Frequency-space domain alternate iteration seismic random noise suppression method and system

Abstract

The invention discloses a frequency-space domain alternate iteration seismic random noise suppression method and system, and the method comprises the steps: converting a seismic signal from a time-space domain to a frequency-space domain, and obtaining a frequency slice through the data of a fixed frequency domain index # imgabs0 #; arranging the frequency slices into a Hankel matrix, performing singular value decomposition, and selecting singular values and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; obtaining a reconstructed frequency slice, and taking a next frequency slice to complete denoising in a space direction; single seismic trace data of a fixed spatial domain index are taken from the frequency-spatial domain data, the single seismic trace data are arranged into a Hankel matrix and singular value decomposition is carried out, and singular values and corresponding singular vectors are selected to carry out rank reduction reconstruction on the Hankel matrix; and performing back-diagonal average to obtain reconstructed single seismic channel data, taking next seismic channel data, and converting the obtained denoised frequency-space domain data back to a time-space domain to obtain a signal after noise suppression.

Description

Background Art

[0002] Seismic exploration is the main means of finding underground resources such as oil and natural gas. Due to various interferences such as natural environment and human factors, seismic data collected in the field usually contain a large amount of random noise, which causes the effective signal reflected by the underground medium to be seriously obscured and cannot be observed. Suppressing seismic random noise is extremely important for the subsequent stacking, migration, interpretation and other work of seismic data. The existing methods for suppressing seismic random noise mainly include: Prior art 1: Transformation-based noise suppression method Such as short-time Fourier transform and wavelet transform, the seismic data is transformed from the time-space domain to its respective transform domain, and then the values ​​belonging to the random noise area are filtered out, and finally the inverse transformation is performed back to the time-space domain to obtain the seismic data after noise suppression.

[0003] Disadvantages of prior art 1: The effective signal and random noise in actual seismic data are usually mixed together in the transform domain, which makes it impossible to completely suppress the random noise and easily damages the effective signal.

[0004] Prior art 2: fx Noise suppression method in the domain like fx Domain prediction filtering, Cadzow filtering, etc., use the continuity of the effective signal in the spatial direction and the discontinuity of random noise in the spatial direction to predict the effective signal and suppress the random noise.

[0005] Disadvantages of prior art 2: When the random noise in seismic data is strong, fx The noise suppression method in the domain will result in a large amount of residual random noise. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a frequency-space domain alternating iterative seismic random noise suppression method and system in response to the above-mentioned deficiencies in the prior art. The method uses two-dimensional seismic data, transforms it into the frequency-space domain, and then performs rank reduction denoising on the seismic data alternately in the frequency direction and the spatial direction, and can perform iterative denoising repeatedly, thereby suppressing the random noise more thoroughly, thereby solving the technical problem that the existing noise suppression methods cannot completely suppress the random noise.

[0007] The present invention adopts the following technical solutions: The seismic random noise suppression method of frequency-space domain alternating iteration includes the following steps: The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform, and a fixed frequency domain index is taken from the frequency-space domain data. The data is used to obtain frequency slices; Arrange the frequency slices into a Hankel matrix, perform singular value decomposition on the Hankel matrix, select the first r largest singular values ​​and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; The Hankel matrix reconstructed by reduced rank is averaged inversely along the diagonal line to obtain the reconstructed frequency slice, and the next frequency slice is taken until all frequency slices are processed to complete the spatial denoising. Take fixed spatial domain indicators in frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​decomposed by singular value, and the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix; The Hankel matrix reconstructed by reduced rank is averaged inversely along the diagonal line to obtain the reconstructed single seismic channel data. The next seismic channel data is taken until all seismic channels are processed and the frequency direction denoising is completed to obtain the denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0008] Preferably, the seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform. channel signal The expression in the frequency-space domain is for:

[0009] in, is the number of time domain sampling points of the signal, is the time domain indicator, is the frequency domain indicator, The unit of the imaginary part in complex number operations.

[0010] Preferably, the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix as follows:

[0011] in, For the i Large singular values, is the corresponding left singular vector, is the corresponding right singular vector, is the Hankel matrix obtained after rank reduction reconstruction.

[0012] Preferably, the Hankel matrix is ​​subjected to singular value decomposition as follows:

[0013] in, is the rank of the matrix, usually the smaller of the number of rows and columns.

[0014] Preferably, the single-channel data are arranged into a Hankel matrix and the Hankel matrix is ​​subjected to singular value decomposition as follows:

[0015] in, For the i Large singular values, is the corresponding left singular vector, is the corresponding right singular vector.

