Data channel correlation-based sampling point determination method, apparatus, device, and medium

Abstract

The application provides a sampling point determination method and device based on data trace correlation, equipment and medium, belonging to the technical field of seismic exploration. The method first obtains part of the sampling points of the irregular system by sampling the regular observation system, then determines the correlation between adjacent data traces based on the seismic data of the part of the sampling points, and finally determines part of the sampling points as the sampling points of the irregular system based on the correlation between adjacent data traces. Since the part of the sampling points is determined based on the correlation between adjacent data traces, the connection between the sampling points of the irregular observation system and the collected data is established, and the reconstruction quality of the irregular data can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, and in particular to a sampling point determination method and device based on data channel correlation, equipment and a medium. BACKGROUND

[0002] With the continuous development of seismic exploration technology, the amount of data collected in seismic exploration increases sharply, which makes the cost of seismic acquisition higher and higher. In the related technology, a compressed sensing seismic exploration technology is used to reduce the cost of seismic acquisition. The key of the compressed sensing seismic exploration technology lies in the reconstruction and recovery of irregular data, and the quality of the reconstruction of irregular data is related to the distribution of sampling points of the irregular observation system. Therefore, there is an urgent need for a sampling point determination method of an irregular observation system which can improve the reconstruction quality of irregular data. SUMMARY

[0003] The embodiments of the present application provide a sampling point determination method and device based on data channel correlation, equipment and a medium, which can improve the reconstruction quality of irregular data. The technical solution is as follows:

[0004] In one aspect, a scanning signal generation method is provided, the method comprising:

[0005] Based on the geological information of the construction area and a plurality of first sampling points in the regular observation system designed for the construction area, simulation is performed to obtain seismic data corresponding to each first sampling point;

[0006] Based on the undersampling rate corresponding to the irregular observation system and the number of the plurality of first sampling points, a first number and a second number are determined, wherein the first number is used to represent the number of second sampling points of the irregular observation system, and the second number is less than the first number;

[0007] Each endpoint of the construction area is determined as a second sampling point, and a plurality of first sampling points inside the construction area are sampled based on the difference between the second number and the number of endpoints of the construction area to obtain a plurality of second sampling points, wherein the number of the plurality of second sampling points obtained by sampling is the difference between the second number and the number of endpoints of the construction area;

[0008] Based on the plurality of second sampling points obtained at present, the construction area is triangularly divided to obtain a plurality of triangular regions;

[0009] For each triangular region, based on the seismic data corresponding to the second sampling points in the triangular region, a data channel correlation parameter corresponding to the triangular region is determined;

[0010] determine a triangle region with the maximum data channel correlation based on the data channel correlation parameter corresponding to each triangle region, perform sampling on the first sampling points in the triangle region at least once to obtain at least one second sampling point;

[0011] repeat the steps of performing triangle division on the construction region based on the currently obtained multiple second sampling points to obtain multiple triangle regions, determining the data channel correlation parameter corresponding to each triangle region based on the seismic data corresponding to the second sampling points in the triangle region, determining a triangle region with the maximum data channel correlation based on the data channel correlation parameter corresponding to each triangle region, and performing sampling on the first sampling points in the triangle region at least once to obtain at least one second sampling point until the number of obtained second sampling points reaches the first number.

[0012] In a possible implementation, the step of performing triangle division on the construction region based on the currently obtained multiple second sampling points to obtain multiple triangle regions comprises:

[0013] determining two second sampling points closest to the second sampling point based on the second sampling point located at any endpoint of the construction region, and constructing a triangle region by the second sampling point and the two second sampling points closest to the second sampling point;

[0014] repeating the step of determining two second sampling points closest to the second sampling point based on any second sampling point in the two second sampling points determined last time and constructing a triangle region by the second sampling point and the two second sampling points closest to the second sampling point until all the second sampling points are traversed.

[0015] In a possible implementation, the step of determining the data channel correlation parameter corresponding to the triangle region based on the seismic data corresponding to the second sampling points in the triangle region comprises:

[0016] grouping the three second sampling points in the triangle region in pairs to obtain three sampling point combinations;

[0017] for each sampling point combination, determining the data channel correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination;

[0018] determining the maximum data channel correlation parameter in the data channel correlation parameters corresponding to the three sampling point combinations as the data channel correlation parameter corresponding to the triangle region.

[0019] In a possible implementation, the determining of the data trace correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination comprises:

[0020] The data trace correlation parameter corresponding to the sampling point combination is determined based on the seismic data corresponding to the two second sampling points in the sampling point combination and first relationship data, the first relationship data being used to represent a relationship among the seismic data corresponding to the two second sampling points, the transpose of the seismic data corresponding to one of the two second sampling points, and the data trace correlation parameter corresponding to the two second sampling points.

[0021] In a possible implementation, the relationship between the data trace correlation parameter corresponding to the triangular region and the seismic data corresponding to the plurality of second sampling points in the triangular region is represented as:

[0022]

[0023] wherein μ j represents the data trace correlation parameter corresponding to the jth triangular region, max is a maximum function, y i，j represents the seismic data corresponding to the ith second sampling point in the jth triangular region, represents the transpose of the seismic data corresponding to the ith second sampling point in the jth triangular region, y s，j represents the seismic data corresponding to the s th second sampling point in the jth triangular region, wherein the value ranges of i and s are interval [1, 3], and i≠s.

