Seismic coherent noise suppression method using frequency spatial domain eigenvector projection

Abstract

The application discloses a seismic coherent noise suppression method using frequency space domain characteristic vector projection, and transforms seismic signals from a time-space domain to a frequency-space domain; data of a certain fixed frequency point in the frequency-space domain data is taken to obtain a frequency slice; a prediction step is selected to construct a forward prediction signal vector, a data matrix and an AR model equation; a covariance matrix of the data matrix is calculated and is subjected to characteristic decomposition, large characteristic values are taken and corresponding characteristic vectors are projected to a specified direction; a solution of the AR model equation is obtained to filter a signal to be predicted; a backward prediction signal vector, a data matrix and an AR model equation are constructed to perform reverse prediction; the results of the forward and reverse prediction are averaged to obtain a processing result of the frequency slice; until all the frequency slices are processed; the processed signal is transformed back to the time-space domain by using Fourier inverse transformation to obtain a signal after noise suppression.

Description

Technical Field

[0001] This invention belongs to the field of exploration geophysics technology, specifically relating to a method for suppressing seismic coherent noise using frequency spatial domain eigenvector projection. Background Technology

[0002] Seismic exploration is a primary means of finding underground resources such as oil and natural gas. However, due to various interferences from the natural environment and human factors, seismic data acquired in the field typically contains multiple types of noise. The vast majority of this noise can be categorized into two forms: random noise and coherent noise. Compared to random noise, coherent noise is often more energetic and exhibits continuity in the spatiotemporal domain, making it more destructive to seismic data and more difficult to suppress. Suppressing seismic coherent noise is extremely important for subsequent work such as stacking, migration, and interpretation of seismic data. Currently available methods for suppressing seismic coherent noise mainly include:

[0003] Existing technology 1: Transform-based noise suppression method

[0004] Transformers such as the FK transform and Radon transform transform seismic data from the time-space domain to their respective transform domains. Then, values ​​belonging to the coherent noise region are filtered out, and finally, the data is transformed back to the time-space domain to obtain the noise-suppressed seismic data.

[0005] Disadvantages of prior art 1:

[0006] In actual seismic data, effective signals and coherent noise are usually mixed together, and filtering out coherent noise will damage some of the effective signals.

[0007] Existing technology 2: Noise suppression method based on matrix singular value decomposition

[0008] For example, Cadzow filtering constructs a Hankel matrix of seismic data in the frequency domain. Based on the principle that the rank of the matrix does not exceed the number of linear in-phase axes in the seismic data, the Hankel matrix is ​​subjected to singular value decomposition and then rank reduction to suppress noise.

[0009] Disadvantages of prior art 2:

[0010] As the dimensionality of earthquake data increases, the matrix dimension also increases, resulting in a large computational load and slow processing speed for singular value decomposition. Summary of the Invention

[0011] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a method for suppressing seismic coherent noise using frequency-spatial domain eigenvector projection. After transforming two-dimensional seismic data to the frequency-spatial domain, eigenvector projection is performed on the covariance matrix of the data matrix, and then the seismic data is reconstructed using the projected eigenvectors to achieve the purpose of suppressing coherent noise. This method is used to solve the technical problems of poor coherent noise suppression effect and low computational efficiency of existing seismic data denoising methods.

[0012] The present invention adopts the following technical solution:

[0013] A seismic coherent noise suppression method utilizing frequency spatial domain eigenvector projection includes the following steps:

[0014] S1. Use Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and take data at a fixed frequency point in the frequency-space domain data to obtain a frequency slice;

[0015] S2. Select the prediction step size, use the frequency slice obtained in step S1 to construct the signal vector, data matrix and AR model equation for forward prediction, calculate the covariance matrix of the data matrix and perform eigenvalue decomposition, take the largest eigenvalue and project the corresponding eigenvector in the specified direction.

[0016] S3. Reconstruct the covariance matrix using the eigenvectors projected in step S2 to obtain the solution of the AR model equation, and then filter the signal to be predicted to obtain the positive prediction result.

[0017] S4. Construct the backward prediction signal vector, data matrix, and AR model equation, and perform prediction again from the reverse direction to obtain the backward prediction result.

[0018] S5. Average the result of the forward prediction obtained in step S3 and the result of the reverse prediction obtained in step S4 to obtain the processing result of the frequency slice. Take the next frequency slice until all frequency slices have been processed to obtain complete frequency-spatial domain data.

