Ground surface area point location optimization layout method and device based on compressed sensing

Abstract

The invention discloses a ground surface area point location optimization layout method and device based on compressed sensing, and the method comprises the steps: obtaining initial offset data, and taking the initial offset data as current offset data; repeatedly executing the following steps until an iteration termination condition is met, and outputting the optimal offset data: carrying out 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; deleting repeated point locations in the SPS file subjected to offset optimization to obtain compressed sensing data; performing simulation reconstruction on the compressed sensing data according to a preset empty point proportion and the number of coverage times; performing simulation reconstruction feasibility verification by comparing the simulation reconstruction result with the theoretical deployment SPS acquisition data attribute to obtain a reconstruction feasibility verification result; and when it is determined that an iteration termination condition is not satisfied according to the reconstruction feasibility verification result, adjusting the current offset data. The point location optimization accuracy is high.

Description

Technical Field

[0001] The present invention relates to the technical field of optimizing the layout of seismic acquisition design points, and particularly to a system and method for optimizing the layout of surface area points based on compressive sensing. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention described in the claims. The descriptions herein are not admitted to be prior art merely because they are included in this section.

[0003] Before the field implementation of seismic exploration, generally in combination with geological exploration tasks (depth of the target layer), the observation system adopted for the project is determined through methods such as analysis of old data and model forward modeling, mainly including parameters such as the number of receiving lines, receiving line spacing, receiving point spacing (trace spacing), shooting point line spacing, shooting point spacing, maximum offset, etc., and a preset SPS (Shell Prospecting Support Files) format file is formed.

[0004] During the field implementation, according to the field obstacle reconnaissance information, on-site optimization of the preset SPS shot and receiver point positions is carried out, and corresponding marks (flags, mounds, paint, paper strips, etc.) are made. For some projects with non-stake number construction, only the coordinates of the optimized positions are collected, and no physical marks of the positions are made in the field.

[0005] In traditional geophysical exploration, data sampling follows the Nyquist sampling theorem in terms of time and space (to recover the original signal without distortion from a discrete signal, the sampling frequency should be greater than twice the highest frequency of the signal). However, after the concept of compressive sensing was proposed, it transcended and extended the classical sampling theorem, attracting wide attention in fields such as image processing, geophysics, medical imaging, computer science, signal processing, and applied mathematics. The compressive sensing theory creatively combines the L1-norm minimization sparse constraint with the offset matrix to obtain an optimal result for the sparse signal reconstruction performance.

[0006] Since the proposal of compressive sensing, many oil companies in the industry have conducted pilot tests based on compressive sensing and achieved good results in indoor data reconstruction. However, in the scheme design stage, generally, methods such as offsetting after point densification, Jitter sampling, or square sampling are used for point sparsification to achieve cost savings in exploration or a higher acquisition density under the same investment.

[0007] When the compressive sensing sparse design points based on the above methods are implemented on the surface, restricted by the point layout, situations such as inability to layout and a large number of points needing to be offset easily occur, and there is no technology in the industry for surface area-based compressive sensing scheme design. Summary of the Invention

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing the layout of surface area points based on compressive sensing, which can perform point position optimization based on compressive sensing during surface area construction (especially in complex surface areas), with high accuracy, and improve the flexibility of field point layout on the surface (especially in complex surfaces) while reducing exploration costs. The method includes:

[0009] Obtain initial offset data and use it as the current offset data;

[0010] 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:

[0011] Perform point position 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 position offset optimization includes point position offset optimization and / or geophone point offset optimization;

[0012] Delete the duplicate points in the SPS file after offset optimization to obtain compressive sensing data;

[0013] Simulate and reconstruct the compressive sensing data according to the preset empty point ratio and coverage times to obtain a simulation reconstruction result;

[0014] Verify the feasibility of the simulation reconstruction by comparing the reconstructed simulation result with the attributes of the theoretical deployment SPS acquisition data to obtain a reconstruction feasibility verification result;

[0015] When it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not met, adjust the current offset data.