[0024] In a possible implementation, the determining of the first quantity and the second quantity based on the undersampling rate corresponding to the irregular observation system and the quantity of the plurality of first sampling points comprises:

[0025] The product of the undersampling rate and the quantity of the plurality of first sampling points is determined as the first quantity.

[0026] Half of the first quantity is determined as the second quantity, or a first ratio input by a user is acquired, and the product of the first quantity and the first ratio is determined as the second quantity.

[0027] In a possible implementation, the method further comprises:

[0028] After the quantity of the obtained second sampling points reaches the first quantity, the second sampling points in the complex structure region in the construction region are encrypted or moved.

[0029] In another aspect, a sampling point determination apparatus based on a data trace correlation is provided, and the apparatus comprises:

[0030] simulate, based on the geological information of the construction area and the plurality of first sampling points in the regular observation system designed for the construction area, to obtain seismic data corresponding to each first sampling point;

[0031] determine, based on the under-sampling rate corresponding to the irregular observation system and the quantity of the plurality of first sampling points, a first quantity and a second quantity, wherein the first quantity is used to represent the quantity of the second sampling points of the irregular observation system, and the second quantity is less than the first quantity;

[0032] sample, based on the difference between the second quantity and the quantity of the endpoints of the construction area, the plurality of first sampling points inside the construction area to obtain a plurality of second sampling points, wherein the quantity of the plurality of second sampling points obtained by sampling is the difference between the second quantity and the quantity of the endpoints of the construction area;

[0033] divide, based on the plurality of second sampling points obtained at present, the construction area into a plurality of triangular regions;

[0034] determine, for each triangular region, a data channel correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points inside the triangular region;

[0035] The sampling module is further configured to determine, based on the data channel correlation parameter corresponding to each triangular region, a triangular region with the maximum data channel correlation, sample at least once the first sampling points in the triangular region to obtain at least one second sampling point;

[0036] The division module, the parameter determination module and the sampling module are further configured to repeat the steps of dividing, based on the plurality of second sampling points obtained at present, the construction area into a plurality of triangular regions, determining, for each triangular region, a data channel correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points inside the triangular region, determining, based on the data channel correlation parameter corresponding to each triangular region, a triangular region with the maximum data channel correlation, and sampling at least once the first sampling points in the triangular region to obtain at least one second sampling point until the quantity of the second sampling points obtained reaches the first quantity.

[0037] In a possible implementation, the dividing module is configured to: determine two second sampling points closest to a second sampling point located at any endpoint of the construction area, and form a triangular area by the second sampling point and the two second sampling points closest to the second sampling point; and repeat the step of determining two second sampling points closest to a second sampling point located at any endpoint of the construction area based on any one of the two second sampling points determined in the last time, and forming a triangular area by the second sampling point and the two second sampling points closest to the second sampling point until all the second sampling points are traversed.

[0038] In a possible implementation, the parameter determining module comprises:

[0039] The grouping unit is configured to group the three second sampling points in the triangular area in pairs to obtain three sampling point combinations.

[0040] The first determining unit is configured to determine, for each sampling point combination, a data channel correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination.

[0041] The second determining unit is configured to determine a maximum data channel correlation parameter in the data channel correlation parameters corresponding to the three sampling point combinations as the data channel correlation parameter corresponding to the triangular area.

[0042] In a possible implementation, the first determining unit is configured to determine the data channel correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination and first relationship data, and the first relationship data is used to represent a relationship among the seismic data corresponding to the two second sampling points, a transpose of the seismic data corresponding to one of the two second sampling points, and the data channel correlation parameter corresponding to the two second sampling points.

[0043] In a possible implementation, the relationship between the data channel correlation parameter corresponding to the triangular area and the seismic data corresponding to the plurality of second sampling points in the triangular area is represented as:

[0044]

[0045] wherein μ j represents the data channel correlation parameter corresponding to the jth triangular area, max is a maximum function, y i，j represents the seismic data corresponding to the ith second sampling point in the jth triangular area, represents a transpose of the seismic data corresponding to the ith second sampling point in the jth triangular area, y s，jdenotes seismic data corresponding to the s-th second sampling point in the j-th triangular area, wherein, the value ranges of i and s are [1, 3], and i≠s.

[0046] In a possible implementation, the number determining module is configured to determine a product of the undersampling rate and the number of the plurality of first sampling points as the first number, determine half of the first number as the second number, or obtain a first ratio input by a user and determine a product of the first number and the first ratio as the second number.

[0047] In a possible implementation, the apparatus further includes:

[0048] The adjusting module is configured to encrypt or move the second sampling points in the complex structure area in the construction area after the obtained number of second sampling points reaches the first number.

[0049] In another aspect, a computer device is provided, which includes a processor and a memory, and the memory stores at least one program code, which is loaded and executed by the processor to implement the sampling point determination method based on data trace correlation as any of the above implementation manners.

[0050] In another aspect, a computer readable storage medium is provided, which stores at least one program code, which is loaded and executed by a processor to implement the sampling point determination method based on data trace correlation as any of the above implementation manners.

[0051] In another aspect, a computer program product is provided, which includes at least one program code, which is loaded and executed by a processor to implement the sampling point determination method based on data trace correlation as any of the above implementation manners.

