A mass seismic data processing method, system, device, medium and program

Abstract

The application relates to a mass seismic data processing method, system, device, storage medium and computer program, the method comprising: acquiring seismic data, performing data preprocessing on the seismic data to obtain a processing data set; performing offset and azimuth division on the processing data set to obtain a common offset common azimuth gather; performing center point domain grouping on the common offset common azimuth gather to obtain a common center point gather; and performing five-dimensional data regularization on the common center point gather to obtain a target data set. The application improves the processing efficiency of mass seismic data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, system, device, medium, and program for processing massive seismic data. Background Technology

[0002] With the continuous development of seismic exploration technology, the amount of seismic data collected in the field has exploded, from a few TB initially to tens of TB or even more today. Traditional data processing methods often fall short when faced with such massive amounts of data, and the problems of slow processing speed and high computing cost are becoming increasingly prominent, seriously restricting the efficiency and accuracy of seismic data interpretation.

[0003] Existing technologies for processing massive seismic data mainly focus on computer aspects such as parallel computing and distributed storage. In the static correction stage of seismic data, methods such as resampling, shortening record length, adjusting offset, or thinning receiver lines can achieve data compression and simplification to some extent. However, although these methods have achieved certain results in the static correction stage, their impact on improving overall processing efficiency is still limited because the computational load of static correction itself in the entire seismic data processing flow is relatively limited. In the OVT (Output Transmission Time) stage, data is combined and superimposed within a small range to achieve 3D reconstruction, but data compression is limited, and the volume offset time is still relatively long.

[0004] Therefore, improving the processing efficiency and processing cycle of massive seismic data has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, system, device, medium, and program for processing massive seismic data to solve the problem of poor efficiency in processing massive seismic data.

[0006] Firstly, this application provides a method for processing massive seismic data, including:

[0007] Seismic data is acquired, and the seismic data is preprocessed to obtain a processed dataset;

[0008] The processed dataset is divided into offset and azimuth segments to obtain a common offset and common azimuth gather;

[0009] The common offset moment and common azimuth angle gathers are grouped by center point domain to obtain common center point gathers;

[0010] Five-dimensional data regularization is performed on the common center point gather to obtain the target dataset.

[0011] In some embodiments, the data preprocessing of the seismic data to obtain a processed dataset includes:

[0012] The earthquake data was cleaned and denoised to obtain uncontaminated earthquake data.

[0013] Data indexes for each earthquake data are generated in a preset database based on the uncontaminated earthquake data.

[0014] The uncontaminated seismic data is associated and stored according to the data index to obtain a processed dataset.

[0015] In some embodiments, the step of dividing the processed dataset into offset and azimuth segments to obtain a common offset and common azimuth gather includes:

[0016] Obtain the maximum non-vertical distance of the processed dataset;

[0017] Data smaller than the maximum non-vertical distance are selected from the processed dataset to obtain the offset moment dataset;

[0018] The azimuth angles of the offset moment dataset are discretized according to a preset azimuth angle interval;

[0019] Based on the azimuth angle, the offset moment dataset is rectangularly sorted to obtain a common offset moment common azimuth angle trace set.

[0020] In some embodiments, the step of performing rectangular sorting on the offset moment dataset based on the azimuth angle to obtain a common offset moment common azimuth angle gather includes:

[0021] Determine the 0-degree azimuth angle of the offset moment dataset, and use the 0-degree azimuth angle and its axisymmetric azimuth as the centerline;

[0022] Obtain the coverage count of the offset moment dataset, and determine the rectangle width based on the coverage count;

[0023] The rectangular region is determined based on the center line and the width of the rectangle.

[0024] Data points less than half the width of the rectangle and located at the center line are selected from the offset moment dataset to form a common offset moment and common azimuth angle gather.

