Methods, apparatus, equipment, media, and programs for filtering and suppressing random noise.

Abstract

This disclosure relates to the field of petroleum exploration technology, and in particular to a method, apparatus, equipment, medium, and program product for filtering and suppressing random noise. The method includes: performing a two-dimensional Hilbert transform on seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data; calculating the local dip angle based on the real part of the frequency response function; calculating the coefficients of each polynomial in a non-stationary polynomial fit and the coefficients of each fitted polynomial based on the local dip angle; and filtering the seismic data based on the polynomial coefficients and the fitted polynomial coefficients. By using the two-dimensional Hilbert transform to accurately estimate the local dip angle in seismic data and performing non-stationary filtering based on the local dip angle, the signal-to-noise ratio of the seismic data can be significantly improved, random noise can be effectively suppressed, and structural information such as faults and horizons in the seismic data can be preserved, thereby improving the accuracy and reliability of seismic data interpretation.

Description

Technical Field

[0001] This disclosure relates to the field of petroleum exploration technology, and in particular to a method, apparatus, equipment, medium, and program product for filtering and suppressing random noise. Background Technology

[0002] Seismic exploration is a crucial technique for detecting underground structures and resources, and the quality of seismic data directly impacts the accuracy of exploration results. However, seismic data is susceptible to various forms of random noise during acquisition and processing. Several methods have been devised to suppress this random noise and improve the quality of seismic data.

[0003] However, while suppressing noise, these existing methods may damage or obscure useful structural information in seismic data, such as the continuity of micro-faults or horizons. In addition, they also have certain limitations in terms of processing speed, adaptability, and computational efficiency. Therefore, there is an urgent need to develop a new technology that can effectively suppress random noise while protecting key structural information in seismic data. Summary of the Invention

[0004] In view of the above problems, this disclosure is made to provide a method, apparatus, device, medium and program product for filtering random noise suppression.

[0005] In a first aspect, this disclosure provides a method for suppressing filtered random noise, including:

[0006] Perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data;

[0007] Calculate the local tilt angle based on the real part of the frequency response function;

[0008] Based on the local tilt angle, calculate the coefficients of each polynomial in the non-stationary polynomial fitting and the coefficients of each fitted polynomial.

[0009] The seismic data is filtered based on the polynomial coefficients and the fitted polynomial coefficients.

[0010] Furthermore, according to one aspect of the filtering random noise suppression method of this disclosure, the real part of the frequency response function includes a real part in the x-direction and a real part in the y-direction;

[0011] Based on the real part of the frequency response function, the local tilt angle is calculated, including:

[0012] Calculate the ratio of the real part in the x-direction to the real part in the y-direction;

[0013] The local tilt angle is obtained based on the sum of the ratio and a preset constant.

[0014] Furthermore, according to one aspect of the random noise suppression method of this disclosure, the method further includes: calculating the coefficients of each polynomial of the non-stationary polynomial fitting and the coefficients of each fitted polynomial based on the local tilt angle, including:

[0015] To obtain the mathematical relationship between earthquake data, prediction errors, and the coefficients of various polynomials;

[0016] Based on the mathematical relationship, the coefficients of each polynomial are calculated by minimizing the prediction error;

[0017] The Tikhonov regularization method is used to fit the non-stationary polynomial to obtain the mathematical relationship between the regularization term and the coefficients of the fitted polynomial.

[0018] Based on the mathematical relationship between the regularization term and the coefficients of the fitted polynomial, the coefficients of each fitted polynomial are calculated.

[0019] Furthermore, according to one aspect of the filtering random noise suppression method of this disclosure, it further includes: filtering the seismic data based on the polynomial coefficients and the fitted polynomial coefficients, including:

[0020] Based on the local tilt angle and the non-stationary polynomial fitting, the structure-guided filter expression is obtained;

[0021] The seismic data is filtered based on the structure-guided filtering expression, the coefficients of each polynomial, and the coefficients of each fitted polynomial.

[0022] Furthermore, according to one aspect of the random noise suppression method of this disclosure, after filtering the seismic data, the method further includes:

[0023] The filtered seismic data is then post-processed, including data normalization and amplitude adjustment.

[0024] Furthermore, the method for suppressing random noise according to one aspect of this disclosure further includes: before performing a two-dimensional Hilbert transform on the seismic data, it further includes:

[0025] The seismic data is preprocessed, including the removal of seismic data trends and DC components.

[0026] Secondly, this disclosure provides a random noise suppression device, comprising:

[0027] The transformation module is used to perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data.

