Seismic data acquisition method, apparatus, and computer device

Abstract

The application discloses a seismic data acquisition method, device and computer equipment, and belongs to the technical field of geophysical exploration. The application provides a seismic data acquisition method. According to position information of each shot point in high-density first original seismic data, a first sampling matrix is constructed, the first sampling matrix is divided into a plurality of sub-matrices, then according to a shot point missing ratio parameter, the number of missing shot points of each sub-matrix is determined, and the first sampling matrix is subjected to missing over-limit processing based on the number of missing shot points of each sub-matrix and a continuous missing upper limit parameter. In this way, the uniformity of sampling can be maintained, and in the obtained target sampling matrix, continuous large-scale vacancies will not appear in any row or column, so that the seismic data can be accurately reconstructed according to the target sampling matrix, and the accuracy of reconstructed seismic data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration. In particular, it relates to a seismic data acquisition method, device and computer equipment. BACKGROUND

[0002] The compressed sensing technology is an efficient and economical data acquisition method. At present, the technology has been widely applied to seismic data acquisition. The core content of the compressed sensing technology is a shot point random sampling method. The seismic data is reconstructed through random sampling, so that high-density seismic data acquisition is not required, thereby reducing the cost of seismic data acquisition.

[0003] The shot point random sampling method in the related art is a global sampling method. The method randomly samples the entire seismic work area, but the method cannot guarantee the uniformity of local sampling, and a large range of continuous empty sampling may occur, resulting in poor accuracy of the reconstructed seismic data. SUMMARY

[0004] The embodiments of the present application provide a seismic data acquisition method, device and computer equipment, which can improve the accuracy of reconstructed seismic data. The specific technical solutions are as follows:

[0005] In one aspect, the embodiments of the present application provide a seismic data acquisition method, which comprises:

[0006] obtaining first original seismic data, obtaining position information of each shot point from the first original seismic data, the first original seismic data being obtained by sampling or simulating a first seismic work area according to a first sampling interval, the first sampling interval being smaller than a preset sampling interval;

[0007] constructing a first sampling matrix based on the position information of each shot point;

[0008] dividing the first sampling matrix into a plurality of sub-matrices based on a preset matrix block parameter;

[0009] determining the number of missing shot points of each sub-matrix based on a preset shot point missing ratio parameter;

[0010] performing missing over-limit processing on the first sampling matrix based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter, to obtain a second sampling matrix;

[0011] determining a target sampling matrix based on the second sampling matrix and the first original seismic data;

[0012] obtaining second original seismic data, the second original seismic data being obtained by sampling according to the target sampling matrix, the sampling interval of the target sampling matrix being greater than the preset sampling interval;

[0013] reconstruct the second original seismic data based on the target sampling matrix to obtain target reconstructed seismic data.

[0014] In a possible implementation, the matrix block parameter includes a column block parameter and a row block parameter.

[0015] The dividing the first sampling matrix into a plurality of sub-matrices based on the preset matrix block parameter includes:

[0016] determining a number of sub-matrices in a vertical direction based on the row block parameter and a number of rows of the first sampling matrix;

[0017] determining a number of sub-matrices in a horizontal direction based on the column block parameter and a number of columns of the first sampling matrix;

[0018] dividing the first sampling matrix into a plurality of sub-matrices based on the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction.

[0019] In another possible implementation, the determining the number of missing shots of each sub-matrix based on the preset missing shot ratio parameter includes:

[0020] For each sub-matrix, determining a theoretical number of missing shots and an actual number of missing shots based on the preset missing shot ratio parameter, a position of the sub-matrix in the first sampling matrix, a first number of shots of the sub-matrix and a second number of shots of each other sub-matrix, the first number of shots being a number of elements contained in the sub-matrix, the each other sub-matrix being a sub-matrix before the position of the sub-matrix in the first sampling matrix according to a preset order, the theoretical number of missing shots being a number of shots theoretically corresponding to the sub-matrix and the each other sub-matrix, and the actual number of missing shots being a number of shots actually corresponding to the each other sub-matrix;

[0021] taking a difference between the theoretical number of missing shots and the actual number of missing shots as the number of missing shots of the sub-matrix.

[0022] In another possible implementation, the determining process of the theoretical number of missing shots based on the preset missing shot ratio parameter, the position of the sub-matrix in the first sampling matrix, the first number of shots of the sub-matrix and the second number of shots of each other sub-matrix includes:

[0023] determining a sum of the first number of shots of the sub-matrix and the second number of shots of the each other sub-matrix to obtain a third number of shots;

[0024] determining a product of the third number of shot points and the shot point missing ratio parameter;

[0025] rounding down the product to obtain the theoretical number of shot points.

[0026] In another possible implementation, the missing over-limit processing of the first sampling matrix based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter to obtain a second sampling matrix comprises:

[0027] performing shot point random missing processing on each sub-matrix based on the number of missing shot points of each sub-matrix to obtain a plurality of processed sub-matrices;

[0028] If there is a matrix region in a third sampling matrix composed of the plurality of processed sub-matrices, in which the number of continuous missing shot points is greater than the continuous missing upper limit parameter, performing position interchanging between a missing shot point at a preset position in the matrix region and any shot point in a processed sub-matrix to which the preset position belongs, until the number of continuous missing shot points in any row and any column in the third sampling matrix is not greater than the continuous missing upper limit parameter, to obtain a second sampling matrix.

[0029] In another possible implementation, the determining of the target sampling matrix based on the second sampling matrix and the first original seismic data comprises:

[0030] determining a product of the second sampling matrix and the first original seismic data to obtain first sampling seismic data;

[0031] performing iterative reconstruction on the first sampling seismic data based on the second sampling matrix to obtain first reconstructed seismic data;

[0032] determining a first energy of the first original seismic data and an energy difference between the first original seismic data and the first reconstructed seismic data;

[0033] determining a first ratio of the first energy to the energy difference, and performing logarithmic processing on the first ratio to obtain a reconstructed signal-to-noise ratio;

[0034] if the reconstructed signal-to-noise ratio is not less than a preset reconstructed signal-to-noise ratio, regarding the second sampling matrix as a target sampling matrix.

[0035] In another possible implementation, the iterative reconstruction of the sampling seismic data based on the second sampling matrix to obtain first reconstructed seismic data comprises:

[0036] determining a difference between a unit matrix and the second sampling matrix, and regarding the difference as a fourth sampling matrix;

[0037] transforming the second reconstructed seismic data to obtain third reconstructed seismic data, the second reconstructed seismic data being seismic data obtained in the last iteration reconstruction;

[0038] determining a product of the fourth sampling matrix and the third reconstructed seismic data to obtain third sampled seismic data;

[0039] determining a sum of the third sampled seismic data and the first sampled seismic data, and taking the sum as fourth reconstructed seismic data, the fourth reconstructed seismic data being seismic data obtained in the current iteration reconstruction;

[0040] taking the fourth reconstructed seismic data as second reconstructed seismic data, performing the step of determining a difference between the unit matrix and the second sampling matrix, and taking the difference as a fourth sampling matrix, until a preset iteration number is reached, and taking seismic data obtained in the iteration when the preset iteration number is reached as the first reconstructed seismic data.

[0041] In another possible implementation, the position information of each shot point includes a horizontal coordinate and a vertical coordinate of the shot point; and the first sampling interval includes a vertical sampling interval and a horizontal sampling interval.

