Observation system design method based on fracture target and storage medium

Abstract

The invention belongs to the technical field of seismic data sampling, and particularly relates to an observation system design method based on a fracture target and a storage medium. The method comprises the following steps: determining candidate sampling points of a target work area; dividing the target work area into a fracture distribution area and a non-fracture distribution area, and distributing sampling points with different sampling rates; obtaining a first compressed sensing matrix according to the sampling points distributed in the fracture distribution area; updating the first compressed sensing matrix until the sampling point number of the fracture distribution area reaches the set sampling point number; obtaining a second compressed sensing matrix according to the sampling points distributed in the non-fracture distribution area; updating the second compressed sensing matrix until the sampling point number of the non-fracture distribution area reaches the set sampling point number; and carrying out fine tuning on all sampling points to determine a final observation system. According to the invention, non-uniform sampling of the fractured target is completed, the sampling rate is reduced, and the pertinence of the observation system to the fractured exploration target is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic data sampling, and particularly relates to a method for designing an observation system based on fracture targets and a storage medium. Background Art

[0002] In seismic exploration, the relative position relationship between the excitation point and the receiving area is called an observation system. In a two-dimensional observation system, the excitation point and the receiving area are usually longitudinal profile line observations, while in a three-dimensional observation system, it is a multi-line and multi-shot beam relationship. Currently, in order to reduce the workload in the design of the observation system, a sparse sampling method based on compressive sensing has been promoted in many places. It sparsifies globally to determine the sampling points, but the global sparsity method cannot reflect the pertinence of the observation system to the local development situation. For example, the prior art CN109407143B discloses a method for designing an irregular observation system for seismic exploration based on compressive sensing. The method for designing an irregular observation system for seismic exploration based on compressive sensing includes: Step 1, determining the work area range, determining the range of shot points and receiving points, the number of shot points, receiving lines, and receiving points to be designed; Step 2, designing the receiving lines, and optimizing the layout position of the receiving lines according to the work area range and the number of receiving lines; Step 3, designing the receiving points, and optimizing the layout position of the receiving points according to the determined receiving line positions; Step 4, designing the shot points, and optimizing the layout position of the shot points according to the shot point layout range; Step 5, generating an irregular optimized observation system based on compressive sensing according to the optimized layout positions of the shot points and receiving points. The method for designing an irregular observation system for seismic exploration based on compressive sensing minimizes the reconstruction signal error through the optimization of the observation system, providing accurate data for fine reservoir prediction and oil and gas exploration. However, this observation system design method sparsely samples the entire work area and lacks pertinence to the exploration of locally developed fractures, which may result in unsatisfactory imaging effects due to insufficient sampling of the fracture target area.

[0003] Therefore, for exploration areas targeted at fractures, where fractures are locally developed and the global sparsity method cannot reflect the pertinence of the observation system to fracture targets, a method for designing an observation system based on fracture targets and a storage medium are proposed. Summary of the Invention

[0004] In order to solve the above technical problems existing in the prior art, the present invention provides a method for designing an observation system based on fracture targets and a storage medium, aiming to adopt different sampling rates for fracture distribution areas and non-fracture distribution areas, and use the sparse sampling method to complete the non-uniform sampling of fracture targets, thereby improving the pertinence of the observation system to fracture exploration targets while reducing the sampling rate.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A design method for an observation system based on fracture targets, comprising:

[0007] S1. Define a regular observation system for the target work area, and determine the candidate sampling points in the target work area according to the regular observation system;

[0008] S2. Divide the target work area into a fracture distribution area and a non-fracture distribution area, specify the sparse sampling quantity for the entire work area, specify the ratio of the sampling rates of the fracture and non-fracture areas, and allocate the sampling points with different sampling rates to the fracture distribution area and the non-fracture distribution area according to the total sparse sampling quantity;

[0009] S3. Obtain a first compressive sensing matrix based on the sampling points allocated to the fracture distribution area;

[0010] S4. Traverse all the candidate sampling points in the fracture distribution area, add each candidate sampling point to the first compressive sensing matrix, then calculate the maximum cross-correlation value of the first compressive sensing matrix, and update the first compressive sensing matrix according to the maximum cross-correlation value until the number of sampling points in the fracture distribution area reaches the set number of sampling points;