[0016] In a second aspect, an embodiment of the present invention further provides a device for optimizing the layout of surface area points, which can perform point position optimization based on compressive sensing during surface area construction (especially in complex surface areas), with high accuracy, and improve the flexibility of field point layout on the surface (especially in complex surfaces) while reducing exploration costs. The device includes:

[0017] An offset data module for obtaining initial offset data and using it as the current offset data;

[0018] An iteration module for repeating the following steps until the iteration termination condition is met, and outputting the current offset data when the iteration termination condition is met as the optimal offset data:

[0019] An offset optimization module for performing point position 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 position offset optimization includes point position offset optimization and / or geophone point offset optimization;

[0020] A compressive sensing data acquisition module, configured to delete repeated points in the SPS file after offset optimization to obtain compressive sensing data;

[0021] A reconstruction module, configured to perform simulated reconstruction on the compressive sensing data according to a preset blank shot ratio and coverage times to obtain a simulated reconstruction result;

[0022] A feasibility verification module, configured to verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction result with the attributes of the theoretical deployment SPS acquisition data to obtain a reconstruction feasibility verification result;

[0023] An adjustment module, configured to adjust the current offset data when it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not satisfied.

[0024] In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for optimizing the layout of points in the surface area based on compressive sensing is implemented.

[0025] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for optimizing the layout of points in the surface area based on compressive sensing is implemented.

[0026] In a fifth aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned method for optimizing the layout of points in the surface area based on compressive sensing is implemented.

[0027] In an embodiment of the present invention, initial offset data is obtained and used as the current offset data. The following steps are repeatedly executed until an iteration termination condition is met, and the current offset data when the iteration termination condition is met is output as the optimal offset data: Based on construction observation system parameters, a theoretical deployment SPS file, and the current offset data, point position offset optimization is performed to obtain an SPS file after offset optimization, where the point position offset optimization includes point position offset optimization and / or geophone point offset optimization; duplicate point positions in the SPS file after offset optimization are deleted to obtain compressed sensing data; according to a preset empty shot ratio and coverage times, the compressed sensing data is simulated and reconstructed to obtain a simulation reconstruction result; by comparing the simulation reconstruction result with the attributes of the theoretical deployment SPS acquisition data, a feasibility verification of the simulation reconstruction is performed to obtain a reconstruction feasibility verification result; when it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not met, the current offset data is adjusted. Compared with the prior art solutions for point position sparsification by means of offset after point position encryption, Jitter sampling, or quadrat sampling, the embodiment of the present invention iteratively determines the optimal offset parameter. During the iteration, compressed sensing data is obtained through point position offset optimization and deletion of duplicate point positions, and a feasibility verification after simulation reconstruction is performed. Through the above steps, it is possible to perform point position optimization based on compressed sensing during construction in the surface area (especially the complex surface area), with high accuracy, and improve the flexibility of surface (especially complex surface) field point position layout while reducing exploration costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0029] Figure 1 is a flowchart of a method for optimizing the layout of surface area point positions based on compressed sensing in an embodiment of the present invention;

[0030] Figure 2 is a legend of the layout point positions in an embodiment of the present invention;

[0031] Figure 3 is an example of point position offset optimization in an embodiment of the present invention;

[0032] Figure 4 is an example of key index marking in an embodiment of the present invention;

[0033] Figure 5 is a diagram showing the deletion of duplicate point positions in the SPS file after offset optimization in an embodiment of the present invention;

[0034] Figure 6 This is an example of the theoretically designed point positions in the embodiments of the present invention;

[0035] Figure 7 This is an example of the point positions after offset optimization in the embodiments of the present invention;

[0036] Figure 8 This is an example of the statistical distance between point positions in the embodiments of the present invention;

[0037] Figure 9 This is a schematic diagram of the retained point positions and the point positions that can be eliminated (the first preset distance is less than 25 m) when deleting duplicate point positions;

[0038] Figure 10 This is a schematic diagram of the retained point positions and the point positions that can be eliminated (the first preset distance is less than 20 m) when deleting duplicate point positions;

[0039] Figure 11 This is a structural block diagram of the device for optimizing the layout of point positions in the surface area based on compressive sensing in the embodiments of the present invention;

[0040] Figure 12 This is a schematic diagram of the computer device in the embodiments of the present invention. Detailed implementation manners