[0025] In some embodiments, the step of grouping the common offset moments and common azimuth angles into center point domains to obtain common center point gathers includes:

[0026] Obtain the coordinates of the receiving point and the excitation point corresponding to the common offset moment common azimuth angle gather;

[0027] The common offset moment and common azimuth angle gathers are grouped according to the coordinates of the receiving point and the excitation point to obtain multiple common center point gathers.

[0028] In some embodiments, grouping the common offset moment and common azimuth angle gathers according to the coordinates of the receiving point and the excitation point to obtain multiple common center point gathers includes:

[0029] Calculate the center coordinates of the line connecting the receiving point and the excitation point for each dataset based on the coordinates of the receiving point and the excitation point, and obtain multiple center point coordinates.

[0030] The multi-channel seismic data are grouped according to the coordinates of the center point to obtain multiple common center point gathers.

[0031] In some embodiments, the step of performing five-dimensional data regularization on the common centroid gather to obtain target data includes:

[0032] Obtain the center point coordinates of the common center point gather, the projection of the common center point gather in the direction of the center point coordinates, and the time;

[0033] Based on the center point coordinates, the projection of the common center point gather in the direction of the center point coordinates, and the time, the common center point gather is reconstructed to the preset regularized grid center point to obtain the target data.

[0034] Secondly, this application provides a seismic data processing apparatus, comprising:

[0035] The preprocessing module is used to acquire seismic data, perform data preprocessing on the seismic data, and obtain a processed dataset.

[0036] The partitioning module is used to partition the processed dataset by offset and azimuth angle to obtain a common offset and common azimuth angle gather;

[0037] The center point grouping module is used to group the common offset moment and common azimuth angle gathers in the center point domain to obtain common center point gathers.

[0038] The five-dimensional data regularization module is used to perform five-dimensional data regularization on the common center point gather to obtain the target dataset.

[0039] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0040] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above aspects.

[0041] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described above.

[0042] This application provides a method, system, device, medium, and program for processing massive seismic data. It identifies and processes anomalous information through data cleaning to avoid biases in subsequent analysis. Noise reduction minimizes noise pollution, improving data accuracy. The uncontaminated seismic data is indexed and stored in a pre-defined database to obtain a processed dataset, facilitating subsequent data retrieval and analysis and improving processing efficiency. Dividing the dataset into common offset and common azimuth gathers makes seismic data interpretation more intuitive and enhances anisotropic interpretation capabilities. Centroid domain grouping yields more focused and clearer seismic data. Common centroid gathers help eliminate noise and interference caused by differences in excitation and receiver locations, improving the signal-to-noise ratio and facilitating data interpretation, thus optimizing processing efficiency. Five-dimensional data regularization eliminates irregularities and noise in seismic data, improving overall data quality. Reconstructing common centroid gathers to the center points of a regularized grid results in a more uniform and regular data distribution, reducing errors and uncertainties in data interpolation and improving processing efficiency. Attached Figure Description

[0043] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0044] Figure 1 A flowchart illustrating a method for processing massive seismic data provided in an embodiment of this application;

[0045] Figure 2 A functional module diagram of an earthquake data processing device provided in an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the structure of an electronic device for a massive seismic data processing method provided in an embodiment of this application.

[0047] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions of this application, and to fully understand and implement the process of how this application uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. The embodiments of this application and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this application.

[0049] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0050] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0051] This application provides a method for processing massive seismic data. The executing entity of this massive seismic data processing method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the system provided in this application embodiment: a server, a terminal, etc. In other words, the massive seismic data processing method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0052] Example 1

[0053] Figure 1 This is a flowchart illustrating a method for processing massive seismic data provided in an embodiment of this application, as shown below. Figure 1 As shown, the massive seismic data processing method includes:

[0054] S1. Acquire earthquake data, perform data preprocessing on the earthquake data, and obtain a processed dataset.

[0055] In this embodiment of the invention, the data preprocessing includes data cleaning and noise reduction of the seismic data to remove or correct errors, anomalies or missing values ​​in the seismic data. At the same time, removing or reducing noise can extract valid seismic data and ensure the reliability of the seismic data.

