Common-offset vector sheet domain pre-stack time migration method and device and medium

Abstract

The invention discloses a common-offset vector sheet field pre-stack time migration method and device and a medium, and relates to the field of seismic exploration imaging, and the method comprises the steps: obtaining a common-offset vector sheet gather; performing five-dimensional regularization processing on the common offset vector film gather to obtain an interpolated common offset vector film gather; pre-stack time migration processing is carried out on the interpolated common offset vector plate gather, and a common offset vector plate imaging result gather is obtained; and overlapping and cutting off the imaging result gather of the common-offset vector plate to obtain the post-stack profile data of the seismic data of the common-offset vector plate domain. According to the method, part of operations required by the OVT domain pre-stack time migration, such as the generation of an OVT gather and the generation of a spiral gather, are realized facing a Spark framework, and the applicability of an OVT processing technology under a modern big data computing framework is improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration imaging, and particularly to a common offset vector tile domain prestack time migration method, device and medium. Background Art

[0002] High-quality seismic data acquisition and processing are of great guiding significance for oil and gas resource exploration work. In recent years, the requirements for seismic exploration accuracy in oil and gas exploration work have become increasingly high, and conventional three-dimensional seismic acquisition and processing technologies can no longer well meet the needs of oil and gas resource exploration.

[0003] The common offset vector tile (OVT) technology is an ideal technology for wide-azimuth, wide-frequency, and high-density seismic data processing. It can make full use of the information carried by wide-azimuth high-density seismic data, improve the processing effect, and improve the processing accuracy. The prestack time migration in the OVT domain retains the azimuth information in the final imaging gather, improves the lateral resolution of the imaging gather, improves the imaging accuracy of the underground structure, and provides a high-quality data basis for subsequent tasks such as azimuthal anisotropy analysis and fracture prediction. At present, the Apache Spark framework has been used in a variety of commercial big data business scenarios, but its application in high-performance scientific computing has not been widely carried out.

[0004] Therefore, how to provide an OVT domain prestack time migration method based on the Spark framework is of great significance for the research of scientific computing models in the context of big data, and is an important issue that the industry urgently needs to solve. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a common offset vector tile domain prestack time migration method, device and medium to solve the problem that the Apache Spark framework has been used in a variety of commercial big data business scenarios, but its application in high-performance scientific computing has not been widely carried out.

[0006] According to a first aspect, an embodiment of the present invention provides a common offset vector tile domain prestack time migration method, the method comprising:

[0007] Obtaining a common offset vector tile gather;

[0008] Performing five-dimensional regularization processing on the common offset vector tile gather to obtain an interpolated common offset vector tile gather;

[0009] Performing prestack time migration processing on the interpolated common offset vector tile gather to obtain a common offset vector tile imaging result gather;

[0010] Stack and cut the common source-receiver offset vector slice imaging result gather to obtain the post-stack seismic data profile data in the common source-receiver offset vector slice domain.

[0011] Combined with the first aspect, in the first embodiment of the first aspect, the obtaining of the common source-receiver offset vector slice gather specifically includes:

[0012] Obtain initial observation data;

[0013] Perform decimation processing on the initial observation data to obtain the common source-receiver offset vector slice index information, and extract and combine the same common source-receiver offset vector slice index information into a common source-receiver offset vector slice gather.

[0014] Combined with the first embodiment of the first aspect, in the second embodiment of the first aspect, the performing of decimation processing on the initial observation data to obtain the common source-receiver offset vector slice index information, and extracting and combining the same common source-receiver offset vector slice index information into a common source-receiver offset vector slice gather specifically includes:

[0015] Perform decimation processing on the initial observation data to obtain the common source-receiver offset vector slice index information;

[0016] Calculate the corresponding common source-receiver offset vector slice piece number of the initial observation data according to the common source-receiver offset vector slice index information;

[0017] Adjust the common source-receiver offset vector slice piece number as the index key, and extract and combine the same common source-receiver offset vector slice index information into a common source-receiver offset vector slice gather according to the index key.