[0016] Preferably, the single channel data are arranged into a Hankel matrix as follows:

[0017] in, N is the number of Fourier transform points, is a fixed spatial domain indicator, The number of columns of the matrix is ​​manually selected.

[0018] Preferably, the fixed spatial domain index , get the data of single channel signal in frequency domain: .

[0020] Preferably, the denoised frequency-space domain data is obtained Specifically: The reconstructed matrix According to the inverse process of arranging the Hankel matrix, the anti-diagonal average is performed, that is, the elements on each anti-diagonal line of the matrix are averaged to restore the one-dimensional single seismic channel data; the spatial domain index is changed , take out the next data, repeat until all seismic channels are processed; repeat steps S1 to S4, alternately perform frequency and spatial direction rank reduction denoising until the preset number of iterations is reached, and obtain the denoised frequency-space domain data .

[0021] Preferably, the noise suppressed signal for:

[0022] in, is the number of time domain sampling points of the signal, is the time domain indicator, is the frequency domain indicator, The unit of the imaginary part in complex number operations.

[0023] In a second aspect, an embodiment of the present invention provides a seismic random noise suppression system with frequency-space domain alternating iteration, comprising: The transformation module uses Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and takes fixed frequency domain indicators in the frequency-space domain data. The data is used to obtain frequency slices; The first reconstruction module arranges the frequency slices into a Hankel matrix, performs singular value decomposition on the Hankel matrix, and selects the first r largest singular values ​​and corresponding singular vectors to perform reduced-rank reconstruction on the Hankel matrix; The denoising module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed frequency slice, and then takes the next frequency slice until all frequency slices are processed to complete the spatial denoising. The second reconstruction module takes fixed spatial domain indicators in the frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​decomposed by singular value, and the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix; The output module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed single seismic channel data, and then takes the next seismic channel data until all seismic channels are processed, completing frequency direction denoising to obtain denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0024] In a third aspect, a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned frequency-space domain alternating iteration seismic random noise suppression method when executing the computer program.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned frequency-space domain alternating iteration seismic random noise suppression method.

[0026] In a fifth aspect, a chip comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned frequency-space domain alternating iteration seismic random noise suppression method when executing the computer program.

[0027] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned frequency-space domain alternating iteration seismic random noise suppression method.

[0028] Compared with the prior art, the present invention has at least the following beneficial effects: A seismic random noise suppression method of frequency-space domain alternating iteration, firstly, the seismic signal is changed from the time-space domain to the frequency-space domain, then the Hankel matrix is ​​constructed in the frequency direction, and the rank reduction reconstruction is performed to complete the frequency direction denoising, then the Hankel matrix is ​​constructed again in the space direction, and the rank reduction reconstruction is performed to complete the space direction denoising, and then the rank reduction denoising can be performed again from the frequency and space directions, and the alternating iteration is performed in this way, and finally the seismic random noise is suppressed. Compared with the conventional seismic coherent noise suppression method, the present invention has a more thorough suppression effect on random noise and a higher fidelity to the effective signal.

[0029] Furthermore, the seismic signal is subjected to Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain. The purpose is to make the effective signal present a linear phase characteristic in the frequency-space domain. At the same time, the random noise does not have a linear phase characteristic in the frequency-space domain, which facilitates the subsequent removal of the random noise.

[0030] Furthermore, the first r largest singular values ​​and their corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix, with the aim of retaining the main singular components corresponding to the large singular values ​​dominated by effective signals, while removing the secondary singular components dominated by random noise.

[0031] Furthermore, by arranging the single-channel data into a Hankel matrix and performing singular value decomposition on the Hankel matrix, the effective signal characteristics in the single-channel data can be fully extracted, so that the effective signal components are concentrated in the components corresponding to the larger singular values, which is convenient for the subsequent suppression of random noise.

[0032] Furthermore, the reconstructed matrix is ​​averaged along the anti-diagonal line according to the inverse process of permuting the Hankel matrix. That is, the elements on each anti-diagonal line of the matrix are averaged to restore the one-dimensional single-channel data. The purpose is to restore the single-channel data after dimensionality increase by dimensionality reduction. At the same time, taking the average of the elements can also suppress random noise to a certain extent.

