Seismic data trace gathering processing method, device and medium based on spark

Abstract

The application discloses a kind of based on Spark's seismic data extraction gather processing method, equipment and medium.Method includes: inputting seismic gather data in distributed file system, the partitioning of input seismic gather data;According to the way of partitioning, construct the shot gather RDD of seismic gather data;Shot gather RDD is converted to trace data RDD;According to the key value of each element in trace data RDD, construct key value RDD;According to the key value sorting mode of target gather, reduce and sort processing is carried out to key value RDD, obtains new key value RDD;According to new key value RDD, extract RDD partition model, based on the repartitioning of trace data RDD in partition model;To the partition data of repartitioning parallel processing, the trace data of same gather in each partition is reduced, and according to the trace sorting rule inside gather, trace is sorted;After completing the processing of all partitions, the new RDD data of target gather type is obtained.The processing efficiency of extraction gather is improved, and storage space is saved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of seismic exploration data processing, and more particularly relates to a seismic data gather extraction processing method, device and medium based on Spark. BACKGROUND

[0002] In seismic exploration, the gather types of seismic data are various, including common shotpoint gather, common receiver gather, common midpoint gather and common offset gather. In the process of seismic data processing, different gather types need to be converted multiple times. The process of extracting one gather type into another gather type is called gather extraction processing.

[0003] There are two widely used gather extraction processing methods. One is to extract the gather data according to the new data arrangement mode to form a new gather data body. This gather extraction method will produce multiple sets of seismic data, which requires a large amount of storage space. With the increase of exploration scale, the data volume increases exponentially, and multiple data types are stored independently, which seriously wastes storage space. The second method is to keep the seismic data body in one type, and only establish a data index for the seismic data according to the new type. Through the reordering of the data index, different types of data can be obtained. This gather extraction processing method can greatly save storage space, but since the data type itself does not change, the discontinuous reading of data reduces the data processing efficiency when parallel processing is performed, which is difficult to meet the efficiency requirements of large-scale data processing. SUMMARY

[0004] The purpose of the present application is to provide a seismic data gather extraction processing method, device and medium based on Spark, which can improve the storage space utilization rate and computing efficiency of the gather extraction process.

[0005] In a first aspect, the present application provides a seismic data gather extraction processing method based on Spark, comprising:

[0006] Step S1: inputting seismic gather data into a distributed file system, partitioning the input seismic gather data, and each partition corresponding to a data block;

[0007] Step S2: constructing a shot gather RDD (abstract resilient distributed dataset) of the seismic gather data according to the partitioning mode, the elements in the shot gather RDD being shot gathers, each element corresponding to a key-value pair of a shot gather, the key in the key-value pair being a shot number, and the value being all trace data corresponding to the shot number;

[0008] Step S3: converting the shot set RDD into a trace data RDD, elements in the trace data RDD being trace data, each element corresponding to a new key-value pair, the key in the new key-value pair being a control parameter of the extracted target trace set type, and the value being a trace data body;

[0009] Step S4: constructing a key value RDD according to the key value of each element in the trace data RDD;

[0010] Step S5: performing reduction and sorting processing on the key value RDD according to the key value sorting mode of the target trace set, reducing elements with the same key value in the same trace set, and obtaining a new key value RDD;

[0011] Step S6: extracting an RDD partition model according to the new key value RDD, re-partitioning the trace data RDD based on the partition model, and merging trace data of the same trace set in the same partition according to the organization form of the target trace set;

[0012] Step S7: performing parallel processing on the re-partitioned partition data, reducing trace data of the same trace set in each partition, and sorting traces according to the trace sorting rule inside the trace set; after processing all the partitions, obtaining new RDD data of the target trace set type.

[0013] Optionally, the step S1 comprises:

[0014] Importing seismic data requiring trace set extraction into an HDFS distributed file system;

[0015] Performing re-partitioning processing on the seismic data loaded into the HDFS distributed file system, and storing the re-partitioned seismic data in the form of data blocks, trace set data in each data block being complete, and each trace set data not being allocated in two partitions.