[0042] The first sampling matrix is constructed based on the position information of each shot point, and includes:

[0043] determining a maximum value of the horizontal coordinate and a minimum value of the horizontal coordinate in the shot point coordinate, and a maximum value of the vertical coordinate and a minimum value of the vertical coordinate;

[0044] determining a first difference between the maximum value of the horizontal coordinate and the minimum value of the horizontal coordinate, and a second difference between the maximum value of the vertical coordinate and the minimum value of the vertical coordinate;

[0045] determining a second ratio of the first difference to the horizontal sampling interval, taking the second ratio as an integer, to obtain a first value, adding 1 to the first value to obtain a column number of the first sampling matrix;

[0046] determining a third ratio of the second difference to the vertical sampling interval, taking the third ratio as an integer, to obtain a second value, and adding 1 to the second value to obtain a row number of the first sampling matrix;

[0047] based on the row number and the column number of the first sampling matrix, initializing a value of an element at each shot point position to 1 to obtain the first sampling matrix.

[0048] In another aspect, an embodiment of the present application provides a seismic data acquisition device, which includes:

[0049] The first acquisition module is configured to acquire first original seismic data, and acquire position information of each shot point from the first original seismic data, wherein the first original seismic data is obtained by collecting or simulating a first seismic work area according to a first sampling interval, and the first sampling interval is smaller than a preset sampling interval.

[0050] The construction module is configured to construct a first sampling matrix based on the position information of each shot point.

[0051] The division module is configured to divide the first sampling matrix into a plurality of sub-matrices based on preset matrix block parameters.

[0052] The first determination module is configured to determine a number of missing shot points of each sub-matrix based on a preset shot point missing ratio parameter.

[0053] The processing module is configured to perform missing over-limit processing on the first sampling matrix based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter, to obtain a second sampling matrix.

[0054] The second determination module is configured to determine a target sampling matrix based on the second sampling matrix and the first original seismic data.

[0055] The second acquisition module is configured to acquire second original seismic data, wherein the second original seismic data is obtained by collecting a second seismic work area according to the target sampling matrix, and a sampling interval of the target sampling matrix is greater than the preset sampling interval.

[0056] The reconstruction module is configured to reconstruct the second original seismic data based on the target sampling matrix, to obtain target reconstructed seismic data.

[0057] In a possible implementation, the matrix block parameters include column block parameters and row block parameters.

[0058] The division module is configured to determine a number of sub-matrices in a vertical direction based on the row block parameters and a number of rows of the first sampling matrix, determine a number of sub-matrices in a horizontal direction based on the column block parameters and a number of columns of the first sampling matrix, and divide the first sampling matrix into a plurality of sub-matrices based on the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction.

[0059] In a possible implementation, the first determining module is configured to: for each sub-matrix, determine a theoretical number of missing shots and an actual number of missing shots based on a preset shot missing ratio parameter, a position of the sub-matrix in the first sampling matrix, a first number of shots of the sub-matrix, and a second number of shots of each other sub-matrix, wherein the first number of shots is a number of elements contained in the sub-matrix, the other sub-matrix is a sub-matrix before the position of the sub-matrix in the first sampling matrix according to a preset order, the theoretical number of missing shots is a number of shots that the sub-matrix and the other sub-matrix theoretically correspond to, and the actual number of missing shots is a number of shots that the other sub-matrix actually corresponds to; and determine a number of missing shots of the sub-matrix as a difference between the theoretical number of missing shots and the actual number of missing shots.

[0060] In a possible implementation, the first determining module is configured to: determine a sum of the first number of shots of the sub-matrix and the second number of shots of each other sub-matrix to obtain a third number of shots; determine a product of the third number of shots and the shot missing ratio parameter; and obtain the theoretical number of missing shots by rounding down the product.

[0061] In a possible implementation, the processing module is configured to: perform shot random missing processing on each sub-matrix based on a number of missing shots of the sub-matrix to obtain a plurality of processed sub-matrices; and if there is a matrix region in which a number of continuously missing shots is greater than the upper limit of continuous missing in a third sampling matrix composed of the plurality of processed sub-matrices, perform position interchanging between a missing shot at a preset position in the matrix region and any shot in a processed sub-matrix to which the preset position belongs, until the number of continuously missing shots in any row and any column in the third sampling matrix is not greater than the upper limit of continuous missing, to obtain a second sampling matrix.

[0062] In a possible implementation, the second determining module is configured to: determine a product of the second sampling matrix and the first original seismic data to obtain first sampling seismic data; perform iterative reconstruction on the first sampling seismic data based on the second sampling matrix to obtain first reconstructed seismic data; determine a first energy of the first original seismic data and an energy difference between the first original seismic data and the first reconstructed seismic data; determine a first ratio of the first energy to the energy difference, and perform logarithm processing on the first ratio to obtain a reconstruction signal-to-noise ratio; and if the reconstruction signal-to-noise ratio is not less than a preset reconstruction signal-to-noise ratio, determine the second sampling matrix as a target sampling matrix.

[0063] In another possible implementation, the second determining module is configured to determine a difference between the unit matrix and the second sampling matrix, and take the difference as a fourth sampling matrix; perform transform processing on second reconstructed seismic data to obtain third reconstructed seismic data, the second reconstructed seismic data being seismic data obtained by last iteration reconstruction; determine a product of the fourth sampling matrix and the third reconstructed seismic data to obtain third sampling seismic data; determine a sum of the third sampling seismic data and the first sampling seismic data, and take the sum as fourth reconstructed seismic data, the fourth reconstructed seismic data being seismic data obtained by current iteration reconstruction; take the fourth reconstructed seismic data as the second reconstructed seismic data, and perform the step of determining a difference between the unit matrix and the second sampling matrix, and taking the difference as the fourth sampling matrix, until a preset iteration number is reached, and take seismic data obtained by iteration when the preset iteration number is reached as the first reconstructed seismic data.

[0064] In another possible implementation, the position information of each shot point includes a horizontal coordinate and a vertical coordinate of the shot point; and the first sampling interval includes a vertical sampling interval and a horizontal sampling interval.

[0065] The construction module is configured to determine a maximum value of the horizontal coordinate and a minimum value of the horizontal coordinate in the shot point coordinates, and a maximum value of the vertical coordinate and a minimum value of the vertical coordinate; determine a first difference between the maximum value of the horizontal coordinate and the minimum value of the horizontal coordinate, and a second difference between the maximum value of the vertical coordinate and the minimum value of the vertical coordinate; determine a second ratio of the first difference to the horizontal sampling interval, take the second ratio as an integer, to obtain a first numerical value, add 1 to the first numerical value, to obtain a column number of the first sampling matrix; determine a third ratio of the second difference to the vertical sampling interval, take the third ratio as an integer, to obtain a second numerical value, add 1 to the second numerical value, to obtain a row number of the first sampling matrix; and initialize a value of an element at each shot point position to 1 based on the row number and the column number of the first sampling matrix, to obtain the first sampling matrix.

[0066] In another aspect, an embodiment of the present application provides a computer device, which comprises a processor and a memory, and the memory stores at least one program code, the at least one program code is loaded and executed by the processor to implement operations performed in the seismic data acquisition method.

[0067] In another aspect, an embodiment of the present application provides a computer readable storage medium, which stores at least one program code, the at least one program code is loaded and executed by a processor to implement operations performed in the seismic data acquisition method.

[0068] In another aspect, an embodiment of the present application provides a computer program product or a computer program, the computer program product or the computer program comprising computer program code stored in a computer readable storage medium. A processor of a computer device reads the computer program code from the computer readable storage medium, and the processor executes the computer program code to implement operations performed in the seismic data acquisition method described in the embodiments of the present application.

