阅读说明：本技术 一种地震数据的非规则优化采集方法、装置、设备及介质 (Irregular optimization acquisition method, device, equipment and medium for seismic data ) 是由 单小彩 吕尧 周永健 杨长春 于 2020-11-13 设计创作，主要内容包括：本发明公开了一种地震数据的非规则优化采集方法、装置、设备及介质,该方法包括,执行微调循环：针对待优化采样矩阵Φ_N,根据基于交替方向贪心序贯的采样减少方案,更新采样矩阵为Φ_(N-n),根据基于交替方向贪心序贯的采样增加方案,更新采样矩阵为Φ_N′。根据压缩感知理论、预设数量m和整体优化终止条件确定是否结束循环,及输出最终优化采样矩阵。本发明提供的方法、装置、设备及介质用以解决现有的非规则地震采集方案易陷入局部最优,计算量大和不适合复杂地形区域的技术问题。提供了一种整体微调寻优,适合复杂地形区域,并降低采样成本的地震采集策略。(The invention discloses a method, a device, equipment and a medium for irregular optimization acquisition of seismic data, wherein the method comprises the following steps of: for a sampling matrix phi to be optimized N Updating the sampling matrix to phi according to a sampling reduction scheme based on alternating direction greedy sequence N‑n According to a sampling increasing scheme based on alternating direction greedy sequence, updating a sampling matrix to phi N '. And determining whether to finish the cycle or not according to the compressed sensing theory, the preset number m and the overall optimization termination condition, and outputting a final optimization sampling matrix. The method, the device, the equipment and the medium provided by the invention are used for solving the technical problems that the existing irregular seismic acquisition scheme is easy to fall into local optimization, large in calculation amount and not suitable for a complex terrain area. Provides an integral fine-tuning optimizing and fitting deviceAnd the seismic acquisition strategy integrates complex terrain areas and reduces the sampling cost.)

1. An irregular optimization acquisition method for seismic data, comprising:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the overall optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the overall optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

2. The method of claim 1, wherein the global optimization termination condition is:

the fine adjustment amplitude n reaches a fine adjustment amplitude minimum value n set in advance_set。

3. The method of claim 1, wherein the alternating direction greedy sequential based sampling reduction scheme is:

performing a traversal reduction loop:

for the sampling matrix phi_NRandomly selecting a sampled point as a candidate point; traversing all sampled points along the x direction of the candidate points, and respectively calculating phi_NReducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point;

traversing all sampled points along the y direction of the candidate points after replacement, and respectively calculating phi_NReducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point; judging whether the replaced candidate point enables the mu value to be minimum in the x direction and the y direction; if not, returning and repeating the traversal reduction cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if so, the candidate point is sampled from the sampling matrix phi_NDeleting and updating the sampling matrix to phi_N-1；

Judging whether the number of the deleted sampling points reaches the fine adjustment amplitude n; if not, then for phi_N-1Repeating the traversal reduction cycle until the number of deleted sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N-n。

4. The method of claim 1, wherein the alternating direction greedy sequential based sample addition scheme is:

executing a traversal increase loop:

for sampling matrix phi_N-nRandomly selecting an unsampled point as a candidate point; traversing all the non-sampling points along the x direction of the candidate points, and respectively calculating phi_N-nIncreasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the current candidate point;

y-direction along the candidate point after replacementTraversing all the non-sampling points to respectively calculate phi_N-nIncreasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the candidate point;

judging whether the replaced candidate point enables the mu value to be minimum in the x direction and the y direction; if not, returning and repeating the traversal increasing cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if yes, adding the candidate point into a sampling matrix phi_N-nUpdating the sampling matrix to phi_N-n+1；

Judging whether the number of the increased sampling points reaches a fine adjustment amplitude n; if not, then for phi_N-n+1Repeating the traversal increase cycle until the number of increased sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N′。

5. The method of claim 1, further comprising a sampling matrix generation scheme to be optimized:

setting a sampling matrix phi to be optimized_NIs phi_kAnd k is greater than 0 and less than N, randomly selecting an unsampled point as a candidate point, and executing the following first loop:

according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

and ending the first loop, and starting from the candidate point after replacement, executing the following second loop:

traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if it isAdding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; if not, repeating the circulation, adding new sampling points and updating the initial acquisition matrix until the end condition of the generation stage is met; if so, outputting the updated initial acquisition matrix as the acquisition matrix phi to be optimized_N。

