Complex earth surface three-dimensional work area designated starting point shot point grouping method, device, equipment, storage medium and product

Abstract

The invention provides a complex surface three-dimensional work area designated starting point shot point grouping method, device and equipment, a storage medium and a product, relates to the field of petroleum seismic exploration, and aims to realize balanced grouping of vibroseis shot points. The method comprises the following steps: obtaining vibroseis shot point data and specified starting shot point data, and further obtaining a shot point set and a starting shot point set; acquiring a connected distance matrix of all shot points; key coefficients of shot points are solved according to the connected distance matrix, and a key coefficient sequence is formed; obtaining a relation matrix according to the connected distance matrix and the key coefficient sequence; obtaining a degree matrix according to the relation matrix; calculating a corresponding Laplacian transformation matrix of the degree matrix; calculating according to the Laplacian transformation matrix to obtain a feature vector; standardizing a matrix formed by the feature vectors according to rows to obtain a feature matrix; taking each row in the feature matrix as a sample, and clustering by using a K-means clustering method to obtain a classification cluster division C; and the classification cluster divides C into shot point groups.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of oil seismic exploration, in particular, to a method, device, equipment, storage medium and product for grouping vibroseis shot points in a complex surface three-dimensional work area with a specified starting point. BACKGROUND

[0002] In oil seismic exploration, the application of vibroseis as a seismic exploration source is becoming more and more widespread. With the expansion of the scale of exploration projects, the number of vibroseis shot points needed for use in each project or work area is also increasing. In order to better organize and manage these shot points and improve exploration efficiency, it is usually necessary to group these shot points for management. However, due to the influence of factors such as complex surface conditions and the location restrictions of the vibroseis vehicle, it is often difficult to achieve balanced grouping of vibroseis shot points. SUMMARY

[0003] Embodiments of the present application provide a method, device, equipment, storage medium and product for grouping vibroseis shot points in a complex surface three-dimensional work area with a specified starting point, aiming to solve the problem of difficulty in achieving balanced grouping of vibroseis shot points due to the influence of factors such as complex surface conditions and the location restrictions of the vibroseis vehicle.

[0004] The first aspect of embodiments of the present application provides a method for grouping vibroseis shot points in a complex surface three-dimensional work area with a specified starting point, comprising:

[0005] obtaining vibroseis shot point data and specified starting shot point data;

[0006] obtaining a shot point set and a starting shot point set from the vibroseis shot point data and the specified starting shot point data; the starting shot point set is a subset of the shot point set;

[0007] obtaining a connectivity distance matrix of all shot points;

[0008] for each shot point in the shot point set, calculating a key coefficient of the shot point according to the connectivity distance matrix to form a key coefficient sequence;

[0009] obtaining a relationship matrix from the connectivity distance matrix and the key coefficient sequence;

[0010] obtaining a degree matrix from the relationship matrix;

[0011] calculating a corresponding Laplace transform matrix of the degree matrix;

[0012] calculating an eigenvector of a self-adjoint matrix from the Laplace transform matrix;

[0013] normalizing a matrix composed of the eigenvector by row to obtain an eigenmatrix;

[0014] Each row in the feature matrix is ​​taken as a sample and clustered using the K-means clustering method to obtain a classification cluster C; the classification cluster C is a grouping of shot points.

[0015] In one optional implementation, the step of calculating the critical coefficients of each shot point in the shot point set based on the connectivity distance matrix to form a critical coefficient sequence includes:

[0016] For each shot point p in the set of shot points i According to each shot point p in the shot point set j (j = 0, 1, ..., n-1, j ≠ i), obtain the distance from p from the connectivity distance matrix. i to p j distance m i,j The distance m i,j With the distance m i,j Sequence pairs (j, m) are formed at sequence positions j in the set of shot points. i,j ); the sequence pair (j,m) i,j Add to queue T i ;

[0017] According to the sequence pair (j,m) i,j m in ) i,j Values ​​of the queue T i Sort;

[0018] The queue T is determined according to the following formula. i Each sequence pair t in i,k (j,m i,j ), k = 0, 1, ..., n-2:

[0019]

[0020] m in the connectivity distance matrix is ​​determined according to the following formula. i,j :

[0021]

