A bin attribute statistics method, device and index file of a super large observation system

Abstract

The application provides a bin attribute statistics method, device and super-large observation system of index file, solves the problem that the calculation efficiency of an algorithm module cannot meet the demand of the super-large observation system of the index file, and comprises the following steps: obtaining a unit template and rolling parameters of an observation system; obtaining a bin coverage frequency array of the unit template based on the unit template parameters; obtaining an observation system coverage frequency array and a bin index array based on the bin coverage frequency array of the unit template; merging the coverage frequencies of the same bin based on the observation system coverage frequency and the bin index array; obtaining the offset and the azimuth of the observation system based on the unit template parameters and the rolling parameters of the observation system; traversing all data through the bin index to obtain statistical data; traversing the offset data in each bin with data in the offset data file; traversing the offset and the azimuth data file, establishing a two-dimensional array, segmenting different dimension data, and performing data statistics.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of seismic exploration and development, and particularly relates to a bin attribute statistical method, a device and a super-large observation system of an index file. BACKGROUND

[0002] With the rapid development of oil exploration and development technology, the application of high-density large-number three-dimensional seismic exploration is increasing, and new technologies such as high-density and irregular design are continuously applied, which brings geometric multiple growth of operation amount, and higher requirements for the calculation efficiency of algorithm modules. For example, in the development of a completely uniform and symmetrical super-high-density observation system demonstration, only the bin attribute result calculation needs to consume 260 GB of disk space, which has far exceeded the performance range of the existing acquisition design interface software. The existing bin attribute statistical method cannot meet the analysis and evaluation of such an observation system. In the face of the layout of a super-large observation system, how to efficiently and accurately statistically analyze the bin attribute of the observation system has become a problem that technicians are urgently concerned about. In the traditional design, technicians usually calculate the coordinates of each shotpoint and geophone point according to the layout, and calculate the coordinates of each cmp point according to each pair of shotpoint and geophone point of the observation system. This method is only suitable for the statistical analysis of the bin attribute of a small observation system. In the face of a super-large observation system, especially in the layout of a uniform high-density observation system, due to the high-density arrangement, the bin attribute calculation amount and data storage space are quite large, which will waste a lot of time and manpower to participate in the bin attribute calculation and statistical work of the observation system. SUMMARY

[0003] Therefore, the present application provides a bin attribute statistical method, a device and a super-large observation system of an index file, which solves the problem that the bin attribute calculation amount and data storage space are quite large, the calculation efficiency of the algorithm module cannot meet the needs of the super-large observation system of the index file, and a lot of time and manpower are needed to participate in the bin attribute calculation and statistical work of the observation system.

[0004] In a first aspect, an embodiment of the present application provides a bin attribute statistical method, which includes:

[0005] obtaining a unit template parameter and an observation system rolling parameter;

[0006] obtaining a unit template bin coverage frequency array based on the unit template parameter;

[0007] obtaining an observation system coverage frequency array based on the unit template bin coverage frequency array;

[0008] obtaining a bin index array based on the observation system coverage frequency array;

[0009] merging the same bin coverage frequency based on the observation system coverage frequency and the bin index array to compress the coverage frequency file.

[0010] obtaining the offset and azimuth of the observation system based on the unit template parameter and the observation system rolling parameter;

[0011] all data are traversed by bin index to obtain the coverage times, offset and azimuth statistical data;

[0012] the offset data in each bin with data existing in the offset data file are traversed to establish the grid map statistical data file;

[0013] the offset and azimuth data file is traversed to establish a two-dimensional array, segment different dimension data and perform data statistics.

[0014] In an embodiment, the unit template parameter comprises shotpoint coordinates and receiverpoint coordinates; and the obtaining the unit template bin coverage times array based on the unit template parameter comprises:

[0015] obtaining the unit template coverage times based on the shotpoint coordinates and the receiverpoint coordinates;

[0016] obtaining the unit template bin coverage times array based on the unit template coverage times.

[0017] In an embodiment, the obtaining the observation system coverage times array based on the unit template bin coverage times array comprises: rolling and superimposing the observation system rolling parameter first in the inline direction and then in the x-line direction to obtain the observation system coverage times array.

[0018] In an embodiment, the merging the same bin coverage times based on the observation system coverage times and the bin index array and compressing the coverage times file comprises: merging adjacent same unit template bin coverage times and modifying the coverage values of the bin index array corresponding to the merged unit template bin coverage times.

[0019] In an embodiment, the unit template parameter comprises shotpoint coordinates and receiverpoint coordinates; and the obtaining the offset and azimuth of the observation system based on the unit template parameter and the observation system rolling parameter comprises: obtaining the offset and azimuth of the observation system based on the shotpoint coordinates and the receiverpoint coordinates; initializing the bin coverage times based on the grid size and rolling and superimposing the offset and azimuth of the observation system first in the inline direction and then in the x-line direction according to the observation system rolling parameter to obtain the observation system offset and azimuth.

[0020] In one embodiment, the traversing all data through the bin index to obtain the number of coverages, offset, azimuth statistics data comprises: traversing all data through the bin index, and obtaining the number of coverages, offset, azimuth statistics data according to the range increment, start point and end point of statistics.

[0021] In one embodiment, the traversing offset data in each bin with data in the offset data file to establish a grid graph statistics data file comprises: traversing offset data in each bin with data in the offset data file, calculating the number of data of the bin in a range, and increasing a record on the corresponding grid statistics data.

[0022] In one embodiment, the traversing the offset and azimuth data file, establishing a two-dimensional array, dividing different dimension data, and performing data statistics comprises: traversing the offset and azimuth data file, and performing statistics on the offset and azimuth information of each CMP point in the offset and azimuth data file according to the offset and azimuth division range defined by a rose graph, establishing a two-dimensional array, the first dimension is divided according to the offset step, the second dimension is divided according to the azimuth step, and data statistics is performed.

[0023] In a second aspect, an embodiment of the present application provides a bin attribute statistics device, comprising:

[0024] A data acquisition module is configured to acquire a unit template parameter and an observation system rolling parameter.