6. The method of claim 5, wherein the generation phase termination condition is:

the updated initial sampling matrix Φ_k+1The number k +1 of the sampling points reaches the preset number N of the sampling points; or the updated initial sampling matrix Φ_k+1Has been smaller than the value mu set in advance_set。

7. An irregular optimization acquisition device for seismic data, comprising a fine tuning loop module for:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the integral optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the integral optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

8. The apparatus of claim 7, further comprising a sampling matrix to be optimized generation module to:

setting a sampling matrix phi to be optimized_NIs phi_kAnd k is greater than 0 and less than N, randomly selecting an unsampled point as a candidate point, and executing the following first loop:

according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

and ending the first loop, and starting from the candidate point after replacement, executing the following second loop:

traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if yes, adding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; such asIf not, repeating the circulation, adding new sampling points and updating the initial acquisition matrix until the end condition of the generation stage is met; if so, outputting the updated initial acquisition matrix as the acquisition matrix phi to be optimized_N。

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-6 are implemented when the program is executed by the processor.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

Technical Field

The invention relates to the technical field of sampling, in particular to an irregular optimization acquisition method, device, equipment and medium for seismic data.

Background

The traditional seismic exploration data acquisition adopts regular sampling construction, and shot points and demodulator probes are uniformly distributed on a regular grid. According to the shannon-nyquist sampling theorem in the classical signal processing, the sampling frequency must be more than twice the bandwidth of the original signal in order to maintain the information in the original signal without distortion in the acquired data.

In order to describe the fine features of the target volume, more accurate signals are often acquired, and therefore, the sampling points are encrypted in space. Such an acquisition mode greatly increases the amount of data acquired, resulting in a drastic increase in acquisition costs.

In addition, in the construction of a complex area, the situation that detectors cannot be arranged easily or even in cliff, rivers, ravines, villages, industrial areas and the like is often encountered, so that certain difficulty is brought to the regular acquisition scheme, and even the situation that sampling points cannot be arranged evenly for acquisition occurs. If a large amount of raw data is lost due to the reasons, the exploration quality and the imaging effect of the whole work area are seriously influenced, thereby causing difficulty in data interpretation.

Disclosure of Invention

In view of the above, the present invention has been developed to provide a method, apparatus, device and medium for irregular optimized acquisition of seismic data that overcomes or at least partially solves the above-mentioned problems.

In a first aspect, a method for irregular optimized acquisition of seismic data is provided, including:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the overall optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the overall optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

Optionally, the overall optimization termination condition is: the fine adjustment amplitude n reaches a fine adjustment amplitude minimum value n set in advance_set。

Optionally, the alternating direction greedy sequential-based sampling reduction scheme is as follows: performing a traversal reduction loop: for the sampling matrix phi_NRandomly selecting a sampled point as a candidate point; traversing all sampled points along the x direction of the candidate points, and respectively calculating phi_NReducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point; traversing all sampled points along the y direction of the candidate points after replacement, and respectively calculating phi_NReducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point; judging whether the replaced candidate point enables the mu value to be minimum in the x direction and the y direction; if not, returning and repeating the traversal reduction cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if it isThen the candidate point is sampled from the sampling matrix phi_NDeleting and updating the sampling matrix to phi_N-1(ii) a Judging whether the number of the deleted sampling points reaches the fine adjustment amplitude n; if not, then for phi_N-1Repeating the traversal reduction cycle until the number of deleted sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N-n。

Optionally, the sampling increasing scheme based on alternating direction greedy sequence is as follows: executing a traversal increase loop: for sampling matrix phi_N-nRandomly selecting an unsampled point as a candidate point; traversing all the non-sampling points along the x direction of the candidate points, and respectively calculating phi_N-nIncreasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the current candidate point; traversing all the non-sampling points along the y direction of the candidate points after replacement, and respectively calculating phi_N-nIncreasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the candidate point; judging whether the replaced candidate point enables the mu value to be minimum in the x direction and the y direction; if not, returning and repeating the traversal increasing cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if yes, adding the candidate point into a sampling matrix phi_N-nUpdating the sampling matrix to phi_N-n+1(ii) a Judging whether the number of the increased sampling points reaches a fine adjustment amplitude n; if not, then for phi_N-n+1Repeating the traversal increase cycle until the number of increased sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N′。

