Irregular data reconstruction method and device, electronic equipment and storage medium

Abstract

This application relates to a method, apparatus, electronic device, and storage medium for reconstructing irregular data, belonging to the field of geophysical exploration technology. The method includes: acquiring irregular data; performing pre-reconstruction processing on the irregular data to generate first data; pre-processing the first data to generate second data; obtaining a signal-to-noise ratio (SNR) control factor for the second data; and reconstructing the second data using the SNR control factor to generate third data. By selecting different reconstruction strategies based on regions with different SNR, irregular seismic data in compressed sensing exploration technology is reconstructed, improving the SNR and accuracy of reconstructed irregular acquired data and enhancing seismic imaging quality.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to methods, apparatus, electronic devices and storage media for reconstructing irregular data. Background Technology

[0002] With the rapid pace of social development, oil and gas exploration targets are becoming increasingly complex, leading to ever-increasing demands for refined seismic data. While high-density seismic data acquisition can meet the sampling requirements of seismic signals, its production costs are prohibitively high. Efficient seismic data acquisition methods based on compressed sensing theory overcome the limitations of the Nyquist sampling theorem and represent a crucial method for reducing costs and increasing efficiency in seismic acquisition. This will drive the rapid development of synchronous source co-location technology and simultaneously promote the development of corresponding data processing and imaging technologies. Compressed sensing technology primarily involves assuming that the measured data contains signals with sparse properties, then using appropriate basis functions to characterize these sparse properties, ensuring that these sparse characteristics are reflected by the basis functions. The key to compressed sensing seismic exploration technology lies in the reconstruction and recovery of irregular data; the quality of this reconstruction directly determines the success or failure of compressed sensing exploration technology.

[0003] In the existing technology, the commonly used reconstruction technology is the data reconstruction technology based on sparse domain constraint inversion. This technology is based on the fact that the data to be processed is sparse or compressible in a certain transform domain. By constructing constraint terms, it uses optimization methods to recover irregular data, which has high reconstruction accuracy.

[0004] However, existing data reconstruction techniques based on sparse domain constraint inversion cannot select different reconstruction strategies for data with different signal-to-noise ratios. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention are proposed to provide a method, apparatus, electronic device and storage medium for irregular data reconstruction that overcomes or at least partially solves the above problems.

[0006] According to a first aspect of the present invention, a method for reconstructing irregular data is provided, characterized in that the method comprises:

[0007] Obtain irregular data;

[0008] The irregular data is pre-reconstructed to generate the first data;

[0009] The first data is preprocessed to generate the second data;

[0010] Obtain the signal-to-noise ratio control factor of the second data;

[0011] The second data is reconstructed using the signal-to-noise ratio control factor to generate the third data.

[0012] Optionally, obtaining irregular data includes:

[0013] Data collected using compressed sensing exploration technology.

[0014] Optionally, the first data includes: first simulated data and first collected data.

[0015] Optionally, the preprocessing of the first data to generate the second data includes:

[0016] The first data is preprocessed to generate the second simulated data and the second collected data;

[0017] Remove the second simulated data to generate second data, wherein the second data is the second collected data;

[0018] Optionally, the signal-to-noise ratio control factor for obtaining the second data includes:

[0019] Determine whether the second data meets preset conditions, the preset conditions include: a first preset condition and a second preset condition, wherein the first preset condition includes meeting seismic migration imaging processing, and the second preset condition includes data quality requirements;

[0020] If the first preset condition is met, the signal-to-noise ratio control factor is 1;

[0021] If the first preset condition is not met, the signal-to-noise ratio control factor is 0;

[0022] If the second preset condition is met, obtain the signal-to-noise ratio of the second data;

[0023] When the signal-to-noise ratio of the second data is greater than a preset threshold, the signal-to-noise ratio control factor is 1;

[0024] When the signal-to-noise ratio (SNR) of the second data is less than a preset threshold, the SNR control factor is 0. Optionally, when the SNR control factor is 1, it includes: not changing the SNR of the second data.

[0025] Optionally, when the signal-to-noise ratio control factor is 0, it includes: increasing the signal-to-noise ratio of the second data.