[0056] In this embodiment of the invention, the earthquake data can be obtained by using programming languages ​​such as Python and Java to connect to a preset database via the HTTP protocol, sending an HTTP request to obtain the data, and the earthquake data includes the earthquake location, magnitude, and waveform data.

[0057] In this embodiment of the invention, the step of preprocessing the seismic data to obtain a processed dataset includes:

[0058] The earthquake data was cleaned and denoised to obtain uncontaminated earthquake data.

[0059] Data indexes for each earthquake data are generated in a preset database based on the uncontaminated earthquake data.

[0060] The uncontaminated seismic data is associated and stored according to the data index to obtain a processed dataset.

[0061] In detail, the data cleaning refers to identifying and processing abnormal data, removing and correcting the abnormal data; the noise reduction process refers to reducing noise in data containing noise pollution to eliminate noise interference to the greatest extent possible.

[0062] Furthermore, by generating data indexes for each earthquake data in a preset database, and then associating and storing the earthquake data according to the data indexes to obtain a processed dataset, it is convenient to search and group the processed dataset in the future, without having to traverse the database one by one to query a specific earthquake data, thereby improving data processing efficiency.

[0063] In this embodiment of the invention, abnormal information is identified and processed through data cleaning to avoid biases in subsequent analysis. Noise reduction processing minimizes noise pollution, thereby improving data accuracy. The uncontaminated seismic data is then used to generate information indexes for each seismic data point in a pre-defined database and stored in association to obtain a processed dataset, facilitating subsequent data retrieval and analysis and improving data processing efficiency.

[0064] S2. Divide the processed dataset into offset and azimuth angles to obtain a common offset and common azimuth angle gather.

[0065] In this embodiment of the invention, the offset distance usually refers to the distance from the excitation point to the center of the nearest geophone array. It can also specifically refer to the vertical offset distance, that is, the horizontal distance from the excitation point to the geophone (in a vertical seismic profile). Taking land exploration as an example, the shot point excitation approximates the underground medium as an elastic medium. The excitation signal propagates through the underground medium and passes through the center of the geophone array on the surface to collect seismic data, i.e., process the dataset. The geophone refers to the seismic receiver.

[0066] The common offset and common azimuth gather refers to a gather in seismic data where the offset of each receiver point is equal. This type of gather is very useful in seismic data processing and analysis to help better understand and analyze the propagation characteristics of seismic waves and underground structures.

[0067] In this embodiment of the invention, the step of dividing the processed dataset into offset and azimuth segments to obtain a common offset and common azimuth gather includes:

[0068] Obtain the maximum non-vertical distance of the processed dataset;

[0069] Data smaller than the maximum non-vertical distance are selected from the processed dataset to obtain the offset moment dataset;

[0070] The azimuth angles of the offset moment dataset are discretized according to a preset azimuth angle interval;

[0071] Based on the azimuth angle, the offset moment dataset is rectangularly sorted to obtain a common offset moment common azimuth angle trace set.

[0072] In detail, the present invention will classify all data points with the same or similar offsets into the same subset based on the offset distance of each data point, forming an offset distance dataset. The azimuth angle of the offset distance dataset will be discretized according to a preset azimuth angle interval. The azimuth angle refers to the angle between the line connecting the source point and the receiver and a certain reference direction (such as due north). The offset distance dataset will be rectangularly sorted according to the azimuth angle to obtain common offset and common azimuth angle gathers. Each gather contains all data within a specific offset distance and azimuth angle range.

[0073] Further, selecting data smaller than the maximum non-vertical distance from the processed dataset includes using the maximum non-vertical distance as a diameter, where the maximum non-vertical distance refers to the maximum non-vertical distance of all processed data in the processed dataset, drawing a circle on the processed dataset with the diameter, and deleting the track dataset outside the circle, thereby obtaining the selected data, i.e., the offset moment dataset.

