Seismic fault feature enhancement identification method and system based on optimal solution

Abstract

The application provides a kind of based on optimal solution Seismic Fault Feature Enhancement Identification Method and system, it is related to Seismic Fault Feature Identification technical field.The method includes obtaining Seismic Fault Image Data, feature seed point is picked up, and forms Seismic Fault Attribute Image;Seismic Fault Attribute Image is carried out nonlinear smoothing processing, and forms preprocessed fault image;Each feature seed point in preprocessed fault image is carried out dynamic programming, and the corresponding optimal surface path is determined;Different optimal surface paths are smoothed, and optimal selection score is determined;Optimal selection score graph is constructed according to optimal selection score;According to optimal selection score graph, fault feature information is extracted.The method enhances Seismic Fault Attribute Image, suppresses noise features irrelevant to fault, and at the same time makes fault features more clear and continuous, so as to improve the accuracy and efficiency of fault interpretation.

Description

Technical Field

[0001] This invention relates to the field of earthquake fault feature recognition technology, and more specifically, to an earthquake fault feature enhancement and recognition method and system based on optimal solutions. Background Technology

[0002] Seismic fault interpretation technology is a crucial aspect of geological exploration and the petroleum industry, relying on the accurate analysis of structural and stratigraphic features in seismic images. Existing technologies mainly include the following seismic attribute analysis methods:

[0003] 1. Semblance: Marfurt et al. (1998) proposed a semblance-based seismic attribute analysis method to detect the continuity or discontinuity of seismic reflections. Although semblance is effective in some cases, it can be sensitive to noise and has limitations in fault feature tracking.

[0004] 2. Coherency: Marfurt et al. (1999) and Li and Lu (2014) further developed coherency-based seismic attribute analysis techniques to enhance fault features in seismic images. However, these methods may not be accurate enough when dealing with complex geological structures.

[0005] 3. Variance: Van Bemmel and Pepper (2000) proposed variance-based seismic attribute analysis to quantify the variability of seismic data. Analysis of variance helps identify discontinuities in seismic images, but further optimization may be needed to distinguish between noise and fault features.

[0006] 4. Curvature: Roberts (2001) and Di & Gao (2016) used curvature properties to analyze fault and stratigraphic variations in seismic images. Curvature analysis can provide information about fault shape and orientation, but may be insufficient in terms of the continuity of fault features.

[0007] 5. Gradient Magnitude: Aqrawi and Boe (2011) used the gradient magnitude property to improve fault segmentation. While gradient magnitude can highlight abrupt changes in seismic images, it may be difficult to distinguish different types of geological features.

[0008] 6. Eigenstructure-Based Coherence Computations: Gersztenkorn and Marfurt (1999) proposed a characteristic-structure-based method to improve fault characteristics in seismic images. This method can improve the accuracy of fault interpretation in some cases, but may require complex computational processes.

[0009] 7. Ant Tracking Method: Pedersen et al. (2002, 2003) proposed a tracking method that simulates ant behavior to enhance fault features along fault paths. Although this method is effective in some cases, it may require significant computational resources and may not be efficient enough when dealing with large-scale datasets.

[0010] Therefore, designing an optimal solution-based method and system for enhancing and identifying seismic fault features, thereby enhancing the seismic fault attribute image, suppressing noise features unrelated to the fault, and making the fault features clearer and more continuous, thus improving the accuracy and efficiency of fault interpretation, is an urgent problem to be solved. Summary of the Invention

[0011] The purpose of this invention is to provide a seismic fault feature enhancement and identification method based on optimal solutions. This method automatically selects seed points using a dynamic programming algorithm, calculates the optimal surface path, and enhances fault features through nonlinear smoothing and selection mechanisms. This method not only improves the clarity and continuity of fault features but also has high efficiency, enabling rapid processing of large-scale seismic datasets.

[0012] The present invention also aims to provide an optimal solution-based seismic fault feature enhancement and recognition system. This system uses a data acquisition unit to calibrate feature seed points in fault images. Based on this, a preprocessing unit performs nonlinear smoothing and noise suppression. Finally, an identification and analysis unit determines the optimal surface path and selects scores to extract fault data. The combination of these different units forms a highly efficient whole for extracting fault feature information, which is the essential material basis for this process.