[0011] S5. Obtain a second compressive sensing matrix based on the sampling points allocated to the non-fracture distribution area;

[0012] S6. Traverse all the candidate sampling points in the non-fracture distribution area, add each candidate sampling point to the second compressive sensing matrix, then calculate the maximum cross-correlation value of the second compressive sensing matrix, and update the second compressive sensing matrix according to the maximum cross-correlation value until the number of sampling points in the non-fracture distribution area reaches the set number of sampling points;

[0013] S7. Fine-tune all the sampling points in the fracture distribution area and all the sampling points in the non-fracture distribution area to determine the final first compressive sensing matrix and second compressive sensing matrix.

[0014] Further, in step S1, determining the candidate sampling points in the target work area according to the regular observation system specifically includes: defining a regular grid for the target work area according to the distribution of the shot points and receiving points of the regular observation system, and each point on the regular grid is a candidate sampling point.

[0015] Furthermore, defining the regular grid for the target work area specifically includes:

[0016] S11. Determine the scope of the target work area, and determine the layout scope of the receiving points and shot points in the target work area according to the original observation system;

[0017] S12. Take the overall distribution scope of the shot points and receiving points as the distribution scope of the regular grid, and divide the regular grid according to the bin size in the original observation system.

[0018] Further, in step S2, the target work area is divided into a fracture distribution area and a non-fracture distribution area, specifically including:

[0019] S21. Determine the coordinate of each end point of the fracture zones distributed in the target work area. The coordinates of each end point are denoted as [X1, Y1], [X2, Y2], [X3, Y3],...;

[0020] S22. By comparing the coordinate values of each fracture end point, obtain the maximum and minimum X coordinates and Y coordinates respectively, denoted as XMAX, XMIN, YMAX, and YMIN. Divide the entire target work area into a fracture distribution area and a non-fracture distribution area according to this coordinate range.

[0021] Further, in step S3, the first compressive sensing matrix is obtained according to the sampling points allocated in the fracture distribution area, specifically including:

[0022] S31. Obtain the sparse matrix Ψ1 according to the sampling points allocated in the fracture distribution area, specifically expressed as:

[0023] Ψ1 = F(E1 n )

[0024] where the matrix E1 n represents the n-order identity matrix, and the order of the matrix is determined by the number of sampling points in the fracture distribution area. F represents the Fourier transform;

[0025] Step S32: Sample the sparse matrix using the sampling matrix to obtain the first compressive sensing matrix θ1, specifically expressed as:

[0026]

[0027] where the elements in the sampling matrix indicate whether sampling is performed or not.

[0028] Further, in step S4, calculate the maximum cross-correlation value of the first compressive sensing matrix, and then determine the minimum value from all the maximum cross-correlation values. Use the candidate sampling point corresponding to the minimum value as the new sampling point to update the first compressive sensing matrix.

[0029] Furthermore, assume that after any candidate sampling point in the fracture distribution area is added to the first compressive sensing matrix, the first compressive sensing matrix is denoted as θ1'. Assume that any two column vectors of the first compressive sensing matrix θ1' are Ψ i and Ψ j , and the maximum cross-correlation value between the column vectors is:

[0030]

[0031] where μ represents the column vector Ψi and Ψ j The maximum cross - correlation value between them.

[0032] Furthermore, the updated first compressive sensing matrix is represented by as

[0033]

[0034] where represents the sampling points corresponding to the minimum maximum cross - correlation value of the first compressive sensing matrix.

[0035] Further, in step S5, the second compressive sensing matrix is obtained according to the sampling points allocated in the non - fracture distribution area, specifically including:

[0036] S51. Obtain the sparse matrix Ψ2 according to the sampling points allocated in the fracture distribution area, specifically represented as:

[0037] Ψ2 = F(E2 n )

[0038] where the matrix E2 n represents the n - order identity matrix, the order of the matrix is determined by the number of sampling points in the non - fracture distribution area, and F represents the Fourier transform;

[0039] S52. Sample the sparse matrix using the sampling matrix to obtain the first compressive sensing matrix θ2, specifically represented as:

[0040]

[0041] where the elements in the sampling matrix indicate whether to sample or not.

