[0030] In an embodiment of the present application, initial offset data is obtained and used as current offset data; the following steps are repeatedly performed until an iteration termination condition is met, and the current offset data when the iteration termination condition is met is output as optimal offset data: point offset optimization is performed based on construction observation system parameters, a theoretically deployed SPS file, and current offset data to obtain an offset-optimized SPS file, where the point offset optimization includes point offset optimization and / or detection point offset optimization; duplicate points in the offset-optimized SPS file are deleted to obtain compressed sensing data; the compressed sensing data is simulated and reconstructed according to a preset empty shot ratio and number of coverages to obtain a simulated reconstruction result; the feasibility of the simulated reconstruction is verified by comparing the simulated reconstruction result with the properties of the theoretically deployed SPS acquisition data to obtain a reconstruction feasibility verification result; when it is determined that the iteration termination condition is not met according to the reconstruction feasibility verification result, the current offset data is adjusted. Compared with the prior art scheme of using point encryption followed by offset, jitter sampling or quadrat sampling to perform point thinning, the embodiment of the present application iteratively determines the optimal offset parameters. During the iteration, compressed sensing data is obtained by optimizing point offsets and deleting duplicate points, and feasibility verification is performed after simulated reconstruction. Through the above steps, point optimization based on compressed sensing can be achieved during construction in surface (especially complex surface) areas with high accuracy, and the flexibility of field point layout on the surface (especially complex surface) can be improved while reducing exploration costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0032] FIG1 is a flow chart of a method for optimizing the layout of surface points based on compressed sensing in an embodiment of the present application;

[0033] FIG2 is a diagram illustrating the layout of points in an embodiment of the present application;

[0034] FIG3 is an example of point offset optimization in an embodiment of the present application;

[0035] FIG4 is an example of key index marking in an embodiment of the present application;

[0036] FIG5 is a diagram illustrating deletion of duplicate points in an SPS file after offset optimization according to an embodiment of the present application;

[0037] FIG6 is an example of theoretical design points in an embodiment of the present application;

[0038] FIG7 is an example of a point after offset optimization in an embodiment of the present application;

[0039] FIG8 is an example of point distance statistics in an embodiment of the present application;

[0040] FIG9 is a schematic diagram of retained points and removable points (the first preset distance is less than 25 m) when deleting duplicate points;

[0041] FIG10 is a schematic diagram of retained points and removable points (the first preset distance is less than 20 m) when deleting duplicate points;

[0042] FIG11 is a structural block diagram of a device for optimizing the layout of surface points based on compressed sensing according to an embodiment of the present application;

[0043] FIG12 is a schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0045] The inventor believes that the vibroseis exploration technology is to use artificial vibroseis to control the spectral characteristics of the signal generated by it to excite seismic waves for exploration. The vibroseis acquisition technology has become an indispensable means for oil exploration with its advantages of environmental protection, safety and flexibility. In order to increase the efficiency of exploration operations, multiple vibroseis vehicles are often required to operate at the same time, and the pre-planned source points are visited in sequence within the specified area and vibrated one by one. In order to avoid harmonic interference between the source vehicles, when two source vehicles with a close distance need to be vibrated at the same time, it is necessary to control the excitation time of the two source vehicles so that they meet the given "time distance rule (TD rule)" condition. The vibroseis excitation problem can be described as how to allocate the source points and plan the vibration path of each vehicle in the environment of multiple source vehicles so that the parallel operation time is the shortest under the condition of meeting the TD rule. At present, the main method used is manual control to carry out excitation path planning and control, and there is a large room for improvement in efficiency. This application aims to design a targeted intelligent excitation optimization algorithm to further improve efficiency and reduce exploration costs.

[0046] It should be noted that the method of the present application is effective for optimizing the layout of surface points based on compressed sensing, and is also applicable to simple optimizing the layout of surface points.