Optionally, the method further includes a sampling matrix generation scheme to be optimized: setting a sampling matrix phi to be optimized_NIs phi_kAnd k is greater than 0 and less than N, randomly selecting an unsampled point as a candidate point, and executing the following first loop: according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; choosing the unsampled point with the smallest μChanging the candidate points; and ending the first loop, and starting from the candidate point after replacement, executing the following second loop: traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point; judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if yes, adding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; if not, repeating the circulation, adding new sampling points and updating the initial acquisition matrix until the end condition of the generation stage is met; if so, outputting the updated initial acquisition matrix as the acquisition matrix phi to be optimized_N。

Optionally, the generating phase termination condition is: the updated initial sampling matrix Φ_k+1The number k +1 of the sampling points reaches the preset number N of the sampling points; or the updated initial sampling matrix Φ_k+1Has been smaller than the value mu set in advance_set。

In a second aspect, an irregular optimized acquisition device for seismic data is provided, comprising a fine tuning loop module configured to:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_NMu value of `Whether or not less than sampling matrix phi_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the integral optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the integral optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

Optionally, the apparatus further includes a to-be-optimized sampling matrix generation module, configured to: setting a sampling matrix phi to be optimized_NIs phi_kAnd k is greater than 0 and less than N, randomly selecting an unsampled point as a candidate point, and executing the following first loop: according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point; and ending the first loop, and starting from the candidate point after replacement, executing the following second loop: traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point; judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if yes, adding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; if not, repeating the circulation, adding new sampling points and updating the initial acquisition matrix until the end condition of the generation stage is met; if so, outputting the updated initial acquisition matrix as the acquisition matrix phi to be optimized_N。

In a third aspect, an electronic device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to the first aspect.

The technical scheme provided by the embodiment of the invention at least has the following technical effects or advantages:

the method, the device, the equipment and the medium for irregular optimization acquisition of seismic data provided by the embodiment of the invention combine a sampling reduction scheme based on alternating direction greedy sequence and a sampling increase scheme based on alternating direction greedy sequence to optimize a sampling matrix from a sampling matrix phi to be optimized_NIs updated to phi_N' and judging the updated sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value of the sampling matrix is used for judging whether to accept the fine adjustment, the fine adjustment trend is to enable the sampling matrix to better meet the requirement of better integrity, and the reliability of the sampling result is ensured. Further, the cycle is executed to determine the final optimized sampling matrix by taking whether the rejection times reach the preset number m as a cycle judgment condition and gradually reducing the fine tuning amplitude n. Therefore, the final optimized sampling matrix which is subjected to fine tuning optimization does not need to perform mechanical average division and average dense setting of sampling points on the region, even in the region with complex topography, the scheme of the application can be used, and candidate point optimization is not performed in the region which cannot be sampled so as to determine the appropriate sampling points. The scheme has small calculation amount and high overall performance, and is updated based on overall fine tuning optimizationThe sampling point can ensure the reliability of the sampling result, needs less sampling number compared with the conventional regular sampling, is not easy to trap in the local optimal solution which is easy to trap in other irregular sampling schemes, and can greatly reduce the sampling cost while ensuring the sampling effect.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

FIG. 1 is a flow chart of a method for irregular optimized acquisition of seismic data in an embodiment of the invention;

FIG. 2 is a diagram illustrating an embodiment of obtaining a sampling matrix Φ to be optimized_NA flow chart of the method of (1);

FIG. 3 is a diagram illustrating an embodiment of obtaining a sampling matrix Φ to be optimized_NSchematic diagram one;

FIG. 4 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NSchematic diagram two of (a);

FIG. 5 is a diagram illustrating an embodiment of obtaining a sampling matrix Φ to be optimized_NSchematic diagram three of (a);

FIG. 6 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NIs as shown in scheme four;

FIG. 7 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NSchematic diagram five of (a);

FIG. 8 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NSchematic diagram six;

FIG. 9 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NA seventh schematic view;

FIG. 10 is a diagram illustrating obtaining a sampling matrix Φ to be optimized according to an embodiment of the present invention_NSchematic diagram eight of (a);

FIG. 11 is a flow chart illustrating an alternate direction greedy sequential based sampling reduction scheme according to an embodiment of the present invention;

FIG. 12 is a flow chart illustrating an alternative orientation greedy sequential-based sample increment scheme according to an embodiment of the present invention;