[0033] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0034] In summary, the present invention adopts alternating iteration in the space-frequency direction for denoising. Compared with the traditional method of denoising only in the space direction, it improves the correlation of the effective signal in the frequency direction and enables the denoising process to be repeated alternately in the space and frequency directions, which suppresses random noise more thoroughly and has higher fidelity for the effective signal.

[0035] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 It is a flow chart of the present invention; Figure 2 is a two-dimensional seismic signal image containing random noise; Figure 3 is a schematic diagram of the Cadzow filtering method results, where (a) is the noise suppression result and (b) is the difference profile; FIG4 is a schematic diagram of the results of the method of the present invention, wherein (a) is the noise suppression result, and (b) is the difference profile; Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 The present invention is a block diagram of an electronic device provided according to an embodiment.

[0038] Among them, 60. computer equipment; 61. processor; 62. memory; 63. computer program; 600. electronic device; 610. processing unit; 620. storage unit; 6201. random access storage unit; 6202. cache storage unit; 6203. read-only storage unit; 6204. program / utility; 6205. program module; 630. bus; 640. display unit; 650. input / output interface; 660. network adapter; 700. external device. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0041] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0042] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0043] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0044] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0045] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0046] The present invention provides a seismic random noise suppression method by alternating frequency-space domain iteration. Two-dimensional seismic data is used, and after being transformed into the frequency-space domain, the data is subjected to Hankel matrix construction and rank reduction reconstruction alternately from the spatial direction and the frequency direction in an iterative manner, so as to achieve the purpose of suppressing random noise.

[0047] Example 1 See also Figure 1 The present invention provides a frequency-space domain alternating iterative seismic random noise suppression method, comprising the following steps: S1. Use Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, take the data of a certain fixed frequency point in the frequency-space domain data, and obtain the frequency slice; Perform discrete Fourier transform on each signal in the two-dimensional seismic data. channel signal For:

[0048] in, is the number of time domain sampling points of the signal, is the time domain indicator, is the frequency domain indicator, is the unit of the imaginary part in complex number operations, is the expression of the signal in the frequency-space domain.

[0049] Fixed frequency domain indicators , and obtain the frequency domain index The corresponding frequency slices are:

[0050] in, is the number of channels of 2D seismic data.

[0051] S2, arranging the frequency slices into a Hankel matrix and performing singular value decomposition on the Hankel matrix, selecting the first few larger singular values ​​and the corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; Arrange frequency slices into a Hankel matrix :

[0052] Pair Matrix Perform singular value decomposition:

[0053] in, For the i Large singular values, is the corresponding left singular vector, is the corresponding right singular vector.

[0054] Select the first r largest singular values ​​and the corresponding singular vector pair matrix The reduced rank reconstruction is performed as follows:

[0055] S3, perform anti-diagonal averaging on the reconstructed Hankel matrix to obtain a reconstructed frequency slice, and take the next frequency slice until all frequency slices are processed to complete spatial denoising; The reconstructed matrix According to the inverse process of arranging the Hankel matrix, the anti-diagonal average is performed, that is, the elements on each anti-diagonal line of the matrix are averaged to restore the one-dimensional frequency slice; the frequency domain index is changed , take out the next frequency slice, and repeat until all frequency slices are processed.

[0056] S4, taking data of a single seismic channel in the frequency-space domain data, arranging the single seismic channel data into a Hankel matrix and performing singular value decomposition on the Hankel matrix, selecting the first several larger singular values ​​and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; Fixed spatial domain index , get the data of single channel signal in frequency domain:

[0057] in, N is the number of Fourier transform points.

[0058] Arrange single-channel data into a Hankel matrix :

[0059] Pair Matrix Perform singular value decomposition:

[0060] in, For the iLarge singular values, is the corresponding left singular vector, is the corresponding right singular vector.

[0061] Select the first r largest singular values ​​and the corresponding singular vector pair matrix Perform reduced rank reconstruction:

[0062] S5. Perform anti-diagonal averaging on the reconstructed Hankel matrix to obtain the reconstructed single-channel data. Take the next channel data until all channels are processed and denoise the frequency direction to obtain the denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0063] The reconstructed matrix According to the inverse process of arranging the Hankel matrix, the anti-diagonal average is performed, that is, the elements on each anti-diagonal line of the matrix are averaged to restore the one-dimensional single seismic channel data. , take out the next data, repeat until all seismic channels are processed; repeat steps S1 to S4, alternately perform frequency and spatial direction rank reduction denoising until the preset number of iterations is reached, and obtain the denoised frequency-space domain data .

[0064] The signal after noise suppression for:

[0065] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "platform".