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following further elaborates on the embodiments of the present invention with reference to the accompanying drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0042] The inventors believe that the vibroseis exploration technology uses artificial seismic sources to excite seismic waves for exploration by controlling the spectral characteristics of the generated signals. The vibroseis acquisition technology has become an indispensable means for oil exploration due to its advantages of environmental protection, safety, and flexibility. To increase the efficiency of exploration operations, multiple vibroseis trucks often need to operate simultaneously, visiting the pre-planned seismic source point positions in sequence within a specified area and starting vibration one by one. To avoid harmonic interference between the vibroseis trucks, when two vibroseis trucks with a relatively short distance need to start vibration simultaneously, it is necessary to control the excitation times of the two vibroseis trucks to meet the conditions of the given "time-distance rule (TD rule)". The vibroseis excitation problem can be described as how to allocate the seismic source point positions and plan the vibration start paths of each vehicle in an environment with multiple vibroseis trucks, so that the parallel operation time is the shortest under the conditions of meeting the TD rule. Currently, the artificial control method is mainly used for excitation path planning and control, and there is still much room for improvement in efficiency. The present invention aims to design a targeted intelligent excitation optimization algorithm to further improve efficiency and reduce exploration costs.

[0043] It should be noted that the method of the present invention has obvious effects on the optimal layout of surface area points based on compressive sensing, and is also applicable to the simple optimal layout of surface area points.

[0044] Figure 1 FIG. is a flowchart of the method for optimizing the layout of surface area points based on compressive sensing in an embodiment of the present invention, including:

[0045] Step 101, obtain initial offset data and use it as the current offset data;

[0046] Step 102, repeatedly execute the following steps until the iteration termination condition is met, and use the current offset data when the iteration termination condition is met as the optimal offset data for output:

[0047] 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 geophone point offset optimization;

[0048] Step 1022, delete the duplicate points in the SPS file after offset optimization to obtain compressive sensing data;

[0049] Step 1023, perform simulated reconstruction on the compressive sensing data according to the preset empty point ratio and coverage times to obtain a simulated reconstruction result;

[0050] Step 1024, verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction result and the attributes of the theoretical deployment SPS acquisition data to obtain a reconstruction feasibility verification result;

[0051] Step 1025, when it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not met, adjust the current offset data.

[0052] In the embodiment of the present invention, compared with the prior art solution of performing point sparsification by means of point densification followed by offset, Jitter sampling, or sampling square, the embodiment of the present invention iteratively determines the optimal offset parameter. During the iteration, point offset optimization and duplicate point deletion are performed to obtain compressive sensing data, and the feasibility verification after simulated reconstruction is carried out. Through the above steps, it is possible to perform compressive sensing-based point optimization during surface area construction based on compressive sensing, with high accuracy, and improve the flexibility of surface field point layout based on compressive sensing while reducing exploration costs.

[0053] In one embodiment, the offset data includes an offset method and corresponding offset parameters;

[0054] When the offset method is rectangular offset, the offset parameters are the maximum offset amounts in the X direction and Y direction of the preset rectangle;

[0055] When the offset mode is circular offset, the offset parameter is the radius of the preset circle;

[0056] When the offset mode is elliptical offset, the offset parameters are the lengths of the major and minor semi-axes of the preset ellipse.

[0057] The initial offset data can be given by the user. Offset data is a kind of data for the user to select other positions on-site when encountering special situations at the designed point positions and unable to implement on-site, such as the direction of offset and the maximum offset distance, etc.

[0058] In step 1021, based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data, point position offset optimization can be performed on both the point positions and the geophone points, or only one of the point positions and the geophone points can be optimized, depending on the actual situation of the project or the user's requirements. Offset optimization is the process of selecting appropriate point positions. For example, the point position includes X and Y coordinates, and optimization is to change the XY coordinates and select an appropriate position. Generally, it is based on the obstacle information in the work area. For example, if there are obstacles such as houses at the theoretical design point position in the field and construction cannot be carried out on-site, a suitable point position is selected according to the current offset data. Figure 2 This is the legend of the point positions arranged in the embodiments of the present invention. Figure 3 This is an example of point position offset optimization in the embodiments of the present invention, where Figure 3 the offset mode is rectangular offset, and the offset amounts in both the X and Y directions are 25 m.