[0028] The first calculation module is used to calculate the local tilt angle based on the real part of the frequency response function;

[0029] The second calculation module is used to calculate the coefficients of each polynomial and the coefficients of each fitted polynomial based on the local tilt angle.

[0030] The filtering module is used to filter the seismic data based on the polynomial coefficients and the fitted polynomial coefficients.

[0031] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method of one aspect above.

[0032] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of one aspect above.

[0033] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method described in one of the above aspects.

[0034] As will be described in detail below, a method, apparatus, device, medium, and program product for suppressing random noise according to embodiments of this disclosure utilizes a two-dimensional Hilbert transform to accurately estimate the local dip angle in seismic data and performs non-stationary filtering processing on the seismic data based on the local dip angle. This significantly improves the signal-to-noise ratio of the seismic data, effectively suppresses random noise, and protects structural information in the seismic data, such as faults and horizons, thereby improving the accuracy and reliability of seismic data interpretation. This method has significant application advantages in seismic exploration data processing and can significantly improve the quality and efficiency of seismic data interpretation.

[0035] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0036] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0037] Figure 1 This is a flowchart illustrating a method for suppressing random noise filtering according to an embodiment of the present disclosure.

[0038] Figure 2This is a flowchart illustrating another method for suppressing filtered random noise according to an embodiment of the present disclosure.

[0039] Figure 3 The illustration shows an image of raw seismic data applied according to an embodiment of this disclosure.

[0040] Figure 4 The illustration shows a filtered seismic data image applied according to an embodiment of the present disclosure.

[0041] Figure 5 The image shown is a noise-removed image obtained by applying an embodiment of this disclosure.

[0042] Figure 6 The illustration shows an image of raw seismic data applied according to an embodiment of this disclosure.

[0043] Figure 7 The illustration shows a denoised seismic data image applied according to an embodiment of this disclosure.

[0044] Figure 8 The image shown is a noise-removed image obtained by applying an embodiment of this disclosure.

[0045] Figure 9 This is a schematic diagram of the structure of a random noise suppression device according to an embodiment of the present disclosure.

[0046] Figure 10 This is a schematic diagram illustrating the structure of a computer device according to an embodiment of the present disclosure.

[0047] Figure 11 This is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. Detailed Implementation

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

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

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

[0051] Seismic exploration is a crucial technique for detecting underground structures and resources, and the quality of seismic data directly impacts the accuracy of exploration results. However, seismic data is susceptible to various forms of random noise during acquisition and processing. Several methods have been devised to suppress this random noise and improve the quality of seismic data.

[0052] However, while suppressing noise, these existing methods may damage or obscure useful structural information in seismic data, such as the continuity of micro-faults or horizons. In addition, they also have certain limitations in terms of processing speed, adaptability, and computational efficiency. Therefore, there is an urgent need to develop a new technology that can effectively suppress random noise while protecting key structural information in seismic data.

[0053] The above description, with reference to the accompanying drawings, illustrates a method, apparatus, device, medium, and program product for suppressing random noise according to embodiments of the present disclosure. Through non-iterative local dip angle calculation and non-stationary polynomial fitting techniques, it can significantly improve the signal-to-noise ratio of seismic data, effectively suppress random noise, and simultaneously protect structural information in the seismic data, such as faults and horizons, thereby improving the accuracy and reliability of seismic data interpretation. This method has significant application advantages in seismic exploration data processing, and can significantly improve the quality and efficiency of seismic data interpretation.

[0054] To facilitate understanding of this embodiment, a detailed description of the random noise suppression method disclosed in this disclosure is provided first. The execution entity of the random noise suppression method provided in this disclosure is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, this random noise suppression method can be implemented by a processor calling computer-readable instructions stored in memory.

[0055] Example 1

[0056] like Figure 1 The diagram shows a flowchart of a random noise suppression method provided in this embodiment of the present disclosure, the method comprising S101-S104:

[0057] S101: Perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data.

[0058] The real part of the frequency response function includes the real part in the x-direction and the real part in the y-direction, that is, the real part HT of the frequency response function of the (P) Hilbert transform of two-dimensional seismic data in the x-direction. x And the real part HT of the frequency response function in the y direction y .

[0059] Optionally, before performing a two-dimensional Hilbert transform on the seismic data, the following steps are also included:

[0060] Seismic data is preprocessed, including the removal of seismic data trends and DC components, to improve the accuracy of subsequent processing.