[0074] In this embodiment of the invention, the offset moment dataset is rectangularly sorted according to the azimuth angle to obtain a common offset moment common azimuth angle gather, including:

[0075] Determine the 0-degree azimuth angle of the offset moment dataset, and use the 0-degree azimuth angle and its axisymmetric azimuth as the centerline;

[0076] Obtain the coverage count of the offset moment dataset, and determine the rectangle width based on the coverage count;

[0077] The rectangular region is determined based on the center line and the width of the rectangle.

[0078] Data points less than half the width of the rectangle and located at the center line are selected from the offset moment dataset to form a common offset moment and common azimuth angle gather.

[0079] Specifically, the number of coverages refers to the number of data points measured at different azimuth angles.

[0080] Furthermore, in the offset moment dataset, each data point has an azimuth angle. The axisymmetric azimuth of 0 degrees is 180 degrees. Using 0 degrees and 180 degrees (or -180 degrees) as center lines, these two lines divide the dataset into two symmetrical parts. The coverage count of the offset moment dataset is obtained. Based on the distribution of the coverage count, a suitable rectangle width can be determined to include enough data points while avoiding excessive redundant data. Data points from the offset moment dataset that are less than half the width of the rectangle from the center lines are selected. That is, the distance from each data point to the center lines (0 degrees and 180 degrees) is calculated, and those data points whose distance is less than half the width of the rectangle are selected. This forms a common offset moment common azimuth gather.

[0081] In this embodiment of the invention, by dividing the dataset into common offset and common azimuth gathers, the interpretation of seismic data can be made more intuitive. Common offset and common azimuth gathers retain the original azimuth information and retain more of the subsurface anisotropic anomaly response than traditional common reflection point gathers, thereby enhancing the anisotropic interpretation capability.

[0082] S3. The common offset moment and common azimuth angle gathers are grouped by center point domain to obtain common center point gathers.

[0083] In this embodiment of the invention, the step of grouping the common offset moments and common azimuth angles into center point domains to obtain common center point gathers includes:

[0084] Obtain the coordinates of the receiving point and the excitation point corresponding to the common offset moment common azimuth angle gather;

[0085] The common offset moment and common azimuth angle gathers are grouped according to the coordinates of the receiving point and the excitation point to obtain multiple common center point gathers.

[0086] In detail, the common center point gather includes the coordinates of the common center point. Each receiver point and excitation point will have its specific coordinate position. The coordinates of the receiver point and excitation point are calculated to obtain the center point coordinates, i.e., CMP. Based on the calculated center point coordinates, multiple common offset and common azimuth gathers are grouped.

[0087] In this embodiment of the invention, the step of grouping the common offset moment and common azimuth angle gathers according to the coordinates of the receiving point and the excitation point to obtain multiple common center point gathers includes:

[0088] Calculate the center coordinates of the line connecting the receiving point and the excitation point for each dataset based on the coordinates of the receiving point and the excitation point, and obtain multiple center point coordinates.

[0089] The multi-channel seismic data are grouped according to the coordinates of the center point to obtain multiple common center point gathers.

[0090] In detail, in seismic exploration, the receiving point refers to the location where a seismic receiver (such as a geophone) is installed, and the coordinates of the receiving point refer to the specific location of the seismic receiver on the ground, usually expressed in latitude and longitude or coordinates relative to a reference point; the excitation point refers to the location where artificial seismic excitation is carried out (such as using explosives or air guns), and the coordinates of the excitation point refer to the specific location of the excitation point on the ground, also expressed in latitude and longitude or coordinates relative to a reference point.

[0091] Furthermore, for each dataset, there exists a receiver point and a trigger point. The line connecting these two points can be considered as the propagation path of a seismic data. The center coordinates of the line can be calculated using the arithmetic mean method, i.e., (x-coordinate of receiver point + x-coordinate of trigger point) / 2 and (y-coordinate of receiver point + y-coordinate of trigger point) / 2. In seismic data processing, when multiple datasets have the same center coordinates of the line connecting the receiver point and the trigger point, their center coordinates are called common center coordinates, which are used to identify seismic wave data.