[0022] k is determined according to the following formula. i,j and all non-zero k i,j Values ​​constitute queue TK i middle:

[0023]

[0024] For the queue TK i Sort the values ​​included in the list;

[0025] The key coefficient k is determined according to the following formula.i :

[0026]

[0027] In an alternative embodiment, the relationship matrix is obtained according to the communication distance matrix and the key coefficient sequence, including:

[0028] a in the relationship matrix is determined according to the following formula: i,j :

[0029]

[0030] In an alternative embodiment, the degree matrix is obtained according to the relationship matrix, including:

[0031] d in the degree matrix is determined according to the following formula: i,j :

[0032]

[0033] In an alternative embodiment, the corresponding Laplace transform matrix of the degree matrix is calculated, including:

[0034] The degree matrix is multiplied by the relationship matrix to obtain an intermediate matrix;

[0035] The intermediate matrix is multiplied by the degree matrix to obtain the Laplace transform matrix.

[0036] In an alternative embodiment, each row in the feature matrix is taken as a sample to cluster by using the K-means clustering method to obtain a classification cluster partition C, including:

[0037] Each row in the feature matrix is taken as a sample of ns dimensions, and there are n samples in total;

[0038] The K-means clustering method is used for clustering to obtain a classification cluster partition C; the classification cluster partition C is {c1, c2,..., c ns}.

[0039] The second aspect of the embodiment of the application provides a complex surface three-dimensional work area specified starting point shot grouping device, including:

[0040] A first acquisition module is configured to acquire vibroseis shot data and specified starting shot data;

[0041] A point set determination module is configured to obtain a shot point set and a starting shot point set according to the vibroseis shot data and the specified starting shot data; the starting shot point set is a subset of the shot point set;

[0042] The second obtaining module is configured to obtain a connectivity distance matrix of all shot points.

[0043] The first processing module is configured to obtain a key coefficient of each shot point in the shot point set according to the connectivity distance matrix, and form a key coefficient sequence.

[0044] The second processing module is configured to obtain a relationship matrix according to the connectivity distance matrix and the key coefficient sequence.

[0045] The third processing module is configured to obtain a degree matrix according to the relationship matrix.

[0046] The fourth processing module is configured to calculate a corresponding Laplace transform matrix of the degree matrix.

[0047] The fifth processing module is configured to calculate an eigenvector of a self-adjoint matrix according to the Laplace transform matrix.

[0048] The sixth processing module is configured to normalize a matrix composed of the eigenvector by row to obtain an eigenmatrix.

[0049] The classification module is configured to take each row in the eigenmatrix as a sample, perform clustering by using a K-means clustering method, and obtain a classification cluster division C; the classification cluster division C is a shot point grouping.

[0050] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the shot point grouping method for a complex three-dimensional work area with a specified starting point.

[0051] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the shot point grouping method for a complex three-dimensional work area with a specified starting point.

[0052] In a fifth aspect, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to implement the steps of the shot point grouping method for a complex three-dimensional work area with a specified starting point.

[0053] In the embodiments of the present disclosure, based on a clustering algorithm, a key parameter matrix containing seismic features is introduced, and the information amount of the starting shot point and other shot points on the key parameters is differentiated, so that the balanced grouping of the vibrator shot points containing the starting point is finally realized, the production efficiency of the field vibrator is improved, the cost is reduced, more accurate and reliable data support is provided for seismic exploration, and the exploration efficiency and accuracy are improved. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0055] Figure 1 is a step flow chart of a complex surface three-dimensional work area specified starting point shot point grouping method according to an embodiment of the present application;

[0056] Figure 2 is a data graph of a shot point to be grouped in a complex surface three-dimensional work area specified starting point shot point grouping method according to an embodiment of the present application;

[0057] Figure 3 is a data graph of a starting shot point to be grouped in a complex surface three-dimensional work area specified starting point shot point grouping method according to an embodiment of the present application;

[0058] Figure 4 is a data graph of a grouping result in a complex surface three-dimensional work area specified starting point shot point grouping method according to an embodiment of the present application;

[0059] Figure 5 is a schematic diagram of an electronic device in the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0061] In the drawings, sometimes the size of the constituent elements, the thickness of the layers or the regions are exaggerated for the sake of clearness, and therefore, any one of the implementations of the present disclosure is not necessarily limited to the size shown in the drawings. The shapes and the sizes of the components in the drawings do not reflect the actual proportions. Furthermore, the drawings schematically show ideal examples, and any one of the implementations of the present disclosure is not limited to the shapes or the values shown in the drawings.