[0061] S102: Calculate the local tilt angle based on the real part of the frequency response function.

[0062] Specifically, it includes:

[0063] Calculate the ratio of the real part in the x-direction to the real part in the y-direction;

[0064] The local tilt angle is obtained based on the sum of the ratio and a preset constant.

[0065] Specifically, the formula for calculating the local tilt angle is as follows:

[0066]

[0067] Where α represents the local tilt angle, HT x (P) represents the real part of the frequency response function of the Hilbert transform of 2D seismic data (P) in the x-direction. y (P) represents the real part of the frequency response function of the Hilbert transform of the two-dimensional seismic data (P) in the y direction, and ∈ represents a preset constant, that is, a small constant added to avoid the division by zero problem.

[0068] S103: Based on the local tilt angle, calculate the coefficients of each polynomial in the non-stationary polynomial fitting and the coefficients of each fitted polynomial.

[0069] Specifically, the following steps are included:

[0070] The mathematical relationships between earthquake data, prediction errors, and the coefficients of various polynomials are obtained as follows:

[0071]

[0072] Where E(x) represents the prediction error, f(x) represents the seismic data, and J i (x) represents a basis function, a i (x) represents the polynomial coefficient of the i-th polynomial.

[0073] Based on the above mathematical relationships, the coefficients of each polynomial are calculated by minimizing the prediction error;

[0074] The Tikhonov regularization method is applied to the fitting of the non-stationary polynomial to obtain the mathematical relationship between the regularization term and the coefficients of the fitted polynomial, as follows:

[0075] Γ(a i,TV )=‖E(x)‖ 2 +λ||Da i,TV || 2

[0076] Where e(x) represents the prediction error, λ represents the regularization parameter, D represents the differential operator, and Γ(a i,TV ) represents the regularization term, a i,TV Let represent the fitting polynomial coefficients of the i-th polynomial.

[0077] Based on the mathematical relationship between the regularization term and the coefficients of the fitted polynomial, the coefficients of each fitted polynomial are calculated.

[0078] S104: Based on the polynomial coefficients and the fitted polynomial coefficients, the seismic data is filtered.

[0079] Specifically, it includes:

[0080] Based on the local tilt angle and the non-stationary polynomial fitting, the structure-guided filter expression is obtained as follows:

[0081]

[0082] Where f(x) represents the seismic data, f′(x) represents the filtered seismic data, and a i (x) represents the polynomial coefficient of the i-th polynomial, a i,TV (x) represents the coefficients of the fitted polynomial after regularization.

[0083] Based on the above structure-guided filtering expression, the coefficients of each of the polynomials, and the coefficients of each of the fitted polynomials, the seismic data is filtered.

[0084] Optionally, after filtering the seismic data, the method further includes:

[0085] The filtered seismic data undergoes post-processing, including data normalization and amplitude adjustment, to meet the needs of interpretation and further analysis.

[0086] This embodiment provides the following test data obtained based on this solution, such as... Figures 3-5 As shown:

[0087] like Figure 3 The image shown is a raw seismic data image.

[0088] like Figure 4 The image shown is a filtered (denoised) seismic data image.

[0089] like Figure 5 The image shown is the noise-removed image, i.e. Figure 3 and Figure 4 The differences.

[0090] Example 2

[0091] like Figure 2 The diagram shown is another flowchart of a random noise suppression method provided in this disclosure, the method comprising S201-S206:

[0092] S201: Data Preprocessing: Perform necessary preprocessing on seismic data, including detrending and DC component removal, to improve the accuracy of subsequent processing.

[0093] S202: Two-dimensional Hilbert transform: Performs a two-dimensional Hilbert transform on seismic data.

[0094] The two-dimensional Hilbert transform is used to accurately estimate the local dip angle in seismic data, and this dip angle information is used to implement non-stationary filtering of the seismic data. The two-dimensional Hilbert transform can provide a sensitive measure of the local variation trend of the seismic wavefield, thereby guiding the design of filters to adapt to the non-stationary characteristics of seismic data.

[0095] S203: Local dip angle calculation: Calculate the local dip angle α based on the 2D seismic data P. The formula for calculating the local dip angle is:

[0096]

[0097] Where α represents the local tilt angle, HT x (P) represents the real part of the frequency response function of the Hilbert transform of 2D seismic data (P) in the x-direction. y (P) represents the real part of the frequency response function of the Hilbert transform of the two-dimensional seismic data (P) in the y direction, and ∈ represents a preset constant, that is, a small constant added to avoid the division by zero problem.