[0047] FIG1 is a flow chart of a method for optimizing the layout of surface points based on compressed sensing in an embodiment of the present application, including:

[0048] Step 101: Obtain initial offset data and use it as current offset data;

[0049] Step 102: Repeat the following steps until an iteration termination condition is met, and output the current offset data when the iteration termination condition is met as the optimal offset data:

[0050] Step 1021: Perform point offset optimization based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data to obtain an SPS file after offset optimization. The point offset optimization includes point offset optimization and / or detection point offset optimization.

[0051] Step 1022: Delete duplicate points in the SPS file after the offset optimization to obtain compressed sensing data.

[0052] Step 1023: simulate and reconstruct the compressed sensing data according to the preset null point ratio and coverage times to obtain a simulated reconstruction result;

[0053] Step 1024 , by comparing the simulated reconstruction results with the attributes of the theoretically deployed SPS collected data, the feasibility of the simulated reconstruction is verified to obtain a reconstruction feasibility verification result;

[0054] Step 1025 : When it is determined that the iteration termination condition is not satisfied according to the reconstruction feasibility verification result, the current offset data is adjusted.

[0055] In an embodiment of the present application, compared with the prior art scheme of using point encryption followed by offset, jitter sampling or quadrat sampling to perform point thinning, the embodiment of the present application iteratively determines the optimal offset parameters. During the iteration, compressed sensing data is obtained by optimizing point offsets and deleting duplicate points, and feasibility verification is performed after simulated reconstruction. Through the above steps, point optimization based on compressed sensing can be achieved during construction in surface areas based on compressed sensing, with high accuracy, and while reducing exploration costs, the flexibility of surface field point layout based on compressed sensing is improved.

[0056] In one embodiment, the offset data includes an offset method and corresponding offset parameters;

[0057] When the offset mode is rectangular offset, the offset parameter is the maximum offset of the preset rectangle in the X and Y directions;

[0058] When the offset mode is circular offset, the offset parameter is the radius of the preset circle;

[0059] When the offset mode is elliptical offset, the offset parameter is the length of the major and minor axes of the preset ellipse.

[0060] Initial offset data can be user-defined. Offset data is used to select alternative locations on-site when a design point encounters a special situation and cannot be implemented on-site. For example, it specifies the direction of offset and the maximum offset distance.

[0061] In step 1021, the point offset optimization is performed based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data. Both the point and the detection point can be optimized, or only one of the point and the detection point can be optimized, depending on the actual situation of the project or the user's requirements. Offset optimization is the process of selecting a suitable point. For example, the point position contains X and Y coordinates. Optimization is to change the XY coordinates and select a suitable position. Generally, it is based on the obstacle information of the work area. For example, the theoretical design point has obstacles such as houses in the wild, and it is impossible to construct on site. A suitable point is selected according to the current offset data. Figure 2 is an example of point layout in an embodiment of the present application. Figure 3 is an example of point offset optimization in an embodiment of the present application, wherein, in Figure 3, the offset method is rectangular offset, and the offset in both the X and Y directions is 25m.

[0062] In one embodiment, duplicate points in the offset-optimized SPS file are deleted to obtain compressed sensing data, including:

[0063] For any points in the SPS file after the migration optimization, the points whose distance between them is less than the first preset distance are marked with key indexes. The key indexes are marked with ascending natural numbers, such as 1, 2, 3, etc.;

[0064] Delete all points with key indexes greater than 1 to obtain compressed sensing data.

[0065] Through the above steps, the goal of retaining only one point is achieved. Figure 4 is an example of key indexing and marking in an embodiment of the present application. Figure 5 is an illustration of deleting duplicate points in an SPS file after offset optimization in an embodiment of the present application. Corresponding to Figure 4, the circles in Figure 5 represent retained points and the squares represent deleted points.

[0066] In one embodiment, the compressed sensing data is simulated and reconstructed according to a preset empty shot ratio and coverage times, including:

[0067] The five-dimensional data interpolation method is used to simulate and reconstruct the compressed sensing data according to the preset empty shot ratio and coverage times.

