Offset imaging method and device, electronic equipment and storage medium

Abstract

The invention provides a migration imaging method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining the number of calculation units in a calculation cluster and the total number of to-be-migrated shot gathers of seismic data; based on the number and the total number of the to-be-offset shot gathers, distributing the number of the to-be-offset shot gathers of each calculation unit, so that each calculation unit performs offset operation on the to-be-offset shot gathers corresponding to the number of the to-be-offset shot gathers to obtain an offset result; and superposing and sorting the migration results of the calculation units to obtain the migration data volume of the seismic data, so that load balancing of calculation tasks of the calculation units can be realized, and the calculation efficiency of migration imaging can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of oil exploration and development, and in particular to a migration imaging method, device, electronic equipment and storage medium. Background Art

[0002] Reverse time migration is based on the two-way wave equation. The theoretical method is perfect and the solution is accurate. It can image complex media with high precision. However, due to the huge storage and computing capacity, this technology will always face certain challenges in the large-scale promotion and application of actual production. Facing this problem, many geophysicists are committed to improving the computing efficiency of reverse time migration technology. The main technical solution is to optimize the algorithm itself and adopt parallel acceleration at the process level and thread level. However, with the development of high-density acquisition in recent years, the amount of collected seismic data has increased sharply, which has brought huge challenges in computing efficiency and data storage IO process. Traditional reverse time migration imaging technology based on high-performance computing mainly adopts the MPI parallel framework under centralized storage. However, affected by the efficiency of data storage and data IO, the computing efficiency of the centralized storage parallel algorithm faces the risk of serious decline as the amount of seismic data increases. In addition, the parallel processes based on the MPI parallel algorithm need to communicate with each other. The communication time itself will affect the computing efficiency. Moreover, the interdependent characteristics of the processes must ensure that each process runs successfully during the entire operation, otherwise the entire operation will fail. Although the breakpoint protection function can be used to achieve job continuation, it is difficult to achieve dynamic management of the job by relying on manual retransmission, which further increases the time cost of production operations. Summary of the invention

[0003] In response to the above problems, the present application provides a method, device, electronic device and storage medium for offset imaging, which can improve the calculation efficiency of offset imaging.

[0004] The present application provides a migration imaging method, comprising:

[0005] Obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data;

[0006] Allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result;

[0007] The migration results of each calculation unit are superimposed and sorted to obtain the migration data volume of the seismic data.

[0008] In some embodiments, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers includes:

[0009] The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0010] In some embodiments, the step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes:

[0011] The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

[0012] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data includes:

[0013] Acquisition of seismic data;

[0014] The seismic data are divided into common shot gather data, and the total number of pseudo-migrated shot gathers is obtained based on the index numbers of the common shot gather data.

[0015] In some embodiments, the computing cluster is a computing cluster based on the Spark parallel framework, the computing units are GPUs, and each GPU performs parallel processing.

[0016] The present application provides an offset imaging device, including:

[0017] An acquisition module is used to obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data;

[0018] an allocation module, configured to allocate the number of quasi-shifted shot gathers to each computing unit based on the number and the total number of quasi-shifted shot gathers, so that each computing unit performs a shift operation on the quasi-shifted shot gathers corresponding to the number of quasi-shifted shot gathers to obtain a shift result;

[0019] The calculation module is used to superimpose and sort the migration results of each calculation unit to obtain the migration data volume of the seismic data.

[0020] In some embodiments, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers includes:

[0021] The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0022] In some embodiments, the step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes:

[0023] The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

[0024] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned offset imaging methods is executed.

[0025] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned offset imaging methods.