[0098] S204: Non-stationary polynomial fitting: Based on the calculated local dip angle, a polynomial fitting along the dip angle direction is selected, and the polynomial coefficients a are solved by minimizing the prediction error. i (x) and a i,TV (x).

[0099] Non-stationary polynomial fitting: Non-stationary polynomial fitting estimates the coefficients a by minimizing the prediction error E(x). i (x), whose mathematical model is as follows:

[0100]

[0101] Where E(x) represents the prediction error, f(x) represents the seismic data, and J i (x) represents a basis function, a i (x) represents the polynomial coefficient of the i-th polynomial.

[0102] Tikhonov Regularization: In non-stationary polynomial fitting, the Tikhonov regularization method is used, and its regularization term can be expressed as:

[0103] Γ(a i,TV )=‖E(x)‖ 2 +λ||Da i,TV || 2

[0104] Where E(x) represents the prediction error, λ represents the regularization parameter, D represents the differential operator, and Γ(a) represents the prediction error. i,TV ) represents the regularization term, ai,TV Let represent the fitting polynomial coefficients of the i-th polynomial.

[0105] S205: Structure-guided filtering: Determine the size of the filtering window based on the fitting results, perform filtering, and achieve the protection of structural information and suppression of noise.

[0106] Implementation of structure-guided filtering: Combining local tilt angle and non-stationary polynomial fitting, structure-guided filtering can be expressed as:

[0107]

[0108] Where f(x) represents the seismic data, f′(x) represents the filtered seismic data, and a i (x) represents the polynomial coefficient of the i-th polynomial, a i,TV (x) represents the coefficients of the fitted polynomial after regularization.

[0109] S206: Post-processing of results: Perform necessary post-processing on the filtered seismic data, including data normalization and amplitude adjustment, to meet the needs of interpretation and further analysis.

[0110] This embodiment provides the following test data obtained based on this solution, such as... Figures 6-8 As shown:

[0111] like Figure 6 The image shown is the original seismic data.

[0112] like Figure 7 The image shown is a filtered (denoised) seismic data image.

[0113] like Figure 8 The image shown is the noise-removed image, i.e. Figure 6 and Figure 7 The differences.

[0114] As can be seen from the above images, this embodiment can effectively preserve the geological structural features in seismic images while removing noise, and the stratigraphic features are clearer than those of traditional methods.

[0115] Example 3

[0116] Based on the above embodiments, this disclosure provides a random noise suppression device, such as... Figure 9 As shown, the device includes:

[0117] Transformation module 101 is used to perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data;

[0118] The first calculation module 102 is used to calculate the local tilt angle based on the real part of the frequency response function;

[0119] The second calculation module 103 is used to calculate the coefficients of each polynomial and the coefficients of each fitted polynomial based on the local tilt angle.

[0120] The filtering module 104 is used to filter the seismic data based on the polynomial coefficients and the fitted polynomial coefficients.

[0121] In one or more embodiments, the first computing module 102 is used for:

[0122] Calculate the ratio of the real part in the x-direction to the real part in the y-direction;

[0123] The local tilt angle is obtained based on the sum of the ratio and a preset constant.

[0124] In one or more embodiments, the second computing module 103 is used for:

[0125] To obtain the mathematical relationship between earthquake data, prediction errors, and the coefficients of various polynomials;

[0126] Based on the mathematical relationship, the coefficients of each polynomial are calculated by minimizing the prediction error;

[0127] The Tikhonov regularization method is used to fit the non-stationary polynomial to obtain the mathematical relationship between the regularization term and the coefficients of the fitted polynomial.

[0128] Based on the mathematical relationship between the regularization term and the coefficients of the fitted polynomial, the coefficients of each fitted polynomial are calculated.

[0129] In one or more embodiments, the filtering module 104 is used to:

[0130] Based on the local tilt angle and the non-stationary polynomial fitting, the structure-guided filter expression is obtained;

[0131] The seismic data is filtered based on the structure-guided filtering expression, the coefficients of each polynomial, and the coefficients of each fitted polynomial.

[0132] The random noise suppression device is further configured to: after filtering the seismic data, perform post-processing on the filtered seismic data, the post-processing including data normalization and amplitude adjustment.

[0133] The filtering random noise suppression device is also used to: preprocess the seismic data before performing a two-dimensional Hilbert transform on the seismic data, the preprocessing including removing the seismic data trend and DC component.