[0025] A data processing module is configured to obtain a unit template bin coverage number array based on the unit template parameter, obtain an observation system coverage number array based on the unit template bin coverage number array, obtain a bin index array based on the observation system coverage number array, merge coverage numbers of the same bin based on the observation system coverage number and the bin index array, compress a coverage number file, and obtain offset and azimuth of an observation system based on the unit template parameter and the observation system rolling parameter.

[0026] A traversal module is configured to traverse all data through the bin index to obtain the number of coverages, offset, azimuth statistics data, traverse offset data in each bin with data in the offset data file to establish a grid graph statistics data file, and traverse the offset and azimuth data file, establish a two-dimensional array, divide different dimension data, and perform data statistics.

[0027] In a third aspect, an embodiment of the present application provides an index file super large observation system, comprising the bin attribute statistics device, and performing bin attribute statistics by using the bin attribute statistics method.

[0028] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, and the one or more computer instructions are configured to implement the surface cell attribute statistical method when executed by the processor.

[0029] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is configured to implement the surface cell attribute statistical method when executed by a processor.

[0030] The surface cell attribute statistical method, the device and the super large observation system of the index file provided by the embodiment of the present application generate the surface cell attribute of the super large observation system through the unit template surface cell attribute rolling, do not need to consider the actual layout of the observation system, and quickly calculate through the index file and compress the repeated data, so that the technical personnel are freed from the inconvenience of calculating each pair of shot-receiver of the observation system, the calculation efficiency is improved, the storage space is saved, and the consumption of the computer system resources in the surface cell attribute statistical process is greatly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 Fig. 1 shows a flowchart of a surface cell attribute statistical method according to an embodiment of the present application.

[0032] Figure 2 Fig. 2 shows a structural diagram of a surface cell attribute statistical device according to an embodiment of the present application.

[0033] Figure 3 Fig. 3 shows a flowchart of a surface cell attribute statistical method according to another embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0035] Example 1

[0036] The embodiment provides a surface cell attribute statistical method, which is implemented based on a super large observation system of an index file, as shown in the figure. Figure 1 The surface cell attribute statistical method comprises the following steps.

[0037] Step 01: Obtain the unit template parameter and the observation system rolling parameter.

[0038] Optionally, the parameters of the unit template include the number of detection lines, the number of detection points, the trace interval, the detection line interval, the starting coordinates of the detection points, the number of shot lines, the number of shot points, the shot point interval, the shot line interval, and the shot point coordinates.

[0039] Optionally, the rolling parameters of the observation system include the rolling azimuth, the inline direction rolling distance, the x-line direction rolling distance, the inline direction rolling number, the x-line direction rolling number, and the grid length and width.

[0040] Step 02: obtaining a unit template bin coverage number array based on the unit template parameters, wherein the unit template parameters include shot point coordinates and detection point coordinates; and the obtaining of the unit template bin coverage number array based on the unit template parameters includes:

[0041] Step 021: obtaining a unit template coverage number based on the shot point coordinates and the detection point coordinates.

[0042] Step 022: obtaining a unit template bin coverage number array based on the unit template coverage number.

[0043] Specifically, the unit template coverage number is calculated through the shot point coordinates and the detection point coordinates in the unit template parameters, the bin coverage number rolling template two-dimensional array is initialized according to the grid size, and the unit template bin coverage number array is obtained.

[0044] Step 03: obtaining an observation system coverage number array based on the unit template bin coverage number array, wherein the obtaining of the observation system coverage number array based on the unit template bin coverage number array includes rolling and superimposing the observation system rolling parameters in the inline direction and then in the x-line direction to obtain the observation system coverage number array.

[0045] Specifically, the observation system coverage number array is obtained by rolling and superimposing the unit template bin coverage number array according to the rolling parameters in the inline direction and then in the x-line direction.

[0046] Step 04: obtaining a bin index array based on the observation system coverage number array, wherein the bin index array is calculated based on the observation system coverage number array.

[0047] Step 05: merging the same bin coverage numbers based on the observation system coverage numbers and the bin index array to compress the coverage number file, wherein the merging of the same bin coverage numbers based on the observation system coverage numbers and the bin index array to compress the coverage number file includes merging adjacent same unit template bin coverage numbers and modifying the coverage values of the bin index array corresponding to the merged unit template bin coverage numbers.

[0048] Specifically, according to the observation system coverage times and the bin index array obtained in steps 03 and 04, adjacent same bin coverage times are combined, and the corresponding index array is modified to the coverage value. The compressed coverage time array and the bin index array are combined and saved. For example, the file format in Table 1 can be used for saving.

[0049] Table 1:

[0050] 1 Number of cells in x direction 2 Number of cells in y direction 3 Starting x coordinate of cell 4 Starting y coordinate of cell 5 Size of cell in x direction 6 Size of cell in y direction 7 Azimuth angle of cell 8 1st data 9 2nd data 10 3rd data …… n nth data

[0051] Step 06: obtaining the offset and azimuth of the observation system based on the unit template parameters and the observation system rolling parameters. The unit template parameters include the coordinates of the shot point and the coordinates of the receiver point. The offset and azimuth of the observation system are obtained based on the coordinates of the shot point and the coordinates of the receiver point. The bin coverage times are initialized based on the grid size, and the offset and azimuth of the observation system are rolled and superimposed in the inline direction and then in the x-line direction according to the observation system rolling parameters to obtain the offset and azimuth of the observation system.

[0052] Specifically, first, the offset and azimuth of the unit template are calculated based on the coordinates of the shot point and the coordinates of the receiver point. Then, the bin coverage times are initialized based on the grid size, and the rolling template two-dimensional array is rolled and superimposed in the inline direction and then in the x-line direction according to the rolling parameters to obtain the offset and azimuth of the observation system. The saving format can refer to Table 1.

