Seismic attribute fusion processing method and device, medium, equipment and program product

Abstract

The invention relates to a seismic attribute fusion processing method and device, a medium, equipment and a program product, and relates to the field of seismic data interpretation. The method comprises the following steps: carrying out attribute extraction on coal field pre-stack depth migration seismic data to obtain multiple pieces of attribute data; iteratively updating the initial dictionary corresponding to each piece of attribute data through a K-SVD algorithm, determining an over-complete dictionary and a sparse coefficient matrix corresponding to each piece of attribute data, and obtaining a plurality of over-complete dictionaries and a plurality of sparse coefficient matrixes; and reconstructing according to the plurality of over-complete dictionaries and the plurality of sparse coefficient matrixes to obtain a fused attribute image of the plurality of pieces of attribute data. The multiple pieces of attribute data are iteratively updated, and reconstruction is carried out according to the attribute data after iteration updating, so that a fusion attribute image with higher precision can be obtained.

Description

Technical Field

[0001] The present disclosure relates to the field of seismic data interpretation, and in particular, to a method, apparatus, medium, device, and program product for seismic attribute fusion processing. Background Art

[0002] In pre-stack depth migration seismic data, seismic attributes, as important characteristic quantities for describing geological information such as formation structure, tectonics, and lithology, are indispensable parameters in seismic data interpretation; seismic attributes not only contain rich geological information, which is the result of the overall reaction of various geological factors such as underground formations, tectonics, and lithology, but also can extract many useful information about lithology and reservoir physical properties hidden therein. However, seismic attributes, especially single seismic attributes, cannot completely reflect the true underground morphology. With the continuous improvement of the accuracy requirements for the description of fine structures in coal mining areas, the analysis of single seismic attributes in pre-stack depth migration seismic data of coalfields can no longer meet the requirements of reservoir prediction accuracy. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a method, apparatus, medium, device, and program product for seismic attribute fusion processing.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a method for seismic attribute fusion processing, including:

[0005] Performing attribute extraction on pre-stack depth migration seismic data of a coalfield to obtain a plurality of attribute data;

[0006] Iteratively updating an initial dictionary corresponding to each of the attribute data through a K-SVD algorithm to determine an over-complete dictionary and a sparse coefficient matrix corresponding to each of the attribute data, and obtaining a plurality of over-complete dictionaries and a plurality of sparse coefficient matrices;

[0007] Performing reconstruction according to the plurality of over-complete dictionaries and the plurality of sparse coefficient matrices to obtain a fused attribute image of the plurality of attribute data.

[0008] Optionally, the iteratively updating an initial dictionary corresponding to each of the attribute data through a K-SVD algorithm to determine an over-complete dictionary and a sparse coefficient matrix corresponding to each of the attribute data, and obtaining a plurality of over-complete dictionaries and a plurality of sparse coefficient matrices includes:

[0009] Constructing a first initial dictionary corresponding to first attribute data; where the first attribute data is any one of the plurality of attribute data;

[0010] Iteratively updating each atom in the first initial dictionary;

[0011] When the iteration termination condition is satisfied, determine the first overcomplete dictionary and the first sparse coefficient matrix corresponding to the first attribute data.

[0012] Optionally, the constructing the first initial dictionary corresponding to the first attribute data includes:

[0013] Perform sliding partitioning on the first attribute data to obtain a plurality of attribute block data;

[0014] Construct the first initial dictionary corresponding to the first attribute data according to the plurality of attribute block data.

[0015] Optionally, the iteratively updating each atom in the first initial dictionary includes:

[0016] In each iteration process, determine the first sparse coefficient matrix corresponding to the first initial dictionary through the OMP algorithm;

[0017] Update each atom in the first initial dictionary according to the first sparse coefficient matrix to obtain a second dictionary, and use the second dictionary as the first initial dictionary for the next iteration;

[0018] When the iteration termination condition is satisfied, determine the overcomplete dictionary and the sparse coefficient matrix corresponding to the first attribute data.