[0013] In a first aspect, the present invention provides a method for enhancing and recognizing seismic fault features based on optimal solutions, comprising: acquiring seismic fault image data, picking feature seed points to form a seismic fault attribute image; performing nonlinear smoothing on the seismic fault attribute image to form a preprocessed fault image; performing dynamic planning on each feature seed point in the preprocessed fault image to determine the corresponding optimal surface path; smoothing different optimal surface paths to determine the optimal selection score; constructing an optimal selection score map based on the optimal selection score; and extracting fault feature information based on the optimal selection score map.

[0014] In this invention, the method automatically selects seed points using a dynamic programming algorithm, calculates the optimal surface path, and enhances fault features through nonlinear smoothing and selection mechanisms. This method not only improves the clarity and continuity of fault features but also has high efficiency, enabling rapid processing of large-scale seismic datasets.

[0015] As one possible implementation, the seismic fault attribute image is nonlinearly smoothed to form a preprocessed fault image. This includes: nonlinearly smoothing the seismic fault attribute image according to the following formula to form a preprocessed fault image: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the image after nonlinear smoothing.

[0016] In this invention, the nonlinear smoothing process applied to the seismic fault attribute image is primarily aimed at enhancing fault-related features and suppressing noise to a certain extent. This serves two purposes: firstly, it enhances the fault features to a certain degree; secondly, it ensures that subsequent feature enhancement can be effectively implemented.

[0017] As one possible implementation, dynamic programming is performed on each feature seed point in the preprocessed tomographic image to determine the corresponding optimal surface path, including: setting local windows for different feature seed points; and performing dynamic programming within the local windows corresponding to different feature seed points to form the corresponding optimal surface path.

[0018] In this invention, a local window for feature seed points is established to ensure a more reasonable determination of the optimal surface path within a smaller range, thereby enabling the features in the local area to be fully enhanced.

[0019] As one possible implementation, dynamic programming is performed within the local window corresponding to different feature seed points to form the corresponding optimal surface path, including determining the optimal surface path using the following formula: The constraint is: |y(x+1)-y(x)|<β. Here, y(x) represents the optimal path, G(x,y) is the transposed fault attribute image, and β is the slope constraint.

[0020] In this invention, the optimal surface path is determined through dynamic programming to achieve more efficient data processing.

[0021] As one possible implementation, smoothing different optimal surface paths and determining the optimal selection score includes: applying Gaussian smoothing to different optimal surface paths to determine the corresponding optimal selection score S. k , where k represents the number of the different optimal surface paths.

[0022] In this invention, by smoothing the optimal surface, the continuity of fault attribute values ​​can be reasonably reflected.

[0023] As one possible implementation, an optimal choice score graph is constructed based on the optimal choice scores, including: accumulating all optimal choice scores to establish an optimal choice score graph M(x,y), where:

[0024] In this invention, the optimal selection score is accumulated to further enhance the characteristics of the fault and achieve efficient and accurate identification.

[0025] Secondly, the present invention provides an earthquake fault feature enhancement and recognition system based on optimal solutions. The system includes a data acquisition unit for acquiring earthquake fault image data and picking feature seed points to form an earthquake fault attribute image; a preprocessing unit for acquiring the earthquake fault attribute image formed by the data acquisition unit and performing nonlinear smoothing processing to form a preprocessed fault image; and a recognition and analysis unit for acquiring the preprocessed fault image formed by the preprocessing unit and performing selection score processing based on the optimal surface path to extract fault feature information.

[0026] In this invention, the system uses a data acquisition unit to calibrate feature seed points in the tomographic image. Based on this, a preprocessing unit performs nonlinear smoothing and noise suppression. Finally, an identification and analysis unit determines the optimal surface path and selects scores to extract the tomographic data. The combination of these different units forms a highly efficient whole for extracting tomographic feature information, which is the essential material basis for this process.

[0027] As one possible implementation, the preprocessing unit performs nonlinear smoothing of the seismic fault attribute image using the following formula: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the image after nonlinear smoothing.

[0028] In this invention, there are various ways to process the non-smoothing of earthquake fault attribute images. This application provides a specific implementation method.

[0029] As one possible implementation, the identification and analysis unit determines the optimal surface path for different feature seed points within a set local window.