[0066] Example 2 The present invention provides a seismic random noise suppression system with alternating frequency-space domain iterations, which can be used to implement the above-mentioned seismic random noise suppression method with alternating frequency-space domain iterations. Specifically, the seismic random noise suppression system with alternating frequency-space domain iterations includes a transformation module, a first reconstruction module, a denoising module, a second reconstruction module and an output module.

[0067] Among them, the transformation module uses Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and takes a fixed frequency domain index in the frequency-space domain data. The data is used to obtain frequency slices; The first reconstruction module arranges the frequency slices into a Hankel matrix, performs singular value decomposition on the Hankel matrix, and selects the first r largest singular values ​​and corresponding singular vectors to perform reduced-rank reconstruction on the Hankel matrix; The denoising module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed frequency slice, and then takes the next frequency slice until all frequency slices are processed to complete the spatial denoising. The second reconstruction module takes fixed spatial domain indicators in the frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​decomposed by singular value, and the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix; The output module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed single seismic channel data, and then takes the next seismic channel data until all seismic channels are processed, completing frequency direction denoising to obtain denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0068] Example 3 The present invention provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the seismic random noise suppression method of alternating iterative frequency-space domains, including: The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform, and a fixed frequency domain index is taken from the frequency-space domain data. The frequency slices are obtained by arranging the frequency slices into a Hankel matrix, performing singular value decomposition on the Hankel matrix, selecting the first r largest singular values ​​and the corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; performing anti-diagonal average on the Hankel matrix reconstructed by the reduced rank to obtain the reconstructed frequency slices, taking the next frequency slice until all frequency slices are processed and completing spatial denoising; taking a fixed spatial domain index in the frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​subjected to singular value decomposition. The first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix. The rank-reduced reconstructed Hankel matrix is ​​subjected to anti-diagonal averaging to obtain the reconstructed single seismic channel data. The next seismic channel data is taken until all seismic channels are processed and frequency direction denoising is completed to obtain denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0069] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for suppressing seismic random noise by alternating frequency-space domain iteration in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the system for suppressing seismic random noise by alternating frequency-space domain iteration in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0070] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0071] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0072] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0073] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0074] See also Figure 6 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0075] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0076] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0077] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0078] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0079] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems). Such communication may be performed via an input / output interface 650. In addition, the electronic device 600 may also communicate with one or more networks (e.g., local area networks, wide area networks, and / or public networks, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0080] Example 4 The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0081] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program code. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0082] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network or a wide area network, or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0083] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the frequency-space domain alternating iteration seismic random noise suppression method in the above embodiment; the processor may load and execute the following steps: The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform, and a fixed frequency domain index is taken from the frequency-space domain data. The frequency slices are obtained by arranging the frequency slices into a Hankel matrix, performing singular value decomposition on the Hankel matrix, selecting the first r largest singular values ​​and the corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; performing anti-diagonal average on the Hankel matrix reconstructed by the reduced rank to obtain the reconstructed frequency slices, taking the next frequency slice until all frequency slices are processed and completing spatial denoising; taking a fixed spatial domain index in the frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​subjected to singular value decomposition. The first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix. The rank-reduced reconstructed Hankel matrix is ​​subjected to anti-diagonal averaging to obtain the reconstructed single seismic channel data. The next seismic channel data is taken until all seismic channels are processed and frequency direction denoising is completed to obtain denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

[0084] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0085] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0086] See also Figure 2 , which is the time-space domain seismic data containing a lot of random noise. The horizontal axis is the number of seismic traces, the vertical axis is time, the sampling interval is 2ms, and there are 500 sampling points in total. It is the noisy data used for noise suppression.

[0087] Please refer to Figure 3, where Figure 3 (a) is the noise suppression result of the Cadzow filtering method, and Figure 3 (b) is the noise suppression result of the Cadzow filtering method. Figure 2 The difference profile can basically filter out most of the random noise in the noisy data, but the noise suppression result still contains a lot of random noise residue.

[0088] Please refer to FIG4 , where FIG4 (a) is the noise suppression result of the method of the present invention, and FIG4 (b) is the noise suppression result of the method of the present invention. Figure 2 3, the random noise residue in the noise suppression result of the method of the present invention is less, which means that the random noise suppression effect of the method of the present invention is more thorough, and the difference profile does not contain effective signal components, and the signal-to-noise ratio of the noise suppression result is higher.