[0019] Optionally, the iteration termination condition includes:

[0020] When the number of iterations of the first initial dictionary is greater than a preset iteration number threshold, determine that the iteration termination condition is satisfied;

[0021] Or,

[0022] When the reconstruction error of the first initial dictionary is less than a preset error threshold, stop the iteration and determine that the iteration termination condition is satisfied.

[0023] Optionally, the reconstructing the fused attribute image of the plurality of attribute data according to the plurality of overcomplete dictionaries and the plurality of sparse coefficient matrices includes:

[0024] Perform dictionary fusion on the plurality of overcomplete dictionaries to obtain a fused attribute overcomplete dictionary;

[0025] Perform sparse coefficient fusion on the plurality of sparse coefficient matrices to obtain a sparse fusion matrix;

[0026] Reconstruct the prestack depth migration seismic data of the coalfield according to the attribute fusion overcomplete dictionary and the sparse fusion matrix.

[0027] According to a second aspect of the embodiments of the present disclosure, there is provided a seismic attribute fusion processing device, including:

[0028] An acquisition module, configured to perform attribute extraction on pre-stack depth migration seismic data of a coalfield to obtain a plurality of attribute data;

[0029] A processing module, configured to iteratively update an initial dictionary corresponding to each of the attribute data through a K-SVD algorithm, determine an over-complete dictionary and a sparse coefficient matrix corresponding to each of the attribute data, and obtain a plurality of over-complete dictionaries and a plurality of sparse coefficient matrices;

[0030] A reconstruction module, configured to perform reconstruction according to the plurality of over-complete dictionaries and the plurality of sparse coefficient matrices to obtain a fused attribute image of the plurality of attribute data.

[0031] According to a third aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, on which computer program instructions are stored, and when the program instructions are executed by a processor, the steps of the seismic attribute fusion processing method provided in the first aspect of the present disclosure are implemented.

[0032] According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic device, including:

[0033] A memory, on which a computer program is stored;

[0034] A processor, configured to execute the computer program in the memory to implement the steps of the seismic attribute fusion processing method provided in the first aspect of the present disclosure.

[0035] According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the seismic attribute fusion processing method provided in the first aspect of the present disclosure are implemented.

[0036] Through the above technical solutions, attribute extraction is performed on pre-stack depth migration seismic data of a coalfield to obtain a plurality of attribute data; the initial dictionary corresponding to each of the attribute data is iteratively updated through a K-SVD algorithm, an over-complete dictionary and a sparse coefficient matrix corresponding to each of the attribute data are determined, and a plurality of over-complete dictionaries and a plurality of sparse coefficient matrices are obtained; reconstruction is performed according to the plurality of over-complete dictionaries and the plurality of sparse coefficient matrices to obtain a fused attribute image of the plurality of attribute data. By performing iterative updates on the plurality of attribute data respectively and then performing reconstruction according to the iteratively updated attribute data, a fused attribute image with higher accuracy can be obtained.

[0037] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present disclosure, and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the accompanying drawings:

[0039] Figure 1 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment.

[0040] Figure 2 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment.

[0041] Figure 3 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment.

[0042] Figure 4 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment.

[0043] Figure 5 is a schematic diagram of a seismic attribute fusion processing device 500 shown according to an exemplary embodiment.

[0044] Figure 6 is a block diagram of an electronic device 600 shown according to an exemplary embodiment. Detailed Description of the Invention

[0045] The following provides a detailed description of the specific embodiments of the present disclosure in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0046] It should be noted that all actions of obtaining signals, information, or data in the present disclosure are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located, and with the authorization given by the owner of the corresponding device.

[0047] Figure 1 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment. As Figure 1 shown, the method includes the following steps:

[0048] In step S11, attribute extraction is performed on the pre-stack depth migration seismic data of the coalfield to obtain a plurality of attribute data.

