A fault-preserving seismic image denoising method and device, electronic equipment and medium

Abstract

The present application relates to the technical field of seismic image processing, and particularly relates to a fault-preserving seismic image denoising method and device, electronic equipment and medium, the method comprising: weighting local frequency data of each point at each frequency according to local fault edge constraint weight matrix of each point in seismic image data, to obtain fault edge Hankel matrix of each point at each frequency; determining singular value spectrum damping factor and singular value spectrum of each point at each frequency according to fault edge Hankel matrix of each point at each frequency; determining denoised seismic image according to singular value spectrum damping factor and singular value spectrum of each point at each frequency. Through the method of the present application, the seismic image is denoised, the fault edge information can be prevented from being identified as noise and separated, the serious blurring and distortion of the fault edge of the seismic image is prevented, and the subsequent processing and interpretation work is avoided from being affected.

Description

Technical Field

[0001] This invention relates to the field of seismic image processing technology, and more specifically, to a method, apparatus, electronic device, and medium for seismic image noise reduction that preserves fault lines. Background Technology

[0002] High-quality seismic images better reflect subsurface structures, facilitating subsequent processing and interpretation. However, due to environmental and other factors, seismic data acquisition is inevitably affected by noise, reducing the signal-to-noise ratio of seismic images. Singular value spectrum analysis (SVS) is an effective method for suppressing seismic image noise. SVS uses short-time Fourier transform to convert time-domain seismic image data to the frequency domain. It then uses singular value decomposition to construct a Hankel matrix along the spatial direction to obtain the singular value spectrum. Based on the distribution characteristics of effective seismic signals and noise on the singular value spectrum, a cutoff point is artificially defined. The Hankel matrix vector space is then decomposed into an effective signal subspace and a noise signal subspace. Finally, an inverse short-time Fourier transform is used to recover the effective seismic signal from the frequency domain to the time domain, achieving the goal of suppressing seismic image noise.

[0003] However, since the fault edges in seismic images do not have linear characteristics, the singular value spectrum of the Hankel matrix at the fault edges diverges, causing the fault edge information to be identified as noise and separated. This results in severe blurring and distortion of the fault edges in the seismic images, which in turn affects subsequent processing and interpretation. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, electronic device and medium for denoising seismic images that preserves faults, which can prevent fault edge information from being identified as noise and separated, prevent severe blurring and distortion of fault edges in seismic images, and thus avoid affecting subsequent processing and interpretation.

[0005] In a first aspect, embodiments of this application provide a method for denoising seismic images while preserving fault lines, the method comprising:

[0006] Obtain the local fault edge constraint weight matrix and the local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point.

[0007] Based on the local fault edge constraint weight matrix of each point, the local frequency data of each point at each frequency are weighted to obtain the fault edge Hankel matrix of each point at each frequency.

[0008] Based on the Hankel matrix of the fault edge at each point at each frequency, the singular value spectrum damping factor and singular value spectrum of each point at each frequency are determined; the singular value spectrum damping factor of each point is used to characterize the attenuation weight value of the singular value noise component at each point.

[0009] The noise-reduced seismic image is determined based on the singular value spectrum damping factor and singular value spectrum at each point at each frequency.

[0010] In one possible implementation, obtaining the local fault edge constraint weight matrix at each point in the seismic image data includes:

[0011] Acquire initial fault edge image data corresponding to the seismic image data; wherein the seismic image data and the initial fault edge image data have the same size;

[0012] The initial fault edge image data is binarized according to the preset fault edge threshold to obtain the target fault edge image data.

[0013] Based on the local fault edge image data of each point in the seismic image data and the local fault edge image data of the target fault edge image data, construct the local fault edge constraint weight matrix for each point.

[0014] In one possible implementation, a local fault edge constraint weight matrix is ​​constructed for each point in the seismic image data based on the local fault edge image data of the target fault edge image data, including:

[0015] Substitute the local fault edge image data of each point in the seismic image data into the target fault edge image data into the following formula to obtain the local fault edge constraint weight matrix of each point.