[0053] Step 07: obtaining the coverage times, offset, and azimuth statistical data by traversing all the data through the bin index. The coverage times, offset, and azimuth statistical data are obtained by traversing all the data through the bin index, and the statistical range increment, the starting point, and the ending point are used for fast statistics.

[0054] Specifically, according to the observation system data file obtained in steps 04 and 06, the coverage times, offset, and azimuth statistical data are obtained by traversing all the data through the bin index based on the bin index array, and the statistical range increment, the starting point, and the ending point are used for fast statistics. The file format in Table 2 can be used for saving.

[0055] Table 2:

[0056] 1 Number of statistical data 2 range increment dx ]] 3 Range start point x0 4 Range end point x1 5 1st data 6 2nd data 7 3rd data …… …… n nth data

[0057] Step 08: traversing the offset data in each bin with data in the offset data file to establish a grid chart statistical data file. Wherein, the traversing the offset data in each bin with data in the offset data file to establish a grid chart statistical data file comprises: traversing the offset data in each bin with data in the offset data file, calculating the number of data in the bin within the range, and increasing the record on the corresponding grid statistical data.

[0058] Specifically, the grid chart statistical data file is calculated by the index file. All data are traversed by bin index based on the bin index array, the offset data in each bin with data in the offset data file is traversed, the number of data in the bin within the range is calculated, and the record is increased on the corresponding grid statistical data. A two-dimensional array is established to store the record, and the statistical data result is saved. The saving format can refer to Table Three.

[0059] Table Three:

[0060] 1 Bottom grid number nx ]]> 2 Number of height grids ny 3 Base length increment dx 4 Height length increment dy 5 Base start point value x0 6 Height start point value y0 7 Gird[y0][x0] 8 Gird[y0][x0+dx] 9 Gird[y0][x0+2*dx] 10 Gird[y0][x0+3*dx] …… …… …… Gird[y0+dy][x0] …… …… …… Gird[y0+2*dy][x0] …… …… …… Gird[ny][ny]

[0061] Step 09: traversing the offset and azimuth data file to establish a two-dimensional array, dividing different dimension data, and performing data statistics. Wherein, the traversing the offset and azimuth data file to establish a two-dimensional array, dividing different dimension data, and performing data statistics comprises: traversing the offset and azimuth data file, and according to the offset and azimuth segmentation range defined by the rose chart, the offset and azimuth information of each CMP point is counted, a two-dimensional array is established, the first dimension is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics is performed.

[0062] Specifically, the rose chart statistical data file is calculated by the index file. The offset and azimuth data file is traversed, and according to the offset and azimuth segmentation range defined by the rose chart, the offset and azimuth information of each CMP point is counted, a two-dimensional array is established, the first dimension of the two-dimensional data is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics is performed. The statistical data result is saved. The saving format can refer to Table Four.

[0063] Table Four:

[0064] 1 x-direction grid number nx ]] 2 Number of y direction grids ny 3 X direction step length dx 4 Y direction step length dy 5 X direction start point x0 6 Y direction start point y0 7 Range start point of offset distance 8 Range end point of offset distance 9 Azimuth angle range start point: 0 10 Azimuth angle range end point: 360° 11 Grid index (1, 1) 12 (1，2) …… …… …… (1，n) …… (2，1) …… …… …… (2，n) …… …… …… (m，1) …… …… …… (m, n)

[0065] The bin attribute statistical method provided by the embodiment of the application has the following advantages:

[0066] (1) The application innovatively realizes the fast calculation and statistical method of bin attribute of a super large observation system by using a bin index file.

[0067] (2) The method generates the super large observation system bin attribute through the calculation of the bin attribute of the unit template, improves the speed of the bin attribute statistics, and reduces the data storage amount.

[0068] (3) The method stores the format by self-definition, and compresses the repeated data, thereby reducing the storage space of the statistical data.

[0069] Example 2

[0070] The embodiment provides a bin attribute statistics device 100, as shown in the figure, which comprises a data acquisition module 10, a data processing module 20 and a traversal module 30. Wherein: Figure 2

[0071] The data acquisition module 10 is used for acquiring the unit template parameter and the observation system rolling parameter;

[0072] The data processing module 20 is used for obtaining the unit template bin coverage times array based on the unit template parameter; obtaining the observation system coverage times array based on the unit template bin coverage times array; obtaining the bin index array based on the observation system coverage times array; merging the same bin coverage times based on the observation system coverage times and the bin index array, compressing the coverage times file; obtaining the shot-receiver distance and the azimuth angle of the observation system based on the unit template parameter and the observation system rolling parameter;

[0073] The traversal module 30 is used for traversing all the data through the bin index to obtain the coverage times, the shot-receiver distance and the azimuth angle statistical data; traversing the shot-receiver distance data in each bin with data to establish the grid map statistical data file; traversing the shot-receiver distance and the azimuth angle data file to establish a two-dimensional array, segmenting different dimension data, and performing data statistics.

[0074] Further, the data processing module 20 is also used for obtaining the unit template coverage times based on the shot point coordinates and the receiver point coordinates; obtaining the unit template bin coverage times array based on the unit template coverage times.

[0075] Further, the data processing module 20 is also used for rolling and superimposing the observation system rolling parameter in the inline direction and then in the x-line direction to obtain the observation system coverage times array.

[0076] Further, the data processing module 20 is also used for merging the adjacent same unit template bin coverage times, and modifying the coverage values of the bin index array corresponding to the merged unit template bin coverage times.

[0077] ​Further, the data processing module 20 is further configured to obtain the offset and azimuth of the observation system based on the shot point coordinates and the receiver point coordinates; initialize the bin coverage times based on the grid size, and perform rolling stacking of the offset and azimuth of the observation system in the inline direction and then in the x-line direction according to the rolling parameters of the observation system to obtain the offset and azimuth of the observation system.

[0078] Further, the traversal module 30 is further configured to traverse all the data through the bin index, and obtain the coverage times, offset and azimuth statistical data by fast statistics according to the statistical range increment, the start point and the end point.