[0018] Combined with the second embodiment of the first aspect, in the third embodiment of the first aspect, the interpolated common source-receiver offset vector slice gather is obtained by performing piecewise interpolation processing on each element in the common source-receiver offset vector slice gather through the flatMap and reduceByKey operators.

[0019] Combined with the second embodiment of the first aspect, in the fourth embodiment of the first aspect, the performing of prestack time migration processing on the interpolated common source-receiver offset vector slice gather to obtain the common source-receiver offset vector slice imaging result gather specifically includes:

[0020] Obtain the migration parameters, assemble the migration parameters into a hadoopConf object, and set them to the Spark cluster through the SparkContext; the migration parameters include the migration imaging range.

[0021] Identify the interpolated common source-receiver offset vector slice gather and divide it into several logical slices to obtain the imaging space set;

[0022] Perform a flatMap operation on the imaging space set, and use Kirchhoff prestack time migration to obtain the migration result of each imaging space;

[0023] Taking inline and crossline as the first and second keywords respectively, use the reduceByKey operator to reduce the offset results to obtain the common shot-receiver offset vector slice imaging result gather.

[0024] Combined with the second embodiment of the first aspect, in the fifth embodiment of the first aspect, the common shot-receiver offset vector slice partition number and the common shot-receiver offset vector slice gather form a key-value relationship.

[0025] Combined with the second embodiment of the first aspect, in the sixth embodiment of the first aspect, the stacking and excision of the common shot-receiver offset vector slice imaging result gather to obtain the post-stack profile data of the common shot-receiver offset vector slice domain seismic data specifically includes:

[0026] Display the common shot-receiver offset vector slice imaging result gather according to the common shot-receiver offset vector slice partition number to obtain a snail gather;

[0027] Perform stacking and excision processing on the snail gather to obtain the post-stack profile data of the common shot-receiver offset vector slice domain seismic data.

[0028] According to the second aspect, an embodiment of the present invention further provides a prestack time migration device in the common shot-receiver offset vector slice domain, characterized in that the device includes:

[0029] A data acquisition module for acquiring a common shot-receiver offset vector slice gather;

[0030] A regularization module for performing five-dimensional regularization processing on the common shot-receiver offset vector slice gather to obtain an interpolated common shot-receiver offset vector slice gather;

[0031] An offset processing module for performing prestack time migration processing on the interpolated common shot-receiver offset vector slice gather to obtain a common shot-receiver offset vector slice imaging result gather;

[0032] A stacking and excision module for performing stacking and excision on the common shot-receiver offset vector slice imaging result gather to obtain a post-stack profile of the common shot-receiver offset vector slice domain seismic data.

[0033] According to the third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the steps of any one of the above-mentioned prestack time migration methods in the common shot-receiver offset vector slice domain.

[0034] According to the fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of any one of the above-mentioned prestack time migration methods in the common shot-receiver offset vector slice domain.

[0035] The common shot-receiver offset vector patch domain prestack time migration method, device and medium of the present invention provide a computational implementation of the prestack time migration method in the OVT domain based on the modern big data architecture using the Spark framework. It improves the utilization efficiency of cluster hardware for prestack migration calculations, reduces the sensitivity and dependence of the program on hardware, and improves the maintainability of the cluster and its applications; it uses the JVM native method interface to provide a local efficient implementation for the computationally intensive parts of the tasks, overcoming the deficiencies of the JVM in memory management and computational efficiency to a certain extent and further improving the computational efficiency; it provides the implementation of some operations required for prestack time migration in the OVT domain, such as the generation of OVT gathers and the generation of spiral gathers, etc., for the Spark framework, improving the applicability of OVT processing technology in the modern big data computing framework. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings. The drawings are schematic and should not be construed as imposing any limitation on the present invention. In the drawings:

[0037] Figure 1 A flowchart showing the common shot-receiver offset vector patch domain prestack time migration method provided by the present invention is shown;

[0038] Figure 2 A structural diagram showing the common shot-receiver offset vector patch domain prestack time migration device provided by the present invention is shown;