[0030] In this invention, defining a local window to determine the optimal surface path for feature seed points can better enhance the fault features locally.

[0031] As one possible implementation, the identification and analysis unit determines the optimal selection analysis by performing Gaussian smoothing on the optimal surface path and establishes an optimal selection score map to extract fault feature information.

[0032] In this invention, there are various ways to extract tomographic enhancement features, and this application achieves this by determining the optimal selection score.

[0033] The beneficial effects of the earthquake fault feature enhancement and identification method and system based on optimal solution provided by this invention are as follows:

[0034] This method automatically selects seed points using a dynamic programming algorithm, calculates the optimal surface path, and enhances fault features through nonlinear smoothing and selection mechanisms. This approach not only improves the clarity and continuity of fault features but also boasts high efficiency, enabling rapid processing of large-scale seismic datasets.

[0035] The system uses a data acquisition unit to calibrate feature seed points in the fault image. Based on this, a preprocessing unit performs nonlinear smoothing and noise suppression. Finally, an identification and analysis unit determines the optimal surface path and selects scores to extract the fault data. The combination of these different units forms a highly efficient whole for extracting fault feature information, which is the essential material basis for this process. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating the steps of the earthquake fault feature enhancement and identification method based on optimal solution provided in this embodiment of the invention;

[0038] Figure 2 This is a schematic diagram of the structure of the earthquake fault feature enhancement and identification system based on the optimal solution provided in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0040] Seismic fault interpretation technology is a crucial aspect of geological exploration and the petroleum industry, relying on the accurate analysis of structural and stratigraphic features in seismic images. Existing technologies mainly include the following seismic attribute analysis methods:

[0041] 1. Semblance: Marfurt et al. (1998) proposed a semblance-based seismic attribute analysis method to detect the continuity or discontinuity of seismic reflections. Although semblance is effective in some cases, it can be sensitive to noise and has limitations in fault feature tracking.

[0042] 2. Coherency: Marfurt et al. (1999) and Li and Lu (2014) further developed coherency-based seismic attribute analysis techniques to enhance fault features in seismic images. However, these methods may not be accurate enough when dealing with complex geological structures.

[0043] 3. Variance: Van Bemmel and Pepper (2000) proposed variance-based seismic attribute analysis to quantify the variability of seismic data. Analysis of variance helps identify discontinuities in seismic images, but further optimization may be needed to distinguish between noise and fault features.

[0044] 4. Curvature: Roberts (2001) and Di & Gao (2016) used curvature properties to analyze fault and stratigraphic variations in seismic images. Curvature analysis can provide information about fault shape and orientation, but may be insufficient in terms of the continuity of fault features.

[0045] 5. Gradient Magnitude: Aqrawi and Boe (2011) used the gradient magnitude property to improve fault segmentation. While gradient magnitude can highlight abrupt changes in seismic images, it may be difficult to distinguish different types of geological features.

[0046] 6. Eigenstructure-Based Coherence Computations: Gersztenkorn and Marfurt (1999) proposed a characteristic-structure-based method to improve fault characteristics in seismic images. This method can improve the accuracy of fault interpretation in some cases, but may require complex computational processes.

[0047] 7. Ant Tracking Method: Pedersen et al. (2002, 2003) proposed a tracking method that simulates ant behavior to enhance fault features along fault paths. Although this method is effective in some cases, it may require significant computational resources and may not be efficient enough when dealing with large-scale datasets.

[0048] refer to Figures 1-2 This invention provides a method for enhancing and identifying seismic fault features based on optimal solutions. This method automatically selects seed points using a dynamic programming algorithm, calculates the optimal surface path, and enhances fault features through nonlinear smoothing and selection mechanisms. This method not only improves the clarity and continuity of fault features but also has high efficiency, enabling rapid processing of large-scale seismic datasets.

[0049] The specific steps of the earthquake fault feature enhancement and identification method based on optimal solution are as follows:

[0050] S1: Acquire seismic fault image data, extract feature seed points, and form a seismic fault attribute image.

[0051] Extracting feature seed points is beneficial for subsequent reasonable tomographic feature enhancement processing in local areas.

[0052] S2: Perform nonlinear smoothing on the seismic fault attribute image to form a preprocessed fault image.