[0026] The present application provides a migration imaging method, device, electronic device and storage medium, which obtain the number of computing units in a computing cluster and the total number of quasi-migrated shot sets of seismic data; allocate the number of quasi-migrated shot sets of each computing unit based on the number and the total number of quasi-migrated shot sets, so that each computing unit performs a migration operation on the quasi-migrated shot sets corresponding to the number of quasi-migrated shot sets to obtain a migration result; and superimpose and sort the migration results of each computing unit to obtain a migration data volume of the seismic data, so as to achieve load balancing of computing tasks of the computing units, thereby improving the computing efficiency of migration imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings.

[0028] Figure 1 A schematic diagram of an implementation flow of a shift imaging method provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the implementation flow of another offset imaging method provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of an implementation flow of a reverse time migration algorithm provided in an embodiment of the present application;

[0031] Figure 4 A schematic diagram of a velocity model of a corresponding line provided in an embodiment of the present application;

[0032] Figure 5 A schematic diagram of an offset data body provided in an embodiment of the present application;

[0033] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0034] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0036] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0037] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0039] Based on the problems existing in the related art, an embodiment of the present application provides a method for offset imaging, which is applied to an electronic device, which may be a computer, a mobile terminal, etc. The electronic device may be a controller of a computing cluster, and the function implemented by the offset imaging method provided in the embodiment of the present application may be implemented by calling a program code by a processor of the electronic device, wherein the program code may be stored in a computer storage medium.

[0040] Embodiment 1

[0041] The present application embodiment provides a method for offset imaging. Figure 1 A schematic diagram of the implementation process of a shift imaging method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0042] Step S101, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data.

[0043] In the embodiment of the present application, the computing cluster is a computing cluster based on the Spark parallel framework. The computing cluster has multiple computing units, each computing unit is independent, and there may be no communication between each computing unit. The computing unit may be a GPU. Each computing unit may process data in parallel.

[0044] In an embodiment of the present application, the electronic device can be connected to a computing cluster for communication, obtain identification information of computing units through the computing cluster, and determine the number of computing units through the identification information. The electronic device can obtain seismic data, analyze the seismic data, thereby determining an index number, and determine the total number of quasi-offset shot gathers of the seismic data based on the number of index numbers.

[0045] In some embodiments, the electronic device can be connected to the input device for communication, and the user can input the number of computing units in the computing cluster and the total number of quasi-offset shot gathers of seismic data through the input device, so that the electronic device can obtain the number of computing units in the computing cluster and the total number of quasi-offset shot gathers of seismic data. The input device may include: a mobile phone, a computer, a keyboard, a mouse, etc.

[0046] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of quasi-migrated shot gathers of seismic data includes: obtaining seismic data; dividing the seismic data into common shot gather data, and obtaining the total number of quasi-migrated shot gathers based on the index number of the common shot gather data.

[0047] Step S102, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result.

[0048] In the embodiment of the present application, load balancing needs to be considered when performing allocation.

[0049] In the embodiment of the present application, the total number of quasi-migrated shot gathers may be divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0050] For example, if the number of computational units is N and the total number of shot gathers to be offset is F, then f＝ F N , and obtain the number of quasi-offset shot gathers of each computational unit.

[0051] In an embodiment of the present application, the electronic device can send a shot collection index number corresponding to the number of shot collections to be offset to each computing unit, and each computing unit can obtain the shot collection to be offset based on the index number, and then perform an offset operation on the shot collection to be offset, thereby obtaining an offset result.

[0052] In the embodiment of the present application, after the task division is completed by grouping according to the number of shot sets to be offset, assuming that there are n parallel computing tasks (stages), each computing unit generates its own RDD according to the shot set index, and converts it into a vector through mapPartitions, and performs an offset operation on each RDD separately; further, reduceBykey is used to implement superposition in the offset result RDD of each parallel calculation, thereby outputting the offset result.

[0053] Step S103, superimposing and sorting the migration results of the various calculation units to obtain a migration data volume of the seismic data.

[0054] In the embodiment of the present application, a Union operation can be used to implement result reduction based on the offset result, and then the final offset result, that is, the offset data body, is output by final superposition and sorting.