[0049] Exemplarily, in the prestack depth migration seismic attribute analysis of coalfields, seismic attributes are important tools for describing and quantifying prestack depth migration seismic data of coalfields and are subsets of the information contained in the prestack depth migration seismic data of coalfields. These attributes can extract information related to lithology and reservoir physical properties from the prestack depth migration seismic data of coalfields. The classification of seismic attributes generally includes but is not limited to the following: amplitude attributes, frequency attributes, phase attributes, energy attributes, waveform attributes, correlation attributes, attenuation attributes, ratio attributes, etc.; these attributes can not only be used in fields such as seismic structural interpretation, stratigraphic analysis, reservoir characterization, and reservoir dynamic monitoring, but also play an increasingly important role in coalfield exploration and development. Through seismic attribute analysis, the underground geological structure can be predicted and described more accurately, providing a scientific basis for the efficient development of coal resources. Therefore, attribute extraction can be performed on the prestack depth migration seismic data of coalfields according to the task objective to obtain the attribute data corresponding to the task objective, where the task objective can be multiple attribute data that need to be analyzed.

[0050] In step S12, the initial dictionary corresponding to each piece of the attribute data is iteratively updated through the K-SVD algorithm to determine the overcomplete dictionary and the sparse coefficient matrix corresponding to each piece of the attribute data, obtaining multiple overcomplete dictionaries and multiple sparse coefficient matrices.

[0051] Exemplarily, the K-SVD (K-Singular Value Decomposition) algorithm is an algorithm for designing an overcomplete dictionary for sparse representation. In the process of seismic attribute data processing, this algorithm can be used for denoising and reconstruction of seismic signals. By sparsely representing the seismic attribute data on the overcomplete dictionary and utilizing the different manifestations of seismic signals and random noise in the sparse domain, denoising of the seismic attribute data is realized, and the seismic signals are represented more accurately.

[0052] The process of iteratively updating any piece of attribute data through the K-SVD algorithm includes the following two main steps:

[0053] Sparse coding: Based on the constructed initial dictionary, a pursuit algorithm (such as Orthogonal Matching Pursuit OMP or FOCUSS for underdetermined systems) is used to calculate the sparse coefficients of the attribute data. These coefficients represent the linear combination of the signal on the dictionary atoms, thus approximately representing the original signal.

[0054] Dictionary update: The dictionary is continuously updated according to the sparse coefficients and the observation vector. In each iteration, only one atom of the dictionary is updated while keeping the other atoms unchanged. The update process uses Singular Value Decomposition (SVD) to find the optimal atom and the corresponding sparse coefficients to minimize the reconstruction error.

[0055] Through multiple iterations, an over-complete dictionary corresponding to the attribute data and a corresponding sparse coefficient matrix can be obtained. In summary, by separately performing iterative updates on each of the multiple attribute data through the above K-SVD algorithm, an over-complete dictionary corresponding to each of the multiple attribute data and a sparse matrix corresponding to the over-complete dictionary can be obtained.

[0056] In step S13, based on the multiple over-complete dictionaries and the multiple sparse coefficient matrices, reconstruction is performed to obtain a fused attribute image of the multiple attribute data.

[0057] Exemplarily, by reconstructing the pre-stack depth migration seismic data of the coalfield through the multiple over-complete dictionaries and the multiple sparse coefficient matrices, a fused attribute image of the multiple attribute data with higher accuracy can be obtained. For example, by reconstructing the attribute image corresponding to each attribute data according to the over-complete dictionary corresponding to each attribute data and the sparse coefficient matrix corresponding to the over-complete dictionary, and then fusing the multiple attribute images, a fused attribute image of the multiple attribute data is obtained. Or, by fusing the multiple over-complete dictionaries, then fusing the multiple sparse matrices, and then determining the fused attribute image of the multiple attribute data according to the fusion results of the two.

[0058] Through the above technical solution, attribute extraction is performed on the pre-stack depth migration seismic data of the coalfield to obtain multiple attribute data; the initial dictionary corresponding to each of the attribute data is iteratively updated through the K-SVD algorithm to determine the over-complete dictionary and the sparse coefficient matrix corresponding to each of the attribute data, obtaining multiple over-complete dictionaries and multiple sparse coefficient matrices; based on the multiple over-complete dictionaries and the multiple sparse coefficient matrices, reconstruction is performed to obtain a fused attribute image of the multiple attribute data. By separately performing iterative updates on the multiple attribute data and then performing reconstruction based on the iteratively updated attribute data, a fused attribute image with higher accuracy can be obtained.