[0019] S6. Use inverse Fourier transform to transform the complete frequency-space domain data obtained in step S5 back to the time-space domain to obtain the noise-suppressed signal.

[0020] Specifically, in step S1, the frequency domain index k is fixed, and the corresponding frequency slices are obtained as follows:

[0021] S(k,1), S(k,2), S(k,3),…S(k,M)

[0022] Where M represents the number of traces in the two-dimensional seismic data.

[0023] Furthermore, a Discrete Fourier Transform is performed on each trace of the 2D seismic data. For the m-th trace s(n,m):

[0024]

[0025] Where N is the number of time-domain sampling points of the signal, n is the time-domain index, k is the frequency-domain index, j is the unit of the imaginary part in complex number operations, and S(k,m) is the expression of the signal in the frequency-space domain.

[0026] Specifically, in step S2, the first p0 eigenvalues ​​are taken for reconstruction, and the corresponding eigenvectors are mapped to an all-1 vector v = [1, 1, ..., 1]. T Projection is performed as follows:

[0027]

[0028] Among them, (u i Let (v,v) be the vector dot product, and (v,v) be the square of the magnitude of vector v. This is the feature vector of the shadow queen.

[0029] Specifically, the covariance matrix W of the data matrix is ​​as follows:

[0030]

[0031] Where H represents the conjugate transpose of the matrix, and M... f For data matrices;

[0032] The eigenvalue decomposition of the covariance matrix W is as follows:

[0033]

[0034] Where, σ i Let u be the i-th eigenvalue of the covariance matrix. i Let be the i-th eigenvector of the covariance matrix.

[0035] Specifically, in step S3, forward filtering is performed on the signal to be predicted:

[0036]

[0037] in, M is the signal vector after forward filtering. f For data matrix, This is the forward prediction filter coefficient vector.

[0038] Furthermore, the forward prediction filter coefficient vector for:

[0039]

[0040] Where, σ i Let be the i-th eigenvalue of the covariance matrix W. M is the result of projecting the eigenvector corresponding to the i-th eigenvalue onto an all-one vector. f Let S be a data matrix. f This is the original signal vector.

[0041] Specifically, in step S4, the result of the reverse prediction is as follows:

[0042]

[0043] in, M is the signal vector after back-filtering. b For data matrix, This is the vector of backward prediction filter coefficients.

[0044] Furthermore, the data matrix M b for:

[0045]

[0046] Where k is the frequency domain index, M is the number of traces in the two-dimensional seismic data, and p is the prediction step size.

[0047] Specifically, in step S5, the complete frequency-space domain data... for:

[0048]

[0049] in, This is the signal obtained from backward predictive filtering, where m is the channel number and p is the prediction step size. The signal is obtained by forward prediction filtering, and M is the number of traces in the two-dimensional seismic data.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects:

[0051] This invention utilizes a seismic coherence noise suppression method based on frequency-space domain eigenvector projection. The seismic signal is transformed from the time-space domain to the frequency-space domain, and then frequency slices are used to construct an AR prediction model. The covariance matrix of the data matrix is ​​then subjected to eigenvalue decomposition and eigenvector projection. Finally, the solution of the AR model is obtained using the projected eigenvectors, thus completing the coherence noise suppression. Compared to conventional seismic coherence noise suppression methods, this invention provides more thorough suppression of oblique linear coherence noise, higher fidelity for effective horizontal signals, and requires less computation.

[0052] Furthermore, by performing Discrete Fourier Transform on each channel signal in the 2D seismic data, the in-phase axis signals in the seismic data exhibit a linear phase difference relationship along the spatial direction on a certain discrete frequency slice, which is predictable and facilitates subsequent denoising.

[0053] Furthermore, reconstructing using the first p0 eigenvalues ​​effectively preserves the structure of the linear in-phase axes and removes random noise, transforming the corresponding eigenvectors into an all-1 vector v = [1,1,…,1]. T Projection can effectively preserve the structure of the horizontal in-phase axis in the linear in-phase axis and remove the in-phase axis formed by oblique coherent noise.

[0054] Furthermore, forward prediction filtering of the signal to be predicted can utilize the correlation of the signal in the forward spatial direction to filter the (p+1)th to Mth channels, suppressing random noise and coherent noise.