[0052] The embodiment of the present application provides a sampling point determination method based on data trace correlation. First, partial sampling points of a non-regular system are obtained by sampling a regular observation system, then the correlation between adjacent data traces is determined based on seismic data of the partial sampling points, and the partial sampling points are determined as sampling points of the non-regular system based on the correlation between adjacent data traces. Since the partial sampling points are determined based on the correlation between adjacent data traces, the connection between the sampling points of the non-regular observation system and the collected data is established, and the reconstruction quality of the non-regular data can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0054] Figure 1 is a flow chart of a sampling point determination method based on data channel correlation provided by an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of selecting a second number of sampling points from a regular observation system provided by an embodiment of the present application;

[0056] Figure 3 is a schematic diagram of a triangular partition provided by an embodiment of the present application;

[0057] Figure 4 is a schematic diagram of sampling point distribution of a non-regular observation system provided by an embodiment of the present application;

[0058] Figure 5 is a schematic diagram of sampling point distribution of a non-regular observation system provided by an embodiment of the present application;

[0059] Figure 6 is a schematic diagram of a curve of signal-to-noise ratio changing with under-sampling rate after non-regular data reconstruction provided by an embodiment of the present application;

[0060] Figure 7 is a structural schematic diagram of a sampling point determination device based on data channel correlation provided by an embodiment of the present application;

[0061] Figure 8 is a structural schematic diagram of a sampling point determination device based on data channel correlation provided by an embodiment of the present application;

[0062] Figure 9 is a structural schematic diagram of a terminal provided by an embodiment of the present application;

[0063] Figure 10 is a structural schematic diagram of a server provided by an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail with reference to the drawings.

[0065] The terms "first", "second", "third", and "fourth" and the like in the description and in the claims of the present application and in the accompanying drawings are used for distinguishing between similar objects and are not necessarily used in a sequential or chronological sense. Also, the terms "comprise", "comprising", "including", and "having", and variations thereof in the present description and in the claims of the present application are intended to cover both the case where the specified object is included and the case where the specified object is not included. For example, the expression "a process, a method, a system, a product or an apparatus comprising a series of steps or units" is not limited to the case where the listed steps or units are included, but also covers the case where other steps or units that are not listed are included or the case where other steps or units that are inherent to the process, the method, the product or the apparatus are included.

[0066] Figure 1 is a flowchart of a sampling point determination method based on data trace correlation provided by an embodiment of the present application. An embodiment of the present application takes a computer device as an example for illustrative description. Referring to Figure 1 , the method comprises:

[0067] 101. The computer device simulates, based on the geological information of the construction area and a plurality of first sampling points in a regular observation system designed for the construction area, seismic data corresponding to each first sampling point.

[0068] The regular observation system refers to an observation system in which sampling points are uniformly distributed, wherein the sampling points are used to indicate the positions of seismic excitation points and / or receiving points. The irregular observation system refers to an observation system in which sampling points are not uniformly distributed.

[0069] In an embodiment of the present application, a skilled person first designs a regular observation system that meets high-precision exploration for a construction area, and then selects some sampling points from the sampling points of the regular observation system as sampling points of an irregular observation system by using the method provided by the embodiment of the present application. Subsequently, the seismic data of the irregular observation system is reconstructed and recovered, which can achieve a similar effect to the regular observation system. Since the number of sampling points of the irregular observation system is less than that of the regular observation system, the cost of seismic acquisition is reduced.

[0070] The geological information of the construction area is the geological information obtained before the seismic exploration of the construction area. In some embodiments, the computer device simulates, based on the geological information of the construction area and a plurality of first sampling points in a regular observation system designed for the construction area, seismic data corresponding to each first sampling point, comprising: the computer device constructs a velocity field of the construction area based on the geological information of the construction area, and simulates seismic exploration based on the velocity field of the construction area and the regular observation system to obtain seismic data corresponding to each first sampling point in the regular observation system.

[0071] It should be noted that, in order to distinguish the sampling points of the regular observation system and the sampling points of the irregular observation system, the sampling points of the regular observation system are referred to as first sampling points, and the sampling points of the irregular observation system are referred to as second sampling points.

[0072] 102. The computer device determines a first quantity and a second quantity based on the under-sampling rate corresponding to the irregular observation system and the quantity of the first sampling points, wherein the first quantity is used to represent the quantity of the second sampling points of the irregular observation system, and the second quantity is less than the first quantity.

[0073] The under-sampling rate corresponding to the irregular observation system can be any value, for example, 40%, 50%, 70%, and the like. The under-sampling rate of the irregular observation system is not limited in the embodiments of the present application. In some embodiments, the under-sampling rate corresponding to the irregular observation system is determined by the budget of the seismic exploration. In some embodiments, the under-sampling rate corresponding to the irregular observation system is set by a technician. In some embodiments, the under-sampling rate corresponding to the irregular observation system is determined by an experience value. In some embodiments, the under-sampling rate corresponding to the irregular observation system is an under-sampling rate that makes the quality of the irregular data reconstruction best.

[0074] When the under-sampling rate corresponding to the irregular observation system is 70%, it means that only 70% of the sampling points in the observation system need to be reserved; when the under-sampling rate corresponding to the irregular observation system is 40%, it means that only 40% of the sampling points in the observation system need to be reserved. Therefore, in some embodiments, the computer device determines the first quantity and the second quantity based on the under-sampling rate corresponding to the irregular observation system and the quantity of the first sampling points, comprising: determining the product of the under-sampling rate and the quantity of the first sampling points as the first quantity.

[0075] The second quantity is any quantity less than the first quantity. In some embodiments, the second quantity is half of the first quantity. Therefore, the computer device determines the second quantity, comprising: determining half of the first quantity as the second quantity. In other embodiments, the second quantity is determined by a user. Therefore, the computer device determines the second quantity, comprising: obtaining a first ratio input by the user, and determining the product of the first quantity and the first ratio as the second quantity.