[0062] A vibrator is a seismic source device used in artificial seismic exploration. It simulates seismic waves by generating vibrations on the ground, so that researchers can analyze and interpret underground structures. The vibrator can generate vibration signals with specific frequency, amplitude and duration, which will propagate underground and reflect back, recorded and analyzed by receivers. The vibrator shot point is the arrangement position of the vibrator device on the ground.

[0063] In oil seismic exploration, multiple vibrator shot points need to be arranged in the exploration area to generate a sufficient number and density of vibration signals to cover the entire exploration area. Each shot point will excite vibration signals according to the predetermined time sequence and parameter settings, which will propagate underground and interact with underground structures to produce reflected waves. The reflected waves will be received and recorded by the receivers arranged in the exploration area, and then through data processing and analysis, researchers can infer information about underground structures, such as stratigraphic distribution, oil and gas reservoir location, etc.

[0064] In complex surface conditions, such as mountainous, marshy, forested terrain, surface obstacles such as gullies, rivers, buildings, etc. will affect the arrangement of the vibrator shot point; on the other hand, the vibrator truck as an important device for seismic exploration, its parking and movement are subject to a series of restrictions, in order to ensure the continuity and efficiency of the exploration work, each group needs to contain a group of vibrator trucks, but the distribution of the vibrator truck itself in the work area may be affected by various factors, making it difficult to achieve uniform distribution; these factors make it difficult to achieve relatively balanced grouping of vibrator shot points, affecting the efficiency and accuracy of field exploration.

[0065] Reference Figure 1 , Figure 1 is an embodiment of a complex surface three-dimensional work area designated starting point shot grouping method. As shown in Figure 1 , the method comprises the following steps:

[0066] Step S11: Obtain vibrator shot point data and designated starting shot point data.

[0067] In seismic exploration, shot point data is key information for determining the location of seismic wave sources, and is crucial for subsequent seismic wave propagation simulation, data analysis and geological structure interpretation. The vibrator shot point data in the work area can be read from storage media (such as databases, files, servers, etc.) to obtain the vibrator shot point data.

[0068] The initial shot point is usually located at one end of the seismic line, and its seismic data has different characteristics and information content from other shot points. Because the seismic data of the initial shot point has special geological significance or characteristics, considering and specifying it separately for grouping can better manage and organize the seismic data. Specifying the initial shot point data can be read from a storage medium (such as a database, file, server, etc.), that is, obtaining specific shot point data that are specially designated as initial shot points.

[0069] Step S12: Obtain a shot point set and an initial shot point set according to the vibroseis shot point data and the specified initial shot point data; the initial shot point set is a subset of the shot point set.

[0070] The vibroseis shot point data at least includes parameters such as horizontal and vertical coordinates, which can form a coordinate pair (x, y). Generally, the number of shot points participating in calculation each time does not exceed 100,000. These shot point coordinate pairs form a shot point set P, and the size of the shot point set P is n.

[0071] The specified initial shot point data at least includes parameters such as horizontal and vertical coordinates, which can form a coordinate pair (x, y). These shot point coordinate pairs form an initial shot point set S, and the size of the initial shot point set S is ns. The initial shot point set S is a subset of the shot point set P, that is,

[0072] Step S13: Obtain the connectivity distance matrix of all shot points.

[0073] The connectivity distance matrix, also known as the distance matrix, is a matrix used to represent the distance or connection degree between nodes. A connectivity distance matrix is a two-dimensional array (or matrix), where each element m i,j represents the distance between node i and node j.

[0074] The connectivity distance matrix can be pre-calculated and stored somewhere (such as a file, database, or memory) and read when needed. The calculation can be done by using seismic wave propagation simulation software to predict the time or energy attenuation required for seismic waves to propagate from one shot point to another. These simulation software takes into account factors such as underground structure, formation velocity, topography, attenuation effect, etc., and the simulation results can be directly or indirectly used to generate the connectivity distance matrix. When using, the connectivity distance matrix of all shot points is obtained by directly reading.