[0055] Furthermore, the forward prediction filter coefficient vector is calculated. Multiplying it by the signal matrix constructed during the forward predictive filtering process yields the forward-filtered signal vector, thus suppressing random noise and coherent noise.

[0056] Furthermore, by performing inverse prediction filtering on the signal to be predicted, the correlation of the signal in the inverse spatial direction can be used to filter the first to the (M-p+1)th channels, suppressing random and coherent noise.

[0057] Furthermore, by adding the results of forward prediction filtering and backward prediction filtering, and averaging the overlapping portions, complete frequency-spatial domain data can be obtained. It can complete the signal parts that cannot be predicted during forward or backward prediction, and improve the denoising effect of overlapping parts.

[0058] In summary, this invention improves upon the traditional FX predictive filtering, suppressing not only random noise but also oblique linear coherent noise, while retaining the advantages of the FX predictive filtering model, such as simplicity and high computational efficiency.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] Figure 1 This is a flowchart of the present invention;

[0061] Figure 2 It is a two-dimensional seismic signal containing oblique linear coherent noise;

[0062] Figure 3 The diagram shows the results of the FX prediction filtering method, where (a) is the noise suppression result and (b) is the difference profile.

[0063] Figure 4 This is a schematic diagram of the results of the method of the present invention, where (a) is the noise suppression result and (b) is the difference profile;

[0064] Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0065] Figure 6 This is a block diagram of a chip provided according to an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0068] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0069] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0070] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. 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.

[0071] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0072] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0073] This invention provides a method for suppressing seismic coherence noise using frequency-spatial domain eigenvector projection. It uses two-dimensional seismic data, transforms it to the frequency-spatial domain, projects eigenvectors onto the covariance matrix of the data matrix, and then reconstructs the seismic data using the projected eigenvectors to suppress coherence noise.

[0074] This invention discloses a method for suppressing seismic coherent noise using frequency spatial domain eigenvector projection, comprising the following steps:

[0075] S1. Use Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and take data at a fixed frequency point in the frequency-space domain data to obtain a frequency slice;

[0076] Performing a Discrete Fourier Transform on each trace of the 2D seismic data, for the m-th trace s(n,m):

[0077]

[0078] Where N is the number of time-domain sampling points of the signal, n is the time-domain index, k is the frequency-domain index, j is the unit of the imaginary part in complex number operations, and S(k,m) is the expression of the signal in the frequency-space domain.

[0079] By fixing the frequency domain index k, we can obtain the frequency slice corresponding to that frequency:

[0080] S(k,1), S(k,2), S(k,3),…S(k,M) (2)

[0081] Where M represents the number of traces in the two-dimensional seismic data.

[0082] S2. Select the prediction step size, construct the forward prediction signal vector, data matrix and AR model equation, calculate the covariance matrix of the data matrix, perform eigenvalue decomposition, take the largest eigenvalue and project the corresponding eigenvector into the specified direction.

[0083] With a prediction step size of p, construct the forward prediction signal vector S. f :

[0084] S f =(S(k,p+1),S(k,p+2),…,S(k,M)) T (3)

[0085] Construct data matrix M f :

[0086]

[0087] Construct the AR model equations:

[0088] M f A f =S f (5)

[0089] Among them, A f The vector represents the coefficients of the forward prediction filter to be solved.

[0090] Calculate data matrix M f The covariance matrix W:

[0091]

[0092] Here, the H symbol represents the conjugate transpose of the matrix.

[0093] Perform eigenvalue decomposition on the covariance matrix W:

[0094]

[0095] Where, σ i Let σi be the i-th eigenvalue of the covariance matrix, satisfying σ1≥σ2≥…≥σi p u i Let be the i-th eigenvector of the covariance matrix.

[0096] The first p0 eigenvalues ​​are selected for reconstruction, and these p0 eigenvectors are mapped to an all-1 vector v = [1, 1, ..., 1]. T Projecting upwards:

[0097]

[0098] Among them, (u i ,v)=v H u i , which represents the vector dot product.

[0099] S3. Reconstruct the covariance matrix using the projected eigenvectors to obtain the solution of the AR model equation, and then filter the signal to be predicted.