[0068] In specific implementation, the blank shot ratio is the percentage of actual constructible points compared to the theoretically designed points. For example, if 100 shots are designed and 80 are retained after offset optimization, this is considered 20% blank shots. Generally, users have requirements for blank shots. Coverage times measure the quality of collected data. The more points retained, the closer the coverage times are to the theoretical design. The closer the coverage times are to the design, the better the data collection results. Compressed sensing data reconstruction involves thinning out the designed points to reduce the field implementation workload, thereby lowering implementation costs. Data recovery is performed using some indoor compressed sensing data reconstruction methods to obtain data that matches the original data (i.e., the initial SPS file).

[0069] In one embodiment, the iteration termination condition is that the reconstruction feasibility verification is passed and the thinning and compression ratio of the compressed sensing data meets a preset thinning and compression ratio.

[0070] Among them, the indoor reconstruction of compressed sensing data has requirements for the data thinning method and thinning percentage. For example, if the original design is 100, it is possible to reconstruct it if only 80 or 50 shots are collected in the field. However, if it is less, only 20 shots may not be able to reconstruct the effect of the original 100 shots. In this case, the reconstruction feasibility verification fails.

[0071] When the reconstruction feasibility is verified, the offset rule can be adjusted to be more flexible to seek the optimal offset data without affecting the data quality. That is, when the thinning and compression ratio of the compressed sensing data does not meet the preset thinning and compression ratio, the optimization process should continue. The optimization process is to find a thinning and compression ratio that is acceptable to both users and construction parties, and then terminate.

[0072] The embodiment of the present application requires comparing the simulation reconstruction results and the theoretical deployment SPS acquisition data attributes, wherein the acquisition data attributes are the observation system attributes corresponding to the new SPS file construction plan corresponding to the compressed sensing data.

[0073] The final optimal offset data can guide field construction.

[0074] A specific example is given below to illustrate the specific application of the method proposed in this application.

[0075] The surface of a certain work area is mainly composed of undulating sand dunes, with some scattered pastoralist settlements. The specific information of the SPS file is shown in Table 1.

[0076] Table 1

[0077] As seismic exploration accuracy improves, observation systems are moving toward higher coverage densities. However, this increased density of shot checkpoints also increases construction costs exponentially. The concept of compressed sensing (CS) enables the acquisition of higher coverage density data while reducing exploration costs. Point thinning is commonly performed by offsetting, jitter sampling, or quadrat sampling regular points. However, these thinned points often render them unworkable during CS-based field operations. Pilot experiments were conducted to validate the effectiveness of CS-based point thinning and reconstruction, while also improving the feasibility of the design in CS-based areas such as urban areas, deserts, and oil fields.

[0078] The test area covers 62.5 km² (9.2 x 6.8 cm), primarily desert terrain. The theoretical design includes 50,142 shots and 99,824 receivers. See Figure 6 for an example of the theoretical design points used in this embodiment, namely the SPS file. The receivers are arranged in a 25m x 25m grid, using a 508XT connected to 12 SG-10 receivers. The points are positioned in a 50m x 25m grid, with the shot line running east-west. The point offsets are ±12.5m in the X direction (east-west) and ±25m in the Y direction (north-south). See Figure 7 for an example of the points after offset optimization in this embodiment.

[0079] According to the 25m×12.5m offset layout, there are 2249 shots at points where the distance between any two points is less than the first preset distance (12.5m in this embodiment). Figure 8 is an example of point distance statistics in the embodiment of the present application. Figure 9 is a schematic diagram of the retained points and the removable points (the first preset distance is less than 25m) when the duplicate points are deleted. Figure 10 is a schematic diagram of the retained points and the removable points (the first preset distance is less than 20m) when the duplicate points are deleted. In Figures 9 and 10, the middle circle represents the retained point and the square represents the deleted point. Because it is a pilot test, data collection and construction are carried out at all field points. During reconstruction, the actual points are sparsely distributed according to certain rules, and the data reconstruction (reconstruction results) is compared with the original collected data (theoretical deployment SPS file) to verify the data reconstruction effect based on compressed sensing.