[0059] Figure 2 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment. As Figure 2 shown, step S12 includes the following steps:

[0060] In step S121, a first initial dictionary corresponding to the first attribute data is constructed; where the first attribute data is any one of the multiple attribute data.

[0061] Optionally, step S121 includes the following steps:

[0062] The first attribute data is subjected to sliding block division to obtain multiple attribute block data;

[0063] Based on the multiple attribute block data, a first initial dictionary corresponding to the first attribute data is constructed.

[0064] Exemplarily, before iteratively updating any attribute data through the K-SVD algorithm, it is first necessary to construct an initial dictionary corresponding to the attribute data. Among them, sliding block means defining a window that slides on the attribute data at a certain step size. After each movement, the data area covered by the window is regarded as a block, and these attribute block data can be used to construct the initial dictionary. For example, randomly select some blocks from the data blocks as the atoms of the initial dictionary; or, select some of the most representative blocks in the data as the atoms of the initial dictionary, such as selecting the block with the largest variance; or, cluster or reduce the dimension of the data blocks through algorithms such as K-means or PCA (Principal Component Analysis), and then select the center point or the principal component as the atoms of the initial dictionary. The present disclosure does not limit the manner of constructing the initial dictionary corresponding to each attribute data, but the dimension of the initial dictionary corresponding to each attribute data should be the same.

[0065] In step S122, each atom in the first initial dictionary is iteratively updated.

[0066] In step S123, when the iteration termination condition is satisfied, the first overcomplete dictionary and the first sparse coefficient matrix corresponding to the first attribute data are determined.

[0067] Exemplarily, when iteratively updating any attribute data through the K-means algorithm and when the iteration termination condition is satisfied, the K-means algorithm can output the overcomplete dictionary and the sparse coefficient matrix corresponding to the attribute data.

[0068] Optionally, the iteration termination condition includes:

[0069] When the number of iterations of the first initial dictionary is greater than a preset iteration number threshold, it is determined that the iteration termination condition is satisfied.

[0070] Or,

[0071] When the reconstruction error of the first initial dictionary is less than a preset error threshold, stop the iteration and determine that the iteration termination condition is satisfied.

[0072] Exemplarily, when any of the following conditions is satisfied, it can be determined that the iteration termination condition is satisfied:

[0073] (1) The number of iterations of the first initial dictionary is greater than a preset iteration number threshold

[0074] (2) The reconstruction error of the dictionary is less than a preset error threshold during two adjacent iterative update processes;

[0075] Among them, the reconstruction error can be determined by the reconstruction error calculation formula in the K-means algorithm, which belongs to the publicly available prior art and will not be elaborated in this disclosure. In addition, the preset error threshold can be determined by empirical values, and this disclosure places no restrictions on the preset error threshold.

[0076] Figure 3 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment. As Figure 3 shown, step S122 includes the following steps:

[0077] In step S1221, in each iteration process, the first sparse coefficient matrix corresponding to the first initial dictionary is determined by the OMP algorithm.

[0078] Exemplarily, the Orthogonal Matching Pursuit (OMP) algorithm is a greedy algorithm used to find the sparse representation of a signal given a dictionary. The goal of this algorithm is to minimize the reconstruction error while maintaining sparsity. For example, the orthogonal matching pursuit is performed through the following formula:

[0079]

[0080] where Y represents the first attribute data, and the first attribute data is any one of the multiple attribute data; D represents the dictionary in each iteration process, i represents the number of iterations, D (0) ∈R n×N ; X is the sparse coefficient in the dictionary D; ||x n ||0≤T represents the nth row of X, restricting the sparsity of the signal and measuring the sparse representation ability of the dictionary D; T represents the upper limit of the number of non-zero elements.

[0081] In step S1222, each atom in the first initial dictionary is updated according to the first sparse coefficient matrix to obtain a second dictionary, and the second dictionary is used as the first initial dictionary for the next iteration.