[0016] Optionally, the step S2 comprises:

[0017] Extracting trace set numbers and trace data according to the trace set type of the input seismic data, wherein the trace set numbers are shot numbers, and the trace data corresponding to the trace set is all trace data of the same shot number;

[0018] Establishing a key-value pair taking the shot number as the key and all trace data corresponding to the shot number as the value;

[0019] Constructing a shot set RDD according to the key-value pair, each element in the shot set RDD corresponding to a key-value pair of a shot set.

[0020] Optionally, the step S3 comprises:

[0021] The trace data RDD is converted by using a Flatmap conversion function, to generate the trace data RDD, wherein the Flatmap processing is parallel processing, and the parallel granularity corresponds to the number of partitions.

[0022] An element of the trace data RDD is a data structure corresponding to a key-value pair of a trace data.

[0023] Optionally, the step S4 comprises:

[0024] The key value of each element in the trace data RDD is extracted, and the trace data value is replaced by a null value to form a new key-value pair, to construct the key value RDD.

[0025] Optionally, the step S5 comprises:

[0026] The key value RDD is reordered according to a new trace set key sorting mode;

[0027] The elements with the same key value are reduced together by using a reduceByKey conversion function, to form a key value trace set model, and a new key value RDD is formed.

[0028] Optionally, the step S7 comprises:

[0029] The repartitioned trace data RDD is obtained, and the partition data is processed in parallel, the trace data of the same trace set in each partition is reduced, and the trace is sorted according to the trace sorting rule in the trace set;

[0030] After the processing of all partitions is completed, a new RDD data of a target trace set type is obtained.

[0031] Optionally, after the step S7, the step S8 is further included:

[0032] When the new RDD data is transmitted to the next processing:

[0033] If output is required, the RDD is directly output to obtain the target trace set data;

[0034] If further processing is required, the new RDD is directly transmitted to a lower processing module for processing.

[0035] In a second aspect, the present application provides an electronic device, which comprises:

[0036] at least one processor; and

[0037] a memory in communication connection with the at least one processor; wherein

[0038] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the seismic data gather processing method of the first aspect.

[0039] In a third aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the seismic data gather processing method of the first aspect.

[0040] The present application has the following beneficial effects:

[0041] The present application utilizes Spark parallel technology to perform parallel acceleration on seismic data gather processing, and obtain efficient gather processing, first partitions input seismic gather data, constructs an abstract resilient distributed dataset (RDD) according to the partition, then restructures the key-value pairs of elements of the RDD according to the gather type of the gather, then converts the RDD into a new RDD with the gather as the basic element according to the new key-value pairs, and performs reduction processing on the gathers in the new RDD according to the same key-value, obtains the data arrangement mode of the new gather, finally outputs the new gather to complete the gather processing, the seismic data gather parallel computing method based on Spark parallel technology realizes efficient gather processing of large-scale seismic data, avoids the storage requirement of intermediate data, reduces the gather processing cost of data, and improves the economic benefit.

[0042] The system of the present application has other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent detailed description incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0043] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the figures, and wherein:

[0044] Figure 1 A step diagram of a seismic data gather processing method based on Spark according to the present application is shown.

[0045] Figure 2 A gather before gather arrangement mode diagram in a seismic data gather processing method based on Spark according to an embodiment of the present application is shown.

[0046] Figure 3 A schematic diagram showing an arrangement of traces in a gather after extracting a trace gather in a Spark-based seismic data trace gather processing method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0047] The present invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention may be implemented in various forms and is not limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present invention more thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0048] Example 1

[0049] Figure 1 A step diagram of a Spark-based seismic data gather processing method according to the present invention is shown.

[0050] like Figure 1 As shown, a Spark-based seismic data gather processing method includes:

[0051] Step S101: inputting seismic gather data into a distributed file system, partitioning the input seismic gather data, and each partition corresponds to a data block;

[0052] This step specifically includes:

[0053] Import the seismic data that need to be extracted into the HDFS distributed file system;

[0054] The seismic data loaded into the HDFS distributed file system is repartitioned. The repartitioned seismic data is stored in the form of data blocks. The gather data in each data block is complete, and each gather data is not distributed in two partitions.