[0055] The present application provides a migration imaging method, which obtains the number of computing units in a computing cluster and the total number of quasi-migrated shot sets of seismic data; allocates the number of quasi-migrated shot sets to each computing unit based on the number and the total number of quasi-migrated shot sets, so that each computing unit performs a migration operation on the quasi-migrated shot sets corresponding to the number of quasi-migrated shot sets to obtain a migration result; and superimposes and sorts the migration results of each computing unit to obtain a migration data body of the seismic data, thereby achieving load balancing of computing tasks of the computing units, thereby improving the computing efficiency of migration imaging.

[0056] Embodiment 2

[0057] Based on the problems existing in the related art, an embodiment of the present application provides a method for offset imaging, which is applied to an electronic device, which may be a computer, a mobile terminal, etc. The function implemented by the offset imaging method provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic device, wherein the program code may be stored in a computer storage medium, and the method includes:

[0058] Step S201, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data.

[0059] In the embodiment of the present application, the computing cluster is a computing cluster based on the Spark parallel framework. The computing cluster has multiple computing units, each computing unit is independent, and there may be no communication connection between each computing unit. The computing unit may be a GPU. Each computing unit may process data in parallel.

[0060] In an embodiment of the present application, the electronic device can be connected to a computing cluster for communication, obtain identification information of computing units through the computing cluster, and determine the number of computing units through the identification information. The electronic device can obtain seismic data, analyze the seismic data, thereby determining an index number, and determine the total number of quasi-offset shot gathers of the seismic data based on the number of index numbers.

[0061] In some embodiments, the electronic device can be communicatively connected to an input device, and a user can input the number of computing units in the computing cluster and the total number of quasi-offset shot sets of seismic data through the input device, so that the electronic device obtains the number of computing units in the computing cluster and the total number of quasi-offset shot sets of seismic data.

[0062] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of quasi-migrated shot gathers of seismic data includes: obtaining seismic data; dividing the seismic data into common shot gather data, and obtaining the total number of quasi-migrated shot gathers based on the index number of the common shot gather data.

[0063] Step S202, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result.

[0064] In the embodiment of the present application, load balancing needs to be considered when performing allocation.

[0065] In the embodiment of the present application, the total number of the quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0066] For example, if the number of computational units is N and the total number of shot gathers to be offset is F, then f＝ F N , and obtain the number of quasi-offset shot gathers of each computational unit.

[0067] In an embodiment of the present application, the electronic device can send a shot collection index number corresponding to the number of shot collections to be offset to each computing unit, and each computing unit can obtain the shot collection to be offset based on the index number, and then perform an offset operation on the shot collection to be offset, thereby obtaining an offset result.

[0068] In the embodiment of the present application, after the task division is completed by grouping according to the number of shot sets to be offset, assuming that there are n parallel computing tasks (stages), each computing unit generates its own RDD according to the shot set index, and converts it into a vector through mapPartitions, and performs an offset operation on each RDD separately; further, reduceBykey is used to implement superposition in the offset result RDD of each parallel calculation, thereby outputting the offset result.

[0069] Step S203, superimposing the migration results of each calculation unit and sorting them according to the test line number to obtain the migration data volume of the seismic data.

[0070] The method provided in the embodiment of the present application reduces data IO time because the intermediate results of the entire calculation process do not need to be written to the disk, and there is no communication between the various calculation tasks, which reduces the time consumption of communication, and has a great promoting effect on improving the efficiency of the algorithm as a whole.

[0071] Embodiment 3

[0072] Based on the above embodiments, the present application provides another migration imaging method, the purpose of which is to improve the efficiency of reverse time migration imaging technology based on the Spark parallel framework and GPU overall grouping strategy, and to store seismic data based on a distributed file system. In addition, YARN job management is applied to achieve dynamic management of jobs, avoiding the failure of the entire job due to the failure of a single node job. The market adaptability of reverse time migration is improved, providing strong technical support for the generation of reverse time migration with larger data in the future.