[0082] Exemplarily, the K-means algorithm is used to iteratively update any dictionary. In each iteration process, each atom in the dictionary needs to be updated in turn. After each atom in the dictionary is updated and the iteration termination condition of the K-means algorithm is not satisfied, the dictionary after this iteration update is used as the initial dictionary for the next iteration. Among them, the specific process of iteratively updating the initial dictionary by the K-means algorithm is prior art and this disclosure places no restrictions on it. In addition, the setting of relevant parameters in the algorithm can be empirical values or can be obtained by analyzing the experimental results after multiple experiments, and this disclosure places no restrictions on it. For example, the dictionary is updated through the following formula:

[0083]

[0084] Among them, Y represents the first attribute data, X is the sparse coefficient in the dictionary D, D represents the dictionary in each iteration process, i represents the iteration number, and d j represents the j-th column of the dictionary D, and x j represents the j-th row of X; E k represents the error after extracting the d k column, and d k represents the j-th column of the dictionary D, and x k represents the j-th row of X; the error can be reduced by adjusting d d x d .

[0085] To ensure the sparsity of X, record the columns where the non-zero elements in x k are located, and remove the zero element terms to obtain x ′ k , and only retain the columns in E k corresponding to the non-zero elements of x k to obtain E k ′ k , and record the corresponding dictionary column as d ′ k , and then perform singular value decomposition on E k ′ :

[0086] W k ′ k =U△V T

[0087] Among them, E k ′ is an m×n matrix; U is an m×m unitary matrix, △ is a semi-positive definite m×n diagonal matrix; V T is the conjugate transpose of V and is an n×n unitary matrix. In the above formula, replace d ′ k with the first column of U, and multiply the largest singular value of △ by the first row of V T to replace x ′ k to complete the dictionary update.

[0088] In step S1223, when the iteration termination condition is satisfied, determine the overcomplete dictionary and the sparse coefficient matrix corresponding to the first attribute data.

[0089] Exemplarily, in the case where the iteration termination condition is satisfied, the K-means algorithm can output the overcomplete dictionary corresponding to the attribute data and the sparse coefficient matrix corresponding to the overcomplete dictionary.

[0090] Figure 4 is a flowchart of a seismic attribute fusion processing method shown according to an exemplary embodiment. As Figure 4 shown, step S13 includes the following steps:

[0091] In step S131, dictionary fusion is performed on the multiple overcomplete dictionaries to obtain a fused attribute fusion overcomplete dictionary.

[0092] Exemplarily, for dictionary fusion of the multiple overcomplete dictionaries, the elements at the same position of the multiple overcomplete dictionaries can be summed and then averaged as the value at the same position of the attribute fusion overcomplete dictionary.

[0093] In step S132, sparse coefficient fusion is performed on the multiple sparse coefficient matrices to obtain a sparse fusion matrix.

[0094] Exemplarily, for sparse coefficient fusion of the multiple sparse coefficient matrices, the elements at the same position of the multiple sparse coefficient matrices can be summed and then averaged as the value at the same position of the sparse fusion matrix.

[0095] In step S133, the prestack depth migration seismic data of the coalfield is reconstructed according to the attribute fusion overcomplete dictionary and the sparse fusion matrix.

[0096] Exemplarily, according to the K-means algorithm, seismic data can be reconstructed through an overcomplete dictionary and the sparse coefficient matrix corresponding to the overcomplete dictionary. Therefore, the prestack depth migration seismic data of the coalfield can be reconstructed according to the attribute fusion overcomplete dictionary and the sparse fusion matrix.

[0097] Through the above technical solution, attribute extraction is performed on the prestack depth migration seismic data of the coalfield to obtain multiple attribute data; the initial dictionary corresponding to each attribute data is iteratively updated through the K-SVD algorithm to determine the overcomplete dictionary and the sparse coefficient matrix corresponding to each attribute data, obtaining multiple overcomplete dictionaries and multiple sparse coefficient matrices; reconstruction is performed according to the multiple overcomplete dictionaries and the multiple sparse coefficient matrices to obtain a fused attribute image of the multiple attribute data. By performing iterative updates on the multiple attribute data respectively and then reconstructing according to the iteratively updated attribute data, a fused attribute image with higher accuracy can be obtained.

