A structure-oriented bilateral filtering seismic image processing method and system

Abstract

The application provides a structure-oriented bilateral filtering seismic image processing method and system, and relates to the technical field of seismic image processing. The method comprises the following steps: obtaining seismic image input information, and performing initial data processing to form to-be-processed seismic image data; performing structure tensor processing on the to-be-processed seismic image data to determine the structure direction and characteristics of the to-be-processed seismic image; determining tensor filtering coefficients according to the structure direction and characteristics of the to-be-processed seismic image; performing structure-oriented smoothing processing on the to-be-processed seismic image data according to the tensor filtering coefficients to form smoothed seismic image data; and performing bilateral filtering processing on the smoothed seismic image data to form characteristic seismic image data. The method can remove noise in the seismic image while retaining geological structure characteristics such as faults and strata in the image, thereby improving the interpretation accuracy of the seismic image and the reliability of geological analysis.

Description

Technical Field

[0001] This invention relates to the field of seismic image processing technology, and more specifically, to a structure-guided bilateral filtering seismic image processing method and system. Background Technology

[0002] Seismic image processing is a crucial aspect of geophysics, involving the extraction of useful information from seismic data for geological structure analysis. Existing techniques, such as isotropic Gaussian filters, are widely used for image denoising and smoothing. However, these methods have limitations when processing seismic images. For instance, while Gaussian filters effectively suppress noise, they also tend to smooth out important geological structural features, such as faults and strata, thus reducing the geological interpretation value of the image.

[0003] In the field of seismic image processing, anisotropic diffusion filters, such as those developed by Weickert (1999) and Fehmers, are widely used. The methods proposed in (2003) are designed to preserve the edge and structural features of images while smoothing noise. These methods achieve adaptive smoothing of image structure by using spatially varying diffusion coefficients. Although these techniques improve the processing of seismic images to some extent, they still face the challenge of balancing noise suppression and geological feature preservation when processing seismic images with complex structural features.

[0004] Furthermore, the bilateral filter, proposed by Tomasi and Manduchi (1998), achieves image denoising and edge preservation by combining spatial and value domain filtering kernels. However, traditional bilateral filters are not widely used in seismic image processing because they are not well-suited to the sinusoidal reflections and geological structures commonly found in seismic images.

[0005] Therefore, designing a structure-oriented bilateral filtering seismic image processing method and system to improve the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata is an urgent problem to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a structure-guided bilateral filtering seismic image processing method. By combining structure-guided smoothing and bilateral filtering techniques, it effectively preserves the geological structural features in seismic images while removing noise. Furthermore, by adjusting the smoothing filter coefficients according to the structure tensor and applying a bilateral filter for image processing, it achieves higher geological structural feature preservation and noise suppression effects compared to traditional methods.

[0007] The present invention also aims to provide a structure-guided bilateral filtering seismic image processing system. The system acquires raw seismic image data through a data acquisition unit, and then performs preprocessing (structure-guided smoothing and bilateral filtering) through a data initialization unit to ensure the effectiveness of subsequent processing. The smoothing and filtering unit completes the structure-guided smoothing and bilateral filtering of the seismic image data to be processed, achieving the effect of effectively preserving the geological structural features in the seismic image while removing noise. The three structural units are closely integrated to form a highly efficient processing system, providing an important material basis for the effective realization of seismic image processing functions.

[0008] In a first aspect, the present invention provides a structure-guided bilateral filtering seismic image processing method, comprising: acquiring seismic image input information and performing initial data processing to form seismic image data to be processed; performing structural tensor processing on the seismic image data to be processed to determine the structural orientation and features of the seismic image to be processed; determining tensor filtering coefficients based on the structural orientation and features of the seismic image to be processed; performing structure-guided smoothing processing on the seismic image data to be processed based on the tensor filtering coefficients to form smoothed seismic image data; and performing bilateral filtering processing on the smoothed seismic image data to form featured seismic image data.

[0009] In this invention, the method combines structure-guided smoothing and bilateral filtering techniques to effectively preserve geological structural features in seismic images while removing noise. Furthermore, by adjusting the smoothing filter coefficients based on the structure tensor and applying a bilateral filter for image processing, it achieves higher geological structural feature preservation and noise suppression capabilities compared to traditional methods.

[0010] As one possible implementation, seismic image input information is acquired and initial data processing is performed to form seismic image data to be processed, including: initial data processing including but not limited to smoothing and filtering.

