Data organization and preprocessing method, device and medium for node gather data

Abstract

The present application belongs to the technical field of petroleum seismic exploration, and specifically discloses a data organization and preprocessing method, equipment and medium for node gather data. The method disclosed by the present application comprises the following steps: firstly, acquiring node gather data; then, on the one hand, reorganizing and processing the node gather data of all nodes; on the other hand, partitioning according to the spatial distribution of the nodes; then, selecting a seed node, and processing the target core area data and the target ring area data of the seed node respectively to obtain superimposed data; then, extracting coarse flattening lines from the superimposed data; finally, flattening the target core area data and the target ring area data of all nodes in the spatial sub-regions. The present application can improve the continuity, regularity and consistency of the overall node gather data, and lay a good foundation for subsequent data processing. The present application can be widely applied to the processing of node gather data in petroleum seismic exploration.

Description

Technical Field

[0001] This invention belongs to the field of petroleum seismic exploration technology, specifically relating to a data organization and preprocessing method, equipment, and medium for nodal gather data. Background Technology

[0002] In the process of petroleum seismic exploration, nodal acquisition equipment has gradually become the mainstream equipment in seismic exploration due to its light weight, convenient deployment, low cost, and high construction efficiency, and is widely used in various petroleum exploration acquisition projects.

[0003] like Figure 1 As shown, in the process of seismic acquisition using nodal devices, the seismic data obtained from the nodal devices is gather data centered on the lookout point. Each data point in the gather corresponds to the response result of a seismic signal from a shot. Affected by factors such as shot location, excitation intensity, propagation path, and environmental noise, there are significant differences between gathers. The data trends between adjacent gathers show poor continuity and obvious jumps. Since the amount of shot data is usually small and the shot length is short during construction, and the distance between shot points is far, these factors further lead to problems such as large jumps, large differences, poor continuity, poor consistency, and lack of prominent regularity in adjacent gather data. It is very difficult to directly use nodal gather data for analysis and processing.

[0004] Due to the complexity and diversity of nodal gather data, some processing tasks, such as quality control and initial arrival picking, become very difficult. This is especially true for low signal-to-noise ratio data collected in complex regions with poor regularity, making it difficult to carry out and implement data analysis based on nodal gathers.

[0005] Currently, in the process of organizing node data, the data organization method of merging multiple adjacent shot lines into one shot line is adopted. The shot line length becomes longer, the data continuity becomes better, and the regularity becomes more apparent. However, the overall regularity and consistency of the data have not yet met the data quality requirements of applications such as quality control and initial arrival picking, and the processing effect based on this data is also unsatisfactory. Summary of the Invention

[0006] The purpose of this invention is to provide a data organization and preprocessing method for node gather data. Using this method can improve the overall continuity, regularity and consistency of node gather data, laying a good foundation for subsequent data processing.

[0007] The second objective of this invention is to provide a terminal device that, when executing its own program, can implement a data organization and preprocessing method for node gather data;

[0008] A third objective of this invention is to provide a computer-readable storage medium for storing a corresponding computer program for a data organization and preprocessing method for node gather data.

[0009] To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0010] A data organization and preprocessing method for node gather data includes the following steps:

[0011] S1. Obtain the gather data of the node;

[0012] S2. On the one hand, according to the set parameters, the gather data of all nodes are reorganized and the gather data of each node is divided into target core area data and target ring area data; on the other hand, the spatial distribution of the nodes in the observation system is partitioned and the space where the nodes are located is divided into several spatial sub-regions.

[0013] S3. Select a seed node in each spatial sub-region, and process the target center region data and target ring region data of the seed node respectively to obtain the superimposed data of several distance subgroups.

[0014] S4. Extract the coarse leveling line from the overlay data of each distance subgroup to obtain the coarse leveling line, coarse leveling position, and coarse leveling time of each distance subgroup.

[0015] S5. Level the data of the target center area and the target ring area of ​​all nodes in the spatial sub-region;

[0016] S6, End.