[0055] Specifically, this step is the seismic data partitioning process:

[0056] Sparkpark parallel processing first determines the parallel granularity of data, that is, the partition strategy of the processed data. The data storage system of Spark parallel data processing is HDFS distributed file system, and the data is stored in different locations of the cluster in a certain data amount, usually 128MB or 512MB small data blocks. The format of the seismic data is stored by trace, and each trace is a standard data format. The trace data is the smallest unit of seismic data and cannot be divided. Above the seismic trace, according to the organization form of the seismic data, a plurality of traces form a trace gather, and different trace gather organization methods represent different meanings, corresponding to different processing methods and effects. According to the storage characteristics of the seismic data, the block size of the HDFS storage and the non-block partition strategy of the seismic trace gather are used to perform parallel block division on the seismic data. After cutting, the size of each data does not exceed the data block capacity of the HDFS file system, and at the same time, each trace gather data is not cut into more than two data blocks.

[0057] Step S102: Constructing a shot gather RDD of the trace gather data according to the partition method, the elements in the shot gather RDD are shot gathers, and each element corresponds to a key-value pair of a shot gather, the key in the key-value pair is a shot number, and the value is all trace data corresponding to the shot number;

[0058] This step specifically includes:

[0059] According to the trace gather type of the input seismic data, the trace gather number and trace data are extracted, wherein the trace gather number is a shot number, and the trace data corresponding to the trace gather is all trace data of the same shot number;

[0060] Taking the shot number as the key and all trace data corresponding to the shot number as the value, a key-value pair is established;

[0061] According to the key-value pair, a shot gather RDD is constructed, and each element in the shot gather RDD corresponds to a key-value pair of a shot gather.

[0062] Specifically, this step is the construction process of the RDD data model:

[0063] According to the trace gather type of the input seismic data, the trace gather number and trace data are extracted. Taking a common shot gather as an example, the trace gather number is a shot number, and the trace data corresponding to the trace gather is all trace data of the same shot number. Taking the shot number as the key (key) and all trace data corresponding to the shot number as the value (value), a key-value pair is established, that is, a key-value pair. The key is the shot number, and the value is the seismic trace data. According to the key-value pair, a shot gather RDD is constructed. Each element in the shot gather RDD corresponds to a key-value pair of a shot gather.

[0064] Step S103: converting the shot gather RDD into a trace data RDD, the elements in the trace data RDD being trace data, each element corresponding to a new key-value pair, the key in the new key-value pair being a control parameter of the extracted target trace gather type, and the value being a trace data body;

[0065] This step specifically includes:

[0066] The shot gather RDD is converted using a Flatmap conversion function to generate a trace data RDD, wherein the Flatmap processing process is parallel processing, and the parallel granularity corresponds to the number of partitions;

[0067] The elements of the trace data RDD are a data structure corresponding to a key-value pair of one trace data.

[0068] Specifically, this step is a reconstruction process based on the RDD of trace data:

[0069] The minimum indivisible unit of the data trace gather is trace data, and the trace gather is a rearrangement and combination of the trace data. After obtaining the seismic data trace gather RDD, the Flatmap conversion function is used to extract the key required for the target trace gather ordering from the trace header for each trace data in each key-value pair of the RDD. For example, if the target trace gather is a common midpoint trace gather (CMP trace gather), the CMP trace gather is ordered according to the inline number, xline number and offset distance of each trace, then the inline, xline and offset values of each trace are extracted to form a new key, and the trace data body corresponding to the key is taken as the value to form a new trace data RDD with traces as elements.

[0070] Step S104: constructing a key value RDD according to the key value of each element in the trace data RDD;

[0071] This step specifically includes:

[0072] The key value of each element in the trace data RDD is extracted, the trace data value is replaced with a null value, a new key-value pair is formed, a key value RDD is constructed, the new RDD value stores the key value of each trace, and the data volume of the RDD is reduced.