[0079] Further, the traversal module 30 is further configured to traverse the offset data in each bin with data in the offset data file, calculate the number of data of the bin in the range to be obtained, and increase the record on the corresponding grid statistical data.

[0080] Further, the traversal module 30 is further configured to traverse the offset and azimuth data file, and perform statistics on the offset and azimuth information of each CMP point in the offset and azimuth data file according to the offset and azimuth segmentation range defined by the rose diagram, establish a two-dimensional array, and perform data statistics by segmentation in the first dimension according to the offset step and segmentation in the second dimension according to the azimuth step.

[0081] Example 3

[0082] The embodiment provides an index file super large observation system, which comprises the bin attribute statistical device in the embodiment two, and adopts the bin attribute statistical method in the embodiment one to perform bin attribute statistics.

[0083] The bin attribute statistical device comprises a data acquisition module 10, a data processing module 20 and a traversal module 30.

[0084] The data acquisition module 10 is configured to acquire the unit template parameters and the observation system rolling parameters.

[0085] The data processing module 20 is configured to obtain the bin coverage times array of the unit template based on the unit template parameters, obtain the observation system coverage times array based on the bin coverage times array of the unit template, obtain the bin index array based on the observation system coverage times array, combine the coverage times of the same bin based on the observation system coverage times and the bin index array, compress the coverage times file, and obtain the offset and azimuth of the observation system based on the unit template parameters and the observation system rolling parameters.

[0086] The traversing module 30 is configured to traverse all data through bin indices to obtain coverage times, offset, and azimuth statistical data; traverse offset data in each bin with data in the offset data file to establish a grid map statistical data file; and traverse the offset and azimuth data file to establish a two-dimensional array, segment different dimension data, and perform data statistics.

[0087] Further, the data processing module 20 is further configured to obtain bin template coverage times based on the shot point coordinates and the receiver point coordinates; and obtain a bin template bin coverage times array based on the bin template coverage times.

[0088] Further, the data processing module 20 is further configured to perform rolling superposition of the observation system rolling parameters in the inline direction and then in the x-line direction to obtain an observation system coverage times array.

[0089] Further, the data processing module 20 is further configured to merge adjacent same bin template bin coverage times and modify coverage values of a bin index array corresponding to the merged bin template bin coverage times.

[0090] Further, the data processing module 20 is further configured to obtain offset and azimuth of an observation system based on the shot point coordinates and the receiver point coordinates; initialize bin coverage times based on a grid size; and perform rolling superposition of the offset and azimuth of the observation system in the inline direction and then in the x-line direction according to the observation system rolling parameters to obtain offset and azimuth of the observation system.

[0091] Further, the traversing module 30 is further configured to traverse all data through bin indices, and obtain coverage times, offset, and azimuth statistical data according to a statistical range increment, a start point, and an end point.

[0092] Further, the traversing module 30 is further configured to traverse offset data in each bin with data in the offset data file, calculate a number of data of the bin in a range to be sought, and increase a record on corresponding grid statistical data.

[0093] Further, the traversing module 30 is further configured to traverse offset and azimuth data files, and perform statistics on offset and azimuth information of each CMP point in the offset and azimuth data files according to offset and azimuth segmentation ranges defined by a rose diagram, establish a two-dimensional array, segment a first dimension according to offset steps, segment a second dimension according to azimuth steps, and perform data statistics.

[0094] The bin attribute statistical method comprises:

[0095] Step 01: Obtain bin template parameters and observation system rolling parameters.

[0096] Optionally, the parameters of the unit template include the number of receiver lines, the number of receiver points, the trace interval, the receiver line interval, the starting coordinates of the receiver points, the number of shot lines, the number of shot points, the shot point interval, the shot line interval, and the shot point coordinates.

[0097] Optionally, the rolling parameters of the observation system include the rolling azimuth, the inline direction rolling interval, the x-line direction rolling interval, the inline direction rolling number, the x-line direction rolling number, and the grid length and width.

[0098] Step 02: obtaining a unit template bin coverage number array based on the unit template parameters, wherein the unit template parameters include shot point coordinates and receiver point coordinates; and the obtaining of the unit template bin coverage number array based on the unit template parameters includes:

[0099] Step 021: obtaining a unit template coverage number based on the shot point coordinates and the receiver point coordinates.

[0100] Step 022: obtaining a unit template bin coverage number array based on the unit template coverage number.

[0101] Specifically, the unit template coverage number is calculated through the shot point coordinates and the receiver point coordinates in the unit template parameters, and a bin coverage number rolling template two-dimensional array is initialized according to the grid size to obtain the unit template bin coverage number array.

[0102] Step 03: obtaining an observation system coverage number array based on the unit template bin coverage number array, wherein the obtaining of the observation system coverage number array based on the unit template bin coverage number array includes rolling and superimposing the observation system rolling parameters in the inline direction and then in the x-line direction to obtain the observation system coverage number array.

[0103] Specifically, the observation system coverage number array is obtained by rolling and superimposing the unit template bin coverage number array obtained in step 02 in the inline direction and then in the x-line direction according to the rolling parameters.

[0104] Step 04: obtaining a bin index array based on the observation system coverage number array, wherein the bin index array is calculated based on the observation system coverage number array.

[0105] Step 05: merging the same cell coverage times based on the observation system coverage times and the cell index array to compress the coverage times file; wherein the merging the same cell coverage times based on the observation system coverage times and the cell index array to compress the coverage times file comprises merging the adjacent same cell template cell coverage times and modifying the coverage values of the cell index array corresponding to the merged cell template cell coverage times.

[0106] Specifically, according to the observation system coverage times and the cell index array obtained in steps 03 and 04, the adjacent same cell coverage times are merged, and the corresponding index array is modified to the coverage value. The compressed coverage times array and the cell index array are saved. For example, the file format in Table 1 can be used for saving.