[0017] As a limitation, step S2, which involves reorganizing the gather data of all nodes into target core region data and target ring region data according to the set parameters, includes the following steps:

[0018] S21. Calculate the distance from the node to each lane and compare it with the set parameters. Lanes with distances less than the set parameters are assigned to the target center area, and all remaining lanes are assigned to the target ring area.

[0019] S22. Processing the data in the target center and target ring regions:

[0020] The target area data is obtained by sorting the track data according to the distance from the track to the node;

[0021] Calculate the spatial angle values ​​from the node to each track in the target ring area. Divide the target ring area into several angle groups based on the spatial angle values. Sort the track data of each angle group in the target ring area according to the distance from the track to the node to obtain the target ring area data.

[0022] As a further limitation, the steps in step S3 for processing the gather data after seed node recombination include:

[0023] The target area data of the seed node is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are superimposed to obtain superimposed data. At the same time, the trace data and group number information in each distance subgroup are recorded.

[0024] Define the data subgroups of the target ring area data of the seed node. Each data subgroup is one or more consecutive angle groups. Then, within each data subgroup, the target ring area data is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are overlaid to obtain overlaid data. At the same time, the trace data and group number information in each distance subgroup are recorded.

[0025] As a limitation on the overlapping process, the overlapping process steps include:

[0026] First, filter and normalize the data for each channel. Then, sum the absolute values ​​or squares of the filtered and normalized data for each channel. Finally, normalize the summed data for each channel.

[0027] As a second limitation, the steps for extracting the coarse leveling line in step S4 include:

[0028] S41. Select the superimposed data with a superimposed channel number greater than the set threshold from the superimposed data of each distance subgroup, and find the starting point of the peak region of each channel in each superimposed data.

[0029] S42. After removing outliers from the starting points, perform trend fitting on the remaining starting points.

[0030] S43. Based on the fitted function, calculate the coarse leveling line, coarse leveling position, and coarse leveling time for each distance subgroup through the grouping range.

[0031] As a third limitation, the step of leveling the bullseye region data of all nodes within the spatial sub-region in step S5 includes:

[0032] S51. Calculate the leveling time of each track in the target area data of all nodes in each spatial sub-region;

[0033] S52. Based on the calculated leveling time and the set time margin, move the track data of all nodes in the target area of ​​the spatial sub-region as a whole, and fill the blanks left by the movement with zeros.

[0034] As a further limitation, the formula for calculating the leveling time includes:

[0035]

[0036] In the formula, The calibration time for the i-th track in the bullseye region, where i is the track number and l is the calibration time for the i-th track. i vi is the distance from the i-th lane to the node, v0 is the velocity value used for target center area leveling, t0 is the time offset for target center area leveling, vi i v represents the fine-tuning speed and time, and v represents the coarse-tuning speed.

[0037] As a fourth limitation, the step of leveling the target ring region data of all nodes within the spatial sub-region in step S5 includes:

[0038] The data for each channel is shifted as a whole according to the coarse leveling time of each channel in each distance subgroup of the target ring area data. If the data length is insufficient, zero padding is performed.

[0039] The matching degree between different positions of adjacent tracks is calculated near the coarse leveling line of the spatial sub-region, and the track data is finely adjusted based on the deviation of the maximum matching position between adjacent tracks.

[0040] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a data organization and preprocessing method for node gather data as described above.

[0041] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the above-described data organization and preprocessing method for node gather data.

[0042] The present invention, by adopting the above-described technical solution, achieves the following technical advancements compared to existing technologies:

[0043] (1) By processing the node gather data, this invention improves the overall continuity, regularity and consistency of the node gather data, and provides a foundation for on-site processing links such as quality control and initial arrival picking based on node gather data;

[0044] (2) The present invention reorganizes the track data of all nodes, making the track data within the group smoother and more consistent;

[0045] (3) The present invention divides the space where the node is located into several sub-regions according to the node’s position in the observation system, which is beneficial for distributing the processing of the node.

[0046] (4) This invention uses seed nodes to extract coarse leveling lines, which provides a basis for calculating the leveling time of each path in all subsequent nodes and for leveling.