[0073] Step S105: reducing and sorting the key value RDD according to the key value ordering mode of the target trace gather, reducing the elements with the same key value in the same trace gather, and obtaining a new key value RDD;

[0074] This step specifically includes:

[0075] The key value RDD is reordered according to the key ordering mode of the new trace gather;

[0076] The same key value elements are reduced together by using a reduceByKey conversion function to form a key value gather model, and a new key value RDD is formed.

[0077] Specifically, steps S104-S105 are a key value based data sorting process:

[0078] The data volume before and after the seismic data gather is very large, the number of traces is large, and the trace data RDD with trace as an element is large in scale, so direct RDD conversion processing is low in efficiency. Therefore, the present application first extracts the key value of each element of the RDD, assigns the value of the RDD element to empty, forms a new key value based RDD, and then reduces and sorts the new RDD according to the key value sorting mode of the target gather, reduces the elements with the same key value in the same gather, and sorts the key values from large to small to obtain the key value RDD.

[0079] Step S106: According to the new key value RDD, extract the RDD partition model, re-partition the trace data RDD based on the partition model, and merge the trace data of the same gather in the same partition according to the organization form of the target gather.

[0080] Step S107: Parallel processing of the re-partitioned partition data, reducing the trace data of the same gather in each partition, and sorting the traces according to the trace sorting rule inside the gather; after processing all the partitions, new RDD data of the target gather type is obtained.

[0081] This step specifically includes:

[0082] Obtain the re-partitioned trace data RDD, parallel process the partition data, reduce the trace data of the same gather in each partition, and sort the traces according to the trace sorting rule inside the gather;

[0083] After processing all the partitions, new RDD data of the target gather type is obtained.

[0084] Specifically, steps S106-S107 are a parallel data by trace conversion process:

[0085] After obtaining the gather reduction RDD of the key value, extract the RDD partition model, re-partition the data body RDD according to the partition model. After obtaining the re-partitioned data, parallel process each partition, extract the corresponding key value seismic trace data from the seismic data according to the key value in each partition, collect the seismic trace data bodies with the same key value together to form a gather data. After parallel processing each element of the seismic trace data RDD, complete the reordering of the seismic trace data, and realize the extraction of the target gather.

[0086] After step S107, step S108 is further included:

[0087] When the new RDD data is passed to the next step processing, the following steps are included:

[0088] If output is needed, the RDD is directly outputted to obtain the target gather data;

[0089] If further processing is needed, the new RDD is directly passed to the next processing module for processing.

[0090] Specifically, the step (6) is a new gather data passing method:

[0091] After the regulation processing of the new gather data is completed, the element description of the new gather data RDD describes the data organization form of the target gather. According to the processing method of the new gather type, the RDD corresponding to the new gather type can be passed to the next processing module for processing. After the next module obtains the RDD of the new gather, the corresponding processing can be directly performed according to the new gather type. Through the RDD passing, the data body corresponding to the new gather type does not need to be outputted, and thus a large amount of data storage space can be saved.

[0092] The method of the application is further described through specific examples.

[0093] Step S201, seismic data re-partitioning:

[0094] Firstly, the seismic data needed to be extracted gather is imported into the distributed file system HDFS. The distributed file system HDFS is distributed in the form of data blocks, and the size of each data block is set to 512BM. According to the characteristics of the seismic data, the basic unit of the seismic data is the trace format data, and according to different organization forms, the multi-trace data is combined into a gather. In the seismic data processing, the gather is generally taken as the basic unit for processing. According to the characteristics of the seismic data and the processing, the seismic data loaded into the HDFS distributed file system is re-partitioned. After the re-partitioning, each data block is smaller than 512BM, and the gather data in each partition is complete, and each gather data is ensured not to be distributed in two partitions.

[0095] Step S202, seismic data RDD construction:

[0096] The seismic data imported into the HDFS is constructed as an RDD in a repartitioning manner. According to the gather type of the input seismic data, the gather number and trace data are extracted. Taking a common shot gather as an example, the gather number is the shot number, and the gather corresponding trace data is all the trace data of the same shot number. A key-value pair is established by taking the shot number as the key and all the trace data corresponding to the shot number as the value, that is, a key-value pair. The key is the shot number, and the value is the seismic trace data. An RDD is constructed according to the key-value pair. Each element in the RDD corresponds to a key-value pair of a shot gather. The parallel division of the RDD is based on the repartitioning number of the seismic data in step S201, and each partition corresponds to a parallel data block.