[0107] Table 1:

[0108] 1 Number of cells in x direction 2 Number of cells in y direction 3 Starting x coordinate of cell 4 Starting y coordinate of cell 5 Size of cell in x direction 6 Size of cell in y direction 7 Azimuth angle of cell 8 1st data 9 2nd data 10 3rd data …… n nth data

[0109] Step 06: obtaining the offset and azimuth of the observation system based on the cell template parameters and the observation system rolling parameters. The cell template parameters include the shotpoint coordinates and the receiverpoint coordinates. The obtaining the offset and azimuth of the observation system based on the cell template parameters and the observation system rolling parameters comprises obtaining the offset and azimuth of the observation system based on the shotpoint coordinates and the receiverpoint coordinates; initializing the cell coverage times based on the grid size, and rolling and superimposing the offset and azimuth of the observation system in the inline direction and then in the x-line direction according to the observation system rolling parameters to obtain the offset and azimuth of the observation system.

[0110] Specifically, first, the cell template offset and azimuth are calculated according to the shotpoint and receiverpoint coordinates. Then, the cell coverage times are initialized based on the grid size, the template two-dimensional array is rolled and superimposed according to the rolling parameters in the inline direction and then in the x-line direction, and the offset and azimuth of the observation system are obtained and saved. The saving format can refer to Table 1.

[0111] Step 07: obtaining the coverage times, offset and azimuth statistical data by traversing all the data through the cell index. The obtaining the coverage times, offset and azimuth statistical data by traversing all the data through the cell index comprises traversing all the data through the cell index, and obtaining the coverage times, offset and azimuth statistical data according to the statistical range increment, the start point and the end point.

[0112] Specifically, according to the observation system data file obtained in step 04 and step 06, all data are traversed based on the bin index array, and the coverage times, offset, and azimuth statistical data are obtained by fast statistics according to the statistical range increment, start point, and end point, and are saved. The file format in Table 2 can be used for saving.

[0113] Table 2

[0114] 1 Number of statistical data 2 range increment dx ]] 3 Range start point x0 4 Range end point x1 5 1st data 6 2nd data 7 3rd data …… …… n nth data

[0115] Step 08: traversing the offset data in each bin with data in the offset data file to establish a grid chart statistical data file. The traversing the offset data in each bin with data in the offset data file to establish a grid chart statistical data file includes: traversing the offset data in each bin with data in the offset data file, calculating the number of data of the bin in the range to be sought, and increasing the record on the corresponding grid statistical data.

[0116] Specifically, the grid chart statistical data file is calculated through the index file. All data are traversed based on the bin index array, and the offset data in each bin with data in the offset data file are traversed; the number of data of the bin in the range to be sought is calculated, and the record on the corresponding grid statistical data is increased. A two-dimensional array is established to store the record, and the statistical data result is saved. The saving format can refer to Table 3.

[0117] Table 3

[0118] 1 Bottom grid number nx ]]> 2 Number of height grids ny 3 Base length increment dx 4 Height length increment dy 5 Base start point value x0 6 Height start point value y0 7 Gird[y0][x0] 8 Gird[y0][x0+dx] 9 Gird[y0][x0+2*dx] 10 Gird[y0][x0+3*dx] …… …… …… Gird[y0+dy][x0] …… …… …… Gird[y0+2*dy][x0] …… …… …… Gird[ny][ny]

[0119] Step 09: traversing the offset and azimuth data file to establish a two-dimensional array, segmenting different dimension data, and performing data statistics. The traversing the offset and azimuth data file to establish a two-dimensional array, segmenting different dimension data, and performing data statistics includes: traversing the offset and azimuth data file, and according to the offset and azimuth segmentation range defined by the rose chart, the offset and azimuth information of each CMP point is counted, a two-dimensional array is established, the first dimension is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics is performed.

[0120] Specifically, the rose diagram statistics file is calculated by the index file. The offset and azimuth information of each CMP point in the offset and azimuth data file is counted according to the offset and azimuth segmentation range defined by the rose diagram to establish a two-dimensional array; the first dimension of the two-dimensional data is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics are performed; and the statistical data results are saved. The saving format can refer to Table 4.

[0121] Table 4:

[0122] 1 x-direction grid number nx ]]> 2 Number of y direction grids ny 3 X direction step length dx 4 Y direction step length dy 5 X direction start point x0 6 Y direction start point y0 7 Range start point of offset distance 8 Range end point of offset distance 9 Azimuth angle range start point: 0 10 Azimuth angle range end point: 360° 11 Grid index (1, 1) 12 (1，2) …… …… …… (1，n) …… (2，1) …… …… …… (2，n) …… …… …… (m，1) …… …… …… (m, n)

[0123] Example 4

[0124] The embodiment provides a bin attribute statistics method, as shown in the following table, the bin attribute statistics method comprises the following steps: Figure 3

[0125] (1) Establishing a unit template

[0126] The parameters of the unit template include: the number of geophone lines, the number of geophone points, the trace interval, the geophone line interval, the geophone point starting coordinates, the number of shot lines, the number of shot points, the shot point interval, the shot line interval, and the shot point starting coordinates.

[0127] (2) Establishing rolling parameters of the observation system

[0128] The rolling parameters include: the rolling azimuth, the inline direction rolling distance, the x-line direction rolling distance, the inline direction rolling times, the x-line direction rolling times, and the grid length and width.

[0129] (3) Calculating the bin coverage times of the unit template

[0130] According to the unit template shot point and geophone point coordinates, the bin coverage times of the unit template are calculated, and the bin coverage times rolling template two-dimensional array is initialized according to the grid size.

[0131] (4) Calculating the observation system coverage times and the bin index file by using the rolling parameters

[0132] According to the bin coverage times array of the unit template obtained in (3), the observation system coverage times array is obtained by rolling and superimposing in the inline direction and then in the x-line direction according to the rolling parameters. The bin index array is calculated according to the observation system coverage times array.

[0133] (5) Merging the same bin coverage times and compressing the coverage times file

[0134] ​According to the observation system coverage times and the bin index array obtained in (4), adjacent same bin coverage times are combined, and the corresponding index array is modified to the coverage value.