[0026] According to a second aspect of the present invention, an irregular data reconstruction apparatus is provided, the apparatus comprising:

[0027] The first acquisition module is used to acquire irregular data;

[0028] The first processing module is used to perform pre-reconstruction processing on the irregular data to generate the first data;

[0029] The second processing module is used to preprocess the first data to generate the second data;

[0030] The second acquisition module is used to acquire the signal-to-noise ratio control factor of the second data;

[0031] The third processing module reconstructs the second data using the signal-to-noise ratio control factor to generate the third data.

[0032] According to a third aspect of the present invention, an electronic device is provided, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0033] Memory, used to store computer programs;

[0034] A processor is used to execute programs stored in memory.

[0035] According to a fourth aspect of the present invention, a computer-readable storage medium is provided on which a computer program is stored.

[0036] This invention provides a method, apparatus, electronic device, and storage medium for reconstructing irregular data. The method involves acquiring irregular data; performing pre-reconstruction processing on the irregular data to generate first data; pre-processing the first data to generate second data; acquiring the signal-to-noise ratio (SNR) control factor of the second data; and reconstructing the second data using the SNR control factor to generate third data. This solves the problem of selecting different reconstruction strategies based on data with different SNRs. By increasing the SNR control factor, different reconstruction strategies are selected according to different SNR regions, reconstructing irregular seismic data in compressed sensing exploration technology, improving the SNR and accuracy of reconstructed irregular acquired data, and enhancing seismic imaging quality. This application optimizes the irregular seismic data reconstruction process by selecting different reconstruction strategies according to different SNR regions, improving both the SNR and data fidelity.

[0037] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 This is a flowchart of an irregular data reconstruction method provided in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of an irregular data reconstruction device provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of a storage medium provided in an embodiment of the present invention;

[0043] Figure 5 This is a flowchart of another irregular data reconstruction method provided in an embodiment of the present invention. Detailed Implementation

[0044] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0045] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0046] The following description, in conjunction with the accompanying drawings, details the method, apparatus, electronic device, and storage medium for irregular data reconstruction provided in this application through specific embodiments and application scenarios.

[0047] The first embodiment of the present invention relates to a method for reconstructing irregular data, the flowchart of which is shown below. Figure 1The above includes:

[0048] Step 101: Obtain irregular data.

[0049] It should be noted that, in this embodiment, irregular data specifically refers to irregular seismic data collected through compressed sensing exploration technology during seismic exploration.

[0050] When conducting seismic exploration in complex areas, it is often necessary to encounter situations where it is difficult or even impossible to place geophones, such as cliffs, rivers, ravines, villages, and industrial areas. This can make data acquisition difficult or even impossible. In order to reduce costs, it may be necessary to reduce the number of geophones in space. Therefore, it is necessary to process the irregular data acquired in step 102.

[0051] Step 102: Perform pre-reconstruction processing on the irregular data to generate the first data.

[0052] It should be noted that in this embodiment, the irregular data undergoes pre-reconstruction processing. The pre-reconstruction processing in this application refers to pre-reconstruction, which uses existing reconstruction techniques to fill in missing data in the irregular seismic data collected by overcompressed sensing exploration technology due to various reasons. The result of pre-processing the original irregular data without generating the first data is very accurate. This step is beneficial for using the reconstruction technology in this application to perform some pre-stack preprocessing before the final reconstruction, such as denoising, static correction, amplitude compensation, etc., because these techniques are applied to the collected data as regular sampled data. In this application, by performing pre-stack preprocessing on regular data, the final data reconstruction result can be accurate.

[0053] It should be noted that the pre-reconstruction processing in this application can choose conventional interpolation techniques, such as inversion-type reconstruction techniques, and this application does not make any specific limitations.

[0054] Step 103: Preprocess the first data to generate the second data.

[0055] It should be noted that in this embodiment, in step 102, the irregular seismic data is pre-reconstructed to generate first data. The first data includes first simulated data and first acquired data. The first acquired data refers to the seismic data actually acquired during the seismic exploration process. The first simulated data refers to the data where, due to geological or human factors, it is impossible to obtain more and denser sampling points during the seismic exploration process, a void 0 is artificially inserted into the actual acquired data. The missing parts in the irregular data (with void 0) are restored using the existing reconstruction technology in step 102 to obtain complete first data (regular data). The first data is then pre-processed. The pre-processing includes, but is not limited to, techniques such as noise reduction, static correction, amplitude compensation, and deconvolution. This application does not make specific limitations on these techniques.