[0076] In the embodiments of the present application, the first quantity indicates the total quantity of the sampling points of the irregular observation system. The difference between the first quantity and the second quantity indicates the quantity of the sampling points of the irregular observation system determined based on the data trace correlation, and the second quantity indicates the quantity of the sampling points of the irregular observation system determined by using other methods.

[0077] 103. The computer device determines each endpoint of the construction area as a second sampling point, and samples the first sampling points in the construction area to obtain the second sampling points based on a difference between the second quantity and the number of endpoints of the construction area, wherein the number of the second sampling points obtained by sampling is the difference between the second quantity and the number of endpoints of the construction area.

[0078] In the embodiments of the present application, the sampling points are first arranged at the endpoints of the construction area, and then the sampling points are arranged in the construction area, and a total of the second quantity of second sampling points are arranged.

[0079] The sampling points are arranged in the construction area by sampling the first sampling points in the construction area. The computer device can use any sampling method to sample the first sampling points in the construction area, and the sampling method used by the computer device is not limited in the embodiments of the present application, which is only exemplarily described by taking the following three embodiments as examples.

[0080] In some embodiments, the computer device uses a random sampling method to sample the first sampling points in the construction area. The computer device samples the first sampling points in the construction area to obtain the second sampling points based on a difference between the second quantity and the number of endpoints of the construction area, including: the computer device randomly samples the first sampling points in the construction area based on the difference between the second quantity and the number of endpoints of the construction area to obtain the second sampling points.

[0081] In other embodiments, the computer device uses a jitter sampling method to sample the first sampling points in the construction area. The computer device samples the first sampling points in the construction area to obtain the second sampling points based on a difference between the second quantity and the number of endpoints of the construction area, including: the computer device jitter samples the first sampling points in the construction area based on the difference between the second quantity and the number of endpoints of the construction area to obtain the second sampling points.

[0082] In other embodiments, the computer device uses a segmented sampling method to sample the first sampling points in the construction area. The computer device samples the first sampling points in the construction area to obtain the second sampling points based on a difference between the second quantity and the number of endpoints of the construction area, including: the computer device segmented samples the first sampling points in the construction area based on the difference between the second quantity and the number of endpoints of the construction area to obtain the second sampling points.

[0083] It should be noted that the embodiments of the present application only exemplarily illustrate the sampling method of "sampling the plurality of first sampling points in the construction area", and of course, other sampling methods such as Poisson sampling method, farthest point sampling method, etc. can also be used.

[0084] It should be noted that in the regular observation system, each end point of the construction area is a first sampling point of the regular observation system, and therefore, the second sampling points obtained in the step 103 are all selected from the first sampling points.

[0085] 104. The computer device performs triangulation on the construction area based on the plurality of second sampling points obtained at present to obtain a plurality of triangular regions.

[0086] In the process of performing triangulation on the construction area based on the plurality of second sampling points obtained at present to obtain a plurality of triangular regions, the computer device takes the plurality of second sampling points obtained at present as end points of the triangular regions. The computer device can use any triangulation method, which is not limited in the embodiments of the present application, and only exemplarily illustrated in the following embodiments.

[0087] In one possible implementation, a sampling point of an end point of the construction area is selected as a starting position, then the two sampling points closest to the sampling point are calculated, a triangle is constructed based on the three sampling points, and the same operation is performed on the subsequent sampling points until all the sampling points are traversed. The computer device performs triangulation on the construction area based on the plurality of second sampling points obtained at present to obtain a plurality of triangular regions, including: determining the two second sampling points closest to the second sampling point based on the second sampling point located at any end point of the construction area, and constructing a triangular region through the second sampling point and the two second sampling points closest to the second sampling point; repeating the step of determining the two second sampling points closest to the second sampling point based on any second sampling point of the two second sampling points determined last time, and constructing a triangular region through the second sampling point and the two second sampling points closest to the second sampling point until all the second sampling points are traversed.

[0088] For example, the second sampling point of an end point of the construction area is taken as a starting position P j , then the two second sampling points P j，1 and P j，2 closest to the second sampling point are calculated, a triangular region is constructed based on the three second sampling points, and the same operation is performed on the subsequent second sampling points until all the second sampling points are traversed.

[0089]

[0090] In the process of performing triangulation on the construction area based on the plurality of second sampling points obtained at present to obtain a plurality of triangular regions, the computer device takes the plurality of second sampling points obtained at present as end points of the triangular regions. The computer device can use any triangulation method, which is not limited in the embodiments of the present application, and only exemplarily illustrated in the following embodiments. denotes the data trace correlation parameter corresponding to the jth triangle region, and P j denotes the two closest sampling points.

[0091] 105、The computer device determines, for each triangle region, a data trace correlation parameter corresponding to the triangle region based on the seismic data corresponding to the second sampling points in the triangle region.

[0092] The data trace correlation parameter corresponding to the triangle region is used to represent the correlation between the seismic traces corresponding to the two sampling points of the triangle region. In a possible implementation, the data trace correlation parameter corresponding to the triangle region is the maximum data trace correlation parameter among the data trace correlation parameters corresponding to any two sampling points of the triangle region.

[0093] In some embodiments, the computer device determines the data trace correlation parameter corresponding to the triangle region based on the seismic data corresponding to the second sampling points in the triangle region, including: grouping the three second sampling points in the triangle region two by two to obtain three sampling point combinations; for each sampling point combination, determining a data trace correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination; and determining the maximum data trace correlation parameter among the data trace correlation parameters corresponding to the three sampling point combinations as the data trace correlation parameter corresponding to the triangle region.