[0059] In one embodiment, the duplicate point positions in the offset-optimized SPS file are deleted to obtain compressed sensing data, including:

[0060] Perform key index marking on the point positions in the offset-optimized SPS file where the distance between any two point positions is less than the first preset distance. The key index marking is ascending natural numbers, such as 1, 2, 3...;

[0061] Delete all the point positions with key index markings greater than 1 to obtain compressed sensing data.

[0062] Through the above steps, the goal of only retaining one point position is achieved. Figure 4 This is an example of performing key index marking in the embodiments of the present invention. Figure 5 This is the illustration of deleting the duplicate point positions in the offset-optimized SPS file in the embodiments of the present invention, corresponding to Figure 4 corresponding, Figure 5 where the circles represent the retained point positions and the squares represent the deleted point positions.

[0063] In one embodiment, according to the preset empty shot ratio and coverage times, the compressed sensing data is simulated and reconstructed, including:

[0064] Adopt a five-dimensional data interpolation method to perform simulated reconstruction on the compressed sensing data according to the preset blank-shot ratio and coverage times.

[0065] In specific implementation, the blank-shot ratio is the percentage of the actually constructible points to the theoretically arranged points above. For example, if 100 shots are designed and 80 shots are retained after offset optimization, it is considered that the blank-shot is 20%. Generally, users have requirements for blank-shots. The coverage times are used to measure the quality of the collected data. The more points are retained, the closer the coverage times are to the theoretical design. The closer the coverage times are to the design, the better the effect of the data collection is. The reconstruction of the compressed sensing data is to perform thinning design on the points, reduce the field implementation workload, that is, reduce the implementation cost, and perform data recovery through some indoor compressed sensing data reconstruction methods to obtain a data effect matching the original data (i.e., the initial SPS file).

[0066] In one embodiment, the iteration termination condition is that the reconstruction feasibility verification is passed and the thinning compression ratio of the compressed sensing data meets the preset thinning compression ratio.

[0067] Among them, for the indoor reconstruction of the compressed sensing data, there are requirements for the data thinning method and the thinning percentage. For example, if the original design is 100 and only 80 or 50 shots are collected in the field, there is a way to reconstruct, but if it is less, only collecting 20 shots may not be able to reconstruct to the effect of the original 100 shots. At this time, the reconstruction feasibility verification fails.

[0068] When the reconstruction feasibility verification is passed, the offset rule can also be adjusted to be more extensive, and the optimal offset data is sought without affecting the data quality. That is, when the thinning compression ratio of the compressed sensing data does not meet the preset thinning compression ratio, the optimization should also continue. The optimization process is to seek a thinning compression ratio that can be accepted by both users and constructors, and then the iteration can be terminated.

[0069] The embodiments of the present invention need to compare the simulated reconstruction results with the attributes of the theoretical deployment SPS collected data. Among them, the collected data attributes are the observation system attributes corresponding to the new SPS file construction plan corresponding to the compressed sensing data.

[0070] The finally determined optimal offset data can guide the field construction.

[0071] A specific embodiment is given below to illustrate the specific application of the method proposed by the present invention.

[0072] The surface of a certain work area is mainly undulating sand dunes, and there are some scattered herdsmen's residential areas. The specific information of the SPS file is shown in Table 1.

[0073] Table 1

[0074]

[0075]

[0076] With the improvement of seismic exploration accuracy, the observation system is developing towards a higher coverage density. However, the increase in the density of shot and receiver points also doubles the construction cost. The concept of compressive sensing enables the acquisition of data with a higher coverage density at a lower exploration cost. Commonly used methods for point position sparsification include offsetting, Jitter sampling, or quadrat sampling for the designed regular points. After sparsification, the points often cannot be constructed during the surface field implementation based on compressive sensing. To verify the reconstruction effect of the points after compressive sensing sparsification and improve the feasibility of the design scheme in areas based on compressive sensing such as urban areas, deserts, and oil fields, a pilot test was conducted.