[0053] The seismic fault attribute image is nonlinearly smoothed to form a preprocessed fault image. This process includes: nonlinearly smoothing the seismic fault attribute image according to the following formula to form a preprocessed fault image: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the image after nonlinear smoothing.

[0054] Nonlinear smoothing of seismic fault attribute images is primarily aimed at enhancing fault-related features and suppressing noise to some extent. This serves two purposes: firstly, it enhances fault features to a certain degree; secondly, it ensures the effective implementation of subsequent feature enhancements.

[0055] S3: Perform dynamic programming on each feature seed point in the preprocessed tomographic image to determine the corresponding optimal surface path.

[0056] Dynamic programming is performed on each feature seed point in the preprocessed tomographic image to determine the corresponding optimal surface path. This includes: setting local windows for different feature seed points; and performing dynamic programming within the local windows corresponding to different feature seed points to form the corresponding optimal surface path. Establishing local windows for feature seed points ensures a more reasonable determination of the optimal surface path within a smaller range, thereby allowing the features in the local region to be fully enhanced.

[0057] Dynamic programming is performed within the local window corresponding to different feature seed points to form the corresponding optimal surface path, including determining the optimal surface path using the following formula: The constraint is: |y(x+1)-y(x)|<β, where y(x) represents the optimal path, G(x,y) is the transposed fault attribute image, and β is the slope constraint. The optimal surface path is determined through dynamic programming to achieve more efficient data processing.

[0058] S4: Smooth different optimal surface paths to determine the optimal selection score.

[0059] Smoothing different optimal surface paths to determine the optimal selection score includes: applying Gaussian smoothing to different optimal surface paths and determining the corresponding optimal selection score S. k , where k represents the number of the different optimal surface paths. By smoothing the optimal surface, the continuity of fault attribute values ​​can be reasonably reflected.

[0060] S5: Construct an optimal selection score graph based on the optimal selection score.

[0061] Constructing an optimal choice score graph based on the optimal choice scores includes: summing all optimal choice scores to establish an optimal choice score graph M(x,y), where: The optimal selection scores are accumulated to further enhance the characteristics of the fault and achieve efficient and accurate identification.

[0062] S6: Select the fractional map based on the optimal selection and extract fault feature information.

[0063] After obtaining the optimal selection score map, reasonable fault feature information can be extracted.

[0064] The present invention also provides an optimal solution-based earthquake fault feature enhancement and recognition system. This system employs the aforementioned optimal solution-based earthquake fault feature enhancement and recognition method, comprising: a data acquisition unit for acquiring earthquake fault image data and picking feature seed points to form an earthquake fault attribute image; a preprocessing unit for acquiring the earthquake fault attribute image formed by the data acquisition unit and performing nonlinear smoothing processing to form a preprocessed fault image; and a recognition and analysis unit for acquiring the preprocessed fault image formed by the preprocessing unit and performing selection score processing based on the optimal surface path to extract fault feature information.

[0065] The system uses a data acquisition unit to calibrate feature seed points in the fault image. Based on this, a preprocessing unit performs nonlinear smoothing and noise suppression. Finally, an identification and analysis unit determines the optimal surface path and selects scores to extract the fault data. The combination of these different units forms a highly efficient whole for extracting fault feature information, which is the essential material basis for this process.

[0066] The preprocessing unit performs nonlinear smoothing of the seismic fault attribute image using the following formula: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the nonlinearly smoothed image. Various methods exist for non-smoothing the seismic fault attribute image; this application provides a specific implementation method.

[0067] The identification and analysis unit determines the optimal surface path for different feature seed points within a defined local window. Defining a local window to determine the optimal surface path for feature seed points allows for better local enhancement of fault features.

[0068] The identification and analysis unit determines the optimal selection analysis by performing Gaussian smoothing on the optimal surface path and establishes an optimal selection score map for fault feature extraction. There are various methods for extracting fault enhancement features; this application achieves this by determining the optimal selection score.

[0069] In summary, the beneficial effects of the earthquake fault feature enhancement and identification method and system based on optimal solution provided in the embodiments of the present invention are as follows:

[0070] This method automatically selects seed points using a dynamic programming algorithm, calculates the optimal surface path, and enhances fault features through nonlinear smoothing and selection mechanisms. This approach not only improves the clarity and continuity of fault features but also boasts high efficiency, enabling rapid processing of large-scale seismic datasets.