[0135] (6) Calculate shot-receiver distance and azimuth file

[0136] First, according to the unit template shot point and receiver point coordinates, the unit template shot-receiver distance and azimuth are calculated, and the bin coverage times rolling template two-dimensional array is initialized according to the grid size. Rolling and stacking are performed in the inline direction and then in the x-line direction according to the rolling parameters, to obtain the observation system shot-receiver distance and azimuth, which are saved in the table one file format.

[0137] (7) Calculate coverage times, shot-receiver distance and azimuth statistical data file through index file

[0138] According to the observation system data files obtained in (4) and (6), all data are traversed through the bin index, and the coverage times, shot-receiver distance and azimuth statistical data are quickly calculated according to the statistical range increment, starting point and end point, and are saved in the table two file format.

[0139] (8) Calculate grid chart statistical data file through index file.

[0140] The shot-receiver distance data in each bin with data in the shot-receiver distance data file are traversed, the number of data of the bin in the range to be solved is calculated, and a record is added on the corresponding grid statistical data. A two-dimensional array is established to store the record, and the statistical data result is saved in the table two file format.

[0141] (9) Calculate rose chart statistical data file through index file

[0142] The shot-receiver distance and azimuth data in the shot-receiver distance and azimuth data file are traversed, the shot-receiver distance and azimuth information of each CMP point is counted according to the shot-receiver distance and azimuth segmentation range defined by the rose chart, a two-dimensional array is established, the first dimension is segmented according to the shot-receiver distance step, the second dimension is segmented according to the azimuth step, data counting is performed, and the statistical data result is saved in the table three file format.

[0143] The bin attribute statistical method provided by the embodiment of the application generates the bin attribute of the super large observation system through the unit template bin attribute rolling, without considering the actual layout of the observation system, and quickly calculates through the index file, and the repeated data is compressed, so that the technical personnel are relieved from the inconvenience of calculating each pair of shot-receiver points of the observation system, the calculation efficiency is improved, the storage space is saved, and the consumption of computer system resources in the bin attribute statistical process is greatly reduced.

[0144] Example 5

[0145] The embodiment provides an electronic device, which can be a mobile phone, a computer or a tablet computer, and the like, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the face element attribute statistical method according to the embodiment. It can be understood that the electronic device can further comprise an input / output (I / O) interface and a communication component.

[0146] The processor is configured to execute all or part of the steps of the face element attribute statistical method according to the embodiment. The memory is configured to store various types of data, which can include, for example, instructions of any application program or method in the electronic device, and application program related data.

[0147] The processor can be an Application Specific Integrated Cricuit (ASIC), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic elements, and is configured to execute the face element attribute statistical method according to the embodiment.

[0148] The memory in the embodiment can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a Static Random Access Memery (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0149] Example 6

[0150] The embodiment further provides a computer readable storage medium. The functional units in various embodiments of the application can be integrated into one processing unit, or each functional unit can exist alone physically, or two or more functional units can be integrated into one unit. When the functions are implemented in the form of software functional units and sold or used as independent products, the software functional units can be stored in a computer readable storage medium.

[0151] Based on the understanding, the technical solutions of the application essentially or the parts that contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the application.

[0152] The aforementioned storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an APP application store, and various media that can store program check codes, in which a computer program is stored, and the computer program is executed by a processor to implement the following method steps:

[0153] Step 01: Obtain unit template parameters and observation system rolling parameters.

[0154] Optionally, the parameters of the unit template include the number of detection lines, the number of detection points, the trace interval, the detection line interval, the starting coordinates of the detection points, the number of shot lines, the number of shot points, the shot point interval, the shot line interval, and the shot point coordinates.

[0155] Optionally, the observation system rolling parameters include a rolling azimuth, an inline direction rolling distance, an x-line direction rolling distance, an inline direction rolling number, an x-line direction rolling number, and a grid length and width.

[0156] Step 02: Obtain a unit template bin coverage number array based on the unit template parameters. The unit template parameters include shot point coordinates and detection point coordinates. The obtaining of the unit template bin coverage number array based on the unit template parameters includes:

[0157] Step 021: Obtain a unit template coverage number based on the shot point coordinates and the detection point coordinates.

[0158] Step 022: Obtain a unit template bin coverage number array based on the unit template coverage number.

[0159] Specifically, the number of unit template coverings is calculated by the shotpoint coordinates and the receiver coordinates in the unit template parameters, and the number of bin coverings is initialized according to the grid size to obtain a unit template bin covering number array.

[0160] Step 03: obtaining an observation system covering number array based on the unit template bin covering number array. The observation system covering number array is obtained by rolling and superimposing the observation system rolling parameters in the inline direction and then in the x-line direction.

[0161] Specifically, the observation system covering number array is obtained by rolling and superimposing the unit template bin covering number array obtained in step 02 in the inline direction and then in the x-line direction according to the rolling parameters.

[0162] Step 04: obtaining a bin index array based on the observation system covering number array. The bin index array is calculated based on the observation system covering number array.

[0163] Step 05: merging the same bin covering numbers based on the observation system covering number and the bin index array to compress the covering number file. The merging of the same bin covering numbers based on the observation system covering number and the bin index array to compress the covering number file includes merging adjacent same unit template bin covering numbers and modifying the covering values of the bin index array corresponding to the merged unit template bin covering numbers.

[0164] Specifically, the adjacent same bin covering numbers are merged according to the observation system covering number and the bin index array obtained in steps 03 and 04, and the corresponding index array is modified to the covering value. The compressed covering number array and the bin index array are saved. For example, the file format in Table 1 can be used for saving.