[0016]

[0017]

[0018]

[0019] Where h(jt,lx) is the local fault edge constraint weight matrix at each point (jt,ix), jt is the temporal sampling value of each point in the seismic image data, lx is the spatial window length of each point in the seismic image data, and C... ′ (jt,jx) represents the value of each point (jt,jx) in the seismic image data within the local fault edge image data, jx represents the spatial direction sampling value of each point in the seismic image data, ix represents the spatial direction sampling value of each point in the local fault edge image data, and C... ′ (jt,ix) represents the value of point (jt,ix) in the local fault edge image data, and hlx represents the half-window length of each point in the spatial direction of the seismic image data.

[0020] In one possible implementation, the singular value spectral damping factor and singular value spectrum of each point at each frequency are determined based on the Hankel matrix of the fault edge at each frequency, including:

[0021] Singular value decomposition is performed on the Hankel matrix of the fault edge at each point at each frequency to obtain the singular value spectrum of each point at each frequency.

[0022] Separate the noise singular spectrum from the singular spectrum of each point at each frequency;

[0023] The mean of the noise singular value spectrum is used as the weight of each singular value noise component in the singular value spectrum to construct the singular value spectrum damping factor at each point at each frequency.

[0024] In one possible implementation, the mean of the noise singular value spectrum is used as the weight of each singular noise component in the singular value spectrum to construct the singular value spectrum damping factor at each point at each frequency, including:

[0025] Substitute the mean and singular values ​​of the noise singular value spectrum into the following formula to construct the singular value spectrum damping factor at each point at each frequency.

[0026]

[0027] Where T is the singular value spectrum damping factor, T(Z) is the damping factor of the Z-th singular value in the singular value spectrum, and Σ1 is the noise singular value spectrum. Let Σ(Z) be the mean of the singular value spectrum of the noise, Σ(Z) be the Zth singular value in the singular value spectrum Σ, L be the number of singular values ​​in the singular value spectrum Σ, and f be the attenuation factor.

[0028] In one possible implementation, the denoised seismic image is determined based on the singular value spectral damping factor and singular value spectrum at each point at each frequency, including:

[0029] The singular spectrum is attenuated by the singular spectrum damping factor at each point at each frequency, and the filtered singular spectrum is obtained.

[0030] The noise-reduced seismic image is determined based on the filtered singular value spectrum of each point at each frequency.

[0031] In one possible implementation, the singular spectrum is attenuated by the singular spectrum damping factor at each point at each frequency to obtain the filtered singular spectrum, including:

[0032] Substituting the singular value spectrum damping factor and singular value spectrum at each point and frequency into the following formula, the filtered singular value spectrum is obtained.

[0033] Σ′(Z)=Σ(Z)T(Z);

[0034] Where Σ′ is the filtered singular value spectrum, and Σ′(Z) is the Zth singular value in the filtered singular value spectrum.

[0035] Secondly, embodiments of this application also provide a fault-preserving seismic image noise reduction device, the method comprising:

[0036] The acquisition module is used to acquire the local fault edge constraint weight matrix and the local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point.

[0037] The weighting module is used to weight the local frequency data of each point at each frequency according to the local fault edge constraint weight matrix of each point, so as to obtain the fault edge Hankel matrix of each point at each frequency.

[0038] The determination module is used to determine the singular value spectrum damping factor and singular value spectrum of each point at each frequency based on the fault edge Hankel matrix of each point at each frequency; the singular value spectrum damping factor of each point is used to characterize the attenuation weight value of the singular value noise component at each point.

[0039] The determination module is also used to determine the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency.

[0040] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the fault-preserving seismic image denoising method according to any of the first aspects.

[0041] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when run by a processor, performs the steps of the fault-preserving seismic image denoising method according to any of the first aspects.

[0042] This application provides a method, apparatus, electronic device, and medium for denoising seismic images while preserving fault boundaries. The method includes: acquiring a local fault edge constraint weight matrix and a local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point; weighting the local frequency data at each frequency for each point according to the local fault edge constraint weight matrix to obtain the fault edge Hankel matrix at each frequency; determining the singular value spectrum damping factor and singular value spectrum for each point at each frequency based on the fault edge Hankel matrix at each frequency; the singular value spectrum damping factor for each point is used to characterize the attenuation weight value of the singular value noise component at each point; and determining the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum at each frequency. This application improves the singular value spectrum analysis method by introducing the local fault edge constraint weight matrix into the singular value spectrum filtering process, thereby avoiding damage to the fault edges. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart of a fault-preserving seismic image noise reduction method provided in an embodiment of this application is shown;

[0045] Figure 2 A schematic diagram of seismic image data provided in an embodiment of this application is shown;

[0046] Figure 3 A schematic diagram of a seismic image after noise reduction using this method, provided in an embodiment of this application, is shown.