[0100] Reconstruct the covariance matrix using the projected eigenvectors:

[0101]

[0102] Using the reconstructed covariance matrix Find the least-squares solution to the AR model equations to obtain the forward prediction filter coefficient vector:

[0103]

[0104] Perform forward filtering on the signal to be predicted:

[0105]

[0106] S4. Construct the backward prediction signal vector, data matrix, and AR model equation, and perform prediction again from the reverse direction;

[0107] With a prediction step size of p, construct the backward prediction signal vector S. b :

[0108] S b =(S(k,1),S(k,2),…,S(k,Mp)) T (12)

[0109] Construct data matrix M b :

[0110]

[0111] Construct the AR model equations:

[0112] M b A b =S b (14)

[0113] Among them, A b Let be the vector of the backward prediction filter coefficients to be solved.

[0114] Similar to step S3, the backward prediction filter coefficient vector is obtained, and backward filtering is performed on the signal to be predicted:

[0115]

[0116] S5. Average the results of the forward and inverse predictions to obtain the processing result for that frequency slice. Move on to the next frequency slice and continue processing until all frequency slices have been processed to obtain complete frequency-spatial domain data.

[0117] The overlapping portions of the signals obtained from the forward and inverse predictions are averaged to obtain the filtered result of the k-th frequency slice:

[0118]

[0119] Change the frequency domain index k, extract the next frequency slice, and repeat steps S2 to S5 until all frequency slices have been processed, obtaining the processed complete frequency-spatial domain data.

[0120] S6. Use inverse Fourier transform to transform the processed signal back to the time-space domain to obtain the noise-suppressed signal.

[0121] The frequency-space domain data obtained by the inverse Fourier transform are processed. Transforming back into the time-space domain, we obtain the noise-suppressed signal:

[0122]

[0123] In another embodiment of the present invention, a seismic coherent noise suppression system utilizing frequency spatial domain eigenvector projection is provided. This system can be used to implement the above-mentioned seismic coherent noise suppression method utilizing frequency spatial domain eigenvector projection. Specifically, the seismic coherent noise suppression system utilizing frequency spatial domain eigenvector projection includes a transformation module, a projection module, a forward prediction module, a backward prediction module, an averaging module, and an output module.

[0124] The transformation module uses Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and extracts data at a fixed frequency point from the frequency-space domain data to obtain a frequency slice;

[0125] The projection module selects the prediction step size, uses the frequency slices obtained from the transformation module to construct the forward prediction signal vector, data matrix and AR model equation, calculates the covariance matrix of the data matrix and performs eigenvalue decomposition, takes the largest eigenvalue and projects the corresponding eigenvector in the specified direction.

[0126] The forward prediction module reconstructs the covariance matrix using the eigenvectors projected by the projection module, obtains the solution to the AR model equation, and then filters the signal to be predicted to obtain the forward prediction result.

[0127] The backward prediction module constructs the backward prediction signal vector, data matrix, and AR model equations, and performs prediction again from the reverse direction to obtain the backward prediction result.

[0128] The averaging module averages the results of the forward prediction obtained by the forward prediction module and the results of the reverse prediction obtained by the reverse prediction module to obtain the processing result of the frequency slice. The next frequency slice is then taken until all frequency slices have been processed to obtain the complete frequency-spatial domain data.

[0129] The output module uses inverse Fourier transform to transform the complete frequency-space domain data obtained from the averaging module back to the time-space domain, thus obtaining the noise-suppressed signal.

[0130] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a seismic coherent noise suppression method utilizing frequency spatial domain eigenvector projection, including:

[0131] The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform. Data at a fixed frequency point is extracted from the frequency-space domain data to obtain a frequency slice. A prediction step size is selected, and the signal vector, data matrix, and AR model equation for forward prediction are constructed using the frequency slices. The covariance matrix of the data matrix is ​​calculated and eigenvalue decomposition is performed. The largest eigenvalue is selected, and the corresponding eigenvector is projected in a specified direction. The covariance matrix is ​​reconstructed using the projected eigenvectors to obtain the solution to the AR model equation. The signal to be predicted is then filtered to obtain the forward prediction result. The signal vector, data matrix, and AR model equation for backward prediction are constructed, and prediction is performed again from the reverse direction to obtain the backward prediction result. The results of the forward and backward predictions are averaged to obtain the frequency slice processing result. The next frequency slice is selected until all frequency slices are processed, resulting in complete frequency-space domain data. Finally, the complete frequency-space domain data is transformed back to the time-space domain using inverse Fourier transform to obtain the noise-suppressed signal.