[0011] In this invention, the initial data processing of the earthquake input image is mainly for a certain degree of data preprocessing, filtering out obvious noise to provide more reasonable image data for subsequent processing and further improve the efficiency of subsequent image data processing. Of course, any preprocessing method that can achieve reasonable noise reduction is acceptable, such as smoothing, filtering, etc.

[0012] As one possible implementation, structure-guided smoothing is performed on the seismic image data to be processed based on the tensor filter coefficients to form smoothed seismic image data. This includes performing partial differential smoothing on the seismic image data to be processed in the following manner to form smoothed seismic image data: Where Q(x) represents smoothed seismic image data, D(x) represents tensor filter coefficients, and α represents the filter half-width. Let P(x) represent the gradient operator, and let P(x) be the seismic image data to be processed.

[0013] In this invention, structure-guided smoothing is mainly performed by combining the direction and features of image data to achieve reasonable smoothing, avoiding the loss of required feature information caused by traditional indiscriminate filtering. This smoothing method can effectively preserve feature information such as faults and strata.

[0014] As one possible implementation, bilateral filtering is performed on smoothed seismic image data to form characteristic seismic image data. This includes combining spatial and value domain filtering kernels to perform bilateral filtering on the smoothed seismic image data Q(x) in the following manner: Where R(P(x)-P(y)) is the range filtering kernel function, S(xy) is the spatial domain filtering kernel function, P(x) is the value of pixel x in the smoothed seismic image data Q(x), and P(y) is the value of pixel y in the smoothed seismic image data Q(x) that is related to P(x).

[0015] In this invention, there are various methods for bilateral filtering. This application adopts a method that combines spatial domain filtering kernel function and value domain filtering kernel function to perform bilateral filtering, so as to achieve better bilateral filtering effect.

[0016] As one possible implementation, the range filtering kernel function R(P(x)-P(y)) is obtained through linear interpolation.

[0017] In this invention, the range filtering kernel function is obtained by linear interpolation, which is efficient and easy to implement.

[0018] As one possible implementation, the range filtering kernel function R(P(x)-P(y)) is approximated by the following linear interpolation method: Wherein, P(x k+1 ) and P(x k ) are the two endpoints of each interval.

[0019] In this invention, the specific linear interpolation method used may vary. This application provides a specific interpolation formula to approximate the value range filtering kernel function.

[0020] Secondly, the present invention provides a structure-guided bilateral filtering seismic image processing system, the system comprising a data acquisition unit for acquiring seismic data information; a data initialization unit for acquiring the seismic data information acquired by the data acquisition unit to form seismic image input information, and performing initial data processing to form seismic image data to be processed; and a smoothing filtering unit for acquiring the seismic image data to be processed formed by the data initialization unit, performing structure-guided smoothing processing to form smoothed seismic image data, and then performing bilateral filtering processing on the smoothed seismic image data to form characteristic seismic image data.

[0021] In this invention, the data acquisition unit acquires raw seismic image data, which is then preprocessed by the data initialization unit to perform structure-guided smoothing and bilateral filtering, ensuring the effectiveness of subsequent processing. The smoothing and filtering unit performs structure-guided smoothing and bilateral filtering on the seismic image data to be processed, effectively preserving the geological structural features in the seismic image while removing noise. These three structural units are closely integrated to form a highly efficient processing system, providing an important material foundation for the effective realization of seismic image processing functions.

[0022] As one possible implementation, the data initialization unit performs initial data processing on the seismic image input information, including but not limited to smoothing and filtering.

[0023] In this invention, the data initialization unit mainly performs initial noise reduction processing on the seismic data information, so that the image data provided to the smoothing filter processing unit is more reasonable and easier to analyze and process efficiently using the smoothing filter method.

[0024] As one possible implementation, the smoothing filter processing unit uses partial derivatives to process the seismic image data to be processed, forming smoothed seismic image data.

[0025] In this invention, the structure-guided smoothing processing method adopted by the smoothing filter processing unit can take various forms. This application provides a partial differential method to achieve smoothing processing of the seismic image to be processed.

[0026] As one possible implementation, the smoothing filtering unit performs bilateral filtering on smoothed seismic image data by combining filtering kernel functions in the spatial domain and the value domain, thereby forming characteristic seismic image data.

[0027] In this invention, there are various ways to implement bilateral filtering of smooth seismic image data. This application provides a method that combines a filtering kernel function of spatial domain and value domain to implement bilateral filtering and form characteristic seismic image data.