[0069] The technical scheme provided by the embodiments of the present application has the beneficial effects that:

[0070] The embodiments of the present application provide a seismic data acquisition method. According to position information of each shot point in high-density first original seismic data, a first sampling matrix is constructed. The first sampling matrix is divided into a plurality of sub-matrices. According to a shot point missing ratio parameter, the number of missing shot points of each sub-matrix is determined. Based on the number of missing shot points of each sub-matrix and a continuous missing upper limit parameter, the first sampling matrix is subjected to missing over-limit processing. In this way, the uniformity of sampling can be maintained, and in the obtained target sampling matrix, there will be no large-scale continuous vacancy in any row or column. Therefore, according to the target sampling matrix, the seismic data can be accurately reconstructed, thereby improving the accuracy of reconstructed seismic data. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is a flowchart of a seismic data acquisition method provided by an embodiment of the present application;

[0072] Figure 2 is a schematic diagram of a 400ms time slice of a high-density first original seismic data 3D common receiver gather provided by an embodiment of the present application;

[0073] Figure 3 is a schematic diagram of a first sampling matrix provided by an embodiment of the present application;

[0074] Figure 4 is a schematic diagram of a sub-matrix subjected to shot point random missing processing provided by an embodiment of the present application;

[0075] Figure 5 is a schematic diagram of a second sampling matrix provided by an embodiment of the present application;

[0076] Figure 6 is a schematic diagram of determining a target sampling matrix based on a second sampling matrix and first original seismic data provided by an embodiment of the present application;

[0077] Figure 7 is a schematic diagram of spatial sampling of first original seismic data through a second sampling matrix provided by an embodiment of the present application;

[0078] Figure 8 is a schematic diagram of first reconstructed seismic data obtained by iteratively reconstructing first sampled seismic data according to an embodiment of the present application;

[0079] Figure 9 is a structural schematic diagram of a seismic data acquisition device according to an embodiment of the present application;

[0080] Figure 10 is a structural block diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the technical solutions and advantages of the present application clearer, the embodiments of the present application are described in further detail below.

[0082] The embodiments of the present application provide a seismic data acquisition method, which is executed by a computer device, referring to Figure 1 , the method comprises:

[0083] Step 101: The computer device acquires first original seismic data, and obtains position information of each shot point from the first original seismic data.

[0084] In this step, the computer device first acquires the acquired first original seismic data, and obtains the position information of each shot point based on the first original seismic data.

[0085] The first original seismic data can be actually acquired in a first seismic work area according to a first sampling interval, or can be simulated in a laboratory according to the geological profile of the first seismic work area according to the first sampling interval. If the first original seismic data is actually acquired in the first seismic work area, the method provided by the present application can be used to optimize the design parameters to obtain a target sampling matrix, which can then be applied to actual acquisition. If the first original seismic data is simulated in a laboratory according to the geological profile of the first seismic work area, the method provided by the present application can be used to optimize the design parameters to obtain a target sampling matrix, which can then be directly applied in the field. Moreover, the first sampling interval is smaller than the preset sampling interval, i.e., the first original seismic data is high-density data acquired or simulated. The computer device can establish a rectangular coordinate system to represent the position information of the shot point through the horizontal coordinate and the vertical coordinate. The first sampling interval includes the vertical sampling interval and the horizontal sampling interval.

[0086] Referring to Figure 2 , Figure 2 is a 400ms time slice of a 3D common receiver gather of high-density first original seismic data, wherein the horizontal axis represents the shot line coordinate and the vertical axis represents the shot point coordinate.

[0087] Step 102: The computer device constructs a first sampling matrix based on the position information of each shot point.

[0088] This step can be obtained by the following steps (1) to (5), comprising:

[0089] (1) The computer device determines the maximum value of the horizontal coordinate and the minimum value of the horizontal coordinate in the shot point coordinate, and the maximum value of the vertical coordinate and the minimum value of the vertical coordinate.

[0090] In this step, the maximum value of the horizontal coordinate can be represented by X max , the minimum value of the horizontal coordinate can be represented by X min , the maximum value of the vertical coordinate can be represented by Y max , and the minimum value of the vertical coordinate can be represented by Y min .

[0091] Continuing to refer to Figure 2 , the maximum value of the horizontal coordinate is 800, the minimum value of the horizontal coordinate is 20, and the horizontal sampling interval is 20. The maximum value of the vertical coordinate is 1000, the minimum value of the vertical coordinate is 0, and the vertical sampling interval is 10.

[0092] (2) The computer device determines the first difference value between the maximum value of the horizontal coordinate and the minimum value of the horizontal coordinate, and the second difference value between the maximum value of the vertical coordinate and the minimum value of the vertical coordinate.

[0093] In this step, the first difference value can be represented by X max -X min , and the second difference value can be represented by Y max -Y min .

[0094] (3) The computer device determines the second ratio of the first difference value to the horizontal sampling interval, rounds down the second ratio to obtain a first value, and adds 1 to the first value to obtain the number of columns of the first sampling matrix.

[0095] In this step, the number of columns of the first sampling matrix can be represented by: Wherein, N represents the number of columns of the first sampling matrix, dX represents the horizontal sampling interval, represents rounding down.

[0096] According to the above data, the number of columns of the first sampling matrix is calculated by the formula to be 40.

[0097] (4) The computer device determines the third ratio of the second difference value to the vertical sampling interval, rounds down the third ratio to obtain a second value, and adds 1 to the second value to obtain the number of rows of the first sampling matrix.

[0098] The number of rows of the first sampling matrix can be represented by Wherein, M represents the number of rows of the first sampling matrix, and dY represents the vertical sampling interval.

[0099] According to the above data, the number of rows of the first sampling matrix is calculated by the formula to be 101.

[0100] (5) The computer device initializes the value of each element at the position of the shot point to 1 based on the number of rows and the number of columns of the first sampling matrix, and obtains the first sampling matrix.

[0101] The computer device generates a matrix with M rows and N columns, initializes the value of each element at the position of the shot point to 1, and obtains the first sampling matrix as shown below:

[0102]

[0103] The first sampling matrix can be represented as A=MxN, where A represents the first sampling matrix.

[0104] For the above data, the computer device generates a 101x40 matrix based on the number of rows 101 and the number of columns 40, initializes the elements at the position of the shot point to 1, and obtains the first sampling matrix, which can be seen in Figure 3 , Figure 3 The values of the circles in the middle are all 1.

[0105] Step 103: The computer device divides the first sampling matrix into multiple sub-matrices based on the preset matrix blocking parameters.

[0106] Among them, the matrix blocking parameters include column blocking parameters and row blocking parameters.

[0107] This step can be realized through the following steps (1) to (3), including:

[0108] (1) The computer device determines the number of sub-matrices in the vertical direction based on the row blocking parameters and the number of rows of the first sampling matrix.

[0109] In this step, the computer device can determine the ratio of the number of rows of the first sampling matrix to the row blocking parameters, and take the ratio to the upper integer to obtain the number of sub-matrices in the vertical direction. This process can be represented by the following formula:

[0110] Among them, I represents the number of sub-matrices in the vertical direction, M represents the number of rows of the first sampling matrix, R represents the row blocking parameters, represents the upper integer.

[0111] (2) The computer device determines the number of sub-matrices in the horizontal direction based on the column blocking parameters and the number of columns of the first sampling matrix.

[0112] In this step, the computer device can determine the ratio of the number of columns of the first sampling matrix to the column blocking parameters, and take the ratio to the upper integer to obtain the number of sub-matrices in the horizontal direction. This process can be represented by the following formula:

[0113] wherein J represents the number of sub-matrices in the horizontal direction, N represents the number of columns of the first sampling matrix, and C represents the column block parameter.