[0132] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fluid composition calculation method in the reservoir stimulation wellbore of this embodiment. To avoid repetition, it will not be described in detail here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the seismic coherent noise suppression system utilizing frequency spatial domain eigenvector projection of this embodiment. To avoid repetition, it will not be described in detail here.

[0133] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0134] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0135] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0136] Furthermore, memory 62 may include both internal storage units and external storage devices of the computer device 60. Memory 62 is used to store computer programs and other programs and data required by the computer device. Memory 62 can also be used to temporarily store data that has been output or will be output.

[0137] Please see Figure 6 The terminal device is a chip. In this embodiment, the chip 600 includes a processor 622, which may be one or more, and a memory 632 for storing computer programs executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to perform the aforementioned seismic coherent noise suppression method utilizing frequency spatial domain eigenvector projection.

[0138] Additionally, chip 600 may also include a power supply component 626 and a communication component 650. The power supply component 626 can be configured to perform power management of chip 600, and the communication component 650 can be configured to enable communication of chip 600, such as wired or wireless communication. Furthermore, chip 600 may also include an input / output (I / O) interface 658. Chip 600 can operate on an operating system stored in memory 632.

[0139] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0140] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the seismic coherent noise suppression method using frequency spatial domain eigenvector projection in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0141] The seismic signal is transformed from the time-space domain to the frequency-space domain using Fourier transform. Data at a fixed frequency point is extracted from the frequency-space domain data to obtain a frequency slice. A prediction step size is selected, and the signal vector, data matrix, and AR model equation for forward prediction are constructed using the frequency slices. The covariance matrix of the data matrix is ​​calculated and eigenvalue decomposition is performed. The largest eigenvalue is selected, and the corresponding eigenvector is projected in a specified direction. The covariance matrix is ​​reconstructed using the projected eigenvectors to obtain the solution to the AR model equation. The signal to be predicted is then filtered to obtain the forward prediction result. The signal vector, data matrix, and AR model equation for backward prediction are constructed, and prediction is performed again from the reverse direction to obtain the backward prediction result. The results of the forward and backward predictions are averaged to obtain the frequency slice processing result. The next frequency slice is selected until all frequency slices are processed, resulting in complete frequency-space domain data. Finally, the complete frequency-space domain data is transformed back to the time-space domain using inverse Fourier transform to obtain the noise-suppressed signal.

[0142] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0143] Please see Figure 2 The data represents time-space domain seismic data containing oblique linear coherent noise. The horizontal axis represents the number of seismic traces, and the vertical axis represents time. The sampling interval is 2ms, and there are 1000 sampling points. This data is used for noise suppression.

[0144] Please see Figure 3 , Figure 3 (a) shows the noise suppression results of the FX predictive filtering method. Figure 3 (b) for Figure 2 The difference profile can basically filter out random noise in noisy data, but the noise suppression results still contain a large amount of oblique linear coherent noise.

[0145] Please see Figure 4 , Figure 4 (a) shows the noise suppression result of the method of the present invention. Figure 4 (b) for Figure 2 The difference in profile, comparison Figure 3 The method of the present invention can not only filter out random noise, but also filter out oblique linear coherent noise, and the differential profile does not contain effective signal components, resulting in a higher signal-to-noise ratio of the noise suppression result.

[0146] In summary, the present invention provides a seismic coherent noise suppression method utilizing frequency spatial domain eigenvector projection. Based on FX predictive filtering, a rank reduction step is added, which improves its suppression effect on random noise. At the same time, the eigenvector is projected, which can effectively suppress oblique linear coherent noise, while retaining the advantages of simple and computationally efficient FX predictive filtering model.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0150] In the embodiments provided by this 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 merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0153] If the integrated module / unit is implemented as 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed 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 electrical carrier signals and telecommunication signals.