[0094] Optionally, the computer device determines the data trace correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination, including: determining the data trace correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination and first relationship data, the first relationship data being used to represent the relationship among the seismic data corresponding to the two second sampling points, the transpose of the seismic data corresponding to one of the two second sampling points, and the data trace correlation parameter corresponding to the two second sampling points.

[0095] Optionally, the relationship between the first relationship data or the data trace correlation parameter corresponding to the triangle region and the seismic data corresponding to the plurality of second sampling points in the triangle region is represented as:

[0096]

[0097] wherein μ j denotes the data trace correlation parameter corresponding to the jth triangle region, and max is a maximum function, y i，j denotes the seismic data corresponding to the ith second sampling point in the jth triangle region, denotes the transpose of the seismic data corresponding to the ith second sampling point in the jth triangle region, y s，jrepresents seismic data corresponding to the s-th second sampling point in the j-th triangular region, where the values of i and s are in the interval [1, 3], and i≠s.

[0098] 106. The computer device determines a triangular region with the largest data channel correlation parameter based on the data channel correlation parameters corresponding to each triangular region, and performs at least one sampling on the first sampling points in the triangular region to obtain at least one second sampling point.

[0099] The computer device can perform one sampling or multiple samplings on the first sampling points in the triangular region. The sampling can be random sampling or other sampling methods, which are not limited in the embodiments of the present application.

[0100] For example, the data channel correlation parameters of the multiple triangular regions are sorted in size order to obtain the largest data channel correlation parameter, and a sampling point is randomly sampled from the first sampling points in the triangular region corresponding to the largest data channel correlation parameter as the second sampling point.

[0101] 107. The computer device repeats the steps of performing triangular division on the construction region based on the multiple second sampling points obtained at present to obtain multiple triangular regions, determining a triangular region with the largest data channel correlation based on the data channel correlation parameters corresponding to each triangular region, and performing at least one sampling on the first sampling points in the triangular region to obtain at least one second sampling point, until the number of the second sampling points reaches the first number.

[0102] In some embodiments, the final irregular observation system can be obtained after the step 107 is performed. In other embodiments, the multiple second sampling points obtained after the step 107 is performed can be adjusted. Optionally, the method further comprises: after the number of the second sampling points reaches the first number, encrypting or moving the second sampling points in the complex structure region in the construction region.

[0103] For example, the computer device determines the position of the complex structure region in the construction region based on the geological information of the construction region or the velocity field of the construction region, moves the second sampling points in the complex structure region to outside the complex structure region, or increases the number of the second sampling points in the complex structure region.

[0104] The sampling point determination method based on data trace correlation provided in this application first obtains a portion of sampling points for irregular systems by sampling regular observation systems. Then, based on the seismic data of these sampling points, the correlation between adjacent data traces is determined. Based on the correlation between adjacent data traces, a portion of sampling points is further determined as sampling points for the irregular systems. Since these sampling points are determined based on the correlation between adjacent data traces, a connection is established between the sampling points and acquired data of the irregular observation system, which can improve the reconstruction quality of irregular data.

[0105] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0106] Next, the performance of the sampling point determination method based on data channel correlation provided in the embodiments of this application in practical applications will be described:

[0107] First, a regular observation system meeting high-precision exploration requirements is designed. The goal is to undersample this regular observation system by 40%, meaning that 40% of the sampling points of the regular observation system are retained as irregular observation points. Half the number of sampling points in the irregular observation system (20% of the total number of sampling points in the regular observation system) is initially selected. Based on this determined number of sampling points, the sampling points at the four endpoints of the construction area are first set up as sampling points for the irregular observation system. Then, jitter sampling is used to set up the remaining sampling points. For example... Figure 2 As shown, Figure 2 The point representation in the observation system indicates the distribution of sampling points. Figure 2 The circles in the diagram represent the distribution of sampling points in the irregular observation system after jitter sampling.

[0108] Next, select Figure 2 The initial sampling point is located at the lower left corner. The positions of the two closest sampling points to this point are calculated, and a triangle is constructed. Then, the next sampling point is selected, and the positions of the two closest sampling points to it are calculated, and a triangle is constructed. This process is repeated until all sampling points in the irregular observation system have been traversed. After traversing all sampling points in the irregular observation system, the construction area is divided into multiple triangular regions, such as... Figure 3 As shown. Calculation Figure 3corresponding to each triangular region, arranging the data channel correlation parameters of all the triangular regions in descending order, determining the triangular region corresponding to the largest data channel correlation parameter, randomly sampling the sampling points in the triangular region in the regular observation system, adding a sampling point, repeating the above steps until the number of sampling points designed for the irregular observation system reaches 40% of the number of sampling points of the regular observation system, and the distribution of the sampling points designed for the irregular observation system at this time is as shown in Figure 4 .

[0109] In addition, the embodiment of the present application also samples the sampling points of the regular observation system in a jitter sampling manner according to an undersampling rate of 40%, and the distribution of the sampling points designed for the irregular observation system obtained is as shown in Figure 5 . Wherein, Figure 4 and Figure 5 The units of the horizontal axis and the vertical axis are length-meters.

[0110] The embodiment of the present application carries out tests according to the distribution of the sampling points in Figure 4 and the distribution of the sampling points in Figure 5 It is found that if the method provided by the embodiment of the present application is used, the signal-to-noise ratio of the reconstructed data is higher than that of the jitter sampling method.