[0165] Table 1:

[0166] 1 Number of cells in x direction 2 Number of cells in y direction 3 Starting x coordinate of cell 4 Starting y coordinate of cell 5 Size of cell in x direction 6 Size of cell in y direction 7 Azimuth angle of cell 8 1st data 9 2nd data 10 3rd data …… n nth data

[0167] Step 06: obtaining the offset and azimuth of the observation system based on the unit template parameters and the observation system rolling parameters. The unit template parameters include the coordinates of the shot point and the coordinates of the receiver point. The obtaining of the offset and azimuth of the observation system based on the unit template parameters and the observation system rolling parameters includes: obtaining the offset and azimuth of the observation system based on the coordinates of the shot point and the coordinates of the receiver point; initializing the bin coverage times based on the grid size, and rolling and stacking the offset and azimuth of the observation system in the inline direction and then in the x-line direction according to the observation system rolling parameters to obtain the offset and azimuth of the observation system.

[0168] Specifically, first, the offset and azimuth of the unit template are calculated according to the coordinates of the shot point and the coordinates of the receiver point. Then, the rolling template two-dimensional array is rolled according to the grid size to initialize the bin coverage times, and is rolled and stacked in the inline direction and then in the x-line direction according to the rolling parameters, so as to obtain the offset and azimuth of the observation system and save them. The saving format can refer to Table 1.

[0169] Step 07: traversing all the data through the bin index to obtain the coverage times, offset and azimuth statistical data. The traversing of all the data through the bin index to obtain the coverage times, offset and azimuth statistical data includes: traversing all the data through the bin index, and quickly counting the coverage times, offset and azimuth statistical data according to the statistical range increment, the start point and the end point.

[0170] Specifically, according to the observation system data file obtained in steps 04 and 06, all the data are traversed through the bin index based on the bin index array, and the coverage times, offset and azimuth statistical data are quickly counted according to the statistical range increment, the start point and the end point, and are saved. The file format in Table 2 can be used for saving.

[0171] Table 2:

[0172] 1 Number of statistical data 2 range increment dx ]] 3 Range start point x0 4 Range end point x1 5 1st data 6 2nd data 7 3rd data …… …… n nth data

[0173] Step 08: traversing the offset data in each bin with data in the offset data file to establish a lattice graph statistical data file. The traversing of the offset data in each bin with data in the offset data file to establish a lattice graph statistical data file includes: traversing the offset data in each bin with data in the offset data file, calculating the number of data of the bin in the range to be solved, and increasing the record on the corresponding grid statistical data.

[0174] Specifically, the grid chart statistical data file is calculated through the index file. All data are traversed based on the bin index array, the offset data in each bin with data in the offset data file is traversed, the number of data in the bin in the range to be obtained is calculated, and a record is added on the corresponding grid statistical data. A two-dimensional array is established to store the record, and the statistical data result is saved. The saving format can refer to Table 3.

[0175] Table 3:

[0176] 1 Bottom grid number nx ]]> 2 Number of height grids ny 3 Base length increment dx 4 Height length increment dy 5 Base start point value x0 6 Height start point value y0 7 Gird[y0][x0] 8 Gird[y0][x0+dx] 9 Gird[y0][x0+2*dx] 10 Gird[y0][x0+3*dx] …… …… …… Gird[y0+dy][x0] …… …… …… Gird[y0+2*dy][x0] …… …… …… Gird[ny][ny]

[0177] Step 09: traversing the offset and azimuth data file, establishing a two-dimensional array, segmenting different dimension data, and performing data statistics. The traversing the offset and azimuth data file, establishing a two-dimensional array, segmenting different dimension data, and performing data statistics includes: traversing the offset and azimuth data file, and according to the offset and azimuth segmentation range defined by the rose chart, the offset and azimuth information of each CMP point is counted, a two-dimensional array is established, the first dimension is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics is performed.

[0178] Specifically, the rose chart statistical data file is calculated through the index file. The offset and azimuth data file is traversed, the offset and azimuth information of each CMP point is counted according to the offset and azimuth segmentation range defined by the rose chart, a two-dimensional array is established, the first dimension of the two-dimensional data is segmented according to the offset step, the second dimension is segmented according to the azimuth step, and data statistics is performed. The statistical data result is saved. The saving format can refer to Table 4.

[0179] Table 4:

[0180] 1 x-direction grid number nx ]]> 2 Number of y direction grids ny 3 X direction step length dx 4 Y direction step length dy 5 X direction start point x0 6 Y direction start point y0 Range start point of offset distance Range end point of offset distance Azimuth angle range start point: 0 Azimuth angle range end point: 360° Grid index (1, 1) (m, n) 7 Shot range start 8 Shot range end 9 Azimuth range start: 0 10 Azimuth range end: 360° 11 Grid index (1, 1) 12 (1，2) …… …… …… (1，n) …… (2，1) …… …… …… (2，n) …… …… …… (m，1) …… …… …… (m, n)

[0181] The bin attribute statistical method provided by the embodiment of the application has the following advantages:

[0182] (1) The application innovatively realizes the fast calculation and statistical method of the bin attribute of the super large observation system using the bin index file.

[0183] (2) The method of the application generates the bin attribute of the super large observation system through the calculation unit template bin attribute rolling, improves the speed of bin attribute statistics, and reduces the data storage amount.

[0184] (3) The method of the application stores the format by self-definition, and compresses the repeated data, thereby reducing the statistical data storage space.

[0185] Those skilled in the art can clearly understand the unit and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application. Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0186] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms. The units described as separate components can be or can not be physically separated, and the components shown as units can be or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0187] The basic principles of the present application are described above in combination with specific embodiments, but it should be pointed out that the advantages, advantages, effects and the like mentioned in the present application are only examples and not limitations, and these advantages, advantages, effects and the like cannot be considered as the must-have of each embodiment of the present application. In addition, the above-mentioned specific details are only for the purpose of example and for the purpose of understanding, and are not limited to the present application, and the above-mentioned details do not limit the present application to the must-use specific details.

[0188] The block diagrams of the devices, apparatuses, equipment, systems involved in the present application are only illustrative examples and are not intended to require or imply the connection, arrangement, configuration shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged and configured in any way.

[0189] It should also be noted that in the devices, equipment and methods of the present application, each component or step can be decomposed and / or recombined. These decompositions and / or recombination should be considered as equivalent solutions of the present application.