[0092] In this embodiment of the invention, more focused and clear seismic data can be obtained by grouping by center point domain. Common center point gathers help to eliminate noise and interference caused by differences in the location of excitation and receiver points, thereby improving the signal-to-noise ratio of the data and facilitating data interpretation, thus optimizing processing efficiency.

[0093] S4. Perform five-dimensional data regularization on the common center point gather to obtain the target dataset.

[0094] In this embodiment of the invention, the five-dimensional data regularization refers to the center point coordinates (x, y), the projection of the common center point gather in the direction of the center point coordinates, and time, where time refers to the time taken for the seismic wave to travel from excitation to reception.

[0095] In this embodiment of the invention, the step of performing five-dimensional data regularization on the common center point gather to obtain target data includes:

[0096] Obtain the center point coordinates of the common center point gather, the projection of the common center point gather in the direction of the center point coordinates, and the time;

[0097] Based on the center point coordinates, the projection of the common center point gather in the direction of the center point coordinates, and the time, the common center point gather is reconstructed to the preset regularized grid center point to obtain the target data.

[0098] In detail, the regularized grid center in seismic exploration refers to a specific area whose size and shape can be adjusted as needed to more effectively collect and analyze seismic data. By reconstructing the common center point gather to the center point of the regularized grid, a more uniform and regular data distribution can be obtained.

[0099] Furthermore, the reconstruction process typically involves interpolation algorithms, such as the Orthogonal Matching Pursuit (OMP) algorithm, which can predict and fill missing data points based on known data points to obtain a more uniform coverage number attribute, thereby performing five-dimensional data regularization to obtain the target dataset.

[0100] In this embodiment of the invention, five-dimensional data regularization can eliminate irregularities and noise in seismic data, thereby improving the overall quality of the data. By reconstructing the common center point gather to the center point of the regularized grid, a more uniform and regular data distribution can be obtained, which helps to reduce errors and uncertainties in the data interpolation process and improve data processing efficiency.

[0101] This invention identifies and processes anomalous information through data cleaning to avoid biases in subsequent analysis. Noise reduction minimizes noise pollution, improving data accuracy. Uncontaminated seismic data is indexed and stored in a pre-defined database to create a processed dataset, facilitating subsequent data retrieval and analysis, and improving data processing efficiency. Dividing the dataset into common offset and common azimuth gathers makes seismic data interpretation more intuitive and enhances anisotropic interpretation capabilities. Centroid domain grouping yields more focused and clearer seismic data. Common centroid gathers help eliminate noise and interference caused by differences in excitation and receiver locations, improving the signal-to-noise ratio and facilitating data interpretation, thus optimizing processing efficiency. Five-dimensional data regularization eliminates irregularities and noise in seismic data, improving overall data quality. Reconstructing common centroid gathers to the center point of a regularized grid results in a more uniform and regular data distribution, reducing errors and uncertainties in data interpolation and improving data processing efficiency.

[0102] Example 2

[0103] like Figure 2 The diagram shown is a functional block diagram of an earthquake data processing device 100 provided in this embodiment.

[0104] The seismic data processing device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the seismic data processing device 100 may include a preprocessing module 101, a partitioning module 102, a center point grouping module 103, and a five-dimensional data regularization module 104. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0105] In this embodiment, the functions of each module / unit are as follows:

[0106] Preprocessing module 101 is used to acquire seismic data, perform data preprocessing on the seismic data, and obtain a processed dataset;

[0107] The partitioning module 102 is used to partition the processing dataset by offset and azimuth angle to obtain a common offset and common azimuth angle gather;

[0108] The center point grouping module 103 is used to group the common offset moment and common azimuth angle gathers in the center point domain to obtain a common center point gather.