[0039] Figure 3 A structural diagram showing the electronic device of the common shot-receiver offset vector patch domain prestack time migration method provided by the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] High-quality seismic data acquisition and processing are of great guiding significance for oil and gas resource exploration. In recent years, the requirements for seismic exploration accuracy in oil and gas exploration have been increasing. Conventional 3D seismic acquisition and processing technologies can no longer meet the needs of oil and gas resource exploration well. In recent years, with the development of high-performance computing technology and acquisition equipment, the high-density 3D seismic exploration technology characterized by "two-widths and one-high" (wide bandwidth, wide azimuth, and high fold) has gradually entered the large-scale commercial application stage, and wide-azimuth seismic exploration has become the mainstream application direction of seismic exploration. In wide-azimuth seismic exploration, the aspect ratio of the seismic acquisition system exceeds 0.5. Such an acquisition system significantly improves the azimuth coverage range of seismic acquisition, increases the fold, and enhances the illumination, which is conducive to obtaining detailed information on the underground wave field.

[0042] Compared with the seismic data processing technology based on the narrow-azimuth acquisition concept, the OVT technology makes full use of the azimuth and offset information brought by the wide-azimuth acquisition system. The prestack time migration in the OVT domain retains the azimuth information in the final migrated gather, improving the lateral resolution of the migrated gather and the imaging accuracy of the underground structure, and providing a high-quality data basis for subsequent tasks such as azimuthal anisotropy analysis and fracture prediction.

[0043] Currently, the prestack time migration in the OVT domain generally adopts the prestack time migration method based on the Kirchhoff integral. The difference between the prestack time migration in the OVT domain and the conventional prestack time migration lies only in the data sorting strategy. The data sorting of the conventional prestack time migration usually divides the migrated data into common midpoint or common offset gathers, and then performs separate migration processing on each gather. The data sorting strategy of the prestack time in the OVT domain is to divide the migrated data into OVT gathers based on the common offset vector slicing as the division standard and then perform separate migration. Therefore, the current key to the implementation of the prestack time migration in the OVT domain lies in the data slicing strategy and the migration calculation.

[0044] For data slicing calculation and migration calculation, in the current methods for processing massive seismic data, it mainly relies on high-performance computing frameworks such as MPI to achieve. With the development of high-performance computing acceleration hardware such as GPU devices, the data calculation tasks have obtained higher computing efficiency by using the acceleration devices. The emergence of big data technology has expanded the implementation methods of high-performance scientific computing applications, simplified the cluster resource management and scheduling work, and made the program writing more focused on the calculation tasks themselves. The program writing and running efficiency are further guaranteed. Currently, the Apache Spark framework has been used in various commercial big data business scenarios, but it has not been widely applied in high-performance scientific computing. Therefore, how to provide an implementation method of the prestack time migration in the OVT domain based on the Spark framework is of great significance for the research of the scientific computing mode in the big data background and is an important issue that the industry urgently needs to solve.

[0045] In order to solve the above problems, a common-offset vector slice domain prestack time migration method is provided in this embodiment, which aims to perform real-time deviation correction processing on the trajectory of the station-depot scene route without relying on third-party data. The common-offset vector slice domain prestack time migration method of the embodiment of the present invention can be used in electronic devices, including but not limited to computers, mobile terminals, wearable smart devices, etc. Figure 1 is a flow chart of a common-offset vector slice domain prestack time migration method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0046] S10. Obtain common-offset vector (OVT) gathers.

[0047] In step S10, first, the cross-arranged track set index corresponding to each data in the initial observation data, i.e., the initial seismic data, is calculated based on the index, and the OVT shard number or OVT slice index of the data in the cross-arranged track set is calculated. This process is completed by the flatMap operator. The OVT shard number is adjusted to be the first index keyword of each track. After obtaining the OVT shard number of each track, the reduceByKey operator is used to reduce all track data with the same OVT shard number to the same track set to form an OVT track set RDD. At this time, each element in the OVT track set RDD is a key-value pair, the key contains at least the OVT shard number of the OVT shard, and the value is the OVT track set.