[0056] Furthermore, such as Figure 5 As shown, step 103, which involves preprocessing the first data to generate the second data, includes:

[0057] Step 1031: Preprocess the first data to generate second simulated data and second collected data.

[0058] It should be noted that the first data is preprocessed. The first data includes the first simulated data and the first acquired data, as described in step 103 above. Therefore, the corresponding second simulated data and second acquired data are generated after preprocessing.

[0059] Step 1032: Remove the second simulated data and generate the second data, wherein the second data is the second collected data.

[0060] It should be noted that in the embodiments of this application, the second data is the second collected data, which is the second simulated data generated by removing the first data and performing preprocessing, that is, the processing result of data that is not actually collected.

[0061] Step 104: Obtain the signal-to-noise ratio control factor of the second data.

[0062] Furthermore, such as Figure 5 As shown, the signal-to-noise ratio control factors for obtaining the second data include:

[0063] Step 1041: Determine whether the second data meets preset conditions. The preset conditions include: a first preset condition and a second preset condition. The first preset condition includes meeting seismic migration imaging processing requirements, and the second preset condition includes data quality requirements.

[0064] It should be noted that the signal-to-noise ratio control factor is determined by whether the subsequent seismic migration imaging processing and the second-party data quality requirements are met, and the value is either 0 or 1. As long as any of the preset conditions are met, there will be a corresponding value.

[0065] Step 1042: If the first preset condition is met, the signal-to-noise ratio control factor is 1; if the first preset condition is not met, the signal-to-noise ratio control factor is 0.

[0066] It should be noted that if the first preset condition is met, i.e., the subsequent seismic migration imaging processing is satisfied, the signal-to-noise ratio of the second data is high, and the strategy of retaining the original data is adopted, with the signal-to-noise ratio control factor set to 1; if the first preset condition is not met, i.e. the subsequent seismic migration imaging processing is not satisfied, the signal-to-noise ratio of the second data is low, and the signal-to-noise ratio of the second data needs to be improved, with the signal-to-noise ratio control factor set to 0.

[0067] Step 1043: If the second preset condition is met, obtain the signal-to-noise ratio of the second data; when the signal-to-noise ratio of the second data is greater than a preset threshold, the signal-to-noise ratio control factor is 1; when the signal-to-noise ratio of the second data is less than the preset threshold, the signal-to-noise ratio control factor is 0.

[0068] It should be noted that the relationship between the signal-to-noise ratio of the second data and the preset threshold is determined. The preset threshold is specified according to the data quality requirements of the second party (Party A). Different second parties (Party A) may determine different thresholds in specific implementations. Therefore, this application does not make specific limitations.

[0069] Step 105: The second data is reconstructed using the signal-to-noise ratio control factor to generate the third data.

[0070] It should be noted that, under normal circumstances, the problem of reconstructing irregular data can be described as follows:

[0071] d obs =Md (Formula 1)

[0072] Where, d obs This represents the collected irregular data, M represents the irregular sampling operator, and d represents the fully collected data.

[0073] Equation 1 can be solved using the least squares optimization method, yielding:

[0074]

[0075] Where R represents the regularization constraint term, This represents the L2 mode.

[0076] The general solution to Equation 2 obtained using the sparse domain iterative thresholding method is expressed as:

[0077] d n+1 =ST λ S H {d n +d obs -Md n} (Formula 3)

[0078] Where, d n S represents the result of the nth iteration, and S represents the sparse transform, which can be Fourier, wavelet, curvelet, shearlet, etc. H The expression represents the inverse transform of the threshold function, S represents the threshold function, typically a hard threshold function or a soft threshold function; T λ This represents the set constraint threshold, λ, which is defined by the user.

[0079] However, the above reconstruction method cannot select different reconstruction strategies according to different signal-to-noise ratio regions, so the signal-to-noise ratio control factor α is added in step 104.