FIG. 13 is a first schematic diagram illustrating a sample reduction scheme in an embodiment of the present invention;

FIG. 14 is a second schematic diagram of a sample reduction scheme in an embodiment of the present invention;

FIG. 15 is a third schematic diagram of a sample reduction scheme in an embodiment of the present invention;

FIG. 16 is a fourth schematic diagram of a sample reduction scheme in an embodiment of the present invention;

FIG. 17 is a fifth schematic illustration of a sample reduction scheme in an embodiment of the present invention;

FIG. 18 is a schematic diagram of a sample addition scheme in an embodiment of the present invention;

FIG. 19 is a schematic diagram of an apparatus for seismic data acquisition in an embodiment of the invention;

FIG. 20 is a schematic diagram of an electronic device in an embodiment of the invention;

fig. 21 is a schematic diagram of a storage medium in an embodiment of the present invention.

Detailed Description

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

The application provides an irregular optimization acquisition method of seismic data, as shown in fig. 1, comprising:

the following fine tuning loop is performed:

step S101, aiming at the sample to be optimizedMatrix phi_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

step S102, for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Step S103, judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

step S104, if less than, will Φ_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

step S105, if not, not changing the current sampling matrix, and increasing the rejection times of the current fine tuning amplitude once;

step S106, judging whether the rejection times reach a preset number m or not, wherein m changes in inverse proportion with the fine adjustment amplitude n;

step S107, if the number of times of rejection reaches the preset number m, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix;

step S108, if yes, determining whether n meets the overall optimization termination condition (fig. 1 assumes that the overall optimization termination condition is n-1);

step S109, if the sampling matrix does not meet the requirement, the fine tuning amplitude n is reduced, and the fine tuning cycle is repeatedly executed on the basis of the current sampling matrix until n meets the integral optimization termination condition;

and step S110, if the sampling matrix is consistent with the optimal sampling matrix, ending the circulation and outputting the final optimal sampling matrix.

Specifically, the irregular optimization acquisition method for seismic data can be applied to a special acquisition device, and can also be applied to equipment such as a computer, and the method is not limited herein.

It should be noted that the present application does not refer to the specific seismic data content of sampling, and the sampling content may be any existing seismic sampling data.

The detailed implementation steps of the irregular optimized acquisition method for seismic data provided by the present application are described in detail below, and refer to fig. 1.

Firstly, determining a sampling matrix phi to be optimized_NPhi of_NThe sampling matrix may be a sampling matrix preset according to experience, or may be a sampling matrix obtained according to a greedy sequential cycle in alternating directions, which is not limited herein.

Specifically, a sampling matrix phi to be optimized is obtained according to greedy sequential circulation of alternate directions_NThe method of (2) is shown in fig. 2. Firstly setting a sampling matrix phi to be optimized_NInitial sampling matrix phi_kWhere k may be equal to 1, or may be equal to 2 or 3, and the like, which is not limited herein. Then, one of the unsampled points is randomly selected as a candidate point, and the following first loop is performed: according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; and selecting the non-sampling point with the minimum mu value to replace the candidate point. Then, the first loop is ended, and the following second loop is executed starting from the candidate point after replacement: traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; and selecting the non-sampling point with the minimum mu value to replace the candidate point.

Judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if yes, adding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; if not, adding a new sampling point and updating the initial acquisition matrix so that k is k +1, and repeating the above cycle until fullA foot generation phase termination condition; if yes, outputting the updated initial acquisition matrix phi_k+1As the acquisition matrix phi to be optimized_N。

Wherein, the generating phase termination condition is as follows: updated initial sampling matrix phi_k+1The number k +1 of the sampling points reaches the preset number N of the sampling points; or the updated initial sampling matrix Φ_k+1Has been smaller than the value mu set in advance_set。

The following provides a specific example to help understand how to obtain the sampling matrix phi to be optimized according to the greedy sequential cycle of alternating directions_NMethod of 20, N:

assume that the sampling scene is a spatial grid point of 10 × 10 as shown in fig. 3 (the open circles are spatial grid points). Suppose that 10 sample points Φ have been determined_kI.e., k-10 (the position is shown as a solid dot in fig. 4), the position of the next sampling point needs to be determined.