[0048] S20, performing five-dimensional regularization processing on the OVT gathers to obtain interpolated OVT gathers.

[0049] In the embodiment of the present invention, each element in the OVT gather RDD is subjected to slice interpolation processing through the flatMap and reduceByKey operators to obtain the interpolated OVT gather RDD.

[0050] S30, performing prestack time migration processing on the interpolated OVT gathers to obtain OVT imaging result gathers.

[0051] The Krichhoff integration method is widely used in pre-stack time migration, and this method is also selected in the embodiment of the present invention to complete the pre-stack time migration calculation.

[0052] According to the obtained migration calculation parameters, a hadoopConf object is constructed and set to the SparkConf object of the Spark application running the migration calculation. According to the imaging space range, the logical imaging space data slices are delineated. The flatMap operator is used to change the structure of each element in the OVT gather RDD. According to the specific configuration of the cluster and considering the cluster performance, the OVT gather elements are re-divided into new OVT gather slices. The logical imaging space data slices are combined with the actual OVT gather slices to form a new OVT imaging space RDD. For each element of the OVT imaging space RDD, the prestack time migration calculation task is performed. Due to the computationally intensive characteristics of the Kirchhoff integral method, this part of the Spark application considers using node-local methods to achieve more efficient CPU utilization and smaller-granular memory management. After completing the calculation tasks of each slice of the imaging space RDD, the reduceByKey operator is used to reduce the calculation results according to the inline and corssline numbers to complete the output of the imaging result RDD. At this point, the OVT domain prestack time migration calculation is completed.

[0053] That is, firstly, the data slices are redivided, the original OVT gather RDD is broken up, and the gather data under the same OVT slice number is combined with a certain imaging space slice to obtain the imaging space RDD. Based on the RDD, the prestack time migration calculation is performed to obtain the migration result for a certain imaging space. With inline and crossline as the first and second keywords respectively, the migration result is reduced using the reduceByKey operator to obtain the imaging result RDD.

[0054] S40, stacking and cutting the OVT imaging result gathers to obtain the post-stack profile of the OVT domain seismic data, which is conducive to further analyzing the migration imaging results.

[0055] In step S40, the OVG spiral gather, i.e., the snail gather, can be further obtained by displaying according to the OVT slice number. OVT offset stacking data, i.e., OVT slice domain seismic data post-stack profile data, can be obtained by performing excision and stacking operations based on the OVG gather or the snail gather.

[0056] The common shot-receiver offset vector patch domain prestack time migration method provided by the present invention structurally processes the trajectory of the acquired original station-yard scene route, then obtains the trajectory mean value of the trajectory structured data within a preset time, and determines whether the current trajectory structured data has an offset based on the trajectory mean value. When an offset occurs, the offset trajectory is corrected based on the travel distance of the pedestrian within the preset acquisition time, realizing real-time correction processing of the trajectory of the station-yard scene route, which can eliminate the trajectory sampling value deviation caused by sporadic interference, does not rely on any third-party map data, and can adapt to the requirements of any third-party map manufacturer and business system for pedestrian navigation data. There is no need to re-develop the system, and it can be accessed in the form of a module at any time.

[0057] In the embodiment of the present invention, step S10 specifically includes:

[0058] S11. Obtain initial observation data, for example, shot gathers, common midpoint or common offset gather, etc.

[0059] The initial observation data can be stored in the electronic device in advance, or can be obtained by the electronic device from the outside. For example, obtained by the electronic device from an external observation device, or extracted from the external acquisition data by the electronic device, etc.

[0060] No specific restrictions are imposed on the specific acquisition form of the initial observation data here, as long as it is ensured that the electronic device can obtain the initial observation data.

[0061] S12. Perform extraction processing on the initial observation data to obtain common shot-receiver offset vector patch (OVT) index information, and extract and combine the same OVT index information into an OVT gather, that is, extract the OVT gather from the initial observation data.