[0097] Step S203, RDD conversion:

[0098] On the basis of the RDD constructed in step S202, the conversion function Flatmap of the RDD is used to perform a conversion operation on the input data RDD to generate a new RDD. The elements of the new RDD are converted from the original shot gather key-value pair to a data structure of one key-value pair per trace. The new key is the control parameter of the extracted target gather type. For example, if the extracted target gather type is a common midpoint gather (CMP gather), the gather can be distinguished by the vertical line number (Inline number) and the horizontal line number (Xline number) of the grid of the seismic data. The key of the element of the new RDD after conversion is the Inline number and the Xline number corresponding to each trace, and the value is the trace data body. The Flatmap processing process is parallel processing, and the parallel granularity is determined by the number of data blocks in step S201.

[0099] Step S204, key-value RDD construction:

[0100] The element of the RDD after conversion in step S203 is a trace, and the key value of each element of the trace data RDD is extracted to replace the trace data value with a null value to form a new key-value pair and construct a new RDD with the key as the main content. The value of the new RDD stores the key value of each trace, thereby reducing the data volume of the RDD.

[0101] Step S205, key-value RDD reduction:

[0102] The RDD generated in step S204 is reordered according to the new gather key sorting mode. The reduceByKey conversion function is used to reduce the elements with the same key value together to form a gather model of the key value and form a new RDD.

[0103] Step S206, seismic data RDD repartitioning:

[0104] The new RDD obtained in step S205 extracts a partition model of the RDD data. According to the partition model, the trace data RDD obtained in step S203 is re-partitioned, and the trace data of the same trace gather is merged in the same partition according to the organization form of the target trace gather.

[0105] In step S207, the seismic data RDD is processed:

[0106] In step S206, the re-partitioned RDD is obtained, the partition data is processed in parallel, the trace data of the same trace gather in each partition is reduced, and the traces are sorted according to the trace sorting rule inside the trace gather. After completing the processing of all partitions, the new RDD data of the target trace gather type is obtained.

[0107] In step S208, the RDD is transmitted:

[0108] The RDD data obtained in step S207 is transmitted to the next processing, if output is required, the RDD is directly output to obtain the target trace gather data. If further processing is required, the RDD data can not be output, and is directly transmitted to the next processing module for processing. The intermediate data of the trace gather can be avoided to occupy a large amount of storage space.

[0109] The arrangement mode of the traces in the trace gather before and after the trace gather is shown in Figure 2 and Figure 3 .

[0110] In summary, the method of the present application constructs the seismic data required for the trace gather into the basic model RDD data model of Spark parallel, and uses the parallel processing method of the RDD data model to quickly complete the trace gather processing of massive seismic data, improves the trace gather processing efficiency, avoids the storage of intermediate data, saves the storage space, and provides an efficient trace gather processing method for massive seismic data processing.

[0111] Embodiment 2

[0112] The electronic device includes:

[0113] at least one processor; and

[0114] a memory connected in communication with the at least one processor; wherein

[0115] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the seismic data trace gather processing method described in embodiment 1.

[0116] An electronic device according to an embodiment of the present disclosure includes a memory and a processor. Specifically, the memory can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM), cache memory, and / or the like. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, and / or the like.

[0117] The processor can be a central processing unit (CPU) or other form of processing unit that has data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is configured to execute the computer-readable instructions stored in the memory.

[0118] Those skilled in the art will understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the present disclosure.

[0119] Detailed descriptions of the embodiments can refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0120] Embodiment 3

[0121] The embodiment provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the seismic data trace gather processing method described in embodiment 1.

[0122] According to an embodiment of the present disclosure, a computer-readable storage medium has non-transitory computer-readable instructions stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the method of each embodiment of the present disclosure described above are performed.