[0114] (3) The computer device divides the first sampling matrix into a plurality of sub-matrices based on the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction.

[0115] The computer device determines the product of the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction to obtain a first number, and divides the first sampling matrix into the first number of sub-matrices, that is, the computer device divides the first sampling matrix into I×J sub-matrices:

[0116]

[0117] wherein each sub-matrix contains the following elements:

[0118] When x<I and y<J:

[0119]

[0120] When x=I and y<J:

[0121]

[0122] When x<I and y=J:

[0123]

[0124] When x=I and y=J:

[0125]

[0126] For example, the row block parameter is 15, the column block parameter is 15, the number of rows of the first sampling matrix is 101, and the number of columns of the first sampling matrix is 40. Figure 3 According to the above formula, the number of sub-matrices in the vertical direction is 7. According to the above formula, the number of sub-matrices in the horizontal direction is 3, and the first sampling matrix can be divided into 7×3 sub-matrices.

[0127] In the embodiments of the present application, the number of rows of a sub-matrix is equal to the row block parameter, and the number of columns of a sub-matrix is equal to the column block parameter. If there is a sub-matrix with a number of remaining rows or columns less than the row block parameter or the column block parameter during the division of the sub-matrices, the actual number of remaining rows or columns is used. For example, the row block parameter is 15, the column block parameter is 15, the number of remaining rows is 10, and the number of remaining columns is 5, and the 10 rows and 5 columns are taken as a sub-matrix.

[0128] Step 104: The computer device determines the number of missing shots of each sub-matrix based on the preset shot missing ratio parameter.

[0129] This step can be implemented through the following steps (1) to (3), comprising:

[0130] (1) For each sub-matrix, the computer device determines the theoretical number of missing shots based on the preset shot missing ratio parameter, the position of the sub-matrix in the first sampling matrix, the first number of shots of the sub-matrix, and the second number of shots of each other sub-matrix.

[0131] Wherein, each other sub-matrix is a sub-matrix before the position of the sub-matrix in the first sampling matrix according to the preset order, and the theoretical number of missing shots is the number of missing shots theoretically corresponding to the sub-matrix and each other sub-matrix.

[0132] This step can be implemented through the following steps (1-1) to (1-3), comprising:

[0133] (1-1) The computer device determines the sum of the first number of shots of the sub-matrix and the second number of shots of each other sub-matrix to obtain the third number of shots.

[0134] The first number of shots is the number of elements contained in the sub-matrix, and the second number of shots is the number of elements contained in each other sub-matrix. For the sub-matrix B ij , the number of elements contained therein can be represented by S ij .

[0135] The preset order can be set and changed as needed, for example, the preset order is row first and column second, or column first and row second. In the embodiments of the present application, only the case where the preset order is row first and column second is taken as an example for description.

[0136] If the preset order is row first and column second, for each sub-matrix, the computer device first determines the number of elements contained in each row sub-matrix before the sub-matrix, and then determines the number of elements contained in each sub-matrix in the row where the sub-matrix is located and before the position of the sub-matrix, and the number of elements contained in the sub-matrix, and the sum of the obtained number of elements is taken as the third number of shots.

[0137] In this step, the third number of shots can be represented by the following formula:

[0138]

[0139] (1-2) The computer device determines the product of the third number of shots and the shot missing ratio parameter.

[0140] In this step, the product can be represented as: Wherein,

[0141] Sij sub-matrix B ij the number of elements contained, S xj sub-matrix B xj the number of elements contained, P represents a shot missing ratio parameter.

[0142] The shot missing ratio parameter can be set and changed as needed, for example, the shot missing ratio parameter is 50% or 30%.

[0143] (1-3) The computer device rounds down the product to obtain the theoretical shot missing number.

[0144] In this step, the theoretical shot missing number can be represented as:

[0145]

[0146] (2) The computer device determines the actual shot missing number.

[0147] The actual shot missing number is the number of missing shots actually corresponding to each sub-matrix. The actual shot missing number is the number of missing shots of each row sub-matrix before the sub-matrix and the number of missing shots of each sub-matrix in the row and before the position of the sub-matrix. The actual shot missing number can be represented by the following formula:

[0148]

[0149] Wherein, Z ij sub-matrix B ij the number of missing shots, Z xj sub-matrix B xj the number of missing shots.

[0150] (3) The computer device takes the difference between the theoretical shot missing number and the actual shot missing number as the shot missing number of the sub-matrix.

[0151] In this step, the shot missing number Z xy of the sub-matrix can be represented as:

[0152]

[0153] In the embodiments of the present application, for each sub-matrix, first determine the total number of elements that should be theoretically zeroed in the sub-matrix and other each sub-matrix before the sub-matrix, then determine the total number of elements that have actually been zeroed in other each sub-matrix before the sub-matrix, and finally determine the difference between the two, which is the number of elements zeroed in the sub-matrix, that is, the shot missing number. In this way, the problem of cumulative rounding error caused by rounding operation and then causing overall missing ratio deviation can be effectively avoided.

[0154] Step 105: The computer device performs shot point random missing processing on each sub-matrix based on the number of missing shot points of each sub-matrix, to obtain a plurality of processed sub-matrices.

[0155] In this step, the computer device performs zero processing on the sub-matrix based on the number of missing shot points of the sub-matrix, see Figure 4 , the process can be: if the number of zeros of the elements in the sub-matrix B xy is less than the number of missing shot points Z xy of the sub-matrix, the computer device randomly selects any element from the sub-matrix, if the value of the element is 0, the element is reselected; if the value of the element is 1, the zero operation is performed, until the number of zeros of the elements in the sub-matrix reaches the number of missing shot points of the sub-matrix, thereby obtaining a plurality of sub-matrices.

[0156] Step 106: If there is a matrix region in the third sampling matrix composed of a plurality of processed sub-matrices, in which the number of continuous missing shot points is greater than the upper limit parameter of continuous missing, the computer device exchanges the positions of the missing shot points at the preset position and any shot point in the processed sub-matrix to which the shot point at the preset position belongs, until the number of continuous missing shot points in any row and any column in the third sampling matrix is not greater than the upper limit parameter of continuous missing, to obtain a second sampling matrix.

[0157] A plurality of processed sub-matrices constitute a third sampling matrix, if there is a matrix region in the third sampling matrix, in which the number of continuous missing shot points is greater than the upper limit parameter of continuous missing, the computer device can exchange the positions of the 0 element at any position in the matrix region and any element with a value of 1 in the processed sub-matrix to which the element at the position belongs, for example, the computer device can exchange the positions of the 0 element at the middle position in the matrix region and any element with a value of 1 in the processed sub-matrix to which the element at the position belongs. Such an iterative loop is performed until the number of continuous 0 elements in all rows and columns is not greater than the upper limit parameter of continuous missing, to obtain a second sampling matrix.

[0158] For example, for the first sampling matrix shown in Figure 3 , the second sampling matrix generated after processing according to the above steps 103-106 can be seen from Figure 5 . Figure 5 It can be seen from that: the number of continuous missing elements in any row and column in the second sampling matrix does not exceed the upper limit parameter of continuous missing 8, realizing the controllable number of continuous 0 values. And, the second sampling matrix can maintain good randomness and uniformity in both global and local ranges, thereby reducing the acquisition cost of seismic data and improving the accuracy of subsequent reconstruction of seismic data.

[0159] Step 107: The computer device determines the target sampling matrix based on the second sampling matrix and the first original seismic data.