Among the non-sampled grid points, an un-sampled point (7,8) is randomly selected as a candidate point (a hollow circle having a dot in the center as shown in fig. 5). Then, all the non-sampled points are traversed along the x direction of the current candidate point, i.e. all the non-sampled grid points on the row with the abscissa of 7 are traversed, as indicated by the arrow in fig. 5, and Φ is calculated respectively_kThe μ value after each unsampled point is increased. The unsampled point with the smallest μ value is selected to replace the candidate point, and assuming that the unsampled point with the smallest μ value is (7,5), (7,5) is taken as the updated candidate point as shown in fig. 6. Then, starting from the candidate point (7,5) after replacement, as shown in fig. 7, all the non-sampled points are traversed along the y direction of (7,5) as indicated by the arrow, that is, all the non-sampled grid points in the column with the ordinate of 5 are traversed, and Φ is calculated respectively_kAnd increasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the candidate point. Assume that the unsampled points that minimize μ values are (1,5) as shown in fig. 8.

Judging whether the candidate points (1,5) after replacement meet the condition that the mu values are minimum in the x direction and the y direction of the candidate points; if not, repeating the above traversal steps with (1,5) as a candidate point until the candidate point after replacement is such that μ in both x and y directionsThe value is minimal. If so (yes in the case of this example), the candidate points (1,5) after replacement are added to the initial sampling matrix Φ as shown in FIG. 9_kUpdating the initial sampling matrix to phi_k+1. Then, one sampling point is gradually increased according to the method until an acquisition matrix phi containing 20 sampling points is obtained as shown in fig. 10_N,N＝20。

Specifically, a greedy sequential cycle with alternating directions is adopted to obtain a sampling matrix phi to be optimized_NThe sampling points do not need to be mechanically and uniformly set in an area, and even in a region with complex terrain, the method can be used for determining the proper sampling points without carrying out candidate point optimization in the region which cannot be sampled. The scheme has small calculated amount and high overall performance, needs less sampling number compared with the conventional regular sampling, is not easy to trap into the local optimal solution which is easy to trap into other irregular sampling schemes, and can greatly reduce the sampling cost while ensuring the sampling effect.

After determining the sampling matrix phi to be optimized_NThereafter, as shown in fig. 1, the process continues to step S101 in fig. 1, and the sampling matrix Φ is referred to_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N.

Specifically, the sampling reduction scheme based on alternating directional greedy sequence is shown in fig. 11:

performing a traversal reduction loop: for the sampling matrix phi to be optimized_N(i.e.,. phi.in FIG. 11)_KAnd K is equal to N), one sampled point is randomly selected as a candidate point. Traversing all sampled points along the x direction of the candidate points, and respectively calculating phi_NAnd reducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point. Then, traversing all sampled points along the y direction of the candidate points after replacement, and respectively calculating phi_NAnd reducing the mu value after each sampled point, and selecting the sampled point with the minimum mu value to replace the candidate point. Next, judging whether the candidate point after replacement enables the mu value to be minimum in the x direction and the y direction;if not, returning to repeatedly execute the traversal reduction cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if so, the candidate point is sampled from the sampling matrix phi_NDeleting and updating the sampling matrix to phi_N-1(ii) a Judging whether the number of the deleted sampling points reaches the fine adjustment amplitude n; if not, then for phi_N-1Repeating the traversal reduction cycle until the number of deleted sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N-n。

Then, step S102 in fig. 1 is executed for Φ_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′。

Specifically, the sampling increment scheme based on alternating directional greedy sequence is shown in fig. 12:

executing a traversal increase loop: for sampling matrix phi_N-n(i.e.,. phi.in FIG. 12)_KK ═ N-N), randomly selecting one non-sampling point as a candidate point; traversing all the non-sampling points along the x direction of the candidate points, and respectively calculating phi_N-nAnd increasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the current candidate point. Then, traversing all the non-sampling points along the y direction of the candidate points after replacement, and respectively calculating phi_N-nAnd increasing the mu value after each non-sampling point, and selecting the non-sampling point with the minimum mu value to replace the candidate point. Judging whether the replaced candidate point enables the mu value to be minimum in the x direction and the y direction; if not, returning and repeating the traversal increasing cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if yes, adding the candidate point into a sampling matrix phi_N-nUpdating the sampling matrix to phi_N-n+1. Then, judging whether the number of the increased sampling points reaches the fine adjustment amplitude n; if not, then for phi_N-n+1Repeating the traversal increase cycle until the number of increased sampling points reaches a fine tuning amplitude n; if so, outputting the updated optimized sampling matrix phi under the current fine tuning amplitude_N′。

A specific example is provided below to help understand the alternating direction greedy sequential based sample reduction scheme and sample increase scheme:

suppose phi_NAs shown in fig. 10, the sampling scene is 10 × 10 spatial grid points (the open circles are the spatial grid points), 20 sampling points (the positions are as shown by the solid dots in fig. 10) have been determined, and fine tuning optimization needs to be performed on the sampling points.