[0073] In the embodiment of the present application, due to the particularity of the algorithm of reverse time migration, each computing unit only needs to complete a single task such as the migration of a shot, and then perform superposition. A large amount of communication is not required in the actual calculation process. Therefore, this characteristic of reverse time migration is naturally compatible with the distributed parallel framework. In the embodiment of the present application, the distributed storage of seismic data is firstly realized based on the Hadoop distributed file system, which solves the storage of massive data very well, and the migration of computing code to data is realized based on the Spark distributed parallel framework, which improves the data IO efficiency and finally realizes the improvement of computing efficiency. Figure 2 A schematic diagram of the implementation flow of another offset imaging method provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, including:

[0074] Seismic data are sorted into common shot collection data, and GPU computing resources and the total number of shot collections to be migrated are counted. That is, the GPU overall grouping strategy is first used to divide the pre-stack shot collection according to the shot index to achieve load balancing of computing tasks in the actual computing unit. The specific implementation is as follows: first, the number of GPUs in the current computing cluster is counted as N, and at the same time, the pre-stack shot collection to be migrated is traversed to obtain the shot number index array F. Thus, the shot collection is comprehensively grouped according to the computing resources, and the load balancing of computing tasks is achieved through balanced allocation. In this way, the sub-shot collection index assigned to each GPU is f, and there is f＝ F N This allows the entire gun index array to be evenly distributed according to the total computing resources, ensuring job parallelism while avoiding redundant computing due to too many total tasks that affect job efficiency.

[0075] Perform shot domain migration and superposition on the shot collection data in the shot collection index RDD grouped according to GPU computing resources, and output the migration result obtained by the computing task;

[0076] Data reduction outputs the final result: the offsets obtained from each calculation task are reduced and superimposed and sorted according to the test line number, and finally the complete offset data body is output.

[0077] Reverse time migration is implemented based on the Spark parallel framework, and data is transferred in the form of RDD. Figure 3 A schematic diagram of an implementation flow of a reverse time migration algorithm provided in an embodiment of the present application is shown in FIG. Figure 3 As shown in the figure, after the task division is completed according to the GPU overall grouping, assuming there are n parallel computing tasks (stages), each computing task generates its own RDD according to the gun set index, and converts it into a vector through mapPartitions, and performs offset operations on each RDD respectively; further, the offset result RDD of each parallel computing is superimposed by using reduceBykey; then the result after each superposition is reduced by using Union operation, and then the final superposition and sorting are performed to output the final offset result. Since the intermediate results of the entire computing process do not need to be written to the disk, the data IO time is reduced, and there is no communication between the computing tasks, which reduces the time consumption of communication, which has a great role in improving the efficiency of the algorithm as a whole.

[0078] In the embodiment of the present application, in order to verify the effect, the rock hill model data was selected for testing. The main parameters of the data are as follows: the calculation grid size is 30m×30m×20m, the main frequency of the Ricker wavelet is selected as 30Hz, the imaging depth is 5000m, the total number of shots is 3333, and the migration results are obtained. Figure 4 A schematic diagram of the velocity model of the corresponding line is given. Figure 5 This is a schematic diagram of an offset data body provided in an embodiment of the present application. By comparing the two figures, it can be seen that the method provided in the embodiment of the present application can achieve high-precision imaging of complex models, and steep-angle structures can be accurately imaged.

[0079] Embodiment 4

[0080] Based on the foregoing embodiments, an embodiment of the present application provides an offset imaging device, wherein each module included in the device and each unit included in each module can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0081] The present application provides an offset imaging device, the offset imaging device comprising:

[0082] An acquisition module is used to obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data;

[0083] an allocation module, configured to allocate the number of quasi-shifted shot gathers to each computing unit based on the number and the total number of quasi-shifted shot gathers, so that each computing unit performs a shift operation on the quasi-shifted shot gathers corresponding to the number of quasi-shifted shot gathers to obtain a shift result;

[0084] The calculation module is used to superimpose and sort the migration results of each calculation unit to obtain the migration data volume of the seismic data.