[0047] (5) When using seed nodes to extract coarse leveling lines, this invention removes outliers in the starting point of the peak region, thereby improving the accuracy of the fitting function.

[0048] (6) Based on the coarse leveling line of the seed node, the present invention performs leveling processing on all node data, which can greatly improve the leveling efficiency and the leveling accuracy.

[0049] This invention belongs to the field of petroleum seismic exploration technology. Using this method can improve the overall continuity, regularity and consistency of node gather data, laying a good foundation for subsequent data processing. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0051] In the attached diagram:

[0052] Figure 1 This is a graph of node gather data collected using existing technologies;

[0053] Figure 2 This is a flowchart of the processing in Embodiment 1 of the present invention;

[0054] Figure 3 To Figure 1 The node gather data graph obtained by the recombination process in Example 1 of the present invention;

[0055] Figure 4 This is a node gather diagram of a certain spatial sub-region obtained after dividing the spatial sub-regions in Example 1;

[0056] Figure 5 To Figure 4 The node gather data diagram after leveling using Example 1. Detailed Implementation

[0057] The preferred embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0058] Example 1: A method for organizing and preprocessing node gather data

[0059] like Figure 2 As shown, this embodiment includes the following steps performed sequentially:

[0060] S1. Obtain the gather data of the nodes collected by the node acquisition device.

[0061] S2. On the one hand, according to the set parameters, the gather data of all nodes are reorganized and the gather data of each node is divided into target core area data and target ring area data. On the other hand, the spatial distribution of the nodes in the observation system is partitioned and the space where the nodes are located is divided into several spatial sub-regions.

[0062] After collecting the gather data of the nodes, it is necessary to reassemble the gather data of all nodes. The steps for reassembling the gather data of each node include:

[0063] First, calculate the distance from the node to each lane and compare it with the set parameters. Lanes that are less than the set parameters are assigned to the bullseye area. After selecting the bullseye area, assign all remaining lanes to the target ring area.

[0064] The target area data is obtained by sorting the track data of the selected target area in ascending order according to the distance from the track to the node.

[0065] Calculate the spatial angle values ​​from each node to each track in the target ring region. Based on these spatial angle values, divide the target ring region into several angle groups. Sort the track data of each angle group in the target ring region in ascending order according to the distance from the track to the node to obtain the target ring region data. For example... Figure 3 As shown, reorganizing the node gathers can significantly improve the consistency of the gather data, resulting in good wavelet consistency and significant wave group regularity.

[0066] To facilitate post-processing of the nodes, after acquiring the node gather data, it is necessary to partition the area according to the spatial distribution of the nodes in the observation system. This involves dividing the node distribution into several equally sized rectangular regions of a specified length and width as spatial sub-regions. It should be noted that the method of dividing the spatial sub-regions is not unique. This embodiment uses the method of dividing into rectangles, which can be modified according to actual conditions, such as grouping several detector lines together and dividing them by a certain length.

[0067] S3. Select a seed node in each spatial sub-region, and process the target center region data and target ring region data of the seed node respectively to obtain the superimposed data of several distance subgroups.

[0068] After dividing the space into sub-regions, the nodes are processed on a per-sub-region basis. The node at the center of each sub-region is selected as the seed node. It should be noted that in this embodiment, the node at the center of the sub-region is selected as the seed node. This can be adjusted according to the actual situation, and can be changed to other places in the sub-region or randomly selected from within the sub-region.

[0069] After selecting the seed node for each spatial sub-region, since step S200 reorganized all nodes, dividing the gather data into target core region data and target ring region data, it is necessary to process the target core region data and target ring region data of the seed node separately:

[0070] The target area data of the seed node is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are overlaid to obtain the overlaid data. At the same time, the trace data and group number information in each distance subgroup are recorded.

[0071] When processing the target ring area data, the data subgroups of the target ring area data of the seed node must first be defined. The data subgroups are one or more consecutive angle groups. Then, within each data subgroup, the target ring area data is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are overlaid to obtain the overlaid data. At the same time, the trace data and group number information in each distance subgroup are recorded.