[0077] The test work area is 62.5 Km² (9.2×6.8). The work area is mainly desert terrain. The theoretical design is 50,142 shots and 99,824 receiver channels. See Figure 6 This is an example of the theoretically designed points in the embodiment of the present invention, that is, the SPS file. The receiver points are in a 25m×25m grid, and 12 SG-10 geophones are externally connected to the 508XT. The point positions are in a 50m×25m grid. The shot lines are along the east-west direction. The offset of the point positions in the X direction (east-west) is ±12.5m, and the offset in the Y direction (north-south) is ±25m. See Figure 7 This is an example of the points after offset optimization in the embodiment of the present invention.

[0078] After arranging the points according to the 25m×12.5m offset, there are 2,249 shots where the distance between any two points is less than the first preset distance (12.5m in this embodiment). Figure 8 This is an example of the point position distance statistics in the embodiment of the present invention. Figure 9 This is a schematic diagram of the retained points and the points that can be excluded (the first preset distance is less than 25m) when deleting duplicate points. Figure 10 This is a schematic diagram of the retained points and the points that can be excluded (the first preset distance is less than 20m) when deleting duplicate points. Figure 9 and Figure 10 At this time, the circles represent the retained points, and the squares represent the deleted points. Because it is a pilot test, data collection and construction are carried out for all field points. During reconstruction, the actual points are sparsified according to certain rules, and the data reconstruction (reconstruction result) is compared with the original collected data (theoretically deployed SPS file) to verify the data reconstruction effect based on compressive sensing.

[0079] During industrial implementation, the theoretical point offset area can be enlarged to increase the flexibility of surface point selection. After offset optimization, the proportion of points with similar distances will increase. Before field implementation, points (bin size) with a distance less than a first preset distance between any two points can be deleted, and only one point is retained to achieve point offset thinning based on compressive sensing.

[0080] An embodiment of the present invention also proposes a device for optimizing and arranging surface area points based on compressive sensing. Its principle is similar to the method for optimizing and arranging surface area points, and will not be elaborated here.

[0081] Figure 11 It is a schematic diagram of the device for optimizing and arranging surface area points based on compressive sensing in an embodiment of the present invention, including:

[0082] The offset data module 1101 is used to obtain initial offset data and use it as the current offset data;

[0083] The iteration module 1102 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:

[0084] 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 offset optimization. The point offset optimization includes point offset optimization and / or geophone point offset optimization;

[0085] The compressive sensing data acquisition module 1104 is used to delete the duplicate points in the SPS file after offset optimization to obtain compressive sensing data;

[0086] The reconstruction module 1105 is used to simulate and reconstruct the compressive sensing data according to the preset empty shot ratio and coverage times to obtain a simulated reconstruction result;

[0087] The feasibility verification module 1106 is used to verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction result and the attributes of the theoretical deployment SPS acquisition data to obtain a reconstruction feasibility verification result;

[0088] The adjustment module 1107 is used to adjust the current offset data when it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not met.

[0089] In one embodiment, the offset data includes an offset method and corresponding offset parameters;

[0090] When the offset method is rectangular offset, the offset parameters are the maximum offset amounts in the X direction and Y direction of the preset rectangle;

[0091] When the offset mode is circular offset, the offset parameter is the radius of a preset circle;

[0092] When the offset mode is elliptical offset, the offset parameters are the lengths of the major and minor semi-axes of a preset ellipse.

[0093] In one embodiment, the compressive sensing data acquisition module is specifically configured to:

[0094] Perform key index marking on points whose distance between any two points in the SPS file after offset optimization is less than a first preset distance, and the key index marking is ascending natural numbers;

[0095] Delete all points with key index markings greater than 1 to obtain compressive sensing data.

[0096] In one embodiment, the reconstruction module is specifically configured to:

[0097] Adopt a five-dimensional data interpolation method to perform simulated reconstruction on the compressive sensing data according to a preset blank shot ratio and coverage times.

[0098] In one embodiment, the iteration termination condition is that the reconstruction feasibility verification passes and the decimation compression ratio of the compressive sensing data meets a preset decimation compression ratio.

[0099] An embodiment of the present invention also provides a computer device, Figure 12 which is a schematic diagram of the computer device in the embodiment of the present invention. The computer device 1200 includes a memory 1210, a processor 1220, and a computer program 1230 stored on the memory 1210 and executable on the processor 1220. When the processor 1220 executes the computer program 1230, the above-mentioned method for optimizing the layout of surface area points based on compressive sensing is implemented.