[0028] The beneficial effects of the structure-guided bilateral filtering seismic image processing method and system provided by this invention are as follows:

[0029] This method combines structure-guided smoothing and bilateral filtering techniques to effectively preserve geological structural features in seismic images while removing noise. Furthermore, by adjusting the smoothing filter coefficients based on the structure tensor and applying a bilateral filter for image processing, it achieves higher geological structural feature preservation and noise suppression capabilities compared to traditional methods.

[0030] The system acquires raw seismic image data through a data acquisition unit, followed by preprocessing using a data initialization unit to perform structure-guided smoothing and bilateral filtering, ensuring the effectiveness of subsequent processing. The smoothing and filtering unit then performs structure-guided smoothing and bilateral filtering on the seismic image data, effectively preserving the geological structural features of the seismic images while removing noise. These three structural units are tightly integrated to form a highly efficient processing system, providing a crucial material foundation for the effective realization of seismic image processing functions. Attached Figure Description

[0031] 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.

[0032] Figure 1 This is a step diagram of the structure-guided bilateral filtering seismic image processing method provided in an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the structure-guided bilateral filtering seismic image processing system provided in an embodiment of the present invention;

[0034] Figure 3 The original seismic image for structure-guided bilateral filtering seismic image processing provided in this embodiment of the invention;

[0035] Figure 4 The image obtained by the conventional tectonic filtering method for structure-guided bilateral filtering seismic image processing provided in the embodiments of the present invention;

[0036] Figure 5 The seismic image is generated by the structure-guided bilateral filtering seismic image processing method provided in the embodiments of the present invention. Detailed Implementation

[0037] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0038] Seismic image processing is a crucial aspect of geophysics, involving the extraction of useful information from seismic data for geological structure analysis. Existing techniques, such as isotropic Gaussian filters, are widely used for image denoising and smoothing. However, these methods have limitations when processing seismic images. For instance, while Gaussian filters effectively suppress noise, they also tend to smooth out important geological structural features, such as faults and strata, thus reducing the geological interpretation value of the image.

[0039] In the field of seismic image processing, anisotropic diffusion filters, such as those developed by Weickert (1999) and Fehmers, are widely used. The methods proposed in (2003) are designed to preserve the edge and structural features of images while smoothing noise. These methods achieve adaptive smoothing of image structure by using spatially varying diffusion coefficients. Although these techniques improve the processing of seismic images to some extent, they still face the challenge of balancing noise suppression and geological feature preservation when processing seismic images with complex structural features.

[0040] Furthermore, the bilateral filter, proposed by Tomasi and Manduchi (1998), achieves image denoising and edge preservation by combining spatial and value domain filtering kernels. However, traditional bilateral filters are not widely used in seismic image processing because they are not well-suited to the sinusoidal reflections and geological structures commonly found in seismic images.

[0041] refer to Figures 1-5 This invention provides a structure-guided bilateral filtering seismic image processing method. This method combines structure-guided smoothing and bilateral filtering techniques to effectively preserve geological structural features in seismic images while removing noise. Furthermore, by adjusting the smoothing filter coefficients based on the structure tensor and applying a bilateral filter for image processing, it achieves higher geological structural feature preservation and noise suppression capabilities compared to traditional methods.

[0042] The specific steps of the structure-guided bilateral filtering seismic image processing method are as follows:

[0043] S1: Acquire seismic image input information and perform initial data processing to form seismic image data to be processed.

[0044] The process involves acquiring seismic image input information and performing initial data processing to form seismic image data to be processed. This initial data processing includes, but is not limited to, smoothing and filtering. The primary purpose of this initial data processing is to preprocess the seismic input image, removing significant noise to provide more suitable image data for subsequent processing and improve the efficiency of subsequent image data processing. Of course, any preprocessing method that achieves reasonable noise reduction is acceptable, such as smoothing and filtering.

[0045] S2: Perform structural tensor processing on the seismic image data to be processed to determine the structural orientation and characteristics of the seismic image.

[0046] The main purpose of processing the structural tensor is to determine the directional features of subsequent structural guidance smoothing, so as to provide the direction of smoothing processing, ensuring that the smoothing processing can effectively remove noise in seismic images while preserving geological structural features.

[0047] S3: Determine the tensor filter coefficients based on the structural orientation and characteristics of the seismic image to be processed.