[0080] During industrial implementation, the theoretical point offset area can be enlarged to increase the flexibility of surface point selection. After the offset optimization, the proportion of points with similar distances will increase. Before field implementation, any points with a distance between them less than a first preset distance (surface size) can be deleted, and only one can be retained to achieve point offset thinning based on compressed sensing.

[0081] The embodiment of the present application also proposes a surface area point optimization layout generation device based on compressed sensing, the principle of which is similar to the surface area point optimization layout generation method, and will not be repeated here.

[0082] FIG11 is a schematic diagram of a device for generating optimized layout of surface area points based on compressed sensing in an embodiment of the present application, comprising:

[0083] The offset data module 1101 is used to obtain initial offset data and use it as current offset data;

[0084] The iteration module 1102 is configured to repeatedly perform the following steps until an iteration termination condition is satisfied, and output the current offset data when the iteration termination condition is satisfied as the optimal offset data:

[0085] The offset optimization module 1103 is used to optimize the point offset based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data to obtain the SPS file after the offset optimization. The point offset optimization includes point offset optimization and / or detection point offset optimization.

[0086] The compressed sensing data acquisition module 1104 is used to delete duplicate points in the SPS file after the offset optimization to obtain compressed sensing data;

[0087] The reconstruction module 1105 is used to simulate and reconstruct the compressed sensing data according to a preset empty shot ratio and coverage times to obtain a simulated reconstruction result;

[0088] The feasibility verification module 1106 is used to verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction results with the attributes of the theoretically deployed SPS collected data, and obtain a reconstruction feasibility verification result;

[0089] The adjustment module 1107 is configured to adjust the current offset data when it is determined that the iteration termination condition is not satisfied according to the reconstruction feasibility verification result.

[0090] In one embodiment, the offset data includes an offset method and corresponding offset parameters;

[0091] When the offset mode is rectangular offset, the offset parameter is the maximum offset of the preset rectangle in the X and Y directions;

[0092] When the offset mode is circular offset, the offset parameter is the radius of the preset circle;

[0093] When the offset mode is elliptical offset, the offset parameter is the length of the major and minor axes of the preset ellipse.

[0094] In one embodiment, the compressed sensing data acquisition module is specifically configured to:

[0095] The points in the SPS file after the migration optimization whose distance between any points is less than the first preset distance are marked with key indexes, and the key indexes are marked as ascending natural numbers;

[0096] Delete all points with key indexes greater than 1 to obtain compressed sensing data.

[0097] In one embodiment, the reconstruction module is specifically configured to:

[0098] The five-dimensional data interpolation method is used to simulate and reconstruct the compressed sensing data according to the preset empty shot ratio and coverage times.

[0099] In one embodiment, the iteration termination condition is that the reconstruction feasibility verification is passed and the thinning and compression ratio of the compressed sensing data meets a preset thinning and compression ratio.

[0100] An embodiment of the present application also provides a computer device. Figure 12 is a schematic diagram of the computer device in the embodiment of the present application. The computer device 1200 includes a memory 1210, a processor 1220, and a computer program 1230 stored in the memory 1210 and executable on the processor 1220. When the processor 1220 executes the computer program 1230, the above-mentioned surface area point optimization layout method based on compressed sensing is implemented.

[0101] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned surface area point optimization layout method based on compressed sensing.

[0102] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned surface area point optimization layout method based on compressed sensing.