[0098] Figure 5 is a schematic diagram of a seismic attribute fusion processing device 500 shown according to an exemplary embodiment. The device 500 includes: an acquisition module 510, a processing module 520, and a reconstruction module 530;

[0099] The acquisition module 510 is configured to perform attribute extraction on the prestack depth migration seismic data of the coalfield to obtain multiple attribute data;

[0100] The processing module 520 is configured to iteratively update the initial dictionary corresponding to each piece of the attribute data through the K-SVD algorithm, determine the over-complete dictionary and the sparse coefficient matrix corresponding to each piece of the attribute data, and obtain a plurality of over-complete dictionaries and a plurality of sparse coefficient matrices;

[0101] The reconstruction module 530 is configured to perform reconstruction according to the plurality of over-complete dictionaries and the plurality of sparse coefficient matrices to obtain a fused attribute image of the plurality of attribute data.

[0102] Optionally, the processing module 520 is configured to:

[0103] Construct a first initial dictionary corresponding to the first attribute data; wherein the first attribute data is any one of the plurality of attribute data;

[0104] Iteratively update each atom in the first initial dictionary;

[0105] When the iteration termination condition is satisfied, determine the first over-complete dictionary and the first sparse coefficient matrix corresponding to the first attribute data.

[0106] Optionally, the processing module 520 is further configured to:

[0107] Perform sliding partitioning on the first attribute data to obtain a plurality of attribute block data;

[0108] Optionally, the processing module 520 is further configured to:

[0109] In each iteration process, determine the first sparse coefficient matrix corresponding to the first initial dictionary through the OMP algorithm;

[0110] Update each atom in the first initial dictionary according to the first sparse coefficient matrix to obtain a second dictionary, and use the second dictionary as the first initial dictionary for the next iteration;

[0111] When the iteration termination condition is satisfied, determine the over-complete dictionary and the sparse coefficient matrix corresponding to the first attribute data.

[0112] Optionally, the iteration termination condition includes:

[0113] When the number of iterations of the first initial dictionary is greater than a preset iteration number threshold, it is determined that the iteration termination condition is satisfied;

[0114] Or,

[0115] When the reconstruction error of the first initial dictionary is less than a preset error threshold, stop the iteration and determine that the iteration termination condition is satisfied.

[0116] Optionally, the reconstruction module 530 is configured to:

[0117] Fuse the multiple overcomplete dictionaries to obtain a fused attribute-fused overcomplete dictionary;

[0118] Fuse the sparse coefficient matrices of the multiple sparse coefficient matrices to obtain a sparse fusion matrix;

[0119] Reconstruct the pre-stack depth migration seismic data of the coalfield according to the attribute-fused overcomplete dictionary and the sparse fusion matrix.

[0120] Construct a first initial dictionary corresponding to the first attribute data according to the multiple attribute block data.

[0121] Through the above technical solution, attribute extraction is performed on the pre-stack depth migration seismic data of the coalfield to obtain multiple attribute data; the initial dictionaries corresponding to each of the attribute data are iteratively updated by the K-SVD algorithm to determine the overcomplete dictionaries and sparse coefficient matrices corresponding to each of the attribute data, obtaining multiple overcomplete dictionaries and multiple sparse coefficient matrices; reconstruction is performed according to the multiple overcomplete dictionaries and the multiple sparse coefficient matrices to obtain a fused attribute image of the multiple attribute data. By performing iterative updates on the multiple attribute data respectively and then performing reconstruction according to the iteratively updated attribute data, a fused attribute image with higher accuracy can be obtained.

[0122] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be elaborated here.

[0123] Figure 6 is a block diagram of an electronic device 600 shown according to an exemplary embodiment. As Figure 6 shown, the electronic device 600 may include: a processor 601, a memory 602. The electronic device 600 may further include one or more of a multimedia component 603, an input / output (I / O) interface 604, and a communication component 605.

[0124] Among them, the processor 601 is used to control the overall operation of the electronic device 600 to complete all or part of the steps in the above-mentioned seismic attribute fusion processing method. The memory 602 is used to store various types of data to support the operation of the electronic device 600. These data may include, for example, instructions for any application or method operating on the electronic device 600, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc. The multimedia component 603 may include a screen and an audio component. Among them, the screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signals can be further stored in the memory 602 or sent through the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 604 provides an interface between the processor 601 and other interface modules. The above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 605 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0125] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned seismic attribute fusion processing method.