[0160] This step can be implemented through the following steps (1) to (6), see Figure 6 , including:

[0161] (1) The computer device determines the product of the second sampling matrix and the first original seismic data to obtain the first sampled seismic data.

[0162] In this step, the computer device can multiply the first original seismic data with the corresponding elements in the second sampling matrix to obtain the first sampled seismic data. The first sampled seismic data can be represented as:

[0163] D1=dot(D,H);

[0164] Wherein, D1 represents the first sampled seismic data, D represents the first original seismic data, H represents the second sampling matrix, and dot(.) represents the corresponding multiplication of the first original seismic data and the elements in the second sampling matrix.

[0165] For example, for the first original seismic data shown in Figure 1 , after spatial sampling by the second sampling matrix, the first sampled seismic data obtained can be seen from Figure 7 , and it can be seen from Figure 7 that the signal continuity of the data sampling is poor.

[0166] (2) The computer device iteratively reconstructs the first sampled seismic data based on the second sampling matrix to obtain the first reconstructed seismic data.

[0167] This step can be implemented through the following steps (2-1) to (2-5), including:

[0168] (2-1) The computer device determines the difference between the unit matrix and the second sampling matrix, and takes the difference as the fourth sampling matrix.

[0169] In this step, F can be used to represent the unit matrix, and the fourth sampling matrix can be represented as: F-H.

[0170] Wherein, the elements on the diagonal line from the top left corner to the bottom right corner of the unit matrix are all 1, and the other elements are all 0.

[0171] (2-2) The computer device transforms the second reconstructed seismic data to obtain the third reconstructed seismic data.

[0172] The second reconstructed seismic data is obtained from the previous iteration. Computer equipment can then perform a series of transformations on the second reconstructed seismic data, including Fourier transform, thresholding, and inverse Fourier transform, to obtain the third reconstructed seismic data.

[0173] The third reconstructed seismic data can be represented as: TE k-1 Where T represents the series of transformations described above, and E k-1 This represents the seismic data obtained from the (k-1)th iteration reconstruction.

[0174] (2-3) The computer equipment determines the product of the fourth sampling matrix and the third reconstructed seismic data to obtain the third sampled seismic data.

[0175] The computer equipment multiplies the third-reconstructed seismic data with each element of the fourth sampling matrix to obtain the third-sampled seismic data. This third-sampled seismic data can be represented as: (FH)TE k-1 .

[0176] (2-4) The computer equipment determines the sum of the third sampled seismic data and the first sampled seismic data, and uses this sum as the fourth reconstructed seismic data.

[0177] The fourth reconstructed seismic data is the seismic data obtained from the current iteration of reconstruction. This fourth reconstructed seismic data can be represented as: E k =D1+(FH)TE k-1 , of which E k This represents the seismic data obtained from the k-th iteration of reconstruction.

[0178] (2-5) The computer equipment uses the fourth reconstructed seismic data as the second reconstructed seismic data and repeats steps (2-1) to (2-5) until the preset number of iterations is reached. The seismic data obtained by iterative reconstruction when the preset number of iterations is reached is used as the first reconstructed seismic data.

[0179] The preset number of iterations can be set and changed as needed, but in this embodiment, no specific limitation is made.

[0180] For example, for Figure 7 The first sampled seismic data shown, after iterative reconstruction through steps (1) to (5) in step 108, yields the first reconstructed seismic data, which can be found in [reference needed]. Figure 8 ,from Figure 8 As can be seen, the continuity of the signal was restored after reconstruction, indicating that the above method can effectively reconstruct seismic data.

[0181] (3) The computer equipment determines the first energy of the first raw seismic data and the energy difference between the first raw seismic data and the first reconstructed seismic data.

[0182] In this step, the process that the computer device determines the first energy of the first original seismic data can be: the computer device determines the sum of squares of each sample value of the first original seismic data to obtain the first energy. The first energy can be represented as: ||D||2.

[0183] The process that the computer device determines the energy difference between the first original seismic data and the first reconstructed seismic data can be: the computer device determines the difference between each sample value of the first original seismic data and each sample value of the first reconstructed seismic data, determines the sum of squares of the difference to obtain the energy difference. The energy difference can be represented as: ||E-D||2. Wherein, E represents the first reconstructed seismic data.

[0184] (4) The computer device determines the first ratio of the first energy and the energy difference, and performs logarithmic processing on the first ratio to obtain the reconstructed signal-to-noise ratio.

[0185] In this step, the first ratio can be represented as: The reconstructed signal-to-noise ratio can be represented as: Wherein, q is a constant, and f is the base of the logarithm.

[0186] Wherein, q and f can be the same or different, for example, q and f are the same, both are 10, and the reconstructed signal-to-noise ratio can be represented as:

[0187] (5) If the reconstructed signal-to-noise ratio is not less than the preset reconstructed signal-to-noise ratio, the computer device takes the second sampling matrix as the target sampling matrix.

[0188] The larger the reconstructed signal-to-noise ratio is, the better the reconstruction effect corresponding to the second sampling matrix is. If the reconstructed signal-to-noise ratio is not less than the preset reconstructed signal-to-noise ratio, the computer device can directly take the second sampling matrix as the target sampling matrix.

[0189] (6) If the reconstructed signal-to-noise ratio is less than the preset reconstructed signal-to-noise ratio, the computer device can adjust at least one of the matrix blocking parameter, the shot missing ratio parameter and the continuous missing upper limit parameter, and then re-executes steps 103 to 107 until the reconstructed signal-to-noise ratio is not less than the preset reconstructed signal-to-noise ratio.

[0190] Wherein, the preset reconstructed signal-to-noise ratio can be set and changed as needed, which is not specifically limited in the embodiment of the present application.

[0191] In the embodiment of the present application, the quantitative evaluation standard of the sampling method is established, the sampling parameters, namely the matrix blocking parameter, the shot missing ratio parameter and the continuous missing upper limit parameter, are optimized based on the reconstructed signal-to-noise ratio, that is, the reconstruction effect, so as to improve the reconstruction accuracy of the compressed sensing seismic data.

[0192] Step 108: The computer device acquires second original seismic data.

[0193] The second original seismic data is seismic data collected in a second seismic work area according to a target sampling matrix, a sampling interval of the target sampling matrix being greater than a preset sampling interval, that is, the second original seismic data is low-density seismic data. The second seismic work area has the same size as the first seismic work area.

[0194] Step 109: The computer device reconstructs the second original seismic data based on the target sampling matrix to obtain target reconstructed seismic data.

[0195] In this step, the computer device multiplies the second original seismic data and corresponding elements in the target sampling matrix to obtain fourth sampling seismic data, and then iteratively reconstructs the fourth sampling seismic data based on the target sampling matrix, that is, performs steps (1) and (2) in step 107 to obtain the target reconstructed seismic data.

[0196] In the embodiments of the present application, low-density seismic data is collected first in actual application, and then high-density seismic data is reconstructed through the target sampling matrix, so that the cost of collecting seismic data can be reduced, and the accuracy of reconstructed seismic data can be improved.

[0197] The embodiments of the present application provide a seismic data acquisition method, according to position information of each shot point in high-density first original seismic data, a first sampling matrix is constructed, the first sampling matrix is divided into a plurality of sub-matrices, then according to a shot point missing ratio parameter, a shot point missing number of each sub-matrix is determined, and based on the shot point missing number of each sub-matrix and a continuous missing upper limit parameter, the first sampling matrix is subjected to missing over-limit processing, so that the uniformity of sampling can be maintained, and there will be no continuous large-scale vacancy in any row or column of the obtained target sampling matrix, so that the seismic data can be accurately reconstructed according to the target sampling matrix, thereby improving the accuracy of reconstructed seismic data.