[0072] The above-mentioned overlay processing method is to first filter and normalize each channel of data, then add the absolute values ​​or squares of each channel of data after filtering and normalization, and finally normalize the added channel data.

[0073] S4. Extract the coarse leveling line from the overlay data of each distance subgroup to obtain the coarse leveling line, coarse leveling position, and coarse leveling time of each distance subgroup.

[0074] After acquiring the superimposed data of the target center and target ring regions, the threshold of each superimposed trace is calculated. Then, from the superimposed data of each distance subgroup, superimposed data with a number of superimposed traces greater than the set threshold is selected. The starting point of the peak region of each trace in the selected superimposed data with a number of superimposed traces greater than the threshold is found. These starting points are generally near the coarse leveling line and can reflect the overall trend of the coarse leveling line. However, outliers in the starting points need to be removed before trend fitting is performed on the remaining starting points. The function used for fitting can adopt a multinomial fitting method. Then, based on the fitted function, the coarse leveling line, coarse leveling position, and coarse leveling time of each distance subgroup can be calculated through the grouping range. The coarse leveling speed of the distance subgroup can be obtained by dividing the distance from the trace to the node in the distance subgroup by the coarse leveling time.

[0075] S5. Based on the extraction of the coarse leveling line of the seed node, level the data of the target center area and the target ring area of ​​all nodes in the spatial sub-region.

[0076] After obtaining the coarse leveling lines for each distance subgroup of the seed node, the data of all nodes can be leveled one by one, taking spatial sub-regions as units. During the leveling process, the data of the target core area and the target ring area of ​​each node need to be leveled separately.

[0077] When calibrating the bullseye region data of nodes, it is necessary to first calculate the calibration time of each trace in the bullseye region data of all nodes in each spatial sub-region. The calculation formula includes:

[0078]

[0079]

[0080] In the formula, The calibration time for the i-th track in the bullseye region, where i is the track number and l is the calibration time for the i-th track. i vi is the distance from the i-th lane to the node, v0 is the velocity value used for target center area leveling, t0 is the time offset for target center area leveling, vi i v represents the fine-tuning speed and time, and v represents the coarse-tuning speed.

[0081] Then, based on the calculated leveling time and the set time margin, the track data of all nodes in the target area within the spatial sub-region are moved as a whole, and the blanks left by the movement are filled with zeros.

[0082] When calibrating the target ring area data of a node, it is necessary to first move the data of each track as a whole according to the coarse calibration time of each track in each distance subgroup of the target ring area data. If the data length is insufficient, zero padding is performed. Then, the matching degree between different positions of adjacent tracks is calculated near the coarse calibration line of the spatial sub-region, and the track data is finely adjusted based on the deviation of the maximum matching position between adjacent tracks.

[0083] like Figure 4 and Figure 5 As shown, comparing the track data before and after leveling, it can be seen that the track data after leveling has stronger overall regularity, the similarity of adjacent group data of adjacent nodes is also very high, and the node data of adjacent positions of adjacent lines also shows good similarity and regularity.

[0084] S6, End.

[0085] In summary, this embodiment can transform the original short line array, numerous jumps and large variations in adjacent channel data, and poor continuity into long group data with strong consistency of adjacent channel data in each group, good wavelet consistency, and significant wave group regularity. Through leveling processing, the consistency of wave group characteristics of different channel data at different time positions can be guaranteed. The preprocessed data shows very good regularity in both horizontal and vertical dimensions, and the data consistency and characteristics are significantly improved. The good data quality laid a good foundation for subsequent data quality control, first arrival picking, and other processing.

[0086] Example 2: A terminal device

[0087] This embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a data organization and preprocessing method for node gather data according to Embodiment 1.

[0088] Example 3: A computer-readable storage medium

[0089] The computer-readable storage medium in this embodiment stores a computer program, which, when executed by a processor, is used to implement a data organization and preprocessing method for node gather data according to Embodiment 1.