[0062] More specifically, step S12 specifically includes:

[0063] S121. Perform extraction processing on the initial observation data to obtain OVT index information, that is, obtain the cross-array gather index corresponding to each trace of data.

[0064] S122. Calculate the corresponding OVT patch number (OVT patch index) of the initial observation data according to the OVT index information, and this step can be completed by the flatMap operator.

[0065] S123. Adjust the OVT shard number as the index keyword, and extract and combine the same OVT index information according to the index keyword to form an OVT gather. After obtaining the OVT shard number of each trace, use the reduceByKey operator to reduce all trace data with the same OVT shard number to the same gather, forming the OVT gather RDD. At this time, each element in the OVT gather RDD is a key-value pair, and the key contains at least the OVT shard number of this OVT shard, and the value is the OVT gather corresponding to this OVT shard.

[0066] For example, the original seismic data is introduced from the file system to form the input RDD. The RDD undergoes a flatMap and reduce operation to be reordered into a common offset gather RDD to ensure the integrity of the shot line, receiver line, offset, and azimuth information of each trace. Then, expand each gather to obtain the inline number and crossline number of each trace data, calculate the OVT shard number or OVT shard index based on the inline number and crossline number, and in the reduce stage, merge the seismic traces with the same OVT shard index into a new gather. The RDD output at this time is called the OVT gather RDD, and each element in the RDD is a key-value pair, where the key contains at least the OVT index number of this OVT shard, and the value part is the OVT gather corresponding to this OVT index.

[0067] Since conventional prestack time migration ignores azimuth information, it shows deficiencies in attributes such as imaging accuracy and lateral resolution of small subsurface structures. The OVT technology is used to process wide-azimuth seismic data to supplement the full azimuth information, thereby improving the characterization accuracy of small subsurface structures and the imaging lateral resolution. Traditional high-performance computing frameworks retain too many interfaces for underlying cluster control that need to be customized by users. Modern big data frameworks gradually separate the computing logic and cluster maintenance tasks through hierarchical abstraction and the provision of systematic components, reducing the degree of dependence of the program on cluster details and enhancing the abstraction ability and maintainability of the program. As a unified computing framework for big data analysis tasks, Apache Spark can build a high-performance computing cluster with characteristics such as high reliability, low maintenance cost, and fast application writing relying on Apache Hadoop series components. Based on the Spark cluster, an efficient implementation adapted to the modern big data framework can be provided for prestack time migration in the OVT domain.

[0068] In the embodiment of the present invention, step S30 specifically includes:

[0069] S31. Obtain the migration parameters, assemble the migration parameters into a hadoopConf object, and set them to the Spark cluster through SparkContext, where the migration parameters include the migration imaging range.

[0070] S32. Identify the interpolated OVT gather, divide it into several logical slices, and obtain the imaging space set.

[0071] S33. Perform a flatMap operation on the imaging space set, and use Kirchhoff prestack time migration to obtain the migration result of each imaging space.

[0072] S34. Use the inline and crossline as the first and second keys respectively, and use the reduceByKey operator to reduce the migration results to obtain the OVT imaging result gather.

[0073] Obtain the migration parameters from the input parameters, including the migration imaging range. Assemble this parameter into a hadoopConf object and set it to the Spark cluster through SparkContext. Since the OVT gather RDD has completed data sharding according to the OVT number, each gather in the OVT gather RDD is identified as split1, split2, ……, splitn. Through the custom data input format interface of Hadoop, the imaging space is divided into several logical slices, mig1, mig2, ……, migm according to the input parameters. Eventually, a total of n * m data slices are generated. Each data slice represents a certain imaging space under a certain OVT gather. These data slices form a new imaging space RDD. Perform a flatMap operation on the imaging space RDD, and use Kirchhoff prestack time migration to obtain the migration result of each imaging space.