[0198] The embodiments of the present application provide a seismic data acquisition device, referring to Figure 9 The device comprises:

[0199] The first acquisition module 901 is configured to acquire first original seismic data, and acquire position information of each shot point from the first original seismic data, the first original seismic data being obtained by collecting or simulating a first seismic work area according to a first sampling interval, the first sampling interval being less than a preset sampling interval;

[0200] The construction module 902 is configured to construct a first sampling matrix based on the position information of each shot point.

[0201] The division module 903 is configured to divide the first sampling matrix into a plurality of sub-matrices based on preset matrix block parameters.

[0202] The first determination module 904 is configured to determine the number of missing shots of each sub-matrix based on a preset shot missing ratio parameter.

[0203] The processing module 905 is configured to perform missing over-limit processing on the first sampling matrix based on the number of missing shots of each sub-matrix and a preset continuous missing upper limit parameter, to obtain a second sampling matrix.

[0204] The second determination module 906 is configured to determine a target sampling matrix based on the second sampling matrix and the first original seismic data.

[0205] The second acquisition module 907 is configured to acquire second original seismic data, which is collected in a second seismic work area according to the target sampling matrix, and the sampling interval of the target sampling matrix is greater than a preset sampling interval.

[0206] The reconstruction module 908 is configured to reconstruct the second original seismic data based on the target sampling matrix, to obtain target reconstructed seismic data.

[0207] In a possible implementation, the matrix block parameters include column block parameters and row block parameters.

[0208] The division module 903 is configured to determine the number of sub-matrices in the vertical direction based on the row block parameters and the number of rows of the first sampling matrix, determine the number of sub-matrices in the horizontal direction based on the column block parameters and the number of columns of the first sampling matrix, and divide the first sampling matrix into a plurality of sub-matrices based on the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction.

[0209] In another possible implementation, the first determination module 904 is configured to, for each sub-matrix, determine a theoretical number of missing shots and an actual number of missing shots based on the preset shot missing ratio parameter, the position of the sub-matrix in the first sampling matrix, a first number of shots of the sub-matrix, and a second number of shots of each other sub-matrix, the first number of shots being the number of elements contained in the sub-matrix, each other sub-matrix being a sub-matrix before the sub-matrix in a preset order in the first sampling matrix, the theoretical number of missing shots being a theoretical number of missing shots corresponding to the sub-matrix and each other sub-matrix, and the actual number of missing shots being an actual number of missing shots corresponding to each other sub-matrix; and determine the number of missing shots of the sub-matrix as the difference between the theoretical number of missing shots and the actual number of missing shots.

[0210] In another possible implementation, the first determining module 904 is configured to determine a sum of the first number of shots of the sub-matrix and the second number of shots of each other sub-matrix to obtain a third number of shots; determine a product of the third number of shots and the shot missing ratio parameter; and take an integer of the product to obtain the theoretical number of shots.

[0211] In another possible implementation, the processing module 905 is configured to perform shot random missing processing on each sub-matrix based on the number of missing shots of each sub-matrix to obtain a plurality of processed sub-matrices; if there is a matrix region in a third sampling matrix composed of the plurality of processed sub-matrices, in which the number of continuous missing shots is greater than the upper limit of continuous missing, exchange the position of any shot in the processed sub-matrix to which the shot at a preset position in the matrix region belongs and the shot at the preset position, until the number of continuous missing shots in any row and any column in the third sampling matrix is not greater than the upper limit of continuous missing, to obtain a second sampling matrix.

[0212] In another possible implementation, the second determining module 906 is configured to determine a product of the second sampling matrix and the first original seismic data to obtain first sampling seismic data; perform iterative reconstruction on the first sampling seismic data based on the second sampling matrix to obtain first reconstructed seismic data; determine a first energy of the first original seismic data and an energy difference between the first original seismic data and the first reconstructed seismic data; determine a first ratio of the first energy to the energy difference, and perform logarithmic processing on the first ratio to obtain a reconstructed signal-to-noise ratio; and if the reconstructed signal-to-noise ratio is not less than a preset reconstructed signal-to-noise ratio, take the second sampling matrix as a target sampling matrix.

[0213] In another possible implementation, the second determining module 906 is configured to determine a difference between a unit matrix and the second sampling matrix, and take the difference as a fourth sampling matrix; perform transformation processing on second reconstructed seismic data to obtain third reconstructed seismic data, the second reconstructed seismic data being seismic data obtained in the last iteration reconstruction; determine a product of the fourth sampling matrix and the third reconstructed seismic data to obtain third sampling seismic data; determine a sum of the third sampling seismic data and the first sampling seismic data, and take the sum as fourth reconstructed seismic data, the fourth reconstructed seismic data being seismic data obtained in the current iteration reconstruction; take the fourth reconstructed seismic data as the second reconstructed seismic data, and perform the step of determining the difference between the unit matrix and the second sampling matrix, and taking the difference as the fourth sampling matrix, until a preset iteration number is reached, and take seismic data obtained in the iteration when the preset iteration number is reached as the first reconstructed seismic data.

[0214] In another possible implementation, the position information of each shot includes a horizontal coordinate and a vertical coordinate of each shot; and the first sampling interval includes a vertical sampling interval and a horizontal sampling interval.

[0215] The construction module 902 is configured to determine a maximum value of the horizontal coordinate and a minimum value of the horizontal coordinate in the shot coordinate, and a maximum value of the vertical coordinate and a minimum value of the vertical coordinate; determine a first difference value of the maximum value of the horizontal coordinate and the minimum value of the horizontal coordinate, and a second difference value of the maximum value of the vertical coordinate and the minimum value of the vertical coordinate; determine a second ratio value of the first difference value and the horizontal sampling interval, take the second ratio value as an integer, to obtain a first numerical value, and add 1 to the first numerical value to obtain a column number of the first sampling matrix; determine a third ratio value of the second difference value and the vertical sampling interval, take the third ratio value as an integer, to obtain a second numerical value, and add 1 to the second numerical value to obtain a row number of the first sampling matrix; initialize a value of an element at each shot position in the first sampling matrix to 1 based on the row number and the column number of the first sampling matrix, to obtain the first sampling matrix.

[0216] The embodiment of the present application provides a seismic data acquisition device, according to position information of each shot point in high-density first original seismic data, a first sampling matrix is constructed, the first sampling matrix is divided into a plurality of sub-matrices, then according to a shot point missing ratio parameter, a shot point missing number of each sub-matrix is determined, and the first sampling matrix is subjected to missing over-limit processing based on the shot point missing number of each sub-matrix and a continuous missing upper limit parameter, so that the uniformity of sampling can be maintained, and in the obtained target sampling matrix, continuous large-scale vacancies will not appear in any row and column, so that the seismic data can be accurately reconstructed according to the target sampling matrix, and therefore the accuracy of reconstructed seismic data is improved.

[0217] Figure 10 A structural block diagram of a computer device 1000 provided by an example embodiment of the present application is shown. The computer device 1000 can be a portable mobile computer device, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a notebook computer, or a desktop computer. The computer device 1000 can also be referred to as a user device, a portable computer device, a laptop computer device, a desktop computer device, and other names.

[0218] Generally, the computer device 1000 includes a processor 1001 and a memory 1002.