[0090] The computer-readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the ASIC can reside in a user equipment. Of course, the processor and the computer-readable storage medium can also exist as discrete components in a communication device. Specifically, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk, etc. The storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

Claims

1. A data organization and preprocessing method for node gather data, characterized in that, Includes the following steps: S1. Obtain the gather data of the node; S2. On the one hand, according to the set parameters, the gather data of all nodes are reorganized and the gather data of each node is divided into target core area data and target ring area data; on the other hand, the spatial distribution of the nodes in the observation system is partitioned and the space where the nodes are located is divided into several spatial sub-regions. S3. Select a seed node in each spatial sub-region, and process the target center region data and target ring region data of the seed node respectively to obtain the superimposed data of several distance subgroups. S4. Extract the coarse leveling line from the overlay data of each distance subgroup to obtain the coarse leveling line, coarse leveling position, and coarse leveling time of each distance subgroup. S5. Level the target core area data and target ring area data of all nodes in the spatial sub-region respectively; The steps for calibrating the data in the target area include: Calculate the calibration time for each track in the bullseye data of all nodes within each spatial sub-region; Based on the calculated calibration time and the set time margin, the track data of all nodes in the target area of ​​the spatial sub-region are moved as a whole, and the blanks left by the movement are filled with zeros. The steps for calibrating the target ring region data include: The data of each channel is moved as a whole according to the coarse leveling time of each channel in each distance subgroup of the target ring area data. If the data length is insufficient, zero padding is performed. The matching degree between different positions of adjacent tracks is calculated near the coarse leveling line of the spatial sub-region, and the track data is finely adjusted based on the deviation of the maximum matching position between adjacent tracks. S6, End.

2. The data organization and preprocessing method for node gather data according to claim 1, characterized in that, Step S2 involves reorganizing the gather data of all nodes according to the set parameters, dividing it into target core region data and target ring region data. S21. Calculate the distance from the node to each lane and compare it with the set parameters. Lanes with distances less than the set parameters are assigned to the target center area, and all remaining lanes are assigned to the target ring area. S22. Process the trace data for the target center region and the target ring region respectively: The target area data is obtained by sorting the track data according to the distance from the track to the node; Calculate the spatial angle values ​​from the node to each track in the target ring area. Divide the target ring area into several angle groups based on the spatial angle values. Sort the track data of each angle group in the target ring area according to the distance from the track to the node to obtain the target ring area data.

3. The data organization and preprocessing method for node gather data according to claim 2, characterized in that, Step S3, which involves processing the target center region data and target ring region data of the seed node, includes the following steps: The target area data of the seed node is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are superimposed to obtain superimposed data. At the same time, the trace data and group number information in each distance subgroup are recorded. Define the data subgroups of the target ring area data of the seed node. Each data subgroup is one or more consecutive angle groups. Then, within each data subgroup, the target ring area data is divided into several distance subgroups according to the set distance interval. The trace data in the distance subgroups are overlaid to obtain overlaid data. At the same time, the trace data and group number information in each distance subgroup are recorded.

4. The data organization and preprocessing method for node gather data according to claim 3, characterized in that, The processing steps of the overlapping process include: First, filter and normalize the data for each channel. Then, sum the absolute values ​​or squares of the filtered and normalized data for each channel. Finally, normalize the summed data for each channel.

5. The data organization and preprocessing method for node gather data according to claim 1, characterized in that, The steps for extracting the coarse leveling line in step S4 include: S41. Select the superimposed data with a superimposed channel number greater than the set threshold from the superimposed data of each distance subgroup, and find the starting point of the peak region of each channel in each superimposed data. S42. After removing outliers from the starting points, perform trend fitting on the remaining starting points. S43. Based on the fitted function, calculate the coarse leveling line, coarse leveling position, and coarse leveling time for each distance subgroup through the grouping range.

6. The data organization and preprocessing method for node gather data according to claim 1, characterized in that, The formula for calculating the leveling time includes: In the formula, For the target area The calibration time for the data For the sequence number of the Dao, For the first The distance from the path to the node. The velocity value used for leveling the target area. The time offset for leveling the target area. To finely calibrate the speed and time, For coarse leveling speed.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a data organization and preprocessing method for node gather data as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement a data organization and preprocessing method for node gather data as described in any one of claims 1 to 6.

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