[0042] Further, in step S6, calculate the maximum cross - correlation value of the second compressive sensing matrix, then determine the minimum value from all the maximum cross - correlation values, and use the candidate sampling points corresponding to the minimum value as the new sampling points to update the second compressive sensing matrix.

[0043] Furthermore, assume that after adding any candidate sampling point in the non - fracture distribution area to the second compressive sensing matrix, at this time the second compressive sensing matrix is represented as θ2’, and assume that any two column vectors of the second compressive sensing matrix θ2’ are Ψ 2i and Ψ 2j , the maximum cross - correlation value between the column vectors is:

[0044]

[0045] where μ2 represents the column vectors Ψ 2i and Ψ 2jThe maximum cross-correlation value between

[0046] Furthermore, the updated second compressive sensing matrix is represented by

[0047]

[0048] wherein, represents the sampling point corresponding to the minimum maximum cross-correlation value of the second compressive sensing matrix.

[0049] Further, in step S7, the time jitter method is used to fine-tune all sampling points in the fracture distribution area and all sampling points in the non-fracture distribution area respectively.

[0050] Furthermore, the time jitter method specifically includes: making random jitters based on the position of each sampling point, performing sampling fine-tuning, aiming at the cross-correlation value of the compressive sensing matrix, finding the first compressive sensing matrix and the second compressive sensing matrix when the target is the smallest, so as to determine the final layout positions of all sampling points.

[0051] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for designing an observation system based on fracture targets is implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The method for designing an observation system based on fracture targets provided by the present invention divides the target work area into a fracture distribution area and a non-fracture distribution area, and uses the sparse sampling method to update the sampling points in the fracture distribution area and the non-fracture distribution area respectively, completing the non-uniform sampling of the fracture targets, reducing the sampling rate, and improving the pertinence of the observation system to the fracture exploration targets. Description of the Drawings

[0054] Figure 1 is the flowchart of the method of the present invention.

[0055] Figure 2 is the schematic diagram of the division of the fracture distribution area and the non-fracture distribution area after the division of the target work area in the present invention.

[0056] Figure 3 is the flowchart of the method for fine-tuning the sampling points by using the time jitter method in the present invention. Detailed Embodiments

[0057] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps described in these embodiments and numerical expressions should not be construed as limiting the scope of the present invention.

[0059] The following description of exemplary embodiments is merely illustrative and in no way limits the present invention and its application or use. Technologies, methods, and devices known to those of ordinary skill in the relevant fields may not be discussed in detail here, but when applicable, these technologies, methods, and devices should be regarded as part of this specification.

[0060] The present invention provides a method for designing an observation system based on fracture targets, as Figure 1 shown, including:

[0061] S1. Define a regular observation system for the target work area, and then define an ideal regular grid for the target work area according to the distribution of shot points and receiving points of the regular observation system. Each point on the regular grid is a candidate sampling point; defining an ideal regular grid for the target work area according to the work area range and the regular observation system specifically includes:

[0062] S11. Determine the target work area range, and determine the distribution ranges of receiving points and shot points in the target work area according to the original observation system (the observation system collected by conventional technical means).

[0063] S12. Take the overall distribution range of shot points and receiving points as the distribution range of the regular grid, and divide the regular grid according to the bin size in the original observation system.

[0064] S2. According to the preliminary exploration results, divide the target work area into a fracture distribution area and a non-fracture distribution area, give the sparse sampling quantity of the entire work area, and give the ratio of the sampling rates of the fracture area and the non-fracture area. The sampling rate of the fracture area is approximately 2-5 times that of the non-fracture area; allocate the sampling points with different sampling rates for the fracture distribution area and the non-fracture distribution area according to the total sparse sampling quantity; in this embodiment, the preliminary exploration results refer to seismic record data, and the target work area model is divided into a fracture distribution area and a non-fracture distribution area according to the seismic record data. The specific division process is as follows:

[0065] S21. Determine the coordinate of each end point of the fracture zone distributed in the target work area, and record the coordinate of each end point as [X1, Y1], [X2, Y2], [X3, Y3],...;

[0066] S22. By comparing the coordinate values of each fracture endpoint, the maximum and minimum X coordinates and Y coordinates are obtained respectively, denoted as XMAX, XMIN, YMAX, and YMIN. According to this coordinate range, the entire target work area 3 is divided into a fracture distribution area 1 and a non-fracture distribution area 2. In this embodiment, the fracture distribution area 1 and the non-fracture distribution area 2 are as Figure 2 shown.