[0074] Considering that the Kirchhoff prestack time migration calculation is a computationally intensive and memory-consuming task, the JVM itself may not be able to provide enough memory to support the entire calculation process. By providing Java native methods, the computing tasks of the nodes can utilize the native code of the nodes. Considering that the current node machines are generally configured with multi-core processors, and thanks to the good computational parallelism of Kirchhoff prestack time migration, there is no strong dependence relationship between the calculations of each thread, and there is only a synchronization relationship with the global data member variables in terms of data IO. Therefore, a thread pool is built to schedule and manage the multi-threaded calculation of the migration tasks. Through the native interface of the JVM, the exchange between local data and the data of the Spark application can be realized, which is convenient for passing the calculation results back to the Spark application. Since the support technologies for multi-threaded calculations by each CPU manufacturer are not unified, there is room for optimization of the local calculation steps for the CPUs of each manufacturer, which can further improve the calculation efficiency.

[0075] After completing the flatMap phase operation, the Spark application needs to perform a reduce operation based on the split number and mig number in the aforementioned data slice. Since the data in each split slice comes from the same OVT gather, there is only one piece of data in the imaging space. This step reduces the results generated in the flatMap phase by inline and crossline as indexes. The output of this part of the result is the result RDD, which is the OVT gather that has completed the prestack time migration in the OVT domain.

[0076] The common-offset vector slice domain prestack time migration device provided by an embodiment of the present invention is described below. The common-offset vector slice domain prestack time migration device described below and the common-offset vector slice domain prestack time migration method described above can be referred to each other.

[0077] In order to solve the above problems, a common-offset vector slice domain prestack time migration device is provided in this embodiment, which is intended to perform real-time deviation correction processing on the trajectory of the station-depot scene route without relying on third-party data. The common-offset vector slice domain prestack time migration device of the embodiment of the present invention can be used in electronic devices, including but not limited to computers, mobile terminals, wearable smart devices, etc. Figure 2 is a schematic structural diagram of a common-offset vector slice domain prestack time migration device according to an embodiment of the present invention. Figure 2 As shown, the device comprises:

[0078] The data acquisition module 10 is used to acquire common-offset vector slice (OVT) gathers.

[0079] In the data acquisition module 10, first, the cross-arranged track set index corresponding to each data in the initial observation data, i.e., the initial seismic data, is calculated based on the index, and the OVT shard number or OVT slice index of the data in the cross-arranged track set is calculated. This process is completed by the flatMap operator. The OVT shard number is adjusted to be the first index keyword of each track. After obtaining the OVT shard number of each track, the reduceByKey operator is used to reduce all track data with the same OVT shard number to the same track set to form an OVT track set RDD. At this time, each element in the OVT track set RDD is a key-value pair, the key contains at least the OVT shard number of the OVT shard, and the value is the OVT track set.

[0080] The regularization module 20 is used to perform five-dimensional regularization processing on the OVT gathers to obtain interpolated OVT gathers.

[0081] In the embodiment of the present invention, each element in the OVT gather RDD is subjected to slice interpolation processing through the flatMap and reduceByKey operators to obtain the interpolated OVT gather RDD.

[0082] The migration processing module 30 is used to perform pre-stack time migration processing on the interpolated OVT gathers to obtain OVT imaging result gathers.

[0083] The Krichhoff integration method is widely used in pre-stack time migration, and this method is also selected in the embodiment of the present invention to complete the pre-stack time migration calculation.

[0084] According to the obtained migration calculation parameters, a hadoopConf object is constructed and set to the SparkConf object of the Spark application running the migration calculation. According to the imaging space range, the logical imaging space data slices are delineated. The flatMap operator is used to change the structure of each element in the OVT gather RDD. According to the specific configuration of the cluster and considering the cluster performance, the OVT gather elements are re-divided into new OVT gather slices. The logical imaging space data slices are combined with the actual OVT gather slices to form a new OVT imaging space RDD. For each element of the OVT imaging space RDD, the prestack time migration calculation task is performed. Due to the computationally intensive characteristics of the Kirchhoff integral method, this part of the Spark application considers using node-local methods to achieve more efficient CPU utilization and smaller-granular memory management. After completing the calculation tasks of each slice of the imaging space RDD, the reduceByKey operator is used to reduce the calculation results according to the inline and corssline numbers to complete the output of the imaging result RDD. At this point, the OVT domain prestack time migration calculation is completed.