[0190] The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0191] In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless explicitly specifically limited otherwise. All directional indications (such as upper, lower, left, right, front, rear, top, bottom, etc.) are intended to facilitate the understanding of relative positions between components and movement conditions, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0192] In addition, reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is explicitly understood that the embodiments described herein can be combined with each other, implicitly and explicitly.

[0193] The above description is only the preferred embodiment of the application, but the protection scope of the application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims. The above description is only the preferred embodiment of the application, and is not intended to limit the application. Any modification, equivalent replacement, etc. made within the spirit and principles of the application should be covered within the protection scope of the application.

Claims

1. A bin attribute statistics method, implemented based on a super large observation system of index files, characterized in that, The method comprises the following steps: obtaining a unit template parameter and an observation system rolling parameter; obtaining a unit template bin coverage times array based on the unit template parameter; obtaining an observation system coverage times array based on the unit template bin coverage times array; obtaining a bin index array based on the observation system coverage times array; merging the same bin coverage times based on the observation system coverage times and the bin index array to compress the coverage times file; obtaining a shot-receiver distance and an azimuth angle of the observation system based on the unit template parameter and the observation system rolling parameter; traversing all data through the bin index to obtain coverage times, shot-receiver distance and azimuth angle statistical data; traversing the shot-receiver distance data in each bin with data in the shot-receiver distance data file to establish a lattice graph statistical data file; traversing the shot-receiver distance and azimuth angle data file to establish a two-dimensional array, segmenting different dimension data and performing data statistics; the step of merging the same bin coverage times based on the observation system coverage times and the bin index array to compress the coverage times file comprises the following steps: merging adjacent same unit template bin coverage times and modifying the coverage value of the bin index array corresponding to the merged unit template bin coverage times; the unit template parameter comprises shot point coordinates and receiver point coordinates; the step of obtaining the shot-receiver distance and the azimuth angle of the observation system based on the unit template parameter and the observation system rolling parameter comprises the following steps: obtaining the shot-receiver distance and the azimuth angle of the observation system based on the shot point coordinates and the receiver point coordinates; initializing bin coverage times based on grid size and rolling and superimposing the shot-receiver distance and the azimuth angle of the observation system in the inline direction and then in the x-line direction according to the observation system rolling parameter to obtain the shot-receiver distance and the azimuth angle of the observation system.

2. The bin properties statistics method of claim 1, wherein, the unit template parameter comprises shot point coordinates and receiver point coordinates; the step of obtaining the unit template bin coverage times array based on the unit template parameter comprises the following steps: obtaining unit template coverage times based on the shot point coordinates and the receiver point coordinates; obtaining the unit template bin coverage times array based on the unit template coverage times.

3. The bin properties statistics method of claim 1, wherein, the step of obtaining the observation system coverage times array based on the unit template bin coverage times array comprises the following step: rolling and superimposing the observation system rolling parameter in the inline direction and then in the x-line direction to obtain the observation system coverage times array.

4. The bin properties statistics method of claim 1, wherein, the step of traversing all data through the bin index to obtain coverage times, shot-receiver distance and azimuth angle statistical data comprises the following step: traversing all data through the bin index, and quickly obtaining coverage times, shot-receiver distance and azimuth angle statistical data according to a statistical range increment, a starting point and an ending point.

5. The bin properties statistics method of claim 1, wherein, the step of traversing the shot-receiver distance data in each bin with data in the shot-receiver distance data file to establish a lattice graph statistical data file comprises the following steps: traversing the shot-receiver distance data in each bin with data in the shot-receiver distance data file, and calculating the number of data of the bin in a range to be sought, and increasing a record on corresponding grid statistical data.

6. The bin properties statistics method of claim 1, wherein, The traversing the offset and azimuth data file, establishing a two-dimensional array, dividing different dimension data, and performing data statistics, including: traversing the offset and azimuth data file, and performing statistics on the offset and azimuth information of each CMP point in the offset and azimuth data file according to the offset and azimuth segmentation range defined by the rose diagram, establishing a two-dimensional array, the first dimension is segmented according to the offset step, and the second dimension is segmented according to the azimuth step, and performing data statistics.

7. A bin attribute statistics device, characterized by Comprise: The data acquisition module is used for acquiring the unit template parameter and the observation system rolling parameter; The data processing module is used for obtaining the unit template bin coverage times array based on the unit template parameter; obtaining the observation system coverage times array based on the unit template bin coverage times array; obtaining the bin index array based on the observation system coverage times array; merging the same bin coverage times based on the observation system coverage times and the bin index array, and compressing the coverage times file; obtaining the offset and azimuth of the observation system based on the unit template parameter and the observation system rolling parameter; The traversal module is used for traversing all the data through the bin index to obtain coverage times, offset and azimuth statistical data; and traversing the offset data in each bin with data in the offset data file to establish a lattice diagram statistical data file; Traversing the offset and azimuth data file, establishing a two-dimensional array, dividing different dimension data, and performing data statistics; The data processing module is also used for merging adjacent same unit template bin coverage times, and modifying the coverage values of the bin index array corresponding to the merged unit template bin coverage times; The unit template parameter comprises shot point coordinates and receiver point coordinates; and the data processing module is also used for obtaining the offset and azimuth of the observation system based on the shot point coordinates and the receiver point coordinates; initializing the bin coverage times based on the grid size, and rolling and superimposing the offset and azimuth of the observation system in the inline direction and then in the x-line direction according to the observation system rolling parameter, to obtain the offset and azimuth of the observation system.

8. A hyperspectral observation system of an index file, characterized by, The bin attribute statistics device in the above claim 7 is included; and the bin attribute statistics method in any one of the above claims 1-6 is used for bin attribute statistics.

9. An electronic device, comprising: The memory is used for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to realize the bin attribute statistics method in any one of the above claims 1-6.

10. A computer readable storage medium characterized by, The computer program is stored in the computer readable storage medium, and the computer program is executed by the processor to realize the bin attribute statistics method in any one of the above claims 1-6.

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