[0080] Equation 3 was optimized by increasing the signal-to-noise ratio control factor α, and the improved reconstruction formula is shown in Equation 4:

[0081] d n+1 =αd obs +(I-αM)ST λ S H {[I-(1-α)M]d n +(1-α)d obs} (Formula 4)

[0082] In Formula 4 above, α is the signal-to-noise ratio control factor, I represents the identity matrix, S is selected as curvelet transform in this embodiment, and T is selected in this application. λ Let λ be a soft thresholding function, and let λ be an adaptive decay threshold. k =τ k,max (1-a (N+1-k) ) represents the threshold selected in the k-th iteration, k = 1, ..., N, where N represents the iteration number, τ k,max It represents the maximum sparsity coefficient of the data in the sparse domain during the k-th iteration.

[0083] Furthermore, after determining the signal-to-noise ratio control factor α in step 104, the second data is reconstructed using the optimized formula 4 above, and the final reconstruction result is output. This final output result is used for subsequent seismic migration image processing, wherein the seismic migration image processing is not specifically limited in this application.

[0084] This specific embodiment provides a method for reconstructing irregular data. The method involves acquiring irregular data; performing pre-reconstruction processing on the irregular data to generate first data; pre-processing the first data to generate second data; acquiring the signal-to-noise ratio (SNR) control factor of the second data; and reconstructing the second data using the SNR control factor to generate third data. This solves the problem of selecting different reconstruction strategies based on data with different SNRs. By increasing the SNR control factor, the reconstruction technology for irregular seismic data is optimized, enabling the selection of different reconstruction strategies based on different SNR regions. This method reconstructs irregular seismic data in compressed sensing exploration, improving the SNR and accuracy of reconstructed irregular data and enhancing seismic imaging quality. This application optimizes the irregular seismic data reconstruction process by selecting different reconstruction strategies based on different SNR regions. While reconstructing missing data, it also improves the SNR of the original data. After reconstruction, the main structural morphology of the original data can still be well recovered, and the recovered signal has a higher degree of consistency with the original data, exhibiting better noise resistance. It improves both the SNR and data fidelity.

[0085] The second embodiment of the present invention relates to an irregular data reconstruction apparatus 200, as shown in the figure. Figure 2 Specifically, the device may include:

[0086] The first acquisition module 201 is used to acquire irregular data;

[0087] The first processing module 202 is used to perform pre-reconstruction processing on the irregular data to generate first data;

[0088] The second processing module 203 is used to preprocess the first data to generate the second data;

[0089] The second acquisition module 204 is used to acquire the signal-to-noise ratio control factor of the second data;

[0090] The third processing module 205 reconstructs the second data using the signal-to-noise ratio control factor to generate the third data.

[0091] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0092] Furthermore, based on the same inventive concept, specific embodiments of this application also provide an electronic device 300, such as... Figure 3 As shown, the system includes a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps:

[0093] Obtain irregular data;

[0094] The irregular data is pre-reconstructed to generate the first data;

[0095] The first data is preprocessed to generate the second data;

[0096] Obtain the signal-to-noise ratio control factor of the second data;

[0097] The second data is reconstructed using the signal-to-noise ratio control factor to generate the third data.

[0098] Based on the same inventive concept, in specific embodiments of this application, when the processor 320 executes the computer program 311, it can implement any of the methods in the embodiments of the present invention.

[0099] Since the electronic devices described in the specific embodiments of this application are devices used to implement the methods of the embodiments of this invention, those skilled in the art can understand the specific structure and variations of the devices based on the methods described in the embodiments of this invention, and therefore will not be described in detail here. All devices used in the methods of the embodiments of this invention fall within the scope of protection of this invention.

[0100] Based on the same inventive concept, specific embodiments of this application also provide storage media corresponding to the methods in the embodiments: This embodiment provides a computer-readable storage medium 400, such as... Figure 4 As shown, a computer program 411 is stored thereon, which, when executed by a processor, performs the following steps:

[0101] Obtain irregular data;

[0102] The irregular data is pre-reconstructed to generate the first data;

[0103] The first data is preprocessed to generate the second data;

[0104] Obtain the signal-to-noise ratio control factor of the second data;

[0105] The second data is reconstructed using the signal-to-noise ratio control factor to generate the third data.

[0106] In practice, when the computer program 411 is executed by the processor, it can implement any of the methods in the specific embodiments of this application.