[0075] By obtaining the connectivity distance matrix of all shot points, the actual connectivity and distance between shot points are considered. This helps to maintain the physical connectivity between shot points during grouping and avoids incorrectly assigning shot points that are far apart to the same group, especially in cases where surface obstacles limit direct connections between shot points.

[0076] Step S14: for each shot point in the shot point set, a key coefficient of the shot point is calculated according to the connected distance matrix, to form a key coefficient sequence.

[0077] A key coefficient is a numerical value defined for a single object, used to represent the key or importance of the object in the system. A key coefficient sequence is a sequence composed of multiple key coefficients, usually corresponding to multiple objects in the system.

[0078] First, the distances in the connected distance matrix are read to form a sequence pair, the sequence pair is sorted, then the sequence pair and the values in the distance matrix are modified according to the required conditions, and finally the key coefficient of the shot point is calculated according to the connected distance matrix, to form a key coefficient sequence.

[0079] Specifically, the key coefficient of each shot point in the shot point set is calculated according to the connected distance matrix, to form a key coefficient sequence, including:

[0080] For each shot point p i in the shot point set, according to each shot point p j in the shot point set, the distance m i,j from p i,j to p i,j is obtained from the connected distance matrix; the distance m i,j forms a sequence pair (j, m i,j ) with the distance m i in the sequence position j in the shot point set; the sequence pair (j, m i,j ) is added to the queue T i,j ;

[0081] The queue T i is sorted according to the m i value in the sequence pair (j, m i,k );

[0082] Each sequence pair t i,j (j, m i,j ) in the queue T i,j is determined according to the following formula, k = 0, 1,..., n-2:

[0083]

[0084] The m i,j in the connected distance matrix is determined according to the following formula:

[0085]

[0086] The k iand all non-zero k i,j values form a queue TK i :

[0087]

[0088] Sort the values included in the queue TK i ;

[0089] The key coefficient k i is determined as follows:

[0090]

[0091] For each shot p i in the shot point set P, according to each shot p j in the shot point set P, from the connected distance matrix M, the distance m i,j from p i to p j is obtained, and a sequence pair (j, m i,j ) is formed with the sequence position j of p i,j in P; the sequence pair (j, m i,j ) is added to the queue T i . The queue T i is sorted according to the value m i,j in the sequence pair (j, m i,j ). The sorting can use the bubble method, and the sorting is arranged from small to large according to the distance m i,j in the sequence pair. Other more efficient sorting algorithms can also be selected according to actual needs, such as quicksort, merge sort, etc. Each sequence pair t i,k (j, m i,j ) in T i is determined as follows (1), k = 0, 1,..., n-2:

[0092]

[0093] Wherein, the set k≤6 and k>6 conditions can be changed according to different needs. When k≤6, the operation t i,k = (j, m i,j ) is performed, and the distance information between the shots with closer distances is retained for subsequent analysis and processing; when k>6, the operation t i,k = (j, 0) is performed, and the distance information between the shots with farther distances is set to 0, indicating that there is no connection relationship or the distance is infinite between them, thereby reducing the complexity of subsequent calculation.

[0094] is determined in the connected distance matrix M as follows (2):

[0095]

[0096] The above operation updates the connected distance matrix M according to the sequence t i = (j, m i,j ) in the queue T i,k , reflecting the changes in connectivity or reachability between some shots.

[0097] Determine k i,j according to the following formula (3), and all non-zero k i,j values constitute the queue TK i :

[0098]

[0099] Sort each value included in the queue TK i . Among them, the sorting can adopt the bubble method, arranged from small to large. Other more efficient sorting algorithms can also be selected according to actual needs, such as quicksort, merge sort, etc.

[0100] Determine the key coefficient k i according to the following formula (4):

[0101]

[0102] The obtained key coefficient k i constitutes a key coefficient sequence K.

[0103] Step S15: Obtain a relationship matrix according to the connected distance matrix and the key coefficient sequence.

[0104] The relationship matrix is a matrix describing the relationship between nodes, and is usually used to represent the connection relationship, similarity or correlation between nodes, etc. Through the information of the connected distance matrix and the key coefficient sequence, the relationship matrix is calculated.