[0047] Figure 4 A schematic diagram of a seismic image after noise reduction using conventional singular spectrum analysis methods, provided in an embodiment of this application, is shown.

[0048] Figure 5 A flowchart of another fault-preserving seismic image noise reduction method provided in an embodiment of this application is shown;

[0049] Figure 6 This illustration shows a structural schematic diagram of a fault-preserving seismic image noise reduction device provided in an embodiment of this application;

[0050] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0052] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0053] To enable those skilled in the art to utilize the content of this application, and in conjunction with the specific application scenario of "earthquake image processing technology," the following embodiments are provided. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application is primarily described within the "earthquake image processing technology field," it should be understood that this is merely an exemplary embodiment.

[0054] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0055] The following is a detailed description of a fault-preserving seismic image denoising method provided in the embodiments of this application.

[0056] Reference Figure 1 The diagram shown is a flowchart illustrating a fault-preserving seismic image denoising method according to an embodiment of this application. The exemplary steps of this embodiment are described below:

[0057] S101. Obtain the local fault edge constraint weight matrix and the local frequency data matrix at each frequency for each point in the seismic image data.

[0058] In this embodiment, two-dimensional seismic image data d(nt, nx) to be denoised are acquired according to a time sampling interval dt; where dt is a preset constant, nt is the number of sampling points in the time direction, and nx is the number of sampling points in the spatial direction; refer to Figure 2 The diagram shown is a schematic representation of seismic image data provided in an embodiment of this application. The local fault edge constraint weight matrix and the local frequency data matrix at each frequency are obtained from the acquired two-dimensional seismic image data. The local fault edge constraint weight matrix at each point is used to characterize the weight of each point as a fault edge point. The local frequency data matrix at each frequency refers to the set of frequency data at each frequency for all points within a preset window length along the spatial direction of each point in the seismic image data. The local fault edge constraint weight matrix at each point is determined by the fault edge image data values ​​of all points within a preset window length along the spatial direction of each point in the seismic image data.

[0059] Here, a fault is a structure in which the Earth's crust fractures under stress, resulting in significant relative displacement of rock blocks on both sides of the fracture surface.

[0060] Further, the local frequency data matrix of each point in the seismic image data at each frequency is obtained through the following steps:

[0061] Step 1: Use the generalized S-transform to transform the time domain data of each point in the seismic image data into frequency data, and obtain the frequency data of each point in the seismic image data at each frequency.

[0062]

[0063] Among them, g jω (jτ,jx) represents the frequency data of each point (jt,jx) in the seismic image data at each frequency. jτ represents the sampling frequency value of each point in the seismic image data in the time direction, jx represents the sampling value of each point in the spatial direction, jω represents the frequency label (frequency slice, an integer), jt represents the sampling time domain value of each point in the seismic image data in the time direction, jx represents the sampling value of each point in the spatial direction, d(jt,jx) represents the point (jt,jx) in the seismic image data, λ and p are scale factors, jt and jτ are both integers, the range of jt is 1≤jt≤nt, the range of jτ is 1≤jτ≤nt, jx is an integer, the range of jx is 1≤jx≤nx, the range of jω is 1≤jω≤nω, nω is the total number of frequencies, e -i2πjωjt In this context, 'i' represents a complex number.

[0064] Here, it should be specifically noted that in the embodiments of this application, all "j*" are a whole identifier, and j is not used as a separate serial number identifier, such as "jτ", "jx", etc. Using the above formula, seismic image data d(nt,nx) can be transformed into time-frequency data g. nω (nt,nx) is used to correlate data in three dimensions: time, space, and frequency.

[0065] Step 2: For each point (jt, jx), set the window length and half-window length in the x-direction as hlx and lx, respectively. Then, extract the set of frequency data of all points within the preset window lengths (hlx and lx) along the spatial direction of each point (jt, jx) from the frequency data of each point in the seismic image data at each frequency, thus obtaining the local frequency data matrix S of each point at each frequency. jω (jt,lx).