[0219] The processor 1001 can include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 1001 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 1001 can also include a main processor and a coprocessor, the main processor being a processor for processing data in an awake state, also known as a CPU (Central Processing Unit), and the coprocessor being a low-power processor for processing data in a standby state. In some embodiments, the processor 1001 can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing content required to be displayed by the display screen. In some embodiments, the processor 1001 can further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

[0220] The memory 1002 can include one or more computer-readable storage media that can be non-transitory. The memory 1002 can also include high-speed random access memory and nonvolatile memory such as one or more disk storage devices, flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction for being executed by the processor 1001 to implement the seismic data acquisition method provided by the method embodiment in the present application.

[0221] In some embodiments, the computer device 1000 can also optionally include a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002, and the peripheral device interface 1003 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 1003 through a bus, a signal line, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007, a positioning assembly 1008, and a power supply 1009.

[0222] The peripheral interface 1003 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002 and the peripheral interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002 and the peripheral interface 1003 can be implemented on a separate chip or circuit board, and the present embodiments are not limited in this regard.

[0223] The radio frequency circuit 1004 is used to receive and send RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 1004 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and the like. The radio frequency circuit 1004 can communicate with other computer devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 1004 can also include NFC (Near Field Communication) related circuitry, and the present application is not limited in this regard.

[0224] The display screen 1005 is configured to display a UI (User Interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 is further configured to capture touch signals on or above the surface of the display screen 1005. The touch signals can be input to the processor 1001 as control signals for processing. In this case, the display screen 1005 can also be configured to provide virtual buttons and / or virtual keyboard, also known as soft buttons and / or soft keyboard. In some embodiments, the display screen 1005 can be one, disposed on the front panel of the computer device 1000; in other embodiments, the display screen 1005 can be at least two, respectively disposed on different surfaces of the computer device 1000 or in a folding design; in other embodiments, the display screen 1005 can be a flexible display screen, disposed on a curved surface or a folding surface of the computer device 1000. Even, the display screen 1005 can also be disposed in an irregular shape other than a rectangle, i.e., a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0225] The camera assembly 1006 is configured to capture images or videos. Optionally, the camera assembly 1006 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is disposed on the front panel of the computer device, and the rear-facing camera is disposed on the back of the computer device. In some embodiments, the rear-facing camera is at least two, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, to realize the background blur function by fusing the main camera and the depth-of-field camera, the panoramic shooting and VR (Virtual Reality) shooting function by fusing the main camera and the wide-angle camera, or other fusion shooting functions. In some embodiments, the camera assembly 1006 can further include a flash. The flash can be a single-color-temperature flash or a dual-color-temperature flash. The dual-color-temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0226] The audio circuit 1007 can include a microphone and a speaker. The microphone is used to collect sound waves of a user and an environment, and convert the sound waves into an electrical signal input to the processor 1001 for processing, or input to the radio frequency circuit 1004 to realize voice communication. The microphone can be multiple for the purpose of stereo sound collection or noise reduction, and arranged at different parts of the computer device 1000. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert an electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker can be a traditional diaphragm speaker, or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only can it convert an electrical signal into a sound wave audible to humans, but also can convert an electrical signal into an inaudible sound wave to humans for ranging purposes. In some embodiments, the audio circuit 1007 can also include a headphone jack.

[0227] The positioning component 1008 is used to position the current geographic location of the computer device 1000 to realize navigation or LBS (Location Based Service). The positioning component 1008 can be a positioning component based on the GPS (Global Positioning System) of the United States, the Beidou system of China, or the Galileo system of Russia.

[0228] The power supply 1009 is used to supply power to various components in the computer device 1000. The power supply 1009 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1009 includes a rechargeable battery, the rechargeable battery can be a wired charging battery or a wireless charging battery. The wired charging battery is a battery charged through a wired line, and the wireless charging battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0229] In some embodiments, the computer device 1000 further includes one or more sensors 1010. The one or more sensors 1010 include, but are not limited to, an acceleration sensor 1011, a gyroscope sensor 1012, a pressure sensor 1013, a fingerprint sensor 1014, an optical sensor 1015, and a proximity sensor 1016.

[0230] The acceleration sensor 1011 can detect the acceleration magnitude in three coordinate axes of a coordinate system established by the computer device 1000. For example, the acceleration sensor 1011 can be used to detect the components of gravitational acceleration in three coordinate axes. The processor 1001 can control the display screen 1005 to display a user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 1011. The acceleration sensor 1011 can also be used for gaming or collection of user motion data.

[0231] The gyroscope sensor 1012 can detect the body direction and rotation angle of the computer device 1000, and the gyroscope sensor 1012 can cooperate with the acceleration sensor 1011 to collect the 3D action of the user on the computer device 1000. According to the data collected by the gyroscope sensor 1012, the processor 1001 can realize the following functions: action sensing (such as changing the UI according to the user's tilt operation), image stabilization when shooting, game control, and inertial navigation.

[0232] The pressure sensor 1013 can be arranged on the side frame of the computer device 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1013 is arranged on the side frame of the computer device 1000, the user's holding signal on the computer device 1000 can be detected, and the left and right hand recognition or shortcut operation can be performed by the processor 1001 according to the holding signal collected by the pressure sensor 1013. When the pressure sensor 1013 is arranged on the lower layer of the display screen 1005, the controllable control on the UI interface can be controlled by the processor 1001 according to the pressure operation of the user on the display screen 1005. The controllable control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0233] The fingerprint sensor 1014 is used to collect the fingerprint of the user, and the identity of the user can be recognized by the processor 1001 according to the fingerprint collected by the fingerprint sensor 1014, or by the fingerprint sensor 1014 according to the collected fingerprint. When the identity of the user is recognized as a trusted identity, the processor 1001 authorizes the user to perform related sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, payment, and changing settings. The fingerprint sensor 1014 can be arranged on the front, back or side of the computer device 1000. When the computer device 1000 is provided with a physical button or a manufacturer's logo, the fingerprint sensor 1014 can be integrated with the physical button or the manufacturer's logo.

[0234] The optical sensor 1015 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 according to the ambient light intensity collected by the optical sensor 1015. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is decreased. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 according to the ambient light intensity collected by the optical sensor 1015.

[0235] The proximity sensor 1016, also referred to as a distance sensor, is usually arranged on the front panel of the computer device 1000. The proximity sensor 1016 is used to collect the distance between the user and the front of the computer device 1000. In one embodiment, when the proximity sensor 1016 detects that the distance between the user and the front of the computer device 1000 gradually decreases, the display screen 1005 is switched from the bright screen state to the screen-off state under the control of the processor 1001; when the proximity sensor 1016 detects that the distance between the user and the front of the computer device 1000 gradually increases, the display screen 1005 is switched from the screen-off state to the bright screen state under the control of the processor 1001.

[0236] Those skilled in the art can understand that the structure shown in the foregoing embodiments is not a limitation on the computer device 1000, and the computer device 1000 can include more or fewer components than those shown in the drawings, or combine certain components, or adopt a different arrangement of components. Figure 10

[0237] The embodiments of the present application further provide a computer readable storage medium, and the computer readable storage medium stores at least one program code. The at least one program code is loaded and executed by a processor to implement the operations performed in the seismic data acquisition method.

[0238] The embodiments of the present application further provide a computer program product or a computer program. The computer program product or the computer program includes computer program code. The computer program code is stored in a computer readable storage medium. The processor of the computer device reads the computer program code from the computer readable storage medium. The processor executes the computer program code, so that the computer device performs the operations performed in the seismic data acquisition method.

[0239] 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.