[0105] Specifically, the relationship matrix is obtained according to the connected distance matrix and the key coefficient sequence, including:

[0106] Determine a i,j in the relationship matrix according to the following formula:

[0107]

[0108] Determine a i,j in the relationship matrix A according to the following formulas (5) and (6):

[0109]

[0110] Step S16: Obtain a degree matrix according to the relationship matrix.

[0111] The degree matrix is a diagonal matrix, where each diagonal element represents the degree (i.e., the number of adjacent nodes) of the corresponding node in the relation matrix, which can be calculated according to the relation matrix.

[0112] Specifically, d in the degree matrix is determined according to the following formula: i,j :

[0113]

[0114] d in the degree matrix D is determined according to the following formula (7): i,j :

[0115]

[0116] The relation matrix and the degree matrix further emphasize the connectivity between the shot points. The relation matrix represents the direct or indirect relationship between the shot points, while the degree matrix provides information on the connectivity degree of each shot point in the network. These information can ensure sufficient connectivity between the shot points in each group when grouping, while minimizing the impact of surface obstacles on grouping.

[0117] Step S17: calculating a corresponding Laplacian transformation matrix of the degree matrix.

[0118] The Laplacian matrix is a matrix that describes the structure of a graph, which can be used to characterize the properties and characteristics of the graph. Calculating the corresponding Laplacian transformation matrix of the degree matrix means obtaining the Laplacian matrix through corresponding operations according to the degree matrix and the relation matrix.

[0119] Specifically, the calculation of the corresponding Laplacian transformation matrix of the degree matrix includes:

[0120] Multiplying the degree matrix and the relation matrix to obtain an intermediate matrix;

[0121] Multiplying the intermediate matrix and the degree matrix to obtain the Laplacian transformation matrix.

[0122] Multiplying the degree matrix and the relation matrix to obtain an intermediate matrix, then multiplying the intermediate matrix and the degree matrix to finally obtain the Laplacian transformation matrix. That is, L = D * A * D, where L is the Laplacian transformation matrix, D is the degree matrix, and A is the relation matrix.

[0123] Step S18: calculating the eigenvector of the self-adjoint matrix according to the Laplacian transformation matrix.

[0124] The Laplacian transformation matrix L is a self-adjoint matrix, which means that the transpose of the matrix is equal to its conjugate. For the Laplacian transformation matrix L, its transpose is equal to itself, so it can be considered as a self-adjoint matrix.

[0125] By performing eigenvalue decomposition on the Laplacian matrix, the eigenvalues and eigenvectors of the self-adjoint matrix are obtained. The eigenvalues represent the characteristics or properties of the self-adjoint matrix, while the eigenvectors represent the directions and magnitudes of the eigenvectors corresponding to these eigenvalues.

[0126] By calculating the Laplacian matrix and its corresponding eigenvalues and eigenvectors, key structures and patterns in the shotpoint network can be identified. These structures and patterns may include special regions formed due to surface obstacles or limitations of the position of the vibrator truck. When grouping, these information can be used to optimize the grouping strategy to better adapt to these limitations.

[0127] Step S19: normalizing the matrix composed of the eigenvectors by row to obtain a feature matrix.

[0128] Row normalization is a process of processing each row in a matrix so that the elements of each row satisfy certain normalization conditions. Common row normalization methods include dividing the elements of each row by the modulus of the elements of that row.

[0129] The matrix composed of the eigenvectors is processed by row normalization, specifically, the elements of each row of the matrix composed of the eigenvectors are divided by the modulus of the elements of that row to obtain a feature matrix F.

[0130] Step S20: taking each row in the feature matrix as a sample and clustering using the K-means clustering method to obtain a classification cluster partition C; the classification cluster partition C is a shotpoint grouping.

[0131] K-means clustering is an unsupervised learning algorithm that divides data points into K predefined clusters. Taking each row in the feature matrix as a sample, clustering using the K-means clustering method obtains a classification cluster partition C; the classification cluster partition C is a shotpoint grouping. This realizes the balanced grouping of the vibrator shotpoints containing the starting point, improves the production efficiency of the field vibrator, and reduces the cost,

[0132] Specifically, the taking each row in the feature matrix as a sample and clustering using the K-means clustering method to obtain a classification cluster partition C includes:

[0133] Taking each row in the feature matrix as an ns-dimensional sample, a total of n samples;

[0134] Clustering using the K-means clustering method obtains a classification cluster partition C; the classification cluster partition C is {c1, c2,..., c ns}.