[0066] The window length lx can be represented by half the window length hlx as: lx = 2hlx + 1, where hlx and lx are both positive integers, and hlx has a range of 1 / 2. This represents rounding down, and the range of lx is 3 ≤ lx ≤ nx.

[0067] Further, the local fault edge constraint weight matrix for each point in the seismic image data is obtained through the following steps:

[0068] Step 1: Obtain the initial fault edge image data C(nt,nx) corresponding to the seismic image data.

[0069] The seismic image data and the initial fault edge image data are of the same size.

[0070] Step 2: Binarize the initial fault edge image data according to the preset fault edge threshold to obtain the target fault edge image data.

[0071] In this embodiment, given a fault edge threshold δ (0≤δ≤1), the input initial fault edge image data C(nt,nx) is transformed to obtain the target fault edge image data C. ′ (nt,nx):

[0072]

[0073] Here, if the data value of a point in the initial fault edge image data is greater than or equal to the fault edge threshold, then that point is not a fault edge. If the data value of a point in the initial fault edge image data is less than the fault edge threshold, then that point is a fault edge. As can be seen from the above formula, the value of fault edge points in the target fault edge image data is 0, and the value of non-fault edge points is 1. These values ​​cannot be interchanged; otherwise, subsequent noise reduction cannot be performed.

[0074] Step 3: Based on the local fault edge image data of each point in the seismic image data and the local fault edge image data of the target fault edge image data, construct the local fault edge constraint weight matrix for each point.

[0075] Specifically, the local fault edge image data of each point in the seismic image data is substituted into the following formula to obtain the local fault edge constraint weight matrix of each point.

[0076]

[0077]

[0078]

[0079] Where h(jt,lx) is the local fault edge constraint weight matrix for each point (jt,ix), jt is the temporal sampling value (time domain) of each point in the seismic image data, lx is the spatial window length of each point in the seismic image data, and C... ′ (jt,jx) represents the value of each point (jt,jx) in the seismic image data within the local fault edge image data, jx represents the spatial direction sampling value of each point in the seismic image data, ix represents the spatial direction sampling value of each point in the local fault edge image data, and C... ′ (jt,ix) represents the value of point (jt,ix) in the local fault edge image data, and hlx represents the half-window length of each point in the spatial direction of the seismic image data.

[0080] S102. Based on the local fault edge constraint weight matrix of each point, the local frequency data of each point at each frequency are weighted to obtain the fault edge Hankel matrix of each point at each frequency.

[0081] In this embodiment of the application, the fault edge Hankel matrix H jt,jx The dimension is (hlx+1)×(hlx+1). The Hankel matrix H of the fault edge at each point at each frequency. jt,jX The following representation is made:

[0082]

[0083] Here, the Hankel matrix exhibits unique characteristics in its data arrangement. Its rank is related to the number of plane waves in different directions, indicating linear signals in different directions. However, discontinuous edges in seismic images lack linear characteristics and are identified as noise. The local fault edge constraint weight matrix weights the local frequency data, effectively preserving fault edge information in the seismic image. Consequently, the Hankel matrix constructed using this data can adaptively adapt to the fault, achieving fault edge protection.

[0084] S103. Based on the Hankel matrix of the fault edge at each point at each frequency, determine the singular value spectrum damping factor and singular value spectrum at each point at each frequency.

[0085] In the embodiments provided in this application, the singular value spectrum damping factor at each point is used to characterize the attenuation weight value of the singular value noise component at each point. Singular value decomposition is performed on the Hankel matrix of the fault edge at each frequency to obtain the singular value spectrum at each frequency; the noise singular value spectrum is separated from the singular value spectrum at each frequency; the mean of the noise singular value spectrum is used as the weight of each singular value component in the singular value spectrum to construct the singular value spectrum damping factor at each frequency for each point.

[0086] S104. Determine the noise-reduced seismic image based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency.

[0087] Specifically, the denoised seismic image is determined by the following steps based on the singular value spectral damping factor and singular value spectrum at each point at each frequency:

[0088] i. The singular spectrum is attenuated by the damping factor of the singular spectrum at each point at each frequency to obtain the filtered singular spectrum.

[0089] Substituting the singular value spectrum damping factor and singular value spectrum at each point and frequency into the following formula, the filtered singular value spectrum is obtained.