[0089] In summary, the present invention provides a frequency-space domain alternating iterative seismic random noise suppression method and system, which constructs Hankel matrices for seismic data in the spatial and frequency directions respectively, performs rank reduction reconstruction denoising, and performs denoising in the spatial and frequency directions by alternating iterative denoising. This denoising method and system can suppress the random noise in the seismic data more thoroughly and make the fidelity of the effective signal higher.

[0090] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0093] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A seismic random noise suppression method based on frequency-space domain alternating iteration, characterized in that: The following steps are involved: The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform, and a fixed frequency domain index is taken from the frequency-space domain data. The data is used to obtain frequency slices; Arrange the frequency slices into a Hankel matrix, perform singular value decomposition on the Hankel matrix, select the first r largest singular values ​​and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; The Hankel matrix reconstructed by reduced rank is averaged inversely along the diagonal line to obtain the reconstructed frequency slice, and the next frequency slice is taken until all frequency slices are processed to complete the spatial denoising. Take fixed spatial domain indicators in frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​decomposed by singular value, and the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix; The Hankel matrix reconstructed by reduced rank is averaged inversely along the diagonal line to obtain the reconstructed single seismic channel data. The next seismic channel data is taken until all seismic channels are processed and the frequency direction denoising is completed to obtain the denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

2. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 1 is characterized in that: The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform. channel signal The expression in the frequency-space domain is for: in, is the number of time domain sampling points of the signal, is the time domain indicator, is the frequency domain indicator, The unit of the imaginary part in complex number operations.

3. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 1, characterized in that: Select the first r largest singular values ​​and the corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix as follows: in, For the i Large singular values, is the corresponding left singular vector, is the corresponding right singular vector, is the Hankel matrix obtained after rank reduction reconstruction.

4. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 3 is characterized in that: The singular value decomposition of the Hankel matrix is ​​as follows: in, is the rank of the matrix, usually the smaller of the number of rows and columns.

5. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 1, characterized in that: Arrange the single-channel data into a Hankel matrix and perform singular value decomposition on the Hankel matrix as follows: in, For the i Large singular values, is the corresponding left singular vector, is the corresponding right singular vector.

6. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 5, characterized in that: Single channel data are arranged into a Hankel matrix as follows: in, N is the number of Fourier transform points, is a fixed spatial domain indicator, The number of columns of the matrix is ​​manually selected.

7. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 5, characterized in that: Fixed spatial domain index , get the data of single channel signal in frequency domain: 。 8. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 1, characterized in that: Get the denoised frequency-spatial domain data Specifically: The reconstructed matrix According to the inverse process of arranging the Hankel matrix, the anti-diagonal average is performed, that is, the elements on each anti-diagonal line of the matrix are averaged to restore the one-dimensional single seismic channel data; the spatial domain index is changed , take out the next data, and repeat until all seismic traces are processed; Repeat steps S1 to S4, alternately perform frequency and spatial rank reduction denoising, until the preset number of iterations is reached, and obtain the denoised frequency-space domain data .

9. The frequency-space domain alternating iterative seismic random noise suppression method according to claim 1, characterized in that: The signal after noise suppression for: in, is the number of time domain sampling points of the signal, is the time domain indicator, is the frequency domain indicator, The unit of the imaginary part in complex number operations.

10. A seismic random noise suppression system with frequency-space domain alternating iteration, characterized in that: include: The transformation module uses Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and takes fixed frequency domain indicators in the frequency-space domain data. The data is used to obtain frequency slices; The first reconstruction module arranges the frequency slices into a Hankel matrix, performs singular value decomposition on the Hankel matrix, and selects the first r largest singular values ​​and corresponding singular vectors to perform reduced-rank reconstruction on the Hankel matrix; The denoising module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed frequency slice, and then takes the next frequency slice until all frequency slices are processed to complete the spatial denoising. The second reconstruction module takes fixed spatial domain indicators in the frequency-space domain data The single seismic channel data are arranged into a Hankel matrix and the Hankel matrix is ​​decomposed by singular value, and the first r largest singular values ​​and corresponding singular vectors are selected to perform rank reduction reconstruction on the Hankel matrix; The output module performs anti-diagonal averaging on the Hankel matrix reconstructed by reduced rank to obtain the reconstructed single seismic channel data, and then takes the next seismic channel data until all seismic channels are processed, completing frequency direction denoising to obtain denoised frequency-space domain data. , using inverse Fourier transform to transform the processed frequency-space domain data Transform back to the time-space domain to obtain the noise-suppressed signal .

Images