[0048] Tensor filter coefficients are guiding coefficients in the smoothing process, ensuring that the filtering process proceeds along the structural direction.

[0049] S4: Based on the tensor filtering coefficients, perform structure-guided smoothing on the seismic image data to be processed to form smoothed seismic image data.

[0050] Based on the tensor filter coefficients, structure-guided smoothing is performed on the seismic image data to be processed to form smoothed seismic image data. This includes: performing partial differential smoothing on the seismic image data to be processed in the following manner to form smoothed seismic image data: Where Q(x) represents smoothed seismic image data, D(x) represents tensor filter coefficients, and α represents the filter half-width. Let P(x) represent the gradient operator, and P(x) be the seismic image data to be processed. Structure-guided smoothing primarily involves combining the direction and characteristics of the image data for reasonable smoothing, avoiding the loss of required feature information caused by traditional indiscriminate filtering. This smoothing method can effectively preserve feature information such as faults and strata.

[0051] S5: Perform bilateral filtering on the smoothed seismic image data to form characteristic seismic image data.

[0052] Bilateral filtering is performed on smoothed seismic image data to form characteristic seismic image data. This includes bilateral filtering of the smoothed seismic image data Q(x) using a filtering kernel that combines spatial and value domains: Where R(P(x)-P(y)) is the range filtering kernel function, S(xy) is the spatial domain filtering kernel function, P(x) is the value of pixel x in the smoothed seismic image data Q(x), and P(y) is the value of pixel y in the smoothed seismic image data Q(x) that is related to P(x). There are various methods for bilateral filtering; this application uses a combination of spatial domain filtering kernel functions and range filtering kernel functions to achieve better bilateral filtering results.

[0053] The range filtering kernel function R(P(x)-P(y)) is obtained through linear interpolation. This application uses linear interpolation to obtain the range filtering kernel function, which is efficient and easy to implement. The range filtering kernel function R(P(x)-P(y)) is approximately obtained through the following linear interpolation method: Wherein, P(x k+1 ) and P(x k The two endpoints of each interval are represented by . The specific linear interpolation method used may vary, and this application provides a specific interpolation formula to approximate the range filtering kernel function.

[0054] This invention also provides a structure-guided bilateral filtering seismic image processing system. This system employs the aforementioned structure-guided bilateral filtering seismic image processing method and includes: a data acquisition unit for acquiring seismic data information; a data initialization unit for acquiring the seismic data information acquired by the data acquisition unit to form seismic image input information, and performing initial data processing to form seismic image data to be processed; and a smoothing filtering unit for acquiring the seismic image data to be processed formed by the data initialization unit, performing structure-guided smoothing processing to form smoothed seismic image data, and then performing bilateral filtering processing on the smoothed seismic image data to form characteristic seismic image data.

[0055] The system acquires raw seismic image data through a data acquisition unit, followed by preprocessing using a data initialization unit to perform structure-guided smoothing and bilateral filtering, ensuring the effectiveness of subsequent processing. The smoothing and filtering unit then performs structure-guided smoothing and bilateral filtering on the seismic image data, effectively preserving the geological structural features of the seismic images while removing noise. These three structural units are tightly integrated to form a highly efficient processing system, providing a crucial material foundation for the effective realization of seismic image processing functions.

[0056] The data initialization unit performs initial data processing on the input seismic image information, including but not limited to smoothing and filtering. The data initialization unit primarily performs initial noise reduction processing on the seismic data information to make the image data provided to the smoothing and filtering unit more reasonable and easier to analyze and process efficiently using smoothing filtering methods.

[0057] The smoothing filter processing unit uses partial differential equations to process the seismic image data to form smoothed seismic image data. The structure-guided smoothing processing method employed by the smoothing filter processing unit can take various forms; this application provides a partial differential equation method to achieve smoothing processing of the seismic image data.

[0058] The smoothing filtering unit performs bilateral filtering on smoothed seismic image data by combining filtering kernel functions in both the spatial and value domains to form characteristic seismic image data. There are various implementation methods for bilateral filtering of smoothed seismic image data; this application provides a method that combines filtering kernel functions in both the spatial and value domains to achieve bilateral filtering and form characteristic seismic image data.