[0090] Σ′(Z)=Σ(Z)T(Z);

[0091] Where Σ′ is the filtered singular value spectrum, Σ′(Z) is the Zth singular value in the filtered singular value spectrum, Σ(Z) is the Zth singular value in the singular value spectrum Σ, T is the singular value spectrum damping factor, and T(Z) is the singular value spectrum damping factor of the Zth singular value in the singular value spectrum.

[0092] ii. Determine the denoised seismic image based on the filtered singular value spectrum of each point at each frequency.

[0093] In this embodiment, the Hankel matrix H′ is reconstructed using the filtered singular value spectrum. jt,jx :H′ jt,jx=UΣ′V T The reconstructed Hankel matrix H′ jt,jx After inverse averaging, the local frequency data matrix g′ is restored. nω (nt,nx) can be used to complete the operation at frequency f. jω The following describes edge-preserving noise reduction processing for the calculation point (jt,jx). The recovered local frequency data matrix g′ for each point at each frequency is then processed. nω The denoised seismic image d′(nt,nx) can be obtained by performing a generalized inverse S-transform on (nt,nx).

[0094] Here, as Figure 3 The image shown is a schematic diagram of a seismic image denoised using this method, as provided in an embodiment of this application; Figure 4 The image shown is a schematic diagram of a seismic image denoised using conventional singular spectrum analysis methods, provided in an embodiment of this application.

[0095] This application provides a seismic image denoising method that preserves fault boundaries. The method includes: acquiring a local fault edge constraint weight matrix and a local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point; weighting the local frequency data at each frequency for each point according to the local fault edge constraint weight matrix to obtain the fault edge Hankel matrix at each frequency; determining the singular value spectrum damping factor and singular value spectrum for each point at each frequency based on the fault edge Hankel matrix at each frequency; the singular value spectrum damping factor for each point is used to characterize the attenuation weight value of the singular value noise component at each point; and determining the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum at each frequency. This application improves the singular value spectrum analysis method by introducing the input seismic image fault edge image data as a constraint into the singular value spectrum filtering process, adaptively constructing a local fault edge constraint weight matrix at the fault edges of the seismic image, thereby avoiding damage to image edges such as faults. Tests on actual seismic images show that the method can suppress noise while preserving image edge information such as faults.

[0096] like Figure 5 The diagram shown is a flowchart illustrating another fault-preserving seismic image denoising method provided in this application embodiment. The exemplary steps of this application embodiment are described below:

[0097] S501. Perform singular value decomposition on the Hankel matrix of the fault edge at each point at each frequency to obtain the singular value spectrum of each point at each frequency.

[0098] In this embodiment of the application, the Hankel matrix H of the fault edge at each point at each frequency is... jt,jxAfter performing singular value decomposition, the singular value spectrum of each point at each frequency can be obtained; the decomposition process is as follows:

[0099] H jt,jx =UΣV T ;

[0100]

[0101] Where U is called the left singular value matrix, V is called the right singular value matrix, T is the transpose, Σ is the singular value spectrum (diagonal matrix), and the elements σ of the singular value spectrum Σ are... m These are different singular values, where m is a positive integer representing the singular value index, and its range is 1≤m≤L.

[0102] S502. Separate the noise singular value spectrum of each point at each frequency.

[0103] Here, as the singular value index m increases, the singular values ​​decrease. On its singular value spectrum Σ, the energy of the effective signal is stronger, resulting in larger singular values ​​concentrated at the beginning; while the noise energy is weaker, resulting in smaller singular values ​​distributed at the end of the spectrum. The difference in singular value values ​​between the effective and noise signals on the singular value spectrum is significant. Furthermore, for noisy and noiseless data at the same position, the singular value spectrum of the former is generally larger than that of the latter.

[0104] Therefore, the critical point σ between the effective signal and noise on the singular value spectrum is first determined using the singular value difference spectrum. k , 1≤k≤L-1. Thus, the noise singular value spectrum Σ1 and the effective signal singular value spectrum Σ2 are automatically separated on the singular value spectrum Σ:

[0105]

[0106]

[0107] S503. Use the mean of the noise singular value spectrum as the weight of each singular value noise component in the singular value spectrum to construct the singular value spectrum damping factor of each point at each frequency.