[0126] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When the program instructions are executed by a processor, the steps of the above-mentioned seismic attribute fusion processing method are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 602 including program instructions, and the above program instructions may be executed by the processor 601 of the electronic device 600 to complete the above-mentioned seismic attribute fusion processing method.

[0127] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device, and the computer program has a code part for executing the above-mentioned seismic attribute fusion processing method when executed by the programmable device.

[0128] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0129] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not separately describe various possible combination methods.

[0130] In addition, any combination can be made between various different embodiments of the present disclosure, as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A seismic attribute fusion processing method, characterized in that: include: Attributes are extracted from coalfield prestack depth migration seismic data to obtain multiple attribute data; Iteratively updating the initial dictionary corresponding to each attribute data by the K-SVD algorithm, determining the super-complete dictionary and the sparse coefficient matrix corresponding to each attribute data, and obtaining multiple super-complete dictionaries and multiple sparse coefficient matrices; Reconstruction is performed according to the multiple overcomplete dictionaries and the multiple sparse coefficient matrices to obtain a fused attribute image of the multiple attribute data.

2. The method according to claim 1, characterized in that: The iterative updating of the initial dictionary corresponding to each attribute data by the K-SVD algorithm, determining the super-complete dictionary and the sparse coefficient matrix corresponding to each attribute data, and obtaining multiple super-complete dictionaries and multiple sparse coefficient matrices, includes: Constructing a first initial dictionary corresponding to the first attribute data; wherein the first attribute data is any attribute data among the plurality of attribute data; Iteratively updating each atom in the first initial dictionary; When an iteration termination condition is met, a first overcomplete dictionary and a first sparse coefficient matrix corresponding to the first attribute data are determined.

3. The method according to claim 2, characterized in that The step of constructing a first initial dictionary corresponding to the first attribute data includes: Slidingly dividing the first attribute data into blocks to obtain a plurality of attribute block data; A first initial dictionary corresponding to the first attribute data is constructed according to the plurality of attribute block data.

4. The method according to claim 2, characterized in that: The iterative updating of each atom in the first initial dictionary comprises: In each iteration process, determining a first sparse coefficient matrix corresponding to the first initial dictionary by using an OMP algorithm; Update each atom in the first initial dictionary according to the first sparse coefficient matrix to obtain a second dictionary, and use the second dictionary as the first initial dictionary for the next iteration; When the iteration termination condition is met, an overcomplete dictionary and a sparse coefficient matrix corresponding to the first attribute data are determined.

5. The method according to claim 2, characterized in that: The iteration termination conditions include: When the number of iterations of the first initial dictionary is greater than a preset iteration number threshold, determining that an iteration termination condition is met; or, When the reconstruction error of the first initial dictionary is less than a preset error threshold, the iteration is stopped to determine whether the iteration termination condition is satisfied.

6. The method according to claim 1, characterized in that The reconstructing according to the multiple overcomplete dictionaries and the multiple sparse coefficient matrices to obtain a fused attribute image of the multiple attribute data includes: Performing dictionary fusion on the multiple super-complete dictionaries to obtain a fused attribute fusion super-complete dictionary; Performing sparse coefficient fusion on the multiple sparse coefficient matrices to obtain a sparse fusion matrix; The coalfield prestack depth migration seismic data is reconstructed according to the attribute fusion overcomplete dictionary and the sparse fusion matrix.

7. A seismic attribute fusion processing device, characterized in that: include: An acquisition module is used to extract attributes from coalfield prestack depth migration seismic data to obtain multiple attribute data; A processing module, used for iteratively updating the initial dictionary corresponding to each attribute data by using a K-SVD algorithm, determining an overcomplete dictionary and a sparse coefficient matrix corresponding to each attribute data, and obtaining a plurality of overcomplete dictionaries and a plurality of sparse coefficient matrices; A reconstruction module is used to reconstruct according to the multiple over-complete dictionaries and the multiple sparse coefficient matrices to obtain a fused attribute image of the multiple attribute data.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement 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 the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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