First, the current sample point and μ value are recorded. The magnitude n of the fine tuning is determined, assuming n is 2. Firstly, n sampling points are sequentially removed by using greedy sequence, and then n sampling points are sequentially added. The method comprises the following specific steps:

as shown in fig. 13, a sampling point is randomly selected as a candidate point (a hollow circle having a dot at the center in fig. 13), for example, (4, 4). Traversing all sampled points along the x direction of the candidate point as shown by the arrow in fig. 13, namely traversing all sampled points of the row with the abscissa of 4, and respectively calculating the μ values after each point is subjected to sampling, and selecting the sampled point with the minimum μ value to replace the candidate point as shown in fig. 14, assuming that the sampled point with the minimum μ value is (4, 3). Then, as shown in fig. 15, all sampled points are traversed along the y direction of the candidate point (4,3) after replacement, that is, all sampled points in the column with the ordinate being 3 are traversed, μ values obtained by sampling each point are respectively calculated, and the sampled point with the smallest μ value is selected to replace the candidate point, assuming that the sampled point with the smallest μ value is still (4, 3). Next, judging whether the candidate point after replacement enables the mu value to be minimum in the x direction and the y direction; if not, returning to repeatedly execute the traversal reduction cycle until the candidate point enables the mu value to be minimum in the x direction and the y direction; if so, the candidate point is sampled from the sampling matrix phi_NDeleting and updating the sampling matrix to phi_N-1. In the case of the example here where (4,3) minimizes the value of μ in both the x and y directions, the sample at (4,3) is removed as shown in fig. 16. And repeating the process, sequentially obtaining candidate points in alternate directions and removing samples until the number of removed sample points reaches n. Since n is 2, let us say that another sampling point (6,5) is removed again as shown in fig. 17, and Φ is obtained_N-n。

Then phi after de-sampling_N-nOn the basis, n sampling points are sequentially added one by one in a greedy sequence, and the method for specifically adding the sampling points and the method for circularly obtaining the sampling matrix phi to be optimized according to the greedy sequence in the alternating direction_NThe method of increasing the sampling point is the same. For example, phi shown in FIG. 17_N-nAnd randomly selecting an unsampled point (5,3) as a candidate point, searching a new candidate point (5,7) which minimizes the mu value in the x direction (a row with the abscissa of 5), and searching a new candidate point which minimizes the mu value in the y direction (a row with the ordinate of 7), and if the new candidate point which minimizes the mu value in the y direction is still (5,7), newly adding the sampled point (5, 7). Then, non-sampling points (1,1) are randomly selected as candidate points, a new candidate point (1,3) with the minimum μ value is searched in the x direction (row with abscissa of 1) in a traversal manner, a new candidate point with the minimum μ value is searched in the y direction (row with ordinate of 3) in a traversal manner, and if the new candidate point with the minimum μ value searched in the y direction is still (1,3), the sampling points (1,3) are newly added, so that the optimized 20 sampling points as shown in fig. 18 are obtained, that is, the optimized sampling matrix Φ under the current fine tuning amplitude_N′。

Next, step S103 in fig. 1 is executed to determine the current sampling matrix Φ according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, execute step S104 to get phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop. If not, step S105 is executed to reject the fine adjustment, and the reject number of the current fine adjustment amplitude is increased once without changing the current sampling matrix.

Then, step S106 is executed to determine whether the rejection number reaches a preset number m. Preferably, m varies inversely with the fine tuning amplitude n.

If not, executing step S107, and returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the pre-thresholdThe number m is set. If yes, step S108 is executed to judge whether n meets the overall optimization termination condition. Wherein the overall optimization termination condition may be: the fine adjustment amplitude n reaches a fine adjustment amplitude minimum value n set in advance_set(assumed to be 1 in fig. 1).