[0067] S3. Obtain the first compressive sensing matrix according to the sampling points allocated in the fracture distribution area; specifically including:

[0068] S31. Obtain the sparse matrix Ψ1 according to the sampling points allocated in the fracture distribution area, specifically expressed as:

[0069] Ψ1 = F(E1 n )

[0070] where the matrix E1 n represents the n-order identity matrix, the order of the matrix is determined by the number of sampling points in the fracture distribution area, and F represents the Fourier transform;

[0071] Step S32: Sample the sparse matrix using the sampling matrix to obtain the first compressive sensing matrix θ1, specifically expressed as:

[0072]

[0073] where the elements in the sampling matrix indicate whether to sample.

[0074] S4. Traverse all candidate sampling points in the fracture distribution area, add any candidate sampling point to the first compressive sensing matrix, and then calculate the maximum cross-correlation value of the first compressive sensing matrix. In this embodiment, after adding the candidate sampling point to the first compressive sensing matrix θ1,

[0075] θ1’ = [Ψ1]

[0076] where the matrix θ1’ represents the first compressive sensing matrix added with the candidate sampling point, and Ψ1 is the added candidate sampling point.

[0077] Let any two column vectors of the first compressive sensing matrix θ1’ after adding the candidate sampling point be Ψ i and Ψ j , and the maximum cross-correlation value between the column vectors is:

[0078]

[0079] where μ represents the maximum cross-correlation value between the column vectors Ψ i and Ψ j .

[0080] Determine the minimum value from all the maximum cross-correlation values, use the candidate sampling point corresponding to the minimum value as the new sampling point, and then update the first compressive sensing matrix.

[0081] In this embodiment, the updated first compressive sensing matrix is denoted by denoted as

[0082]

[0083] where denotes the sampling point corresponding to the minimum of the maximum cross-correlation value of the first compressive sensing matrix.

[0084] Repeat step S4 until the number of sampling points in the fracture distribution area reaches the set number of sampling points.

[0085] S5. Obtain the second compressive sensing matrix according to the sampling points allocated in the non-fracture distribution area; specifically, it includes:

[0086] S51. Obtain the sparse matrix Ψ2 according to the sampling points allocated in the fracture distribution area, specifically expressed as:

[0087] Ψ2 = E(E2 n )

[0088] where the matrix E2 n denotes the n-order identity matrix, the order of the matrix is determined by the number of sampling points in the non-fracture distribution area, and F represents the Fourier transform;

[0089] S52. Sample the sparse matrix using the sampling matrix to obtain the first compressive sensing matrix θ2, specifically expressed as:

[0090]

[0091] where the elements in the sampling matrix indicate whether to sample or not.

[0092] S6. Traverse all candidate sampling points in the non-fracture distribution area, add any candidate sampling point to the second compressive sensing matrix, and then calculate the maximum cross-correlation value of the second compressive sensing matrix.

[0093] In this embodiment, after adding the candidate sampling point to the second compressive sensing matrix θ2, we get

[0094] θ2’ = [Ψ2]

[0095] where the matrix θ2’ represents the second compressive sensing matrix added with the candidate sampling point, and Ψ2 is the added candidate sampling point.

[0096] Let any two column vectors of the second compressive sensing matrix θ2’ with candidate sampling points added be Ψ 2i and Ψ 2j , and the maximum cross-correlation value between the column vectors is:

[0097]

[0098] where μ2 represents the maximum cross-correlation value between the column vectors Ψ 2i and Ψ 2j .

[0099] Determine the minimum value from all the maximum cross-correlation values, use the candidate sampling points corresponding to the minimum value as the new sampling points, and then update the second compressive sensing matrix.

[0100] In this embodiment, the updated second compressive sensing matrix is denoted as ,

[0101]

[0102] where represents the sampling point corresponding to the minimum of the maximum cross-correlation value of the second compressive sensing matrix.