[0111] In addition, the embodiment of the present application also carries out tests for different undersampling rates, and obtains the curve diagram as shown in Figure 6 , Figure 6 The curve where (*) is located represents the change relationship between the signal ratio of the reconstructed data and the undersampling rate when the method provided by the embodiment of the present application is used, Figure 6 The curve where (o) is located represents the change relationship between the signal ratio of the reconstructed data and the undersampling rate when the jitter sampling method is used. According to Figure 6 It can be known that the method provided by the embodiment of the present application is better than the jitter sampling method at a low sampling rate, and the signal-to-noise ratios of the two methods are high and low in the case of an undersampling rate higher than 50%. Therefore, a suitable method can be selected based on the undersampling rate to arrange the sampling points of the irregular observation system.

[0112] Figure 7 is a structural schematic diagram of a sampling point determination device based on data channel correlation provided by the embodiment of the present application, as shown in Figure 7 , the device comprises:

[0113] The simulation module 701 is used to simulate based on the geological information of the construction area and the plurality of first sampling points in the regular observation system designed for the construction area, to obtain the seismic data corresponding to each first sampling point;

[0114] The quantity determination module 702 is configured to determine a first quantity and a second quantity based on the undersampling rate of the irregular observation system and the number of the first sampling points, where the first quantity represents the number of the second sampling points of the irregular observation system, and the second quantity is less than the first quantity.

[0115] The sampling module 703 is configured to determine each endpoint of the construction area as a second sampling point, sample the first sampling points in the construction area based on a difference between the second quantity and the number of the endpoints of the construction area, and obtain a plurality of second sampling points, where the number of the second sampling points obtained by sampling is the difference between the second quantity and the number of the endpoints of the construction area.

[0116] The division module 704 is configured to perform triangular division on the construction area based on the plurality of second sampling points obtained at present, and obtain a plurality of triangular regions.

[0117] The parameter determination module 705 is configured to determine, for each triangular region, a data channel correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points in the triangular region.

[0118] The sampling module 703 is further configured to determine, based on the data channel correlation parameter corresponding to each triangular region, a triangular region with the maximum data channel correlation, sample the first sampling points in the triangular region at least once, and obtain at least one second sampling point.

[0119] The division module 704, the parameter determination module 705 and the sampling module 703 are further configured to repeatedly perform the steps of performing triangular division on the construction area based on the plurality of second sampling points obtained at present, obtaining a plurality of triangular regions, determining, for each triangular region, a data channel correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points in the triangular region, determining, based on the data channel correlation parameter corresponding to each triangular region, a triangular region with the maximum data channel correlation, and sampling the first sampling points in the triangular region at least once to obtain at least one second sampling point, until the number of the second sampling points obtained reaches the first quantity.

[0120] As shown in FIG. 7, Figure 8 in a possible implementation, the division module 704 is configured to determine, based on a second sampling point located at any endpoint of the construction area, two second sampling points closest to the second sampling point, and form a triangular region by the second sampling point and the two second sampling points closest to the second sampling point; and repeatedly perform the steps of determining, based on any second sampling point of the two second sampling points determined last time, two second sampling points closest to the second sampling point, and forming a triangular region by the second sampling point and the two second sampling points closest to the second sampling point, until all the second sampling points are traversed.

[0121] In a possible implementation, the parameter determining module 705 includes:

[0122] The grouping unit 7051 is configured to group the three second sampling points in the triangular region two by two to obtain three sampling point combinations.

[0123] The first determining unit 7052 is configured to determine, for each sampling point combination, a data channel correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination.

[0124] The second determining unit 7053 is configured to determine, as the data channel correlation parameter corresponding to the triangular region, the maximum data channel correlation parameter in the data channel correlation parameters corresponding to the three sampling point combinations.

[0125] In a possible implementation, the first determining unit 7052 is configured to determine, as the data channel correlation parameter corresponding to the sampling point combination, the data channel correlation parameter based on the seismic data corresponding to the two second sampling points in the sampling point combination and first relationship data, the first relationship data being used to represent a relationship among the seismic data corresponding to the two second sampling points, the transpose of the seismic data corresponding to one of the two second sampling points, and the data channel correlation parameter corresponding to the two second sampling points.

[0126] In a possible implementation, the relationship between the data channel correlation parameter corresponding to the triangular region and the seismic data corresponding to the plurality of second sampling points in the triangular region is represented as:

[0127]

[0128] wherein μ j represents the data channel correlation parameter corresponding to the j th triangular region, max is a maximum function, y i，j represents the seismic data corresponding to the i th second sampling point in the j th triangular region, represents the transpose of the seismic data corresponding to the i th second sampling point in the j th triangular region, y s，j represents the seismic data corresponding to the s th second sampling point in the j th triangular region, wherein the value range of i and s is the interval [1, 3], and i≠s.

[0129] In a possible implementation, the quantity determining module 702 is configured to determine, as the first quantity, a product of the undersampling rate and the number of the plurality of first sampling points, determine, as the second quantity, half of the first quantity, or obtain a first ratio value input by a user and determine, as the second quantity, a product of the first quantity and the first ratio value.

[0130] In a possible implementation, the apparatus further includes:

[0131] The adjusting module 706 is configured to encrypt or move the second sampling points in the complex structure region in the construction region after the obtained number of second sampling points reaches the first number.