[0107] The technical solutions provided in the specific embodiments of this application have at least the following technical effects or advantages:

[0108] This specific embodiment provides an irregular data reconstruction device, which acquires irregular data; performs pre-reconstruction processing on the irregular data to generate first data; pre-processes the first data to generate second data; acquires the signal-to-noise ratio (SNR) control factor of the second data; and reconstructs the second data using the SNR control factor to generate third data. This solves the problem of selecting different reconstruction strategies based on data with different SNRs. By increasing the SNR control factor, the reconstruction technology for irregular seismic data is optimized, enabling the selection of different reconstruction strategies based on different SNR regions. This improves the SNR and accuracy of reconstructed irregular seismic data in compressed sensing exploration, thereby enhancing seismic imaging quality. This application optimizes the irregular seismic data reconstruction process by selecting different reconstruction strategies based on different SNR regions. While reconstructing missing data, it also improves the SNR of the original data. After reconstruction, the main structural morphology of the original data can still be well recovered, and the recovered signal has a higher degree of consistency with the original data, exhibiting better noise resistance. It improves both the SNR and data fidelity.

[0109] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0110] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, storable media, and processors. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] In a typical configuration, the computer device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media. Computer-readable media includes both permanent and non-persistent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined in this article, computer-readable media do not include transient media, such as modulated data signals and carrier waves.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0116] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0117] The above provides a detailed description of the irregular data reconstruction method, apparatus, electronic device, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method of reconstructing irregular data, characterized by, The method comprises: acquiring irregular data; performing pre-reconstruction processing on the irregular data to generate first data; performing preprocessing on the first data to generate second data; acquiring a signal-to-noise ratio control factor of the second data; performing reconstruction processing on the second data through the signal-to-noise ratio control factor to generate third data; The acquiring of the signal-to-noise ratio control factor of the second data comprises: determining whether the second data meets preset conditions, wherein the preset conditions comprise a first preset condition and a second preset condition, the first preset condition comprises meeting seismic migration imaging processing, and the second preset condition comprises a data quality requirement; if the first preset condition is met, the signal-to-noise ratio control factor is 1; if the first preset condition is not met, the signal-to-noise ratio control factor is 0; if the second preset condition is met, the signal-to-noise ratio of the second data is acquired; when the signal-to-noise ratio of the second data is greater than a preset threshold, the signal-to-noise ratio control factor is 1; when the signal-to-noise ratio of the second data is less than the preset threshold, the signal-to-noise ratio control factor is 0.

2. The method of claim 1, wherein, The acquiring of the irregular data comprises: acquiring seismic data collected through a compressed sensing exploration technology.

3. The method of claim 1, wherein, The first data comprises first simulation data and first acquisition data.

4. The method of claim 1, wherein, The preprocessing of the first data to generate second data comprises: performing preprocessing on the first data to generate second simulation data and second acquisition data; removing the second simulation data to generate second data, wherein the second data is the second acquisition data.

5. The method of claim 1, wherein, When the signal-to-noise ratio control factor is 1, the signal-to-noise ratio of the second data is not changed.

6. The method of claim 1, wherein, When the signal-to-noise ratio control factor is 0, the signal-to-noise ratio of the second data is improved.

7. An apparatus for reconstructing irregular data, characterized by comprising: comprise: a first acquiring module configured to acquire irregular data; a first processing module configured to perform pre-reconstruction processing on the irregular data to generate first data; a second processing module configured to perform preprocessing on the first data to generate second data; a second acquiring module configured to acquire a signal-to-noise ratio control factor of the second data; a third processing module configured to perform reconstruction processing on the second data through the signal-to-noise ratio control factor to generate third data; The acquiring of the signal-to-noise ratio control factor of the second data comprises: determining whether the second data meets preset conditions, wherein the preset conditions comprise a first preset condition and a second preset condition, the first preset condition comprises meeting seismic migration imaging processing, and the second preset condition comprises a data quality requirement; if the first preset condition is met, the signal-to-noise ratio control factor is 1; if the first preset condition is not met, the signal-to-noise ratio control factor is 0; if the second preset condition is met, the signal-to-noise ratio of the second data is acquired; when the signal-to-noise ratio of the second data is greater than a preset threshold, the signal-to-noise ratio control factor is 1; when the signal-to-noise ratio of the second data is less than the preset threshold, the signal-to-noise ratio control factor is 0.

8. An electronic device, comprising: comprise a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete mutual communication through the communication bus; the memory is configured to store a computer program; A processor for implementing the method of any one of claims 1 to 6 when executing a program stored on a memory.

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

Images