[0085] In the embodiment of the present application, the computing cluster is a computing cluster based on the Spark parallel framework. The computing cluster has multiple computing units, each computing unit is independent, and there may be no communication between each computing unit. The computing unit may be a GPU. Each computing unit may process data in parallel.

[0086] In an embodiment of the present application, the electronic device can be connected to a computing cluster for communication, obtain identification information of computing units through the computing cluster, and determine the number of computing units through the identification information. The electronic device can obtain seismic data, analyze the seismic data, thereby determining an index number, and determine the total number of quasi-offset shot gathers of the seismic data based on the number of index numbers.

[0087] In some embodiments, the electronic device can be connected to the input device for communication, and the user can input the number of computing units in the computing cluster and the total number of quasi-offset shot gathers of seismic data through the input device, so that the electronic device can obtain the number of computing units in the computing cluster and the total number of quasi-offset shot gathers of seismic data. The input device may include: a mobile phone, a computer, a keyboard, a mouse, etc.

[0088] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of quasi-migrated shot gathers of seismic data includes: obtaining seismic data; dividing the seismic data into common shot gather data, and obtaining the total number of quasi-migrated shot gathers based on the index number of the common shot gather data.

[0089] In the embodiment of the present application, load balancing needs to be considered when performing allocation.

[0090] In the embodiment of the present application, the total number of quasi-migrated shot gathers may be divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0091] For example, if the number of computational units is N and the total number of shot gathers to be offset is F, then f＝ F N , and obtain the number of quasi-offset shot gathers of each computational unit.

[0092] In an embodiment of the present application, the electronic device can send a shot collection index number corresponding to the number of shot collections to be offset to each computing unit, and each computing unit can obtain the shot collection to be offset based on the index number, and then perform an offset operation on the shot collection to be offset, thereby obtaining an offset result.

[0093] In the embodiment of the present application, after the task division is completed by grouping according to the number of shot sets to be offset, assuming that there are n parallel computing tasks (stages), each computing unit generates its own RDD according to the shot set index, and converts it into a vector through mapPartitions, and performs an offset operation on each RDD separately; further, reduceBykey is used to implement superposition in the offset result RDD of each parallel calculation, thereby outputting the offset result.

[0094] In the embodiment of the present application, a Union operation can be used to implement result reduction based on the offset result, and then the final superposition and sorting are performed to output the final offset result, that is, the offset data body.

[0095] In some embodiments, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers includes:

[0096] The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0097] In some embodiments, the step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes:

[0098] The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

[0099] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data includes:

[0100] Acquisition of seismic data;

[0101] The seismic data are divided into common shot gather data, and the total number of pseudo-migrated shot gathers is obtained based on the index numbers of the common shot gather data.

[0102] In some embodiments, the computing cluster is a computing cluster based on the Spark parallel framework, the computing units are GPUs, and each GPU performs parallel processing.

[0103] The present application provides a migration imaging device, which obtains the number of computing units in a computing cluster and the total number of quasi-migrated shot sets of seismic data; allocates the number of quasi-migrated shot sets to each computing unit based on the number and the total number of quasi-migrated shot sets, so that each computing unit performs a migration operation on the quasi-migrated shot sets corresponding to the number of quasi-migrated shot sets to obtain a migration result; and superimposes and sorts the migration results of each computing unit to obtain a migration data body of the seismic data, thereby achieving load balancing of computing tasks of the computing units, thereby improving the computing efficiency of migration imaging.

[0104] Embodiment 5

[0105] An embodiment of the present application provides an electronic device; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to realize the connection and communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include a standard wired interface and a wireless interface. The processor 701 is configured to execute the program of the offset imaging method stored in the memory to implement the steps in the offset imaging method provided in the above embodiment.