[0134] The random noise suppression device and the random noise suppression method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0135] Example 4

[0136] Based on the above embodiments, this disclosure provides a computer device for performing the above-described method for suppressing random noise. Please refer to... Figure 10 It illustrates a schematic diagram of a computer device provided by some embodiments of this disclosure. For example... Figure 10 As shown, the computer device 8 includes: a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected via the bus 802. The memory 801 stores a computer program that can run on the processor 800. When the processor 800 runs the computer program, it executes the filtering random noise suppression method provided in any of the foregoing embodiments of this disclosure.

[0137] The memory 801 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 803 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0138] Bus 802 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs. After receiving an execution instruction, the processor 800 executes the program. The random noise suppression filtering method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 800, or implemented by the processor 800.

[0139] The processor 800 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 800 or by instructions in software form. The processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPTA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 801. Processor 800 reads the information in memory 801 and, in conjunction with its hardware, completes the steps of the above method.

[0140] The computer device provided in this disclosure and the random noise suppression method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0141] Example 5

[0142] Based on the above embodiments, this disclosure provides a computer-readable storage medium corresponding to the filtering random noise suppression method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it executes the filtering random noise suppression method provided in any of the foregoing embodiments.

[0143] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0144] The computer-readable storage medium provided in the above embodiments of this disclosure and the filtering random noise suppression method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0145] This disclosure also provides a computer program product; please refer to [reference needed]. Figure 11 The computer program product 600 carries program code, namely computer program 601. The instructions included in the computer program 601 can be used to execute the steps of the filtering random noise suppression method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0146] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0147] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0148] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0149] Additionally, as used herein, the “or” used in a list of items beginning with “at least one” indicates a separate list, such that a list of, for example, “at least one of A, B, or C” means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word “exemplary” does not imply that the described example is preferred or better than other examples.

[0150] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0151] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0152] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0153] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for suppressing random noise through filtering, characterized in that, include: Perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data; Calculate the local tilt angle based on the real part of the frequency response function; Based on the local tilt angle, calculate the coefficients of each polynomial in the non-stationary polynomial fitting and the coefficients of each fitted polynomial. The seismic data is filtered based on the polynomial coefficients and the fitted polynomial coefficients.

2. The random noise suppression method as described in claim 1, characterized in that, The real part of the frequency response function includes a real part in the x-direction and a real part in the y-direction; Based on the real part of the frequency response function, the local tilt angle is calculated, including: Calculate the ratio of the real part in the x-direction to the real part in the y-direction; The local tilt angle is obtained based on the sum of the ratio and a preset constant.

3. The method for suppressing random noise as described in claim 1, characterized in that, Based on the local tilt angle, the coefficients of each polynomial in the non-stationary polynomial fitting and the coefficients of each fitted polynomial are calculated, including: To obtain the mathematical relationship between earthquake data, prediction errors, and the coefficients of various polynomials; Based on the mathematical relationship, the coefficients of each polynomial are calculated by minimizing the prediction error; The Tikhonov regularization method is used to fit the non-stationary polynomial to obtain the mathematical relationship between the regularization term and the coefficients of the fitted polynomial. Based on the mathematical relationship between the regularization term and the coefficients of the fitted polynomial, the coefficients of each fitted polynomial are calculated.

4. The method for suppressing random noise as described in claim 1, characterized in that, Based on the polynomial coefficients and the fitted polynomial coefficients, the seismic data is filtered, including: Based on the local tilt angle and the non-stationary polynomial fitting, the structure-guided filter expression is obtained; The seismic data is filtered based on the structure-guided filtering expression, the coefficients of each polynomial, and the coefficients of each fitted polynomial.

5. The method for suppressing random noise as described in claim 1, characterized in that, After filtering the seismic data, the process also includes: The filtered seismic data is then post-processed, including data normalization and amplitude adjustment.

6. The method for suppressing random noise as described in claim 1, characterized in that, Before performing a two-dimensional Hilbert transform on the seismic data, the following steps are also included: The seismic data is preprocessed, including the removal of seismic data trends and DC components.

7. A random noise suppression device, characterized in that, include: The transformation module is used to perform a two-dimensional Hilbert transform on the seismic data to obtain the real part of the frequency response function of the two-dimensional seismic data. The first calculation module is used to calculate the local tilt angle based on the real part of the frequency response function; The second calculation module is used to calculate the coefficients of each polynomial and the coefficients of each fitted polynomial based on the local tilt angle. The filtering module is used to filter the seismic data based on the polynomial coefficients and the fitted polynomial coefficients.

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

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

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