[0085] The stacking and excision module 40 is used to perform stacking and excision on the OVT imaging result gathers to obtain the post-stack profile of the OVT domain seismic data, which is beneficial for further analysis of the migration imaging results.

[0086] In the stacking and cutting module 40, the OVG spiral gather, i.e., the snail gather, can be further obtained by displaying according to the OVT slice number. OVT offset stacking data, i.e., OVT slice domain seismic data post-stack profile data, can be obtained by performing cutting and stacking operations based on the OVG gather or the snail gather.

[0087] The common shot-receiver offset vector patch domain prestack time migration device provided by the present invention structurally processes the acquired trajectory of the original station-library scene route, then obtains the trajectory mean value of the trajectory structured data within a preset time, and determines whether the current trajectory structured data has an offset based on the trajectory mean value. When an offset occurs, the offset trajectory is corrected based on the traveling distance of the pedestrian within the preset acquisition time, realizing real-time correction processing of the trajectory of the station-library scene route, which can eliminate the deviation of the trajectory sampling value caused by sporadic interference, and does not rely on any third-party map data. At the same time, it can meet the requirements of any third-party map manufacturer and business system for pedestrian navigation data, and there is no need to re-develop the system, and it can be accessed in the form of a module at any time.

[0088] Figure 3 An example of the physical structure diagram of an electronic device is shown as Figure 3 shown. The electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute the common shot-receiver offset vector patch domain prestack time migration method, and the method includes:

[0089] Obtain a common shot-receiver offset vector patch gather;

[0090] Perform five-dimensional regularization processing on the common shot-receiver offset vector patch gather to obtain an interpolated common shot-receiver offset vector patch gather;

[0091] Perform prestack time migration processing on the interpolated common shot-receiver offset vector patch gather to obtain a common shot-receiver offset vector patch imaging result gather;

[0092] Perform stacking excision on the common shot-receiver offset vector patch imaging result gather to obtain the post-stack profile data of the common shot-receiver offset vector patch domain seismic data.

[0093] In addition, when the logic instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs, Read-Only Memories), random access memories (RAMs, Random Access Memories), magnetic disks, or optical discs that can store program codes.

[0094] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the common shot-receiver offset vector patch domain prestack time migration method provided by the above-mentioned various methods. The method includes:

[0095] Obtain a common shot-receiver offset vector patch gather;

[0096] Perform five-dimensional regularization processing on the common shot-receiver offset vector patch gather to obtain an interpolated common shot-receiver offset vector patch gather;

[0097] Perform prestack time migration processing on the interpolated common shot-receiver offset vector patch gather to obtain a common shot-receiver offset vector patch imaging result gather;

[0098] Perform stacking and excision on the common shot-receiver offset vector patch imaging result gather to obtain post-stack profile data of seismic data in the common shot-receiver offset vector patch domain.

[0099] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the execution of the common shot-receiver offset vector patch domain prestack time migration method provided by the above-mentioned various methods. The method includes:

[0100] Obtain a common shot-receiver offset vector patch gather;

[0101] Perform five-dimensional regularization processing on the common shot-receiver offset vector patch gather to obtain an interpolated common shot-receiver offset vector patch gather;

[0102] Perform prestack time migration processing on the interpolated common shot-receiver offset vector patch gather to obtain a common shot-receiver offset vector patch imaging result gather;

[0103] Stack and cut the gather of the common shot-receiver offset vector plot imaging results to obtain the post-stack profile data of the seismic data in the common shot-receiver offset vector plot domain.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0105] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solutions or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A common shot-receiver offset vector patch domain prestack time migration method, characterized in that, The method includes: Obtaining a common shot-receiver offset vector patch gather; Performing five-dimensional regularization processing on the common shot-receiver offset vector patch gather to obtain an interpolated common shot-receiver offset vector patch gather; Performing prestack time migration processing on the interpolated common shot-receiver offset vector patch gather to obtain a common shot-receiver offset vector image result gather; Performing stacking excision on the common shot-receiver offset vector image result gather to obtain post-stack profile data of seismic data in the common shot-receiver offset vector patch domain.