[0103] Repeat step S6 until the number of sampling points in the non-fracture distribution area reaches the set number of sampling points; among them, the set number of sampling points in the fracture area and the non-fracture area are different, and are respectively related to the corresponding sampling rate and the distributed area.

[0104] S7. Determine all sampling points in the fracture distribution area according to the first compressive sensing matrix, and determine all sampling points in the non-fracture distribution area according to the second compressive sensing matrix; use the time jitter method to fine-tune all sampling points in the fracture distribution area and all sampling points in the non-fracture distribution area respectively to determine the final first compressive sensing matrix and the second compressive sensing matrix, and the first compressive sensing matrix and the second compressive sensing matrix are the designed observation systems.

[0105] Use the time jitter method to fine-tune all sampling points in the fracture distribution area and all sampling points in the non-fracture distribution area respectively. As Figure 3 shown, it specifically includes: making random jitters based on the position of each sampling point for sampling fine-tuning, with the cross-correlation value of the compressive sensing matrix as the target, where the calculation method of the cross-correlation value of the compressive sensing matrix is the same as the calculation method of the cross-correlation value between column vectors, and finding the first compressive sensing matrix and the second compressive sensing matrix when the target is the smallest, so as to determine the final layout positions of all sampling points.

[0106] The present invention adopts different sampling rates and uses the sparse sampling method to complete the non-uniform sampling of the fracture target. On the basis of reducing the sampling rate, the targeting of the observation system for fracture exploration targets is improved. It can be applied to the sampling of seismic data, increasing the target targeting while ensuring the sampling effect.

[0107] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned observation system design method based on fracture targets is implemented.

[0108] The above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the examples, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for designing an observation system based on a fracture target, characterized in that Including: S1. Define a regular observation system for the target work area, and determine candidate sampling points in the target work area according to the regular observation system; S2. Divide the target work area into a fracture distribution area and a non-fracture distribution area, specify the sparse sampling quantity for the entire work area, specify the ratio of the sampling rates for the fracture and non-fracture areas, and allocate sampling points with different sampling rates to the fracture distribution area and the non-fracture distribution area according to the total sparse sampling quantity; S3. Obtain a first compressive sensing matrix based on the sampling points allocated to the fracture distribution area; S4. Traverse all candidate sampling points in the fracture distribution area, add each candidate sampling point to the first compressive sensing matrix, then calculate the maximum cross-correlation value of the first compressive sensing matrix, and update the first compressive sensing matrix according to the maximum cross-correlation value until the number of sampling points in the fracture distribution area reaches the set number of sampling points; S5. Obtain a second compressive sensing matrix based on the sampling points allocated to the non-fracture distribution area; S6. Traverse all candidate sampling points in the non-fracture distribution area, add each candidate sampling point to the second compressive sensing matrix, then calculate the maximum cross-correlation value of the second compressive sensing matrix, and update the second compressive sensing matrix according to the maximum cross-correlation value until the number of sampling points in the non-fracture distribution area reaches the set number of sampling points; S7. Fine-tune all sampling points in the fracture distribution area and all sampling points in the non-fracture distribution area to determine the final first compressive sensing matrix and second compressive sensing matrix.

2. The observation system design method according to claim 1, wherein In step S1, determining the candidate sampling points in the target work area according to the regular observation system is specifically: defining a regular grid of the target work area according to the distribution of shot points and receiving points of the regular observation system, and each point on the regular grid is a candidate sampling point.

3. The observation system design method according to claim 2, wherein Defining the regular grid of the target work area specifically includes: S11. Determine the scope of the target work area, and determine the layout scope of receiving points and shot points in the target work area according to the original observation system; S12. Use the overall distribution scope of shot points and receiving points as the distribution scope of the regular grid, and divide the regular grid according to the bin size in the original observation system 4. The observation system design method according to claim 1, wherein In step S2, dividing the target work area into a fracture distribution area and a non-fracture distribution area specifically includes: S21. Determine the coordinate of each end point of the fracture zone distributed in the target work area, and record the coordinate of each end point as [X1, Y1], [X2, Y2], [X3, Y3],...; S22. By comparing the coordinate values of each fracture end point, obtain the maximum and minimum X coordinates and Y coordinates respectively, denoted as XMAX, XMIN, YMAX, YMIN, and divide the entire target work area into a fracture distribution area and a non-fracture distribution area according to this coordinate range.