[0135] Each row in the feature matrix F is taken as a sample of ns dimensions, and there are n samples in total; clustering is performed using a K-means clustering method, and the clustering dimension is ns; the K-means clustering method outputs cluster division C: C={c1, c2,..., c ns The final cluster division C obtained by using the K-means clustering method is the obtained shot grouping, and each c i in the cluster division C contains a series of shot points corresponding to the classification i.

[0136] In this embodiment, based on the clustering algorithm, a key parameter matrix containing seismic features is introduced, and the information amount of the starting shot point and other shot points on the key parameters is differentiated, so that the balanced grouping of the starting shot point of the vibrator is realized, the production efficiency of the field vibrator is improved, the cost is reduced, more accurate and reliable data support is provided for seismic exploration, and the exploration efficiency and accuracy are improved.

[0137] Based on the same inventive concept, an embodiment of the present application discloses a device for grouping shots of a specified starting point in a three-dimensional work area with a complex surface, the device comprising:

[0138] A first acquisition module is configured to acquire vibrator shot data and specified starting shot data.

[0139] A point set determination module is configured to obtain a shot point set and a starting shot point set according to the vibrator shot data and the specified starting shot data; the starting shot point set is a subset of the shot point set.

[0140] A second acquisition module is configured to acquire a connectivity distance matrix of all shots.

[0141] A first processing module is configured to obtain a key coefficient of each shot in the shot point set according to the connectivity distance matrix, and form a key coefficient sequence.

[0142] In an optional embodiment, the first processing module is specifically configured to:

[0143] For each shot p i in the shot point set, a distance m j from p i to p j is obtained from the connectivity distance matrix according to each shot p i,j in the shot point set; the distance m i,j forms a sequence pair (j, m i,j ) at a sequence position j in the shot point set; and the sequence pair (j, m i,j ) is taken as the key coefficient of p i,j) is added to the queue T i ;

[0144] According to the sequence pair (j, m i,j ) in the queue T i,j , the m i value is sorted;

[0145] Each sequence pair t i (j, m i,k ) in the queue T i,j is determined according to the following formula, k = 0, 1,..., n-2:

[0146]

[0147] The m i,j value in the connection distance matrix is determined according to the following formula:

[0148]

[0149] k i,j is determined according to the following formula, and all non-zero k i,j values form a queue TK i :

[0150]

[0151] Each value included in the queue TK i is sorted;

[0152] The key coefficient k i is determined according to the following formula:

[0153]

[0154] The second processing module is configured to obtain a relationship matrix according to the connection distance matrix and the key coefficient sequence.

[0155] In an optional implementation, the second processing module is specifically configured to:

[0156] The a i,j in the relationship matrix is determined according to the following formula:

[0157]

[0158] The third processing module is configured to obtain a degree matrix according to the relationship matrix.

[0159] In an optional implementation, the third processing module is specifically configured to:

[0160] The d i,j in the degree matrix is determined according to the following formula:

[0161]

[0162] The fourth processing module is used to calculate the corresponding Laplace transform matrix of the degree matrix.

[0163] In one alternative implementation, the fourth processing module is specifically used for:

[0164] Multiply the degree matrix by the relation matrix to obtain the intermediate matrix;

[0165] Multiplying the intermediate matrix by the degree matrix yields the Laplace transform matrix.

[0166] The fifth processing module is used to calculate the eigenvectors of the self-adjoint matrix based on the Laplace transform matrix.

[0167] The sixth processing module is used to standardize the matrix composed of the feature vectors by row to obtain the feature matrix.

[0168] The classification module is used to treat each row of the feature matrix as a sample and perform clustering using the K-means clustering method to obtain a classification cluster C; the classification cluster C is a grouping of shot points.