[0100] An embodiment of the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for optimizing the layout of surface area points based on compressive sensing is implemented.

[0101] An embodiment of the present invention also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned method for optimizing the layout of surface area points based on compressive sensing is implemented.

[0102] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0103] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0106] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the layout of surface area points based on compressive sensing, characterized in that, it includes: Obtain 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, perform point offset optimization to obtain the SPS file after offset optimization. The point offset optimization includes point offset optimization and / or geophone point offset optimization; Delete the repeated points in the SPS file after offset optimization to obtain compressive sensing data; According to the preset empty point ratio and coverage times, perform simulated reconstruction on the compressive sensing data to obtain a simulated reconstruction result; Verify the feasibility of simulated reconstruction by comparing the simulated reconstruction result with the attributes of the theoretical deployment SPS acquisition data to obtain a reconstruction feasibility verification result; When it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not met, adjust the current offset data.

2. The method according to claim 1, characterized in that, The offset data includes the offset method and the corresponding offset parameters; When the offset method is rectangular offset, the offset parameter is the maximum offset in the X and Y directions of the preset rectangle; When the offset method is circular offset, the offset parameter is the radius of the preset circle; When the offset method is elliptical offset, the offset parameter is the lengths of the major and minor axes of the preset ellipse.

3. The method according to claim 1, characterized in that, Deleting the repeated points in the SPS file after offset optimization to obtain compressive sensing data includes: Perform key index marking on the points in the SPS file after offset optimization where the distance between any two points is less than the first preset distance. The key index marking is a natural number in ascending order; Delete all points with a key index marking greater than 1 to obtain compressive sensing data.

4. The method according to claim 1, characterized in that, Performing simulated reconstruction on the compressive sensing data according to the preset empty point ratio and coverage times includes: Using a five-dimensional data interpolation method to perform simulated reconstruction on the compressive 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 passes and the decimation compression ratio of the compressive sensing data meets the preset decimation compression ratio.

6. A device for optimizing the layout of surface area points based on compressive sensing, characterized in that, it includes: An offset data module for obtaining the initial offset data and using it as the current offset data; An iteration module for repeating the following steps until the iteration termination condition is met, and outputting the current offset data when the iteration termination condition is met as the optimal offset data: An offset optimization module for performing point offset optimization based on the construction observation system parameters, the theoretical deployment SPS file, and the current offset data to obtain the SPS file after offset optimization. The point offset optimization includes point offset optimization and / or geophone point offset optimization; A compressive sensing data acquisition module, configured to delete duplicate points in the SPS file after offset optimization to obtain compressive sensing data; A reconstruction module, configured to perform simulated reconstruction on the compressive sensing data according to a preset empty point ratio and coverage times to obtain a simulated reconstruction result; A feasibility verification module, configured to verify the feasibility of the simulated reconstruction by comparing the simulated reconstruction result with the theoretical deployment SPS acquisition data attributes to obtain a reconstruction feasibility verification result; An adjustment module, configured to adjust the current offset data when it is determined according to the reconstruction feasibility verification result that the iteration termination condition is not satisfied.

7. The device according to claim 6, wherein, the offset data includes an offset method and corresponding offset parameters; when the offset method is rectangular offset, the offset parameter is the maximum offset amounts in the X direction and Y direction of a preset rectangle; when the offset method is circular offset, the offset parameter is the radius of a preset circle; when the offset method is elliptical offset, the offset parameter is the lengths of the major and minor semi-axes of a preset ellipse.

8. The device according to claim 6, wherein, the compressive sensing data acquisition module is specifically configured to: perform key index marking on points whose distance between any two points in the SPS file after offset optimization is less than a first preset distance, and the key index marking is ascending natural numbers; delete all points with key index markings greater than 1 to obtain compressive sensing data.

9. The device according to claim 6, wherein, the reconstruction module is specifically configured to: perform simulated reconstruction on the compressive sensing data by using a five-dimensional data interpolation method according to a preset empty point ratio and coverage times.

10. The device according to claim 6, wherein, the iteration termination condition is that the reconstruction feasibility verification is passed and the decimation compression ratio of the compressive sensing data meets a preset decimation compression ratio.

11. A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, 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, wherein, 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, wherein, the computer program product includes 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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