[0106] The present application provides a migration imaging method, comprising:

[0107] Obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data;

[0108] Allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result;

[0109] The migration results of each calculation unit are superimposed and sorted to obtain the migration data volume of the seismic data.

[0110] In some embodiments, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers includes:

[0111] The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0112] In some embodiments, the step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes:

[0113] The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

[0114] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data includes:

[0115] Acquisition of seismic data;

[0116] The seismic data are divided into common shot gather data, and the total number of pseudo-migrated shot gathers is obtained based on the index numbers of the common shot gather data.

[0117] In some embodiments, the computing cluster is a computing cluster based on the Spark parallel framework, the computing units are GPUs, and each GPU performs parallel processing.

[0118] Embodiment 6

[0119] In the embodiment of the present application, if the above-mentioned offset imaging method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0120] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the offset imaging method provided in the above embodiment are implemented.

[0121] The present application provides a migration imaging method, comprising:

[0122] Obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data;

[0123] Allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result;

[0124] The migration results of each calculation unit are superimposed and sorted to obtain the migration data volume of the seismic data.

[0125] In some embodiments, allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers includes:

[0126] The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

[0127] In some embodiments, the step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes:

[0128] The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

[0129] In some embodiments, obtaining the number of computing units in the computing cluster and the total number of pseudo-migration shot gathers of seismic data includes:

[0130] Acquisition of seismic data;

[0131] The seismic data are divided into common shot gather data, and the total number of pseudo-migrated shot gathers is obtained based on the index numbers of the common shot gather data.

[0132] In some embodiments, the computing cluster is a computing cluster based on the Spark parallel framework, the computing units are GPUs, and each GPU performs parallel processing.

[0133] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0134] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0135] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0136] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0137] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0138] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0139] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0140] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0141] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A migration imaging method, characterized in that: include: Obtain the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data; Allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers, so that each computing unit performs a migration operation on the quasi-migrated shot gathers corresponding to the number of quasi-migrated shot gathers to obtain a migration result; The migration results of each calculation unit are superimposed and sorted to obtain the migration data volume of the seismic data.

2. The method according to claim 1, characterized in that The allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers comprises: The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

3. The method according to claim 1, characterized in that The step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes: The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

4. The method according to claim 1, characterized in that: The obtaining of the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data includes: Acquisition of seismic data; The seismic data are divided into common shot gather data, and the total number of pseudo-migrated shot gathers is obtained based on the index numbers of the common shot gather data.

5. The method according to any one of claims 1 to 4, characterized in that: The computing cluster is a computing cluster based on the Spark parallel framework, the computing units are GPUs, and each GPU performs parallel processing.

6. An offset imaging device, characterized in that: include: An acquisition module, used to acquire the number of computing units in the computing cluster and the total number of quasi-migration shot gathers of seismic data; an allocation module, configured to allocate the number of quasi-shifted shot gathers to each computing unit based on the number and the total number of quasi-shifted shot gathers, so that each computing unit performs a shift operation on the quasi-shifted shot gathers corresponding to the number of quasi-shifted shot gathers to obtain a shift result; The calculation module is used to superimpose and sort the migration results of each calculation unit to obtain the migration data volume of the seismic data.

7. The offset imaging device according to claim 6, characterized in that: The allocating the number of quasi-migrated shot gathers to each computing unit based on the number and the total number of quasi-migrated shot gathers comprises: The total number of quasi-migrated shot gathers is divided by the number to obtain the number of quasi-migrated shot gathers of each computing unit.

8. The offset imaging device according to claim 6, characterized in that: The step of superimposing and sorting the migration results of the various calculation units to obtain the migration data volume of the seismic data includes: The migration results of each calculation unit are superimposed and sorted according to the test line number to obtain the migration data volume of the seismic data.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the offset imaging method according to any one of claims 1 to 5 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the migration imaging method as described in any one of claims 1 to 5.

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