[0108] In the embodiments of this application, the mean of the noise singular value spectrum is used to characterize the mean of the noise energy on the singular value spectrum.

[0109] Specifically, the mean and singular values ​​of the noise singular value spectrum are substituted into the following formula to construct the singular value spectrum damping factor at each point at each frequency.

[0110]

[0111] Where T is the singular value spectrum damping factor, T(Z) is the damping factor of the Z-th singular value in the singular value spectrum, and Σ1 is the noise singular value spectrum. Let Σ(Z) be the mean of the singular value spectrum of the noise, Σ(Z) be the Zth singular value in the singular value spectrum Σ, L be the number of singular values ​​in the singular value spectrum Σ, and f be the attenuation factor (constant).

[0112] This application provides another method for denoising seismic images while preserving faults. It can construct the singular value spectrum damping factor of each point at each frequency, and then filter the seismic image data. This method can prevent fault edge information from being identified as noise and separated, and prevent severe blurring and distortion of the fault edges of the seismic image, thereby avoiding affecting subsequent processing and interpretation.

[0113] Based on the same inventive concept, this application also provides a fault-preserving seismic image denoising device corresponding to the fault-preserving seismic image denoising method. Since the principle of the device in this application is similar to the fault-preserving seismic image denoising method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0114] Reference Figure 6 The diagram shown is a schematic of a fault-preserving seismic image denoising device provided in an embodiment of this application. The fault-preserving seismic image denoising device includes:

[0115] The acquisition module 601 is used to acquire the local fault edge constraint weight matrix of each point in the seismic image data and the local frequency data matrix at each frequency; the local fault edge constraint weight matrix of each point is used to characterize the weight of each point as a fault edge point.

[0116] The weighting module 602 is used to weight the local frequency data of each point at each frequency according to the local fault edge constraint weight matrix of each point, so as to obtain the fault edge Hankel matrix of each point at each frequency.

[0117] The determination module 603 is used to determine the singular value spectrum damping factor and singular value spectrum of each point at each frequency based on the fault edge Hankel matrix of each point at each frequency; the singular value spectrum damping factor of each point is used to characterize the attenuation weight value of the singular value noise component at each point.

[0118] The determination module 603 is also used to determine the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency.

[0119] In one possible implementation, the acquisition module 601 is specifically used to acquire initial fault edge image data corresponding to the seismic image data; wherein the seismic image data and the initial fault edge image data have the same size; the initial fault edge image data is binarized according to a preset fault edge threshold to obtain target fault edge image data; and a local fault edge constraint weight matrix is ​​constructed for each point in the seismic image data based on the local fault edge image data of each point in the target fault edge image data.

[0120] In one possible implementation, the acquisition module 601 is further configured to:

[0121] Substitute the local fault edge image data of each point in the seismic image data into the target fault edge image data into the following formula to obtain the local fault edge constraint weight matrix of each point.

[0122]

[0123]

[0124]

[0125] Where h(jt,lx) is the local fault edge constraint weight matrix at each point (jt,ix), jt is the temporal sampling value of each point in the seismic image data, lx is the spatial window length of each point in the seismic image data, and C... ′ (jt,jx) represents the value of each point (jt,jx) in the seismic image data within the local fault edge image data, jx represents the spatial direction sampling value of each point in the seismic image data, ix represents the spatial direction sampling value of each point in the local fault edge image data, and C... ′ (jt,ix) represents the value of point (jt,ix) in the local fault edge image data, and hlx represents the half-window length of each point in the spatial direction of the seismic image data.

[0126] In one possible implementation, the determining module 603 is specifically used to perform singular value decomposition on the Hankel matrix of the fault edge at each point at each frequency to obtain the singular value spectrum of each point at each frequency; separate the noise singular value spectrum in the singular value spectrum of each point at each frequency; and use the mean of the noise singular value spectrum as the weight of each singular noise component in the singular value spectrum to construct the singular value spectrum damping factor of each point at each frequency.

[0127] In one possible implementation, the determining module 603 is further configured to:

[0128] Substitute the mean and singular values ​​of the noise singular value spectrum into the following formula to construct the singular value spectrum damping factor at each point at each frequency.