[0132] It should be noted that the sampling point determination apparatus based on data channel correlation provided in the above embodiments only takes the above-mentioned division of functional modules as an example, and in actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above-described functions. In addition, the sampling point determination apparatus based on data channel correlation and the sampling point determination method based on data channel correlation provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0133] In some embodiments, the computer device is provided as a terminal. Figure 9 Figure 9 is a structural block diagram of a terminal provided in an embodiment of the present application. The terminal 900 comprises a processor 901 and a memory 902.

[0134] The processor 901 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 901 can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 901 can be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 901 can also include an AI (Artificial Intelligence) processor for processing machine learning-related computing operations.

[0135] The memory 902 can include one or more computer-readable storage media. The computer-readable storage media can be non-transitory. The memory 902 can also include high-speed random access memory and can include nonvolatile memory, such as one or more magnetic disk storage devices, optical storage devices, flash memory devices, or other nonvolatile solid-state storage devices. In some embodiments, the non-transitory computer-readable storage medium of the memory 902 is used to store at least one program code for being executed by the processor 901 to implement the method for determining sampling points based on data channel correlation provided by the method embodiments of the present application.

[0136] In some embodiments, the terminal 900 can further optionally include a peripheral device interface 903 and at least one peripheral device. The processor 901, the memory 902, and the peripheral device interface 903 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 903 through a bus, a signal line, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 904, a display screen 905, a camera 906, an audio circuit 907, a positioning component 908, and a power supply 909.

[0137] The peripheral device interface 903 can be used to connect at least one peripheral device related to input / output (I / O) to the processor 901 and the memory 902. In some embodiments, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 can be implemented on a separate chip or circuit board, and the present embodiment is not limited in this regard.

[0138] The display screen 905 is configured to display a UI (User Interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 905 is a touch display screen, the display screen 905 is further configured to capture touch signals on or above the surface of the display screen 905. The touch signals can be input to the processor 901 as control signals for processing. In this case, the display screen 905 can be further configured to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 905 can be one, arranged on the front panel of the terminal 900; in other embodiments, the display screen 905 can be at least two, arranged on different surfaces of the terminal 900 or in a folding design; in yet other embodiments, the display screen 905 can be a flexible display screen, arranged on a curved surface or a folding surface of the terminal 900. Even, the display screen 905 can be arranged in an irregular shape other than a rectangle, i.e., a special-shaped screen. The display screen 905 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0139] The power supply 909 is configured to supply power to various components in the terminal 900. The power supply 909 can be AC power, DC power, a disposable battery, or a rechargeable battery. When the power supply 909 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be configured to support fast charging technology.

[0140] Those skilled in the art can understand that the structure shown in FIG. 10 is not a limitation on the terminal 900, and the terminal 900 can include more or fewer components than those shown in the figure, or combine certain components, or adopt a different arrangement of components. Figure 9 Those skilled in the art can understand that the structure shown in FIG. 10 is not a limitation on the terminal 900, and the terminal 900 can include more or fewer components than those shown in the figure, or combine certain components, or adopt a different arrangement of components.

[0141] In some embodiments, the computer device is provided as a server. Figure 10 FIG. 11 is a structural schematic diagram of a server provided by an embodiment of the present application. The server 1000 can have a large difference due to different configurations or performances, and can include one or more processors (CPU) 1001 and one or more memories 1002. The memory 1002 stores at least one program code, which is loaded and executed by the processor 1001 to implement the method provided by each method embodiment described above. Of course, the server can also have a wired or wireless network interface, a keyboard, and an input / output interface, etc., to perform input / output, and can include other components for implementing device functions, which are not described herein.

[0142] The server 1000 is configured to perform the steps performed by the server in the above method embodiments.

[0143] The embodiments of the present application further provide a computer readable storage medium, wherein at least one program code is stored in the computer readable storage medium, and the at least one program code is loaded and executed by a processor to implement the sampling point determination method based on data channel correlation as any of the above implementation manners.

[0144] The embodiments of the present application further provide a computer program product, wherein the computer program product comprises at least one program code, and the at least one program code is loaded and executed by a processor to implement the sampling point determination method based on data channel correlation as any of the above implementation manners.

[0145] In some embodiments, the computer program related to the embodiments of the present application can be deployed to execute on one computer device, or on multiple computer devices located in one place, or on multiple computer devices distributed in multiple places and interconnected through a communication network, and the multiple computer devices distributed in multiple places and interconnected through a communication network can constitute a blockchain system.

[0146] The above is only optional embodiments of the present application, and does not limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining sampling points based on data track correlation, characterized in that, The method includes: Based on the geological information of the construction area and multiple first sampling points in the rule-based observation system designed for the construction area, a simulation is performed to obtain the seismic data corresponding to each first sampling point. Based on the undersampling rate corresponding to the irregular observation system and the number of the plurality of first sampling points, a first quantity and a second quantity are determined, wherein the first quantity is used to represent the number of second sampling points of the irregular observation system, and the second quantity is less than the first quantity; Each endpoint of the construction area is determined as a second sampling point. Based on the difference between the second number and the number of endpoints of the construction area, multiple first sampling points inside the construction area are sampled to obtain multiple second sampling points. The number of multiple second sampling points obtained by sampling is the difference between the second number and the number of endpoints of the construction area. Based on the multiple second sampling points obtained so far, the construction area is triangulated to obtain multiple triangular regions; For each triangular region, the correlation parameters of the data traces corresponding to the triangular region are determined based on the seismic data corresponding to the second sampling point within the triangular region. Based on the data channel correlation parameters corresponding to each triangular region, the triangular region with the highest data channel correlation is determined, and the first sampling point in the triangular region is sampled at least once to obtain at least one second sampling point. The process of repeatedly performing the steps of triangulating the construction area based on the currently obtained multiple second sampling points to obtain multiple triangular regions, determining the data trace correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points in the triangular region, determining the triangular region with the highest data trace correlation based on the data trace correlation parameter corresponding to each triangular region, and sampling the first sampling point in the triangular region at least once to obtain at least one second sampling point, is repeated until the number of obtained second sampling points reaches the first number. The determination of the first quantity and the second quantity based on the undersampling rate corresponding to the irregular observation system and the number of the plurality of first sampling points includes: The product of the undersampling rate and the number of the plurality of first sampling points is determined as the first quantity; The second quantity is determined by taking half of the first quantity; or, the second quantity is determined by obtaining the first ratio input by the user and multiplying the first quantity by the first ratio.