[0123] The computer-readable storage medium described above includes, but is not limited to, an optical storage medium (for example, CD-ROM and DVD), a magneto-optical storage medium (for example, MO), a magnetic storage medium (for example, magnetic tape or a mobile hard disk), a medium with a built-in rewritable non-volatile memory (for example, a memory card), and a medium with a built-in ROM (for example, a ROM cartridge).

[0124] Having described various embodiments of the application, it is to be understood that the above description is meant to be illustrative only, and that many modifications and variations of the embodiments are possible without departing from the scope and spirit of the described embodiments. Many modifications and variations of the described embodiments are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the described embodiments can be practiced otherwise than as specifically described.

Claims

1. A Spark-based seismic data gather processing method, characterized in that: include: Step S1: input the seismic gather data into a distributed file system, partition the input seismic gather data, and each partition corresponds to a data block; Step S2: constructing a shot gather RDD of the seismic gather data in a partitioning manner, wherein the elements in the shot gather RDD are shot gathers, each element corresponds to a key-value pair of a shot gather, the key in the key-value pair is the shot number, and the value is all trace data corresponding to the shot number; Step S3: converting the shot gather RDD into a trace data RDD, wherein the elements in the trace data RDD are trace data, each element corresponds to a new key-value pair, the key in the new key-value pair is the control parameter of the extracted target trace gather type, and the value is the trace data body; Step S4: constructing a key value RDD according to the key value of each element in the track data RDD, including: extracting the key value of each element in the track data RDD, replacing the track data value with a null value to form a new key-value pair, and constructing the key value RDD; Step S5: reducing and sorting the key value RDD according to the key value sorting method of the target gather, reducing the elements with the same key value to the same gather, and obtaining a new key value RDD; Step S6: extracting an RDD partition model according to the new key value RDD, repartitioning the track data RDD based on the partition model, and merging the track data of the same track gather into the same partition according to the organizational form of the target track gather; Step S7: Process the partitioned data in parallel after repartitioning, reduce the trace data of the same trace set in each partition, and sort the traces according to the trace sorting rules within the trace set; after completing the processing of all partitions, obtain new RDD data of the target trace set type.

2. The seismic data gather processing method according to claim 1, characterized in that: The step S1 comprises: Import the seismic data that need to be extracted into the HDFS distributed file system; The seismic data loaded into the HDFS distributed file system is repartitioned. The repartitioned seismic data is stored in the form of data blocks. The gather data in each data block is complete, and each gather data is not distributed in two partitions.

3. The seismic data gather processing method according to claim 1, characterized in that: The step S2 comprises: According to the gather type of the input seismic data, the gather number and the trace data are extracted, wherein the gather number is the shot number, and the trace data corresponding to the gather are all the trace data of the same shot number; Use the gun number as the key and all the data corresponding to the gun number as the value to create a key-value pair; Construct a gun collection RDD based on the key-value pair. Each element in the gun collection RDD corresponds to a key-value pair of a gun collection.

4. The seismic data gather processing method according to claim 1, characterized in that: The step S3 comprises: Using the Flatmap conversion function, the shot gather RDD is converted to generate the track data RDD, wherein the Flatmap processing process is parallel processing, and the parallel granularity corresponds to the number of partitions; An element of the track data RDD is a data structure in which one track data corresponds to one key-value pair.

5. The seismic data gather processing method according to claim 1, characterized in that: The step S5 comprises: Reorder the key value RDD according to the keyword sorting method of the new gather; The reduceByKey transformation function is used to reduce the elements with the same key value together to form a gather model of the key value and form a new key value RDD.

6. The seismic data gather processing method according to claim 1, characterized in that: The step S7 comprises: Get the repartitioned track data RDD, process the partitioned data in parallel, reduce the track data of the same track set in each partition, and sort the tracks according to the track sorting rules within the track set; After processing all partitions, obtain new RDD data of the target gather type.

7. The seismic data gather processing method according to claim 1, characterized in that: After step S7, the method further includes step S8: When passing the new RDD data to the next step of processing: If output is required, directly output the new RDD data to obtain the target gather data; If further processing is required, the new RDD data is directly passed to the lower-level processing module for processing.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions to be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the seismic data extraction and processing method according to any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the seismic data gather processing method according to any one of claims 1 to 7.

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