2. The common shot-receiver offset vector patch domain prestack time migration method according to claim 1, characterized in that, The obtaining of the common shot-receiver offset vector patch gather specifically includes: Obtaining initial observation data; Performing extraction processing on the initial observation data to obtain common shot-receiver offset vector patch index information, and extracting and combining the same common shot-receiver offset vector patch index information into a common shot-receiver offset vector patch gather.

3. The common shot-receiver offset vector patch domain prestack time migration method according to claim 2, characterized in that, The performing of extraction processing on the initial observation data to obtain common shot-receiver offset vector patch index information, and extracting and combining the same common shot-receiver offset vector patch index information into a common shot-receiver offset vector patch gather specifically includes: Performing extraction processing on the initial observation data to obtain common shot-receiver offset vector patch index information; Calculating the corresponding common shot-receiver offset vector patch slice number of the corresponding initial observation data according to the common shot-receiver offset vector patch index information; Adjusting the common shot-receiver offset vector patch slice number as an index key, and extracting and combining the same common shot-receiver offset vector patch index information into a common shot-receiver offset vector patch gather according to the index key.

4. The common shot-receiver offset vector patch domain prestack time migration method according to claim 3, characterized in that, The interpolated common shot-receiver offset vector patch gather is obtained by performing slice interpolation processing on each element in the common shot-receiver offset vector patch gather through flatMap and reduceByKey operators.

5. The common shot-receiver offset vector patch domain prestack time migration method according to claim 3, characterized in that, The performing of prestack time migration processing on the interpolated common shot-receiver offset vector patch gather to obtain a common shot-receiver offset vector image result gather specifically includes: Obtaining migration parameters, assembling the migration parameters into a hadoopConf object, and setting them to the Spark cluster through SparkContext; the migration parameters include the migration imaging range. Performing identification on the interpolated common shot-receiver offset vector patch gather and dividing it into several logical slices to obtain an imaging space set; Performing a flatMap operation on the imaging space set and using Kirchhoff prestack time migration to obtain the migration result of each imaging space; Using inline and crossline as the first and second keys respectively, and using the reduceByKey operator to perform reduction on the migration results to obtain a common shot-receiver offset vector image result gather.

6. The common shot-receiver offset vector patch domain prestack time migration method according to claim 3, characterized in that, The common shot-receiver offset vector patch slice number and the common shot-receiver offset vector patch gather form a key-value relationship.

7. The common shot-receiver offset vector patch domain prestack time migration method according to claim 3, characterized in that, The performing of stacking excision on the common shot-receiver offset vector image result gather to obtain post-stack profile data of seismic data in the common shot-receiver offset vector patch domain specifically includes: Displaying the common shot-receiver offset vector image result gather according to the common shot-receiver offset vector patch slice number to obtain a snail gather; Performing stacking excision processing on the snail gather to obtain post-stack profile data of seismic data in the common shot-receiver offset vector patch domain.

8. A common shot-receiver offset vector patch domain prestack time migration device, characterized in that, The device includes: A data acquisition module for obtaining a common shot-receiver offset vector patch gather; A regularization module for performing five-dimensional regularization processing on the common shot-receiver offset vector patch gather to obtain an interpolated common shot-receiver offset vector patch gather; An offset processing module, configured to perform prestack time migration processing on the interpolated common shot-receiver offset vector slice gather to obtain a common shot-receiver offset vector slice imaging result gather; A stacking excision module, configured to perform stacking excision on the common shot-receiver offset vector slice imaging result gather to obtain a post-stack profile of seismic data in the common shot-receiver offset vector slice domain.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the common shot-receiver offset vector slice domain prestack time migration method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, the steps of the common shot-receiver offset vector slice domain prestack time migration method according to any one of claims 1 to 7 are implemented.

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