[0109] The five-dimensional data regularization module 104 is used to perform five-dimensional data regularization on the common center point gather to obtain the target dataset.

[0110] Example 3

[0111] Figure 3 This is a schematic diagram of the structure of an electronic device for a massive seismic data processing method provided in an embodiment of this application.

[0112] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0113] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0114] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0115] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0116] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0117] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0118] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0119] The processor can communicate with external devices via the communication interface of the I / O bus through wired or wireless networks.

[0120] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0121] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0122] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] Although the embodiments disclosed in this application are as described above, the above content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for processing massive seismic data, characterized in that, The method includes: Seismic data is acquired, and the seismic data is preprocessed to obtain a processed dataset; The processed dataset is divided into offset and azimuth segments to obtain a common offset and common azimuth gather; The common offset moment and common azimuth angle gathers are grouped by center point domain to obtain common center point gathers; Five-dimensional data regularization is performed on the common center point gather to obtain the target dataset.

2. The method for processing massive seismic data according to claim 1, characterized in that, The process of preprocessing the seismic data to obtain a processed dataset includes: The earthquake data was cleaned and denoised to obtain uncontaminated earthquake data. Data indexes for each earthquake data are generated in a preset database based on the uncontaminated earthquake data. The uncontaminated seismic data is associated and stored according to the data index to obtain a processed dataset.

3. The method for processing massive seismic data according to claim 1, characterized in that, The step of dividing the processed dataset into offset and azimuth segments to obtain a common offset and common azimuth gather includes: Obtain the maximum non-vertical distance of the processed dataset; Data smaller than the maximum non-vertical distance are selected from the processed dataset to obtain the offset moment dataset; The azimuth angles of the offset moment dataset are discretized according to a preset azimuth angle interval; Based on the azimuth angle, the offset moment dataset is rectangularly sorted to obtain a common offset moment common azimuth angle trace set.

4. The method for processing massive seismic data according to claim 3, characterized in that, The step of performing rectangular sorting on the offset moment dataset based on the azimuth angle to obtain a common offset moment common azimuth angle gather includes: Determine the 0-degree azimuth angle of the offset moment dataset, and use the 0-degree azimuth angle and its axisymmetric azimuth as the centerline; Obtain the coverage count of the offset moment dataset, and determine the rectangle width based on the coverage count; The rectangular region is determined based on the center line and the width of the rectangle. Data points less than half the width of the rectangle and located at the center line are selected from the offset moment dataset to form a common offset moment and common azimuth angle gather.

5. The method for processing massive seismic data according to claim 1, characterized in that, The process of grouping the common offset moments and common azimuth angles into center point domains to obtain common center point gathers includes: Obtain the coordinates of the receiving point and the excitation point corresponding to the common offset moment common azimuth angle gather; The common offset moment and common azimuth angle gathers are grouped according to the coordinates of the receiving point and the excitation point to obtain multiple common center point gathers.

6. The method for processing massive seismic data according to claim 1, characterized in that, The five-dimensional data regularization performed on the common center point gather yields the target data, including: Obtain the center point coordinates of the common center point gather, the projection of the common center point gather in the direction of the center point coordinates, and the time; Based on the center point coordinates, the projection of the common center point gather in the direction of the center point coordinates, and the time, the common center point gather is reconstructed to the preset regularized grid center point to obtain the target data.

7. A seismic data processing device, characterized in that, The device includes: The preprocessing module is used to acquire seismic data, perform data preprocessing on the seismic data, and obtain a processed dataset. The partitioning module is used to partition the processed dataset by offset and azimuth angle to obtain a common offset and common azimuth angle gather; The center point grouping module is used to group the common offset moment and common azimuth angle gathers in the center point domain to obtain common center point gathers. The five-dimensional data regularization module is used to perform five-dimensional data regularization on the common center point gather to obtain the target dataset.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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