[0059] In summary, the beneficial effects of the structure-guided bilateral filtering seismic image processing method and system provided in the embodiments of the present invention are as follows:

[0060] This method combines structure-guided smoothing and bilateral filtering techniques to effectively preserve geological structural features in seismic images while removing noise. Furthermore, by adjusting the smoothing filter coefficients based on the structure tensor and applying a bilateral filter for image processing, it achieves higher geological structural feature preservation and noise suppression capabilities compared to traditional methods.

[0061] The system acquires raw seismic image data through a data acquisition unit, followed by preprocessing using a data initialization unit to perform structure-guided smoothing and bilateral filtering, ensuring the effectiveness of subsequent processing. The smoothing and filtering unit then performs structure-guided smoothing and bilateral filtering on the seismic image data, effectively preserving the geological structural features of the seismic images while removing noise. These three structural units are tightly integrated to form a highly efficient processing system, providing a crucial material foundation for the effective realization of seismic image processing functions.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] "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.

[0066] The “protocol” mentioned in the embodiments of this application 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. The embodiments of this application do not specifically limit this.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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).

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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 scope of the technology 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 of structurally guided bilateral filtering seismic image processing, characterized in that, include: The seismic image input information is acquired and initial data processing is performed to form the seismic image data to be processed. Structural tensor processing is performed on the seismic image data to be processed to determine the structural orientation and features of the seismic image; Based on the structural orientation and characteristics of the seismic image to be processed, the tensor filter coefficients are determined; Based on the tensor filtering coefficients, the seismic image data to be processed is subjected to structure-guided smoothing to form smoothed seismic image data; The smoothed seismic image data is subjected to bilateral filtering to form characteristic seismic image data.

2. The structure-oriented bilateral filtered seismic image processing method of claim 1, wherein, The process of acquiring seismic image input information and performing initial data processing to form seismic image data to be processed includes: The initial data processing includes, but is not limited to, smoothing and filtering.

3. The structure-oriented bilateral filtered seismic image processing method of claim 2, wherein, The step of performing structure-guided smoothing processing on the seismic image data to be processed based on the tensor filtering coefficients to form smoothed seismic image data includes: The smoothed seismic image data is formed by performing partial differential smoothing on the seismic image data to be processed in the following manner: where Q(x) is the smoothed seismic image data, D(x) is a tensor filter coefficient, and a is a filter half-width, denotes a gradient operator, and P(x) is the seismic image data to be processed.

4. The structure-oriented bilateral filter seismic image processing method of claim 3, wherein, The step of performing bilateral filtering on the smoothed seismic image data to form characteristic seismic image data includes: Combining spatial and value domain filtering kernels, the smoothed seismic image data Q(x) is subjected to bilateral filtering in the following manner: where R(P(x) - P(y)) is a range domain filter kernel function, S(x - y) is a spatial domain filter kernel function, P(x) is a value of a pixel point x in the smoothed seismic image data Q(x), and P(y) is a value of a pixel point y related to P(x) in the smoothed seismic image data Q(x).

5. The method of claim 4, wherein, The range filtering kernel function R(P(x)-P(y)) is obtained through linear interpolation.

6. The structure-guided bilateral filtering seismic image processing method according to claim 5, characterized in that, The range filtering kernel function R(P(x)-P(y)) is approximately obtained through the following linear interpolation method: Wherein, P(x k+1 ) and P(x k ) are the two endpoints of each interval.

7. A structure-guided bilateral filtering seismic image processing system, characterized in that, include: The data acquisition unit is used to collect earthquake data information; The data initialization unit is used to acquire the seismic data information collected by the data acquisition unit to form seismic image input information, and to perform initial data processing to form seismic image data to be processed. The smoothing filter processing unit is used to acquire the seismic image data to be processed formed by the data initialization unit, perform structure-guided smoothing processing to form smoothed seismic image data, and then perform bilateral filtering processing on the smoothed seismic image data to form characteristic seismic image data.

8. The structure-guided bilateral filtering seismic image processing system according to claim 7, characterized in that, The data initialization unit performs initial data processing on the earthquake image input information, including but not limited to smoothing and filtering.

9. The structure-guided bilateral filtering seismic image processing system according to claim 8, characterized in that, The smoothing filter processing unit processes the seismic image data to be processed using partial differential methods to form the smoothed seismic image data.

10. The structure-guided bilateral filtering seismic image processing system according to claim 9, characterized in that, The smoothing filtering processing unit performs bilateral filtering on the smoothed seismic image data by combining a filtering kernel function in the spatial domain and the value domain, thereby forming the characteristic seismic image data.

Images