If not, executing step S109, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the overall optimization termination condition; if yes, step S110 ends the loop and outputs the final optimized sampling matrix.

The theoretical basis of the method provided by the present application, and the method for calculating the μ value therein, are described below: the basic assumption of the compressed sensing theory is that the target signal has sparsity or compressibility, i.e. the target signal or its limited number of components in a certain transform domain is not equal to 0 (corresponding to sparsity) or much larger than 0 (corresponding to compressibility). If the target signal has K components not equal to 0, the signal is called K sparse. Assuming that a target signal with sparsity or compressibility is x, a fourier transform F is adopted for the sparse transform, and an orthogonal basis F is a fourier basis function, so that x ═ F^HS, in which: s is a sparse representation of the signal x in the fourier domain; f^HRepresenting the conjugate transpose of F.

The acquired data may be viewed as the result of multiplying a sampling function or sampling matrix with the target signal. The sampling matrix is recorded as phi, and when full sampling is performed, phi is equal to I, wherein I represents an identity matrix. When sparse sampling is carried out, phi is a matrix formed by a plurality of columns extracted by I, namely only column vectors corresponding to sampling positions are reserved, and sampling data are as follows: y phi x phi F^HS · s ═ Ψ · s. In the formula: psi ═ Φ · F^HIs a perceptual matrix.

According to the compressed sensing theory, a sufficient requirement for successfully reconstructing a K sparse target signal is that a sensing matrix psi ═ phi · F^HSatisfy the RIP condition of limited Isometric Property, i.e. for any K sparse vectors vIt holds (for each integer K1, 2.., the equidistant constant δ of the matrix Ψ is defined as δ when K is the minimum number at which the inequality holds_K). However, solving the matrix that satisfies the RIP condition is an NP-Hard problem, and instead, a calculable criterion may be used for the sampling design. Let the column vector of the matrix Ψ be Ψ_iThe maximum cross-correlation value between column vectors is:

the maximum cross correlation value among the column vectors of the sensing matrix is the maximum non-zero frequency amplitude of the irregular sampling normalized frequency spectrum. In the frequency domain, the spectrum of the sampled data y is the result of the convolution between the spectrum of the sensing matrix Ψ and the spectrum of the sparse representation s of the target signal. Mu is the maximum spectrum leakage caused by that the orthogonality of Fourier base (sparse base) is destroyed due to irregular sampling, and suppressing mu can enable the spectrum of the sensing matrix Ψ to approximate Delta function, otherwise, the spectrum of the sensing matrix Ψ has a plurality of peaks which generate 'spurious frequency' noise in a frequency domain (sparse domain) after being convolved with the sparse expression s.

The smaller μ, the higher the probability that the signal can be reconstructed after irregular sampling. Since psi ═ phi · F^HWherein F is fixed, therefore, when the number of samples is insufficient, the maximum cross-correlation value can be reduced by changing the sampling matrix Φ, i.e. changing the distribution of irregular sampling points, so as to optimize the sampling matrix and improve the probability of target signal reconstruction, i.e.:the optimization problem is non-convex, and the requirements can be met only by finding a local optimal solution during specific optimization.

Based on the same inventive concept, an embodiment of the present invention further provides an irregular optimized acquisition apparatus for seismic data, as shown in fig. 19, including:

a trim cycle module 400 to: the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the integral optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the integral optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

The seismic data acquisition device may be a dedicated acquisition device, or may be a computer, and is not limited herein.

In this embodiment, the apparatus may further include: a to-be-optimized sampling matrix generation module, configured to:

setting a sampling matrix phi to be optimized_NIs phi_kAnd k is greater than 0 and less than N, randomly selecting an unsampled point as a candidate point, and executing the following first loop:

according to the compressed sensing theory, traversing all the non-sampling points along the x direction of the current candidate point, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

and ending the first loop, and starting from the candidate point after replacement, executing the following second loop:

traversing all the non-sampling points along the y direction of the candidate points, and respectively calculating phi_kIncreasing the mu value after each non-sampling point; selecting an unsampled point with the minimum mu value to replace the candidate point;

judging whether the replaced candidate point meets the condition that the mu value is minimum in the x direction and the y direction; if not, repeating the first loop and the second loop until the candidate point after replacement minimizes a μ value in both x and y directions; if yes, adding the replaced candidate point into the initial sampling matrix phi_kUpdating the initial sampling matrix to phi_k+1Judging whether the generation stage termination condition is met or not; if not, repeating the circulation, adding new sampling points and updating the initial acquisition matrix until the end condition of the generation stage is met; if so, outputting the updated initial acquisition matrix as the acquisition matrix phi to be optimized_N。

Since the apparatus described in the embodiment of the present invention is an apparatus used for implementing the method in the embodiment of the present invention, a person skilled in the art can understand the specific structure and the deformation of the apparatus based on the method described in the embodiment of the present invention, and thus the detailed description is omitted here. All devices adopted by the method of the embodiment of the invention belong to the protection scope of the invention.