[0154] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for suppressing seismic coherent noise using frequency spatial domain eigenvector projection, characterized in that, Includes the following steps: S1. Use Fourier transform to transform the seismic signal from the time-space domain to the frequency-space domain, and take data at a fixed frequency point in the frequency-space domain data to obtain a frequency slice; S2. Select the prediction step size, and construct the forward prediction signal vector, data matrix, and AR model equation using the frequency slices obtained in step S1. Calculate the covariance matrix of the data matrix and perform eigenvalue decomposition. Take the largest eigenvalue and project the corresponding eigenvectors in the specified direction. Reconstruct the eigenvalues ​​and transform the corresponding eigenvectors into all-one vectors. Projection is performed as follows: in, For vector dot product, For vectors The square of the modulus, These are the projected feature vectors; S3. Reconstruct the covariance matrix using the eigenvectors projected in step S2 to obtain the solution of the AR model equation, and then filter the signal to be predicted to obtain the positive prediction result. S4. Construct the backward prediction signal vector, data matrix, and AR model equation, and perform prediction again from the reverse direction to obtain the backward prediction result. S5. Average the forward prediction result obtained in step S3 and the reverse prediction result obtained in step S4 to obtain the frequency slice processing result. Take the next frequency slice until all frequency slices have been processed to obtain complete frequency-spatial domain data. S6. Use inverse Fourier transform to transform the complete frequency-space domain data obtained in step S5 back to the time-space domain to obtain the noise-suppressed signal.

2. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 1, characterized in that, In step S1, fixed frequency domain indicators The corresponding frequency slices are as follows: in, This represents the number of traces in the two-dimensional seismic data.

3. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 2, characterized in that, Perform Discrete Fourier Transform on each trace of the 2D seismic data, and then... channel signal In other words: in, The number of time-domain sampling points of the signal. For time domain indicators, For frequency domain indicators, It is the unit of the imaginary part in complex number operations. This is the expression for the signal in the frequency-space domain.

4. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 1, characterized in that, Covariance matrix of the data matrix Specifically: in, To perform the conjugate transpose of a matrix, For data matrices; For covariance matrix The eigenvalue decomposition is as follows: in, The first covariance matrix is ​​the first... 1 eigenvalue, The first covariance matrix is ​​the first... 1 eigenvector.

5. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 1, characterized in that, In step S3, forward filtering is performed on the signal to be predicted: in, This is the signal vector after forward filtering. For data matrix, This is the forward prediction filter coefficient vector.

6. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 5, characterized in that, Forward Predictive Filter Coefficient Vector for: in, Covariance matrix The i-th eigenvalue, The result is the projection of the eigenvector corresponding to the i-th eigenvalue onto an all-one vector. For data matrix, This is the original signal vector.

7. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 2, characterized in that, In step S4, the result of the reverse prediction is as follows: in, This is the signal vector after back-filtering. For data matrix, This is the vector of backward prediction filter coefficients.

8. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 7, characterized in that, Data Matrix for: in, For frequency domain indicators, For the number of traces in two-dimensional seismic data, To predict the step size.

9. The seismic coherence noise suppression method using frequency spatial domain eigenvector projection according to claim 1, characterized in that, In step S5, complete frequency-space domain data for: in, The signal obtained by backward predictive filtering. For the sequence number of the Dao, To predict the step size, The signal obtained by forward predictive filtering. This represents the number of traces in the two-dimensional seismic data.

10. A seismic coherent noise suppression system utilizing frequency spatial domain eigenvector projection, 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 extracts data at a fixed frequency point from the frequency-space domain data to obtain a frequency slice; The projection module selects the prediction step size and uses the frequency slices obtained from the transformation module to construct the forward prediction signal vector, data matrix, and AR model equations. It calculates the covariance matrix of the data matrix and performs eigenvalue decomposition, taking the largest eigenvalue and projecting the corresponding eigenvectors in a specified direction. Reconstruct the eigenvalues ​​and transform the corresponding eigenvectors into all-one vectors. Projection is performed as follows: in, For vector dot product, For vectors The square of the modulus, These are the projected feature vectors; The forward prediction module reconstructs the covariance matrix using the eigenvectors projected by the projection module, obtains the solution to the AR model equation, and then filters the signal to be predicted to obtain the forward prediction result. The backward prediction module constructs the backward prediction signal vector, data matrix, and AR model equations, and performs prediction again from the reverse direction to obtain the backward prediction result. The averaging module averages the forward prediction results obtained from the forward prediction module and the backward prediction results obtained from the backward prediction module to obtain the frequency slice processing results. The next frequency slice is then taken until all frequency slices have been processed to obtain complete frequency-spatial domain data. The output module uses inverse Fourier transform to transform the complete frequency-space domain data obtained from the averaging module back to the time-space domain, thus obtaining the noise-suppressed signal.

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