2. The method of claim 1, wherein, Based on the currently obtained multiple second sampling points, the construction area is triangulated to obtain multiple triangular regions, including: Based on a second sampling point located at any endpoint of the construction area, determine the two second sampling points closest to the second sampling point, and form a triangular region through the second sampling point and the two second sampling points closest to the second sampling point; Repeat the process of determining the two second sampling points closest to the previously determined second sampling point, and forming a triangular region by the second sampling point and the two closest second sampling points, until all second sampling points have been traversed.

3. The method of claim 1, wherein, The determination of data trace correlation parameters corresponding to the triangular region based on the seismic data corresponding to the second sampling point within the triangular region includes: The three second sampling points within the triangular region are grouped in pairs to obtain a combination of three sampling points; For each combination of sampling points, the data trace correlation parameter corresponding to the combination of sampling points is determined based on the seismic data corresponding to the two second sampling points in the combination of sampling points. The largest data channel correlation parameter among the data channel correlation parameters corresponding to the combination of the three sampling points is determined as the data channel correlation parameter corresponding to the triangular region.

4. The method of claim 3, wherein, The step of determining the data trace correlation parameter corresponding to the sampling point combination based on the seismic data corresponding to the two second sampling points in the sampling point combination includes: Based on the seismic data corresponding to the two second sampling points in the sampling point combination and the first relational data, the data trace correlation parameter corresponding to the sampling point combination is determined. The first relational data is used to represent the relationship between the seismic data corresponding to the two second sampling points, the transpose of the seismic data corresponding to one of the two second sampling points, and the data trace correlation parameter corresponding to the two second sampling points.

5. The method according to claim 3 or 4, characterized in that, The relationship between the correlation parameter of the data trace corresponding to the triangular region and the seismic data corresponding to multiple second sampling points within the triangular region is expressed as follows: wherein, represents the data trace correlation parameter corresponding to the jth triangular region, max is a maximum function, represents the seismic data corresponding to the ith second sampling point in the jth triangular region, represents the transpose of the seismic data corresponding to the ith second sampling point in the jth triangular region, represents the seismic data corresponding to the s th second sampling point in the jth triangular region, wherein the value range of i and s is the interval [1, 3], and i≠s.

6. The method of claim 1, wherein, The method further includes: After the number of second sampling points reaches the first number, the second sampling points in the complex structural area of ​​the construction area are encrypted or moved.

7. A sampling point determination device based on data channel correlation, characterized in that, The device includes: The simulation module is used to simulate based on the geological information of the construction area and multiple first sampling points in the rule-based observation system designed for the construction area, and to obtain the seismic data corresponding to each first sampling point. The quantity determination module is used to determine a first quantity and a second quantity based on the undersampling rate corresponding to the irregular observation system and the number of the plurality of first sampling points, wherein the first quantity is used to represent the number of second sampling points of the irregular observation system, and the second quantity is less than the first quantity; The sampling module is used to determine each endpoint of the construction area as a second sampling point, and to sample multiple first sampling points within the construction area based on the difference between the second number and the number of endpoints of the construction area to obtain multiple second sampling points, wherein the number of multiple second sampling points obtained by sampling is the difference between the second number and the number of endpoints of the construction area. The partitioning module is used to triangulate the construction area based on the multiple second sampling points obtained so far, resulting in multiple triangular regions; The parameter determination module is used to determine the data trace correlation parameters corresponding to each triangular region based on the seismic data corresponding to the second sampling point within the triangular region. The sampling module is also used to determine the triangular region with the greatest data channel correlation based on the data channel correlation parameter corresponding to each triangular region, and to sample the first sampling point in the triangular region at least once to obtain at least one second sampling point. The division module, the parameter determination module, and the sampling module are further configured to repeatedly execute the steps of triangulating the construction area based on the currently obtained multiple second sampling points to obtain multiple triangular regions; for each triangular region, determining the data trace correlation parameter corresponding to the triangular region based on the seismic data corresponding to the second sampling points within the triangular region; determining the triangular region with the highest data trace correlation based on the data trace correlation parameter corresponding to each triangular region; and sampling the first sampling point within the triangular region at least once to obtain at least one second sampling point, until the number of obtained second sampling points reaches the first number. The quantity determination module is used to determine the first quantity by multiplying the undersampling rate and the number of the plurality of first sampling points; to determine the second quantity by half of the first quantity; or to obtain a first ratio input by the user and to determine the second quantity by multiplying the first quantity and the first ratio.

8. A computer device, comprising: The computer device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to implement the sampling point determination method based on data channel correlation as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the sampling point determination method based on data channel correlation as described in any one of claims 1 to 6.

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