[0169] In one alternative implementation, the classification is specifically used for:

[0170] Each row in the feature matrix is ​​treated as an n-dimensional sample, for a total of n samples;

[0171] Clustering is performed using the K-means clustering method to obtain a classification cluster partition C; the classification cluster partition C is {c1,c2,...,c...} ns}

[0172] In one exemplary embodiment, the following can be obtained based on steps S11 to S12: Figure 2 Data map of gun points to be grouped and such Figure 3 The initial shot point data map to be grouped; based on steps S13 to S20, the following can be obtained: Figure 4 The grouping results data chart.

[0173] from Figure 2 As can be seen, the distribution of light-colored dots represents the distribution of shot points in the geographic or virtual space of the exploration project. The blank areas represent a boundary or specific area for grouping shot points, and the limitations imposed by this area should be considered when grouping them.

[0174] from Figure 3It can be seen that the distribution of dark points represents the distribution of the specified initial shot points in the geographical space. Since the seismic data of the initial shot points has special geological significance or characteristics, specifying them for grouping can better manage and organize the seismic data

[0175] From Figure 4 It can be seen that different areas represent the results of grouping, according to the grouping of the specified initial shot points, so that each initial shot point is in a group, and the distribution of the shot points in each group is relatively balanced.

[0176] The complex surface three-dimensional work area initial point shot point grouping method provided by the embodiments of the present disclosure is based on a clustering algorithm, introduces a key parameter matrix containing seismic characteristics, and differentiates the information amount of the initial shot points and other shot points on the key parameters. Finally, the balanced grouping of the vibrator shot points containing the initial points is realized, the production efficiency of the field vibrator is improved, the cost is reduced, more accurate and reliable data support is provided for seismic exploration, and the exploration efficiency and accuracy are improved.

[0177] It should be noted that, for the method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present disclosure are not limited to the action sequence described, because according to the embodiments of the present disclosure, certain steps can be performed in other sequences or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of the present disclosure.

[0178] The embodiments of the present disclosure also provide an electronic device, which refers to Figure 5 , Figure 5 is a schematic diagram of an electronic device shown in the embodiments of the present disclosure. As shown in Figure 5 , the electronic device 100 includes a memory 110 and a processor 120, the memory 110 and the processor 120 are communicatively connected through a bus, the memory 110 stores a computer program, the computer program can run on the processor 120, and then realize the steps in the complex surface three-dimensional work area initial point shot point grouping method disclosed by the embodiments of the present disclosure.

[0179] The embodiments of the present disclosure also provide a computer readable storage medium, when the instructions in the computer readable storage medium are executed by the processor of the computer device, the computer device can execute the steps in the complex surface three-dimensional work area initial point shot point grouping method disclosed by the embodiments of the present disclosure.

[0180] The embodiment of the present disclosure further provides a computer program product, comprising a computer program, which can execute the steps in the complex surface three-dimensional work area specified starting point shot grouping method disclosed by the embodiment of the present disclosure when executed by a processor of a computer device.

[0181] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other.

[0182] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices, electronic devices and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal equipment to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal equipment generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus for implementing the functions specified in one block or multiple blocks.

[0183] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer or other programmable data processing terminal equipment to work in a specific way, so that the instructions stored in the computer readable memory generate a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus for implementing the functions specified in one block or multiple blocks.

[0184] These computer program instructions can also be loaded into a computer or other programmable data processing terminal equipment, so that a series of operation steps are executed on the computer or other programmable terminal equipment to generate a computer-implemented process, so that the instructions executed on the computer or other programmable terminal equipment provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1 an apparatus for implementing the functions specified in one block or multiple blocks.

[0185] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0186] Finally, it is to be understood that the phraseology or terminology such as "first" and "second" etc. used herein is merely intended to differentiate one entity or operation from another without necessarily requiring or implying any actual such relationship or order between such entities or operations. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0187] The above provides a complex surface three-dimensional work area starting point shot point grouping method, device, equipment, storage medium and product, and a specific example is applied herein to describe the principle and implementation mode of the application. The above description of the embodiments is only used to help understand the method and its core idea; meanwhile, for those skilled in the art, according to the idea of the application, the specific implementation mode and application range can be changed, and the above description should not be understood as a limitation of the application.