[0129]

[0130] Where T is the singular value spectrum damping factor, T(Z) is the damping factor of the Z-th singular value in the singular value spectrum, and Σ1 is the noise singular value spectrum. Let Σ(Z) be the mean of the singular value spectrum of the noise, Σ(Z) be the Zth singular value in the singular value spectrum Σ, L be the number of singular values ​​in the singular value spectrum Σ, and f be the attenuation factor.

[0131] In one possible implementation, the determining module 603 is specifically used to attenuate the singular value spectrum by using the singular value spectrum damping factor of each point at each frequency to obtain the filtered singular value spectrum; and to determine the denoised seismic image based on the filtered singular value spectrum of each point at each frequency.

[0132] In one possible implementation, the determining module 603 is further configured to:

[0133] Substituting the singular value spectrum damping factor and singular value spectrum at each point and frequency into the following formula, the filtered singular value spectrum is obtained.

[0134] Σ′(Z)=Σ(Z)T(Z);

[0135] Where Σ′ is the filtered singular value spectrum, and Σ′(Z) is the Zth singular value in the filtered singular value spectrum.

[0136] This application provides a fault-preserving seismic image denoising device, which includes: an acquisition module 601, used to acquire the local fault edge constraint weight matrix and the local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point; a weighting module 602, used to weight the local frequency data at each frequency for each point according to the local fault edge constraint weight matrix for each point, to obtain the fault edge Hankel matrix at each frequency for each point; a determination module 603, used to determine the singular value spectrum damping factor and singular value spectrum of each point at each frequency according to the fault edge Hankel matrix at each frequency; the singular value spectrum damping factor for each point is used to characterize the attenuation weight value of the singular value noise component at each point; the determination module 603 is also used to determine the denoised seismic image according to the singular value spectrum damping factor and singular value spectrum of each point at each frequency. This application improves the singular value spectrum analysis method by introducing the local fault edge constraint weight matrix into the singular value spectrum filtering process, thereby avoiding damage to the fault edge.

[0137] like Figure 7As shown in the embodiment of this application, an electronic device 700 includes a processor 701, a memory 702, and a bus. The memory 702 stores machine-readable instructions that can be executed by the processor 701. When the electronic device is running, the processor 701 communicates with the memory 702 through the bus. The processor 701 executes the machine-readable instructions to perform the steps of the fault-preserving seismic image noise reduction method described above.

[0138] Specifically, the memory 702 and processor 701 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 701 runs the computer program stored in the memory 702, it can execute the above-mentioned fault-preserving seismic image noise reduction method.

[0139] Corresponding to the above-mentioned fault-preserving seismic image denoising method, this application embodiment also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the above-mentioned fault-preserving seismic image denoising method.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0141] The modules described as separate components may or may not be physically separate. The components shown as modules 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.

[0142] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0143] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the information processing methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0144] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for denoising seismic images while preserving fault lines, characterized in that, The method includes: The local fault edge constraint weight matrix and the local frequency data matrix at each frequency are obtained for each point in the seismic image data. The local fault edge constraint weight matrix is ​​used to characterize the weight of each point as a fault edge point. The local frequency data matrix at each frequency refers to the set of frequency data of all points within a preset window length along the spatial direction of each point in the seismic image data at each frequency. The step of obtaining the local fault edge constraint weight matrix of each point in the seismic image data includes: obtaining initial fault edge image data corresponding to the seismic image data; wherein the seismic image data and the initial fault edge image data have the same size; binarizing the initial fault edge image data according to a preset fault edge threshold to obtain target fault edge image data; and substituting the local fault edge image data of each point in the seismic image data in the target fault edge image data into the following formula to obtain the local fault edge constraint weight matrix of each point. ； ； ； ； in, For each point The local fault edge constraint weight matrix, These are the time-direction sampled values ​​for each point in the seismic image data. The window length is the spatial orientation of each point in the seismic image data. For each point in the seismic image data The value in the local fault edge image data. These are the spatial orientation sampling values ​​for each point in the seismic image data. These are the spatial orientation sampling values ​​of each point in the local fault edge image data. Midpoint of local fault edge image data The value, This represents the half-window length in the spatial direction of each point in the seismic image data; Based on the local fault edge constraint weight matrix of each point, the local frequency data of each point at each frequency are weighted to obtain the fault edge Hankel matrix of each point at each frequency. Based on the fault edge Hankel matrix of each point at each frequency, the singular value spectrum damping factor and singular value spectrum of each point at each frequency are determined; the singular value spectrum damping factor of each point is used to characterize the attenuation weight value of the singular value noise component at each point. Based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency, the denoised seismic image is determined.