5. The observation system design method according to claim 1, wherein In step S3, obtaining the first compressive sensing matrix based on the sampling points allocated to the fracture distribution area specifically includes: S31. Obtain a sparse matrix Ψ1 based on the sampling points allocated to the fracture distribution area, specifically expressed as: Ψ1 = F(E1 n ) Among them, matrix E1 n represents an n-order identity matrix, the order of the matrix is determined by the number of sampling points in the fracture distribution area, and F represents the Fourier transform; Step S32: Use the sampling matrix to sample the sparse matrix to obtain the first compressed sensing matrix θ1, which is specifically expressed as: Among them, the sampling matrix The elements in it indicate whether sampling is performed or not.

6. The method for designing an observation system according to claim 5, characterized in that In step S4, calculate the maximum cross-correlation value of the first compressive sensing matrix, then determine the minimum value from all the maximum cross-correlation values, and use the candidate sampling point corresponding to the minimum value as the new sampling point to update the first compressive sensing matrix.

7. The observation system design method according to claim 6, wherein After setting any candidate sampling point in the fracture distribution area to be added to the first compressive sensing matrix, the first compressive sensing matrix is denoted as θ1'. Assume that any two column vectors of the first compressive sensing matrix θ1' are Ψ i and Ψ j , and the maximum cross-correlation value between the column vectors is: where μ represents the maximum cross - correlation value between the column vectors Ψ i and Ψ j ​ 8. The method for designing an observation system according to claim 7, wherein The updated first compressive sensing matrix is denoted by and Among them, represents the sampling point corresponding to the minimum maximum cross-correlation value of the first compressed sensing matrix.

9. The observation system design method according to claim 1, wherein In step S5, obtaining the second compressive sensing matrix based on the sampling points allocated to the non-fracture distribution area specifically includes: S51. Obtain the sparse matrix Ψ2 from the sampling points allocated according to the fracture distribution area, which is specifically expressed as: Ψ2 = F(E2 n ) Among them, matrix E2 n represents an n-order identity matrix, the order of the matrix is determined by the number of sampling points in the non-fracture distribution area, and F represents the Fourier transform; S52. Using a sampling matrix Sample the sparse matrix to obtain a first compressive sensing matrix θ2, which is specifically expressed as: Among them, the sampling matrix The elements in it indicate whether sampling is performed or not.

10. The observation system design method according to claim 9, characterized in that, In step S6, calculate the maximum cross-correlation value of the second compressive sensing matrix, and then determine the minimum value from all the maximum cross-correlation values. Use the candidate sampling points corresponding to the minimum value as the new sampling points to update the second compressive sensing matrix.

11. The observation system design method according to claim 10, characterized in that, Suppose that after adding an arbitrary candidate sampling point in the non-fracture distribution area to the second compressive sensing matrix, the second compressive sensing matrix is represented as θ2'. Suppose that any two column vectors of the second compressive sensing matrix θ2' are Ψ 2i and Ψ 2j , and the maximum cross-correlation value between the column vectors is as follows: where μ2 represents the maximum cross-correlation value between the column vectors Ψ 2i and Ψ 2j .

12. The method for designing an observation system according to claim 11, characterized in that, The updated second compressive sensing matrix is denoted by . Among them, represents the sampling point corresponding to the minimum maximum cross-correlation value of the second compressed sensing matrix.

13. The observation system design method according to claim 1, characterized in that, In step S7, use the time jitter method to fine-tune all the sampling points in the fracture distribution area and all the sampling points in the non-fracture distribution area respectively.

14. The observation system design method according to claim 13, characterized in that, The time jitter method specifically includes: making random jitters based on the positions of each sampling point for sampling fine-tuning, aiming at the cross-correlation value of the compressive sensing matrix, and finding the first compressive sensing matrix and the second compressive sensing matrix when the target is minimized, so as to determine the final layout positions of all sampling points.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the observation system design method according to any one of claims 1-14.

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