[0103] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0104] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0105] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0107] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for optimizing the layout of surface points based on compressed sensing, characterized in that: include: Get the initial offset data and use it as the current offset data; Repeat the following steps until the iteration termination condition is met, and output the current offset data when the iteration termination condition is met as the optimal offset data: Based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data, point offset optimization is performed to obtain an SPS file after offset optimization, wherein the point offset optimization includes point offset optimization and / or detection point offset optimization; Delete the duplicate points in the SPS file after the offset optimization to obtain compressed sensing data; According to the preset empty point ratio and coverage times, the compressed sensing data is simulated and reconstructed to obtain the simulated reconstruction result; By comparing the simulated reconstruction results with the attributes of the theoretically deployed SPS acquisition data, the feasibility of the simulated reconstruction is verified and the feasibility of the reconstruction is verified. When it is determined that the iteration termination condition is not satisfied according to the reconstruction feasibility verification result, the current offset data is adjusted.

2. The method according to claim 1, characterized in that The offset data includes an offset method and corresponding offset parameters; When the offset mode is rectangular offset, the offset parameter is the maximum offset of the preset rectangle in the X direction and the Y direction; When the offset mode is circular offset, the offset parameter is the radius of the preset circle; When the offset mode is elliptical offset, the offset parameter is the length of the major and minor semi-axis of the preset ellipse.

3. The method according to claim 1, characterized in that Delete the duplicate points in the SPS file after the migration optimization to obtain compressed sensing data, including: Perform key index marking on points in the SPS file after the offset optimization, where the distance between any points is less than the first preset distance, and the key index marking is an ascending natural number; Delete all points with key indexes greater than 1 to obtain compressed sensing data.

4. The method according to claim 1, characterized in that: According to the preset empty point ratio and coverage times, the compressed sensing data is simulated and reconstructed, including: The five-dimensional data interpolation method is used to simulate and reconstruct the compressed sensing data according to the preset empty point ratio and coverage times.

5. The method according to claim 1, characterized in that The iteration termination condition is that the reconstruction feasibility verification is passed and the thinning and compression ratio of the compressed sensing data meets the preset thinning and compression ratio.

6. A device for optimizing the layout of surface points based on compressed sensing, characterized in that: include: An offset data module is used to obtain initial offset data and use it as current offset data; The iteration module is used to repeatedly execute the following steps until the iteration termination condition is met, and output the current offset data when the iteration termination condition is met as the optimal offset data: An offset optimization module is used to optimize the point offset based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data to obtain the SPS file after the offset optimization. The point offset optimization includes point offset optimization and / or detection point offset optimization. A compressed sensing data acquisition module is used to delete duplicate points in the SPS file after the offset optimization to obtain compressed sensing data; A reconstruction module is used to simulate and reconstruct the compressed sensing data according to a preset empty point ratio and coverage times to obtain a simulated reconstruction result; The feasibility verification module is used to verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction results with the attributes of the theoretically deployed SPS acquisition data, and obtain the reconstruction feasibility verification results; The adjustment module is used to adjust the current offset data when it is determined that the iteration termination condition is not met according to the reconstruction feasibility verification result.

7. The device according to claim 6, characterized in that The offset data includes an offset method and corresponding offset parameters; When the offset mode is rectangular offset, the offset parameter is the maximum offset of the preset rectangle in the X direction and the Y direction; When the offset mode is circular offset, the offset parameter is the radius of the preset circle; When the offset mode is elliptical offset, the offset parameter is the length of the major and minor semi-axis of the preset ellipse.

8. The device according to claim 6, characterized in that The compressed sensing data acquisition module is specifically used for: Perform key index marking on points in the SPS file after the offset optimization, where the distance between any points is less than the first preset distance, and the key index marking is an ascending natural number; Delete all points with key indexes greater than 1 to obtain compressed sensing data.

9. The device according to claim 6, characterized in that The reconstruction module is specifically used to: The five-dimensional data interpolation method is used to simulate and reconstruct the compressed sensing data according to the preset empty point ratio and coverage times.

10. The device according to claim 6, characterized in that The iteration termination condition is that the reconstruction feasibility verification is passed and the thinning and compression ratio of the compressed sensing data meets the preset thinning and compression ratio.

11. 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 computer program, the method according to any one of claims 1 to 5 is implemented.

12. 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, the method according to any one of claims 1 to 5 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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