Claims

1. A method for grouping complex surface three-dimensional work area designated starting point shot points, characterized in that, The method comprises the following steps: obtaining controllable source shot data and specified initial shot data; obtaining a shot point set and an initial shot point set according to the controllable source shot data and the specified initial shot point data; the initial shot point set is a subset of the shot point set; obtaining a connection distance matrix of all shots; for each shot in the shot point set, obtaining a key coefficient of the shot according to the connection distance matrix, and forming a key coefficient sequence; obtaining a relationship matrix according to the connection distance matrix and the key coefficient sequence; obtaining a degree matrix according to the relationship matrix; calculating a corresponding Laplace transform matrix of the degree matrix; calculating an eigenvector of a self-adjoint matrix according to the Laplace transform matrix; normalizing a matrix composed of the eigenvectors by rows to obtain an eigenmatrix; clustering each row in the eigenmatrix as a sample by using a K-means clustering method to obtain a classification cluster partition C; the classification cluster partition C is a shot grouping.

2. The complex overburden 3D work area specifying starting point shot point grouping method according to claim 1, characterized in that, The method further comprises the following steps: for each shot p in the shot point set i , obtaining a distance m j from p i to p j from the connected distance matrix according to each shot p in the shot point set i,j (j = 0, 1,... n-1, j ≠ i); the distance m i,j forms a sequence pair (j, m i,j ) with the distance m i,j at the sequence position j in the shot point set; adding the sequence pair (j, m i,j ) to the queue T i ; According to the sequence of (j, m i,j ) m i,j values are sorted on the queue T i ; Each sequence pair t i in the queue T is determined according to the following formula i,k (j, m i,j ), k = 0, 1,... n - 2: m is determined in the interconnection distance matrix according to the following formula i,j : Determine k according to the following formula i,j and form a queue TK i,j of all non-zero k i values. The queue TK i is sorted for each value included in it; The key coefficient k is determined according to the following formula i :

3. The complex overburden 3D work area designation starting point shot point grouping method according to claim 2, characterized in that, The method further comprises the following steps: The relationship matrix a is determined according to the following formula i,j :

4. The method of claim 3, wherein, The method further comprises the following steps: d in the degree matrix is determined according to the following formula i,j :

5. The complex overburden 3D work area specifying starting point shot point grouping method according to claim 4, characterized in that, The method further comprises the following steps: multiplying the degree matrix and the relationship matrix to obtain an intermediate matrix; multiplying the intermediate matrix and the degree matrix to obtain the Laplace transform matrix.

6. The complex overburden 3D work area specifying starting point shot point grouping method of claim 1, wherein, The method further comprises the following steps: clustering each row in the eigenmatrix as a sample of ns dimensions, and a total of n samples; Classifying the clusters obtained by the K-means clustering method to obtain a classification cluster division C; the classification cluster division C is {c1, c2,..., c ns}.

7. A device for grouping complex terrain three-dimensional work area designated starting point shot points, characterized in that, The device comprises: a first obtaining module configured to obtain controllable source shot data and specified initial shot data; a point set determining module configured to obtain a shot point set and an initial shot point set according to the controllable source shot data and the specified initial shot point data; the initial shot point set is a subset of the shot point set; a second obtaining module configured to obtain a connection distance matrix of all shots; a first processing module configured to, for each shot in the shot point set, obtain a key coefficient of the shot according to the connection distance matrix, and form a key coefficient sequence; a second processing module configured to obtain a relationship matrix according to the connection distance matrix and the key coefficient sequence; a third processing module configured to obtain a degree matrix according to the relationship matrix; a fourth processing module configured to calculate a corresponding Laplace transform matrix of the degree matrix; a fifth processing module configured to calculate an eigenvector of a self-adjoint matrix according to the Laplace transform matrix; a sixth processing module configured to normalize a matrix composed of the eigenvectors by rows to obtain an eigenmatrix; a classification module configured to cluster each row in the eigenmatrix as a sample by using a K-means clustering method to obtain a classification cluster partition C; the classification cluster partition C is a shot grouping.

8. An electronic device, comprising: The device comprises: A processor, a memory, and a computer program stored on the memory and executable on the processor, the processor implementing the complex surface 3D work area start point shot grouping method of any one of claims 1 to 6 when executing the program.

9. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the complex surface 3D work area start point shot grouping method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the complex surface 3D work area start point shot grouping method of any one of claims 1 to 6.

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