Based on the same inventive concept, an electronic device is further provided in the embodiments of the present invention, as shown in fig. 20, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520, where the processor 520 executes the computer program 511 to implement the following steps:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the overall optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the overall optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

In the embodiment of the present invention, when the processor 520 executes the computer program 511, any one of the methods of the embodiment of the present invention may be implemented.

Since the electronic device described in the embodiment of the present invention is a device used for implementing the method in the embodiment of the present invention, a person skilled in the art can understand the specific structure and the deformation of the device based on the method described in the embodiment of the present invention, and thus details are not described herein. All the devices adopted by the method of the embodiment of the invention belong to the protection scope of the invention.

Based on the same inventive concept, the embodiment of the present invention further provides a storage medium corresponding to the method in the embodiment:

the present embodiment provides a computer-readable storage medium 600, as shown in fig. 21, on which a computer program 611 is stored, the computer program 611, when executed by a processor, implementing the steps of:

the following fine tuning loop is performed:

for a sampling matrix phi to be optimized_NSequentially reducing n sampling points according to a sampling reduction scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N-n(ii) a Wherein N is a preset fine tuning amplitude value, and N is greater than N;

for phi_N-nSequentially increasing n sampling points according to a sampling increasing scheme based on alternating direction greedy sequence, and updating the sampling matrix to phi_N′；

Judging the current sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value is the maximum cross-correlation value among column vectors of a sensing matrix in the compressed sensing theory;

if less than, will be phi_NChange to phi_N', end on phi_NAnd for phi_N' repeatedly executing the fine tuning loop;

if not, not changing the current sampling matrix, increasing the rejection times of the current fine tuning amplitude once, and judging whether the rejection times reach a preset number m or not; if not, returning to repeatedly execute the fine adjustment cycle on the basis of the current sampling matrix until the rejection times reach the preset number m; if yes, judging whether n meets the overall optimization termination condition, if not, reducing the fine tuning amplitude n, and returning to repeatedly execute the fine tuning cycle on the basis of the current sampling matrix until n meets the overall optimization termination condition; and if so, ending the circulation and outputting a final optimized sampling matrix.

In particular, when the computer program 611 is executed by a processor, any of the methods of the embodiments of the present invention may be implemented.

The technical scheme provided by the embodiment of the invention at least has the following technical effects or advantages:

the method, the device, the equipment and the medium provided by the embodiment of the invention combine the sampling reduction scheme based on the alternating direction greedy sequence and the sampling increase scheme based on the alternating direction greedy sequence to optimize the sampling matrix from phi_NIs updated to phi_N' and judging the updated sampling matrix phi according to the compressed sensing theory_N' whether the value of μ is less than the sampling matrix Φ_NThe mu value of the sampling matrix is used for judging whether to accept the fine adjustment, the fine adjustment trend is to enable the sampling matrix to better meet the requirement of better integrity, and the reliability of the sampling result is ensured. Furthermore, whether the rejection times reach the preset number m or not is used as a circulation judgment condition, and the fine adjustment amplitude n is gradually reduced to execute circulation to determine a final optimized sampling matrix, so that the final optimized sampling matrix obtained through fine adjustment does not need to mechanically and intensively set sampling points on the area, even in the area with complex terrain, the scheme of the application can be used for determining the proper sampling points without candidate point optimization in the area which cannot be sampled. The scheme has small calculated amount and high overall performance, the sampling point is updated based on integral fine tuning optimization, the reliability of the sampling result can be ensured, less sampling number is needed compared with the conventional regular sampling, the scheme is not easy to fall into the local optimal solution which is easy to fall into by other irregular sampling schemes, and the sampling cost can be greatly reduced while the sampling effect is ensured.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of a gateway, proxy server, system according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.