[0240] The above is only to facilitate those skilled in the art to understand the technical solutions of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.​

Claims

1. A method of seismic data acquisition, characterized by, The method comprises: acquiring first original seismic data, obtaining position information of each shot point from the first original seismic data, the first original seismic data being obtained by collecting or simulating a first seismic work area according to a first sampling interval, the first sampling interval being smaller than a preset sampling interval; constructing a first sampling matrix based on the position information of each shot point; dividing the first sampling matrix into a plurality of sub-matrices based on preset matrix block parameters; determining the number of missing shot points of each sub-matrix based on a preset missing shot point ratio parameter; performing missing over-limit processing on the first sampling matrix based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter, to obtain a second sampling matrix; determining a target sampling matrix based on the second sampling matrix and the first original seismic data; acquiring second original seismic data, the second original seismic data being collected in a second seismic work area according to the target sampling matrix, the sampling interval of the target sampling matrix being greater than the preset sampling interval; reconstructing the second original seismic data based on the target sampling matrix to obtain target reconstructed seismic data.

2. The method of claim 1, wherein, The matrix block parameters comprise column block parameters and row block parameters; The first sampling matrix is divided into a plurality of sub-matrices based on the preset matrix block parameters, which comprises: determining the number of sub-matrices in the vertical direction based on the row block parameters and the number of rows of the first sampling matrix; determining the number of sub-matrices in the horizontal direction based on the column block parameters and the number of columns of the first sampling matrix; dividing the first sampling matrix into a plurality of sub-matrices based on the number of sub-matrices in the vertical direction and the number of sub-matrices in the horizontal direction.

3. The method of claim 1, wherein, The number of missing shot points of each sub-matrix is determined based on the preset missing shot point ratio parameter, which comprises: for each sub-matrix, determining a theoretical missing shot point number and an actual missing shot point number based on the preset missing shot point ratio parameter, the position of the sub-matrix in the first sampling matrix, the first shot point number of the sub-matrix, and the second shot point number of each other sub-matrix, the first shot point number being the number of elements contained in the sub-matrix, each other sub-matrix being a sub-matrix in the first sampling matrix before the position of the sub-matrix in a preset order, the theoretical missing shot point number being the number of theoretically corresponding missing shot points of the sub-matrix and each other sub-matrix, and the actual missing shot point number being the number of actually corresponding missing shot points of each other sub-matrix; the difference between the theoretical missing shot point number and the actual missing shot point number is taken as the number of missing shot points of the sub-matrix.

4. The method of claim 3, wherein, The determination process of the theoretical missing shot point number based on the preset missing shot point ratio parameter, the position of the sub-matrix in the first sampling matrix, the first shot point number of the sub-matrix, and the second shot point number of each other sub-matrix comprises: determining the sum of the first shot point number of the sub-matrix and the second shot point number of each other sub-matrix to obtain a third shot point number; determining the product of the third shot point number and the missing shot point ratio parameter; The product is rounded down to obtain the theoretical number of shot points.

5. The method of claim 1, wherein, The first sampling matrix is subjected to a missing over-limit processing based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter to obtain a second sampling matrix, including: The shot points in each sub-matrix are subjected to a random missing processing based on the number of missing shot points of each sub-matrix to obtain a plurality of processed sub-matrices; If there is a matrix region in a third sampling matrix composed of the plurality of processed sub-matrices, in which the number of continuous missing shot points is greater than the continuous missing upper limit parameter, the missing shot points at a preset position in the matrix region are subjected to a position exchange with any shot point in the processed sub-matrix to which the preset position belongs until the number of continuous missing shot points in any row and any column in the third sampling matrix is not greater than the continuous missing upper limit parameter, thereby obtaining a second sampling matrix.

6. The method of claim 1, wherein, The target sampling matrix is determined based on the second sampling matrix and the first original seismic data, including: A product of the second sampling matrix and the first original seismic data is determined to obtain first sampling seismic data; The first sampling seismic data is subjected to an iterative reconstruction based on the second sampling matrix to obtain first reconstructed seismic data; A first energy of the first original seismic data and an energy difference between the first original seismic data and the first reconstructed seismic data are determined; A first ratio of the first energy to the energy difference is determined, and a logarithm processing is performed on the first ratio to obtain a reconstructed signal-to-noise ratio; If the reconstructed signal-to-noise ratio is not less than a preset reconstructed signal-to-noise ratio, the second sampling matrix is taken as a target sampling matrix.

7. The method of claim 6, wherein, The sampling seismic data is subjected to an iterative reconstruction based on the second sampling matrix to obtain first reconstructed seismic data, including: A difference between a unit matrix and the second sampling matrix is determined, and the difference is taken as a fourth sampling matrix; Second reconstructed seismic data obtained in a previous iteration is subjected to a transformation processing to obtain third reconstructed seismic data; A product of the fourth sampling matrix and the third reconstructed seismic data is determined to obtain third sampling seismic data; A sum of the third sampling seismic data and the first sampling seismic data is determined, and the sum is taken as fourth reconstructed seismic data, which is seismic data obtained in a current iteration; The fourth reconstructed seismic data is taken as second reconstructed seismic data, and the step of determining a difference between a unit matrix and the second sampling matrix and taking the difference as a fourth sampling matrix is executed until a preset iteration number is reached, and seismic data obtained in the iteration when the preset iteration number is reached is taken as the first reconstructed seismic data.

8. The method of claim 1, wherein, The position information of each shot point includes a horizontal coordinate and a vertical coordinate of the shot point; and the first sampling interval includes a vertical sampling interval and a horizontal sampling interval. The first sampling matrix is constructed based on the position information of each shot point, including: Maximum values of horizontal coordinates and minimum values of horizontal coordinates in shot point coordinates, and maximum values of vertical coordinates and minimum values of vertical coordinates are determined. determining a first difference between a maximum value of the abscissa and a minimum value of the abscissa, and a second difference between a maximum value of the ordinate and a minimum value of the ordinate; determining a second ratio of the first difference to the horizontal sampling interval, rounding down the second ratio to obtain a first number, and adding 1 to the first number to obtain a column number of the first sampling matrix; determining a third ratio of the second difference to the vertical sampling interval, rounding down the third ratio to obtain a second number, and adding 1 to the second number to obtain a row number of the first sampling matrix; initializing a value of an element at each shot point position to 1 based on the row number and the column number of the first sampling matrix to obtain the first sampling matrix.

9. A seismic data acquisition apparatus, characterized by, The device comprises: a first acquisition module configured to acquire first original seismic data, and to acquire position information of each shot point from the first original seismic data, the first original seismic data being obtained by collecting or simulating a first seismic work area according to a first sampling interval, the first sampling interval being smaller than a preset sampling interval; a construction module configured to construct a first sampling matrix based on the position information of each shot point; a division module configured to divide the first sampling matrix into a plurality of sub-matrices based on preset matrix block parameters; a first determination module configured to determine a number of missing shot points of each sub-matrix based on a preset shot point missing ratio parameter; a processing module configured to perform missing upper limit processing on the first sampling matrix based on the number of missing shot points of each sub-matrix and a preset continuous missing upper limit parameter to obtain a second sampling matrix; a second determination module configured to determine a target sampling matrix based on the second sampling matrix and the first original seismic data; a second acquisition module configured to acquire second original seismic data, the second original seismic data being obtained by collecting a second seismic work area according to the target sampling matrix, a sampling interval of the target sampling matrix being greater than the preset sampling interval; a reconstruction module configured to reconstruct the second original seismic data based on the target sampling matrix to obtain target reconstructed seismic data.

10. A computer device, comprising: The computer device comprises a processor and a memory, the memory stores at least one program code, the at least one program code is loaded and executed by the processor to implement the seismic data acquisition method in any one of claims 1 to 8.

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