2. The seismic image noise reduction method preserving faults according to claim 1, characterized in that, The step of determining the singular value spectral damping factor and singular value spectrum of each point at each frequency based on the Hankel matrix of the fault edge at each frequency includes: Singular value decomposition is performed on the fault edge Hankel matrix of each point at each frequency to obtain the singular value spectrum of each point at each frequency. Separate the noise singular value spectrum of each point at each frequency; The mean of the noise singular value spectrum is used as the weight of each singular value noise component in the singular value spectrum to construct the singular value spectrum damping factor of each point at each frequency.

3. The seismic image noise reduction method preserving faults according to claim 2, characterized in that, The step of using the mean of the noise singular value spectrum as the weight of each singular value noise component in the singular value spectrum to construct the singular value spectrum damping factor at each point at each frequency includes: Substitute the mean and singular values ​​of the noise singular value spectrum into the following formula to construct the singular value spectrum damping factor at each point at each frequency. ， ； in, The singular value spectrum damping factor. Let Z be the damping factor of the Zth singular value in the singular value spectrum. For the noise singular value spectrum, The mean of the singular value spectrum of the noise. Singular value spectrum The Z-th singular value in the spectrum, where L is the singular value spectrum. The number of singular values ​​in the middle. This is the attenuation factor.

4. The seismic image noise reduction method preserving faults according to claim 3, characterized in that, The step of determining the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency includes: The singular spectrum is attenuated by the singular spectrum damping factor at each point at each frequency to obtain the filtered singular spectrum. The noise-reduced seismic image is determined based on the filtered singular value spectrum of each point at each frequency.

5. The seismic image noise reduction method preserving faults according to claim 4, characterized in that, The process of attenuating the singular value spectrum by using the singular value spectrum damping factor at each point at each frequency to obtain the filtered singular value spectrum includes: Substituting the singular value spectrum damping factor and singular value spectrum at each point and frequency into the following formula, the filtered singular value spectrum is obtained. ； in, This is the filtered singular value spectrum. Let Z be the Zth singular value in the filtered singular value spectrum.

6. A seismic image noise reduction device that preserves fault lines, characterized in that, The device includes: The acquisition module is used to acquire the local fault edge constraint weight matrix and the local frequency data matrix at each frequency for each point in the seismic image data; the local fault edge constraint weight matrix for each point is used to characterize the weight of each point as a fault edge point. The local frequency data matrix of each point at each frequency refers to the set of frequency data of all points within a preset window length along the spatial direction in the seismic image data at each frequency. Specifically, the acquisition module is used to acquire initial fault edge image data corresponding to the seismic image data; wherein the seismic image data and the initial fault edge image data have the same size; binarize the initial fault edge image data according to a preset fault edge threshold to obtain target fault edge image data; substitute the local fault edge image data of each point in the seismic image data in the target fault edge image data into the following formula to obtain the local fault edge constraint weight matrix of each point; ； ； ； ； in, For each point The local fault edge constraint weight matrix, These are the time-direction sampled values ​​for each point in the seismic image data. The window length is the spatial orientation of each point in the seismic image data. For each point in the seismic image data The value in the local fault edge image data. These are the spatial orientation sampling values ​​for each point in the seismic image data. These are the spatial orientation sampling values ​​of each point in the local fault edge image data. Midpoint of local fault edge image data The value, This represents the half-window length in the spatial direction of each point in the seismic image data; The weighting module is used to weight the local frequency data of each point at each frequency according to the local fault edge constraint weight matrix of each point, so as to obtain the fault edge Hankel matrix of each point at each frequency. The determination module is used to determine the singular value spectrum damping factor and singular value spectrum of each point at each frequency based on the fault edge Hankel matrix of each point at each frequency; the singular value spectrum damping factor of each point is used to characterize the attenuation weight value of the singular value noise component of each point. The determining module is further configured to determine the denoised seismic image based on the singular value spectrum damping factor and singular value spectrum of each point at each frequency.

7. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the fault-preserving seismic image denoising method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the fault-preserving seismic image denoising method as described in any one of claims 1 to 5.

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