[0071] The system uses a data acquisition unit to calibrate feature seed points in the fault image. Based on this, a preprocessing unit performs nonlinear smoothing and noise suppression. Finally, an identification and analysis unit determines the optimal surface path and selects scores to extract the fault data. The combination of these different units forms a highly efficient whole for extracting fault feature information, which is the essential material basis for this process.

[0072] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0073] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0074] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0075] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0076] The “protocol” mentioned in this application embodiment may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. This application embodiment does not specifically limit this.

[0077] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0078] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0079] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0080] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0082] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0083] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0084] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0090] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0091] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for enhancing and identifying seismic fault features based on optimal solutions, characterized in that, include: Seismic fault image data is acquired, feature seed points are picked, and a seismic fault attribute image is formed. The earthquake fault attribute image is subjected to nonlinear smoothing to form a preprocessed fault image; Dynamic planning is performed on each feature seed point in the preprocessed tomographic image to determine the corresponding optimal surface path; Smooth the different optimal surface paths to determine the optimal selection score; Construct an optimal selection score graph based on the optimal selection score; Based on the optimized selection of the fractional map, fault feature information is extracted.

2. The earthquake fault feature enhancement and identification method based on optimal solution according to claim 1, characterized in that, The step of performing nonlinear smoothing on the seismic fault attribute image to form a preprocessed fault image includes: The earthquake fault attribute image is nonlinearly smoothed according to the following formula to form the preprocessed fault image: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the image after nonlinear smoothing.

3. The earthquake fault feature enhancement and identification method based on optimal solution according to claim 2, characterized in that, The step of dynamically planning for each feature seed point in the preprocessed tomographic image to determine the corresponding optimal surface path includes: For different feature seed points, a local window is set; Dynamic planning is performed within the local window corresponding to different feature seed points to form the corresponding optimal surface path.

4. The earthquake fault feature enhancement and identification method based on optimal solution according to claim 3, characterized in that, The step of performing dynamic planning within the local window corresponding to different feature seed points to form the corresponding optimal surface path includes: The optimal surface path is determined using the following formula: The constraint is: |y(x+1)-y(x)|<β. Here, y(x) represents the optimal path, G(x,y) is the transposed fault attribute image, and β is the slope constraint.

5. The earthquake fault feature enhancement and identification method based on optimal solution according to claim 4, characterized in that, The process of smoothing different optimal surface paths to determine the optimal selection score includes: Gaussian smoothing is applied to different optimal surface paths to determine the corresponding optimal selection score S. k , where k represents the number of the different optimal surface paths.

6. The earthquake fault feature enhancement and identification method based on optimal solution according to claim 5, characterized in that, The step of constructing an optimal selection score graph based on the optimal selection score includes: Accumulate all the optimal choice scores to construct the optimal choice score graph M(x,y), where:

7. A seismic fault feature enhancement and identification system based on optimal solutions, characterized in that, include: The data acquisition unit is used to acquire seismic fault image data and pick up feature seed points to form a seismic fault attribute image. The preprocessing unit is used to acquire the seismic fault attribute image formed by the data acquisition unit and perform nonlinear smoothing processing to form a preprocessed fault image. The identification and analysis unit is used to acquire the preprocessed tomographic image formed by the preprocessing unit, and to perform selection score processing based on the optimal surface path to extract tomographic feature information.

8. The earthquake fault feature enhancement and identification system based on optimal solution according to claim 7, characterized in that, The preprocessing unit performs nonlinear smoothing of the seismic fault attribute image using the following formula: S(x,y)=F(x,y)+E(x,y)-G(x,y), where F(x,y) represents the cumulative image along the forward direction, E(x,y) represents the cumulative image along the backward direction, G(x,y) is the original fault attribute image, and S(x,y) is the image after nonlinear smoothing.

9. The earthquake fault feature enhancement and identification system based on optimal solution according to claim 8, characterized in that, The identification and analysis unit determines the optimal surface path for different feature seed points within a set local window.

10. The earthquake fault feature enhancement and identification system based on optimal solution according to claim 9, characterized in that, The identification and analysis unit determines the optimal selection analysis by performing Gaussian smoothing on the optimal surface path and establishes an optimal selection score map to extract fault feature information.

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